diff --git a/docs/docs/Integrations/Notion/Conversational_Notion_Agent.json b/docs/docs/Integrations/Notion/Conversational_Notion_Agent.json
index 2b8b4063d..e977769bc 100644
--- a/docs/docs/Integrations/Notion/Conversational_Notion_Agent.json
+++ b/docs/docs/Integrations/Notion/Conversational_Notion_Agent.json
@@ -1 +1,3137 @@
-{"id":"e070f0be-edc4-4512-bb0f-e53307062a26","data":{"nodes":[{"id":"AddContentToPage-ZezUn","type":"genericNode","position":{"x":1416.217259177943,"y":1709.6205867919527},"data":{"type":"AddContentToPage","node":{"template":{"_type":"Component","block_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"block_id","value":"","display_name":"Page/Block ID","advanced":true,"dynamic":false,"info":"The ID of the page/block to add the content.","title_case":false,"type":"str","_input_type":"StrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nfrom typing import Dict, Any, Union\nfrom markdown import markdown\nfrom bs4 import BeautifulSoup\nimport requests\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\n\nclass AddContentToPage(LCToolComponent):\n    display_name: str = \"Add Content to Page \"\n    description: str = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\n    documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        MultilineInput(\n            name=\"markdown_text\",\n            display_name=\"Markdown Text\",\n            info=\"The markdown text to convert to Notion blocks.\",\n        ),\n        StrInput(\n            name=\"block_id\",\n            display_name=\"Page/Block ID\",\n            info=\"The ID of the page/block to add the content.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class AddContentToPageSchema(BaseModel):\n        markdown_text: str = Field(..., description=\"The markdown text to convert to Notion blocks.\")\n        block_id: str = Field(..., description=\"The ID of the page/block to add the content.\")\n\n    def run_model(self) -> Data:\n        result = self._add_content_to_page(self.markdown_text, self.block_id)\n        return Data(data=result, text=json.dumps(result))\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"add_content_to_notion_page\",\n            description=\"Convert markdown text to Notion blocks and append them to a Notion page.\",\n            func=self._add_content_to_page,\n            args_schema=self.AddContentToPageSchema,\n        )\n\n    def _add_content_to_page(self, markdown_text: str, block_id: str) -> Union[Dict[str, Any], str]:\n        try:\n            html_text = markdown(markdown_text)\n            soup = BeautifulSoup(html_text, \"html.parser\")\n            blocks = self.process_node(soup)\n\n            url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\n            headers = {\n                \"Authorization\": f\"Bearer {self.notion_secret}\",\n                \"Content-Type\": \"application/json\",\n                \"Notion-Version\": \"2022-06-28\",\n            }\n\n            data = {\n                \"children\": blocks,\n            }\n\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n\n            return response.json()\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to add content to Notion page. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while adding content to Notion page. {str(e)}\"\n\n    def process_node(self, node):\n        blocks = []\n        if isinstance(node, str):\n            text = node.strip()\n            if text:\n                if text.startswith(\"#\"):\n                    heading_level = text.count(\"#\", 0, 6)\n                    heading_text = text[heading_level:].strip()\n                    if heading_level == 1:\n                        blocks.append(self.create_block(\"heading_1\", heading_text))\n                    elif heading_level == 2:\n                        blocks.append(self.create_block(\"heading_2\", heading_text))\n                    elif heading_level == 3:\n                        blocks.append(self.create_block(\"heading_3\", heading_text))\n                else:\n                    blocks.append(self.create_block(\"paragraph\", text))\n        elif node.name == \"h1\":\n            blocks.append(self.create_block(\"heading_1\", node.get_text(strip=True)))\n        elif node.name == \"h2\":\n            blocks.append(self.create_block(\"heading_2\", node.get_text(strip=True)))\n        elif node.name == \"h3\":\n            blocks.append(self.create_block(\"heading_3\", node.get_text(strip=True)))\n        elif node.name == \"p\":\n            code_node = node.find(\"code\")\n            if code_node:\n                code_text = code_node.get_text()\n                language, code = self.extract_language_and_code(code_text)\n                blocks.append(self.create_block(\"code\", code, language=language))\n            elif self.is_table(str(node)):\n                blocks.extend(self.process_table(node))\n            else:\n                blocks.append(self.create_block(\"paragraph\", node.get_text(strip=True)))\n        elif node.name == \"ul\":\n            blocks.extend(self.process_list(node, \"bulleted_list_item\"))\n        elif node.name == \"ol\":\n            blocks.extend(self.process_list(node, \"numbered_list_item\"))\n        elif node.name == \"blockquote\":\n            blocks.append(self.create_block(\"quote\", node.get_text(strip=True)))\n        elif node.name == \"hr\":\n            blocks.append(self.create_block(\"divider\", \"\"))\n        elif node.name == \"img\":\n            blocks.append(self.create_block(\"image\", \"\", image_url=node.get(\"src\")))\n        elif node.name == \"a\":\n            blocks.append(self.create_block(\"bookmark\", node.get_text(strip=True), link_url=node.get(\"href\")))\n        elif node.name == \"table\":\n            blocks.extend(self.process_table(node))\n\n        for child in node.children:\n            if isinstance(child, str):\n                continue\n            blocks.extend(self.process_node(child))\n\n        return blocks\n\n    def extract_language_and_code(self, code_text):\n        lines = code_text.split(\"\\n\")\n        language = lines[0].strip()\n        code = \"\\n\".join(lines[1:]).strip()\n        return language, code\n\n    def is_code_block(self, text):\n        return text.startswith(\"```\")\n\n    def extract_code_block(self, text):\n        lines = text.split(\"\\n\")\n        language = lines[0].strip(\"`\").strip()\n        code = \"\\n\".join(lines[1:]).strip(\"`\").strip()\n        return language, code\n\n    def is_table(self, text):\n        rows = text.split(\"\\n\")\n        if len(rows) < 2:\n            return False\n\n        has_separator = False\n        for i, row in enumerate(rows):\n            if \"|\" in row:\n                cells = [cell.strip() for cell in row.split(\"|\")]\n                cells = [cell for cell in cells if cell]  # Remove empty cells\n                if i == 1 and all(set(cell) <= set(\"-|\") for cell in cells):\n                    has_separator = True\n                elif not cells:\n                    return False\n\n        return has_separator and len(rows) >= 3\n\n    def process_list(self, node, list_type):\n        blocks = []\n        for item in node.find_all(\"li\"):\n            item_text = item.get_text(strip=True)\n            checked = item_text.startswith(\"[x]\")\n            is_checklist = item_text.startswith(\"[ ]\") or checked\n\n            if is_checklist:\n                item_text = item_text.replace(\"[x]\", \"\").replace(\"[ ]\", \"\").strip()\n                blocks.append(self.create_block(\"to_do\", item_text, checked=checked))\n            else:\n                blocks.append(self.create_block(list_type, item_text))\n        return blocks\n\n    def process_table(self, node):\n        blocks = []\n        header_row = node.find(\"thead\").find(\"tr\") if node.find(\"thead\") else None\n        body_rows = node.find(\"tbody\").find_all(\"tr\") if node.find(\"tbody\") else []\n\n        if header_row or body_rows:\n            table_width = max(\n                len(header_row.find_all([\"th\", \"td\"])) if header_row else 0,\n                max(len(row.find_all([\"th\", \"td\"])) for row in body_rows),\n            )\n\n            table_block = self.create_block(\"table\", \"\", table_width=table_width, has_column_header=bool(header_row))\n            blocks.append(table_block)\n\n            if header_row:\n                header_cells = [cell.get_text(strip=True) for cell in header_row.find_all([\"th\", \"td\"])]\n                header_row_block = self.create_block(\"table_row\", header_cells)\n                blocks.append(header_row_block)\n\n            for row in body_rows:\n                cells = [cell.get_text(strip=True) for cell in row.find_all([\"th\", \"td\"])]\n                row_block = self.create_block(\"table_row\", cells)\n                blocks.append(row_block)\n\n        return blocks\n\n    def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\n        block: dict[str, Any] = {\n            \"object\": \"block\",\n            \"type\": block_type,\n            block_type: {},\n        }\n\n        if block_type in [\n            \"paragraph\",\n            \"heading_1\",\n            \"heading_2\",\n            \"heading_3\",\n            \"bulleted_list_item\",\n            \"numbered_list_item\",\n            \"quote\",\n        ]:\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n        elif block_type == \"to_do\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"checked\"] = kwargs.get(\"checked\", False)\n        elif block_type == \"code\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"language\"] = kwargs.get(\"language\", \"plain text\")\n        elif block_type == \"image\":\n            block[block_type] = {\"type\": \"external\", \"external\": {\"url\": kwargs.get(\"image_url\", \"\")}}\n        elif block_type == \"divider\":\n            pass\n        elif block_type == \"bookmark\":\n            block[block_type][\"url\"] = kwargs.get(\"link_url\", \"\")\n        elif block_type == \"table\":\n            block[block_type][\"table_width\"] = kwargs.get(\"table_width\", 0)\n            block[block_type][\"has_column_header\"] = kwargs.get(\"has_column_header\", False)\n            block[block_type][\"has_row_header\"] = kwargs.get(\"has_row_header\", False)\n        elif block_type == \"table_row\":\n            block[block_type][\"cells\"] = [[{\"type\": \"text\", \"text\": {\"content\": cell}} for cell in content]]\n\n        return block\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"markdown_text":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"markdown_text","value":"","display_name":"Markdown Text","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The markdown text to convert to Notion blocks.","title_case":false,"type":"str","_input_type":"MultilineInput"},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Convert markdown text to Notion blocks and append them to a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Add Content to Page ","documentation":"https://developers.notion.com/reference/patch-block-children","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["markdown_text","block_id","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"AddContentToPage-ZezUn","description":"Convert markdown text to Notion blocks and append them to a Notion page.","display_name":"Add Content to Page "},"selected":false,"width":384,"height":330,"dragging":false,"positionAbsolute":{"x":1416.217259177943,"y":1709.6205867919527}},{"id":"NotionPageCreator-6SCB5","type":"genericNode","position":{"x":1413.9782390799146,"y":2051.645785494985},"data":{"type":"NotionPageCreator","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nfrom typing import Dict, Any, Union\nimport requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageCreator(LCToolComponent):\n    display_name: str = \"Create Page \"\n    description: str = \"A component for creating Notion pages.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"properties_json\",\n            display_name=\"Properties (JSON)\",\n            info=\"The properties of the new page as a JSON string.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageCreatorSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n        properties_json: str = Field(..., description=\"The properties of the new page as a JSON string.\")\n\n    def run_model(self) -> Data:\n        result = self._create_notion_page(self.database_id, self.properties_json)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the created page data\n            output = \"Created page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"create_notion_page\",\n            description=\"Create a new page in a Notion database. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._create_notion_page,\n            args_schema=self.NotionPageCreatorSchema,\n        )\n\n    def _create_notion_page(self, database_id: str, properties_json: str) -> Union[Dict[str, Any], str]:\n        if not database_id or not properties_json:\n            return \"Invalid input. Please provide 'database_id' and 'properties_json'.\"\n\n        try:\n            properties = json.loads(properties_json)\n        except json.JSONDecodeError as e:\n            return f\"Invalid properties format. Please provide a valid JSON string. Error: {str(e)}\"\n\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"parent\": {\"database_id\": database_id},\n            \"properties\": properties,\n        }\n\n        try:\n            response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\n            response.raise_for_status()\n            result = response.json()\n            return result\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Failed to create Notion page. Error: {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._create_notion_page(*args, **kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"database_id","value":"","display_name":"Database ID","advanced":true,"dynamic":false,"info":"The ID of the Notion database.","title_case":false,"type":"str","_input_type":"StrInput"},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"properties_json":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"properties_json","value":"","display_name":"Properties (JSON)","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The properties of the new page as a JSON string.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"A component for creating Notion pages.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Create Page ","documentation":"https://docs.langflow.org/integrations/notion/page-create","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["database_id","notion_secret","properties_json"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionPageCreator-6SCB5","description":"A component for creating Notion pages.","display_name":"Create Page "},"selected":false,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":1413.9782390799146,"y":2051.645785494985}},{"id":"NotionDatabaseProperties-aeWil","type":"genericNode","position":{"x":1004.5753613670959,"y":1713.914531491452},"data":{"type":"NotionDatabaseProperties","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import Dict, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionDatabaseProperties(LCToolComponent):\n    display_name: str = \"List Database Properties \"\n    description: str = \"Retrieve properties of a Notion database.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionDatabasePropertiesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n\n    def run_model(self) -> Data:\n        result = self._fetch_database_properties(self.database_id)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the properties\n            return Data(text=str(result), data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_database_properties\",\n            description=\"Retrieve properties of a Notion database. Input should include the database ID.\",\n            func=self._fetch_database_properties,\n            args_schema=self.NotionDatabasePropertiesSchema,\n        )\n\n    def _fetch_database_properties(self, database_id: str) -> Union[Dict, str]:\n        url = f\"https://api.notion.com/v1/databases/{database_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n        try:\n            response = requests.get(url, headers=headers)\n            response.raise_for_status()\n            data = response.json()\n            properties = data.get(\"properties\", {})\n            return properties\n        except requests.exceptions.RequestException as e:\n            return f\"Error fetching Notion database properties: {str(e)}\"\n        except ValueError as e:\n            return f\"Error parsing Notion API response: {str(e)}\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"database_id","value":"","display_name":"Database ID","advanced":true,"dynamic":false,"info":"The ID of the Notion database.","title_case":false,"type":"str","_input_type":"StrInput"},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Retrieve properties of a Notion database.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Database Properties ","documentation":"https://docs.langflow.org/integrations/notion/list-database-properties","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["database_id","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionDatabaseProperties-aeWil","description":"Retrieve properties of a Notion database.","display_name":"List Database Properties "},"selected":false,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":1004.5753613670959,"y":1713.914531491452}},{"id":"NotionListPages-znA3w","type":"genericNode","position":{"x":1006.1848442547046,"y":2022.7880909242833},"data":{"type":"NotionListPages","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nimport json\nfrom typing import Dict, Any, List, Optional\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionListPages(LCToolComponent):\n    display_name: str = \"List Pages \"\n    description: str = (\n        \"Query a Notion database with filtering and sorting. \"\n        \"The input should be a JSON string containing the 'filter' and 'sorts' objects. \"\n        \"Example input:\\n\"\n        '{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}'\n    )\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-pages\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database to query.\",\n        ),\n        MultilineInput(\n            name=\"query_json\",\n            display_name=\"Database query (JSON)\",\n            info=\"A JSON string containing the filters and sorts that will be used for querying the database. Leave empty for no filters or sorts.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n    class NotionListPagesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database to query.\")\n        query_json: Optional[str] = Field(\n            default=\"\",\n            description=\"A JSON string containing the filters and sorts for querying the database. Leave empty for no filters or sorts.\",\n        )\n\n    def run_model(self) -> List[Data]:\n        result = self._query_notion_database(self.database_id, self.query_json)\n\n        if isinstance(result, str):\n            # An error occurred, return it as a single record\n            return [Data(text=result)]\n\n        records = []\n        combined_text = f\"Pages found: {len(result)}\\n\\n\"\n\n        for page in result:\n            page_data = {\n                \"id\": page[\"id\"],\n                \"url\": page[\"url\"],\n                \"created_time\": page[\"created_time\"],\n                \"last_edited_time\": page[\"last_edited_time\"],\n                \"properties\": page[\"properties\"],\n            }\n\n            text = (\n                f\"id: {page['id']}\\n\"\n                f\"url: {page['url']}\\n\"\n                f\"created_time: {page['created_time']}\\n\"\n                f\"last_edited_time: {page['last_edited_time']}\\n\"\n                f\"properties: {json.dumps(page['properties'], indent=2)}\\n\\n\"\n            )\n\n            combined_text += text\n            records.append(Data(text=text, **page_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_pages\",\n            description=self.description,\n            func=self._query_notion_database,\n            args_schema=self.NotionListPagesSchema,\n        )\n\n    def _query_notion_database(self, database_id: str, query_json: Optional[str] = None) -> List[Dict[str, Any]] | str:\n        url = f\"https://api.notion.com/v1/databases/{database_id}/query\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        query_payload = {}\n        if query_json and query_json.strip():\n            try:\n                query_payload = json.loads(query_json)\n            except json.JSONDecodeError as e:\n                return f\"Invalid JSON format for query: {str(e)}\"\n\n        try:\n            response = requests.post(url, headers=headers, json=query_payload)\n            response.raise_for_status()\n            results = response.json()\n            return results[\"results\"]\n        except requests.exceptions.RequestException as e:\n            return f\"Error querying Notion database: {str(e)}\"\n        except KeyError:\n            return \"Unexpected response format from Notion API\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"database_id","value":"","display_name":"Database ID","advanced":true,"dynamic":false,"info":"The ID of the Notion database to query.","title_case":false,"type":"str","_input_type":"StrInput"},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"query_json":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"query_json","value":"","display_name":"Database query (JSON)","advanced":true,"input_types":["Message"],"dynamic":false,"info":"A JSON string containing the filters and sorts that will be used for querying the database. Leave empty for no filters or sorts.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Pages ","documentation":"https://docs.langflow.org/integrations/notion/list-pages","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["notion_secret","database_id","query_json"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionListPages-znA3w","description":"Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}","display_name":"List Pages "},"selected":false,"width":384,"height":470,"dragging":false,"positionAbsolute":{"x":1006.1848442547046,"y":2022.7880909242833}},{"id":"NotionUserList-C3eGn","type":"genericNode","position":{"x":2260.15497405973,"y":1717.4551881467207},"data":{"type":"NotionUserList","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Retrieve users from Notion.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Users ","documentation":"https://docs.langflow.org/integrations/notion/list-users","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionUserList-C3eGn","description":"Retrieve users from Notion.","display_name":"List Users "},"selected":true,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":2260.15497405973,"y":1717.4551881467207}},{"id":"NotionPageContent-SlL21","type":"genericNode","position":{"x":1826.4242329724448,"y":1715.6365113286927},"data":{"type":"NotionPageContent","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageContent(LCToolComponent):\n    display_name = \"Page Content Viewer \"\n    description = \"Retrieve the content of a Notion page as plain text.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/page-content-viewer\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to retrieve.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageContentSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to retrieve.\")\n\n    def run_model(self) -> Data:\n        result = self._retrieve_page_content(self.page_id)\n        if isinstance(result, str) and result.startswith(\"Error:\"):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the content\n            return Data(text=result, data={\"content\": result})\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_page_content\",\n            description=\"Retrieve the content of a Notion page as plain text.\",\n            func=self._retrieve_page_content,\n            args_schema=self.NotionPageContentSchema,\n        )\n\n    def _retrieve_page_content(self, page_id: str) -> str:\n        blocks_url = f\"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n        try:\n            blocks_response = requests.get(blocks_url, headers=headers)\n            blocks_response.raise_for_status()\n            blocks_data = blocks_response.json()\n            return self.parse_blocks(blocks_data.get(\"results\", []))\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to retrieve Notion page content. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while retrieving Notion page content. {str(e)}\"\n\n    def parse_blocks(self, blocks: list) -> str:\n        content = \"\"\n        for block in blocks:\n            block_type = block.get(\"type\")\n            if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"quote\"]:\n                content += self.parse_rich_text(block[block_type].get(\"rich_text\", [])) + \"\\n\\n\"\n            elif block_type in [\"bulleted_list_item\", \"numbered_list_item\"]:\n                content += self.parse_rich_text(block[block_type].get(\"rich_text\", [])) + \"\\n\"\n            elif block_type == \"to_do\":\n                content += self.parse_rich_text(block[\"to_do\"].get(\"rich_text\", [])) + \"\\n\"\n            elif block_type == \"code\":\n                content += self.parse_rich_text(block[\"code\"].get(\"rich_text\", [])) + \"\\n\\n\"\n            elif block_type == \"image\":\n                content += f\"[Image: {block['image'].get('external', {}).get('url', 'No URL')}]\\n\\n\"\n            elif block_type == \"divider\":\n                content += \"---\\n\\n\"\n        return content.strip()\n\n    def parse_rich_text(self, rich_text: list) -> str:\n        return \"\".join(segment.get(\"plain_text\", \"\") for segment in rich_text)\n\n    def __call__(self, *args, **kwargs):\n        return self._retrieve_page_content(*args, **kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"page_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"page_id","value":"","display_name":"Page ID","advanced":true,"dynamic":false,"info":"The ID of the Notion page to retrieve.","title_case":false,"type":"str","_input_type":"StrInput"}},"description":"Retrieve the content of a Notion page as plain text.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Page Content Viewer ","documentation":"https://docs.langflow.org/integrations/notion/page-content-viewer","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["page_id","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionPageContent-SlL21","description":"Retrieve the content of a Notion page as plain text.","display_name":"Page Content Viewer "},"selected":false,"width":384,"height":330,"dragging":false,"positionAbsolute":{"x":1826.4242329724448,"y":1715.6365113286927}},{"id":"NotionSearch-VS2mI","type":"genericNode","position":{"x":2258.1166047519732,"y":2034.3959294952945},"data":{"type":"NotionSearch","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"filter_value":{"trace_as_metadata":true,"options":["page","database"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"filter_value","value":"page","display_name":"Filter Type","advanced":true,"dynamic":false,"info":"Limits the results to either only pages or only databases.","title_case":false,"type":"str","_input_type":"DropdownInput"},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"query":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"query","value":"","display_name":"Search Query","advanced":true,"dynamic":false,"info":"The text that the API compares page and database titles against.","title_case":false,"type":"str","_input_type":"StrInput"},"sort_direction":{"trace_as_metadata":true,"options":["ascending","descending"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sort_direction","value":"descending","display_name":"Sort Direction","advanced":true,"dynamic":false,"info":"The direction to sort the results.","title_case":false,"type":"str","_input_type":"DropdownInput"}},"description":"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Search ","documentation":"https://docs.langflow.org/integrations/notion/search","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["notion_secret","query","filter_value","sort_direction"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionSearch-VS2mI","description":"Searches all pages and databases that have been shared with an integration.","display_name":"Search "},"selected":false,"width":384,"height":386,"dragging":false,"positionAbsolute":{"x":2258.1166047519732,"y":2034.3959294952945}},{"id":"NotionPageUpdate-6FyYd","type":"genericNode","position":{"x":1827.0574354713603,"y":2055.9948126656136},"data":{"type":"NotionPageUpdate","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nimport requests\nfrom typing import Dict, Any, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom loguru import logger\nfrom langflow.io import Output\n\nclass NotionPageUpdate(LCToolComponent):\n    display_name: str = \"Update Page Property \"\n    description: str = \"Update the properties of a Notion page.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to update.\",\n        ),\n        MultilineInput(\n            name=\"properties\",\n            display_name=\"Properties\",\n            info=\"The properties to update on the page (as a JSON string or a dictionary).\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageUpdateSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to update.\")\n        properties: Union[str, Dict[str, Any]] = Field(\n            ..., description=\"The properties to update on the page (as a JSON string or a dictionary).\"\n        )\n\n    def run_model(self) -> Data:\n        result = self._update_notion_page(self.page_id, self.properties)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the updated page data\n            output = \"Updated page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"update_notion_page\",\n            description=\"Update the properties of a Notion page. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._update_notion_page,\n            args_schema=self.NotionPageUpdateSchema,\n        )\n\n    def _update_notion_page(self, page_id: str, properties: Union[str, Dict[str, Any]]) -> Union[Dict[str, Any], str]:\n        url = f\"https://api.notion.com/v1/pages/{page_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n\n        # Parse properties if it's a string\n        if isinstance(properties, str):\n            try:\n                parsed_properties = json.loads(properties)\n            except json.JSONDecodeError as e:\n                error_message = f\"Invalid JSON format for properties: {str(e)}\"\n                logger.error(error_message)\n                return error_message\n\n        else:\n            parsed_properties = properties\n\n        data = {\"properties\": parsed_properties}\n\n        try:\n            logger.info(f\"Sending request to Notion API: URL: {url}, Data: {json.dumps(data)}\")\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n            updated_page = response.json()\n\n            logger.info(f\"Successfully updated Notion page. Response: {json.dumps(updated_page)}\")\n            return updated_page\n        except requests.exceptions.HTTPError as e:\n            error_message = f\"HTTP Error occurred: {str(e)}\"\n            if e.response is not None:\n                error_message += f\"\\nStatus code: {e.response.status_code}\"\n                error_message += f\"\\nResponse body: {e.response.text}\"\n            logger.error(error_message)\n            return error_message\n        except requests.exceptions.RequestException as e:\n            error_message = f\"An error occurred while making the request: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n        except Exception as e:\n            error_message = f\"An unexpected error occurred: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._update_notion_page(*args, **kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"page_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"page_id","value":"","display_name":"Page ID","advanced":true,"dynamic":false,"info":"The ID of the Notion page to update.","title_case":false,"type":"str","_input_type":"StrInput"},"properties":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"properties","value":"","display_name":"Properties","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The properties to update on the page (as a JSON string or a dictionary).","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Update the properties of a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Update Page Property ","documentation":"https://docs.langflow.org/integrations/notion/page-update","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["page_id","properties","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionPageUpdate-6FyYd","description":"Update the properties of a Notion page.","display_name":"Update Page Property "},"selected":false,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":1827.0574354713603,"y":2055.9948126656136}},{"id":"ToolCallingAgent-50Gcd","type":"genericNode","position":{"x":2186.0530739759893,"y":612.1744804997304},"data":{"type":"ToolCallingAgent","node":{"template":{"_type":"Component","chat_history":{"trace_as_metadata":true,"list":true,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"chat_history","value":"","display_name":"Chat History","advanced":false,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"DataInput"},"llm":{"trace_as_metadata":true,"list":false,"required":true,"placeholder":"","show":true,"name":"llm","value":"","display_name":"Language Model","advanced":false,"input_types":["LanguageModel"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"tools":{"trace_as_metadata":true,"list":true,"required":false,"placeholder":"","show":true,"name":"tools","value":"","display_name":"Tools","advanced":false,"input_types":["Tool","BaseTool"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"handle_parsing_errors":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"handle_parsing_errors","value":true,"display_name":"Handle Parse Errors","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"},"max_iterations":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"max_iterations","value":15,"display_name":"Max Iterations","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"int","_input_type":"IntInput"},"system_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_prompt","value":"","display_name":"System Prompt","advanced":false,"input_types":["Message"],"dynamic":false,"info":"System prompt for the agent.","title_case":false,"type":"str","_input_type":"MultilineInput"},"user_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"user_prompt","value":"{input}","display_name":"Prompt","advanced":true,"input_types":["Message"],"dynamic":false,"info":"This prompt must contain 'input' key.","title_case":false,"type":"str","_input_type":"MultilineInput"},"verbose":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"verbose","value":true,"display_name":"Verbose","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Agent that uses tools","icon":"LangChain","base_classes":["AgentExecutor","Message"],"display_name":"Tool Calling Agent","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["AgentExecutor"],"selected":"AgentExecutor","name":"agent","display_name":"Agent","method":"build_agent","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Message"],"selected":"Message","name":"response","display_name":"Response","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","handle_parsing_errors","verbose","max_iterations","tools","llm","system_prompt","user_prompt","chat_history"],"beta":true,"edited":false,"lf_version":"1.0.17"},"id":"ToolCallingAgent-50Gcd"},"selected":false,"width":384,"height":532,"dragging":false,"positionAbsolute":{"x":2186.0530739759893,"y":612.1744804997304}},{"id":"ChatOutput-TSCup","type":"genericNode","position":{"x":2649.190603849412,"y":841.0466487848925},"data":{"type":"ChatOutput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"data_template":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"data_template","value":"{text}","display_name":"Data Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as output.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sender","value":"Machine","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sender_name","value":"AI","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"session_id","value":"","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"should_store_message":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"should_store_message","value":true,"display_name":"Store Messages","advanced":true,"dynamic":false,"info":"Store the message in the history.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Display a chat message in the Playground.","icon":"ChatOutput","base_classes":["Message"],"display_name":"Chat Output","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"message","display_name":"Message","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","should_store_message","sender","sender_name","session_id","data_template"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ChatOutput-TSCup"},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":2649.190603849412,"y":841.0466487848925},"dragging":false},{"id":"ChatInput-bcq6D","type":"genericNode","position":{"x":557.6262725075026,"y":724.8518930903978},"data":{"type":"ChatInput","node":{"template":{"_type":"Component","files":{"trace_as_metadata":true,"file_path":"","fileTypes":["txt","md","mdx","csv","json","yaml","yml","xml","html","htm","pdf","docx","py","sh","sql","js","ts","tsx","jpg","jpeg","png","bmp","image"],"list":true,"required":false,"placeholder":"","show":true,"name":"files","value":"","display_name":"Files","advanced":true,"dynamic":false,"info":"Files to be sent with the message.","title_case":false,"type":"file","_input_type":"FileInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, MESSAGE_SENDER_NAME_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"list users","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as input.","title_case":false,"type":"str","_input_type":"MultilineInput"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sender","value":"User","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sender_name","value":"User","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"session_id","value":"","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"should_store_message":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"should_store_message","value":true,"display_name":"Store Messages","advanced":true,"dynamic":false,"info":"Store the message in the history.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Get chat inputs from the Playground.","icon":"ChatInput","base_classes":["Message"],"display_name":"Chat Input","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"message","display_name":"Message","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","should_store_message","sender","sender_name","session_id","files"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ChatInput-bcq6D"},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":557.6262725075026,"y":724.8518930903978},"dragging":false},{"id":"ToolkitComponent-2lNG0","type":"genericNode","position":{"x":1731.8884789245508,"y":1378.7846304343796},"data":{"type":"ToolkitComponent","node":{"template":{"_type":"Component","tools":{"trace_as_metadata":true,"list":true,"required":false,"placeholder":"","show":true,"name":"tools","value":"","display_name":"Tools","advanced":false,"input_types":["Tool"],"dynamic":false,"info":"List of tools to combine.","title_case":false,"type":"other","_input_type":"HandleInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import List\r\nfrom langflow.custom import Component\r\nfrom langflow.inputs import HandleInput, MessageTextInput\r\nfrom langflow.template import Output\r\nfrom langflow.field_typing import Tool, Embeddings\r\nfrom langchain.tools.base import BaseTool, StructuredTool\r\nfrom langflow.schema import Data\r\n\r\nclass ToolkitComponent(Component):\r\n    display_name = \"Toolkit\"\r\n    description = \"Combines multiple tools into a single list of tools.\"\r\n    icon = \"pocket-knife\"\r\n\r\n    inputs = [\r\n        HandleInput(\r\n            name=\"tools\",\r\n            display_name=\"Tools\",\r\n            input_types=[\"Tool\"],\r\n            info=\"List of tools to combine.\",\r\n            is_list=True,\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Tools\", name=\"generated_tools\", method=\"generate_toolkit\"),\r\n        Output(display_name=\"Tool Data\", name=\"tool_data\", method=\"generate_tool_data\"),\r\n    ]\r\n\r\n    def generate_toolkit(self) -> List[BaseTool]:\r\n        combined_tools = []\r\n        name_count = {}\r\n        for index, tool in enumerate(self.tools):\r\n            self.log(f\"Processing tool {index}: {type(tool)}\")\r\n            if isinstance(tool, (BaseTool, StructuredTool)):\r\n                processed_tool = tool\r\n            elif hasattr(tool, 'build_tool'):\r\n                processed_tool = tool.build_tool()\r\n            else:\r\n                self.log(f\"Unsupported tool type: {type(tool)}. Attempting to process anyway.\")\r\n                processed_tool = tool\r\n\r\n            original_name = getattr(processed_tool, 'name', f\"UnnamedTool_{index}\")\r\n            self.log(f\"Original tool name: {original_name}\")\r\n\r\n            if original_name not in name_count:\r\n                name_count[original_name] = 0\r\n                final_name = original_name\r\n            else:\r\n                name_count[original_name] += 1\r\n                final_name = f\"{original_name}_{name_count[original_name]}\"\r\n\r\n            if hasattr(processed_tool, 'name'):\r\n                processed_tool.name = final_name\r\n\r\n            self.log(f\"Final tool name: {final_name}\")\r\n\r\n            if isinstance(processed_tool, StructuredTool) and hasattr(processed_tool, 'args_schema'):\r\n                processed_tool.args_schema.name = f\"{final_name}_Schema\"\r\n\r\n            combined_tools.append(processed_tool)\r\n\r\n        debug_info = \"\\n\".join([f\"Tool {i}: {getattr(tool, 'name', f'UnnamedTool_{i}')} (Original: {getattr(tool, '_original_name', 'N/A')}) - Type: {type(tool)}\" for i, tool in enumerate(combined_tools)])\r\n        self.log(\"Final toolkit composition:\")\r\n        self.log(debug_info)\r\n\r\n\r\n        self.status = combined_tools\r\n        return combined_tools\r\n\r\n    def generate_tool_data(self) -> List[Data]:\r\n        tool_data = []\r\n        for tool in self.generate_toolkit():\r\n            tool_data.append(Data(\r\n                data={\r\n                    \"name\": getattr(tool, 'name', 'Unnamed Tool'),\r\n                    \"description\": getattr(tool, 'description', 'No description available')\r\n                }\r\n            ))\r\n        return tool_data","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false}},"description":"Combines multiple tools into a single list of tools.","icon":"pocket-knife","base_classes":["BaseTool","Data"],"display_name":"Toolkit","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["BaseTool"],"selected":"BaseTool","name":"generated_tools","display_name":"Tools","method":"generate_toolkit","value":"__UNDEFINED__","cache":true},{"types":["Data"],"selected":"Data","name":"tool_data","display_name":"Tool Data","method":"generate_tool_data","value":"__UNDEFINED__","cache":true,"hidden":true}],"field_order":["tools"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"ToolkitComponent-2lNG0"},"selected":false,"width":384,"height":292,"dragging":false,"positionAbsolute":{"x":1731.8884789245508,"y":1378.7846304343796}},{"id":"OpenAIModel-BJWIg","type":"genericNode","position":{"x":1718.9773974162958,"y":603.4642741725065},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":true,"required":false,"placeholder":"","show":true,"name":"api_key","value":"","display_name":"OpenAI API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Input","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"json_mode","value":false,"display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"name":"max_tokens","value":"","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"model_kwargs","value":{},"display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model_name","value":"gpt-4o","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"openai_api_base","value":"","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"name":"output_schema","value":{},"display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"seed","value":1,"display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"stream","value":false,"display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_message","value":"","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"temperature","value":"0.2","display_name":"Temperature","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"OpenAIModel-BJWIg"},"selected":false,"width":384,"height":433,"positionAbsolute":{"x":1718.9773974162958,"y":603.4642741725065},"dragging":false},{"id":"Memory-CTQWu","type":"genericNode","position":{"x":1240.7186213296432,"y":1059.5754404393747},"data":{"type":"Memory","node":{"template":{"_type":"Component","memory":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"memory","value":"","display_name":"External Memory","advanced":true,"input_types":["BaseChatMessageHistory"],"dynamic":false,"info":"Retrieve messages from an external memory. If empty, it will use the Langflow tables.","title_case":false,"type":"other","_input_type":"HandleInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n        Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        if self.memory:\n            chat_memory = self.memory\n        else:\n            chat_memory = LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"n_messages":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"n_messages","value":100,"display_name":"Number of Messages","advanced":true,"dynamic":false,"info":"Number of messages to retrieve.","title_case":false,"type":"int","_input_type":"IntInput"},"order":{"trace_as_metadata":true,"options":["Ascending","Descending"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"order","value":"Ascending","display_name":"Order","advanced":true,"dynamic":false,"info":"Order of the messages.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender":{"trace_as_metadata":true,"options":["Machine","User","Machine and User"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sender","value":"Machine and User","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Filter by sender type.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sender_name","value":"","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Filter by sender name.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"session_id","value":"","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{sender_name}: {text}","display_name":"Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Retrieves stored chat messages from Langflow tables or an external memory.","icon":"message-square-more","base_classes":["BaseChatMemory","Data","Message"],"display_name":"Chat Memory","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"messages","display_name":"Messages (Data)","method":"retrieve_messages","value":"__UNDEFINED__","cache":true},{"types":["Message"],"selected":"Message","name":"messages_text","display_name":"Messages (Text)","method":"retrieve_messages_as_text","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["BaseChatMemory"],"selected":"BaseChatMemory","name":"lc_memory","display_name":"Memory","method":"build_lc_memory","value":"__UNDEFINED__","cache":true,"hidden":true}],"field_order":["memory","sender","sender_name","n_messages","session_id","order","template"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"Memory-CTQWu"},"selected":false,"width":384,"height":244,"dragging":false,"positionAbsolute":{"x":1240.7186213296432,"y":1059.5754404393747}},{"id":"Prompt-0dWZu","type":"genericNode","position":{"x":1227.4862876736101,"y":616.3826667128244},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"<Instructions>\nYou are a Notion Agent, an AI assistant designed to help users interact with their Notion workspace. Your role is to understand user requests, utilize the appropriate Notion tools to fulfill these requests, and communicate clearly with the user throughout the process.\n\nGeneral Guidelines:\n\n1. Carefully analyze each user request to determine which tool(s) you need to use.\n\n2. Before using any tool, ensure you have all the necessary information. If you need more details, ask the user clear and concise questions.\n\n3. When using a tool, provide a brief explanation to the user about what you're doing and why.\n\n4. After using a tool, interpret the results for the user in a clear, concise manner.\n\n5. If a task requires multiple steps, outline your plan to the user before proceeding.\n\n6. If you encounter an error or limitation, explain it to the user and suggest possible solutions or alternative approaches.\n\n7. Always maintain a helpful and professional tone in your interactions.\n\n8. Be proactive in offering suggestions or alternatives if the user's initial request can't be fulfilled exactly as stated.\n\n9. When providing information or results, focus on relevance and clarity. Summarize when necessary, but provide details when they're important.\n\n10. If a user's request is unclear or could be interpreted in multiple ways, ask for clarification before proceeding.\n\n11. After completing a task, summarize what was accomplished and suggest any relevant next steps or additional actions the user might want to take.\n\n12. If a user asks about capabilities you don't have or tools you can't access, clearly explain your limitations and suggest alternative ways to assist if possible.\n\nRemember, your primary goal is to assist the user effectively with their Notion-related tasks using the provided tools. Always strive for clarity, accuracy, and helpfulness in your interactions. Adapt your communication style to the user's level of technical understanding and familiarity with Notion.\n\nNow, you're ready to assist the user\n\nToday is: {CURRENT_DATE}\n</Instructions>","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","_input_type":"PromptInput"},"CURRENT_DATE":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"CURRENT_DATE","display_name":"CURRENT_DATE","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","is_input":null,"is_output":null,"is_composition":null,"base_classes":["Message"],"name":"","display_name":"Prompt","documentation":"","custom_fields":{"template":["CURRENT_DATE"]},"output_types":[],"full_path":null,"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","hidden":null,"display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"error":null,"edited":false,"lf_version":"1.0.17"},"id":"Prompt-0dWZu"},"selected":false,"width":384,"height":416,"positionAbsolute":{"x":1227.4862876736101,"y":616.3826667128244},"dragging":false},{"id":"CurrentDateComponent-NSNQ8","type":"genericNode","position":{"x":1092.5108512311297,"y":868.3249850335523},"data":{"type":"CurrentDateComponent","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from datetime import datetime\r\nfrom zoneinfo import ZoneInfo\r\nfrom typing import List\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import DropdownInput, Output\r\nfrom langflow.schema.message import Message\r\n\r\nclass CurrentDateComponent(Component):\r\n    display_name = \"Current Date 🕰️\"\r\n    description = \"Returns the current date and time in the selected timezone.\"\r\n    icon = \"clock\"\r\n\r\n    inputs = [\r\n        DropdownInput(\r\n            name=\"timezone\",\r\n            display_name=\"Timezone\",\r\n            options=[\r\n                \"UTC\",\r\n                \"US/Eastern\",\r\n                \"US/Central\",\r\n                \"US/Mountain\",\r\n                \"US/Pacific\",\r\n                \"Europe/London\",\r\n                \"Europe/Paris\",\r\n                \"Asia/Tokyo\",\r\n                \"Australia/Sydney\",\r\n                \"America/Sao_Paulo\",\r\n                \"America/Cuiaba\",\r\n            ],\r\n            value=\"UTC\",\r\n            info=\"Select the timezone for the current date and time.\",\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Current Date\", name=\"current_date\", method=\"get_current_date\"),\r\n    ]\r\n\r\n    def get_current_date(self) -> Message:\r\n        try:\r\n            tz = ZoneInfo(self.timezone)\r\n            current_date = datetime.now(tz).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\r\n            result = f\"Current date and time in {self.timezone}: {current_date}\"\r\n            self.status = result\r\n            return Message(text=result)\r\n        except Exception as e:\r\n            error_message = f\"Error: {str(e)}\"\r\n            self.status = error_message\r\n            return Message(text=error_message)","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"timezone":{"trace_as_metadata":true,"options":["UTC","US/Eastern","US/Central","US/Mountain","US/Pacific","Europe/London","Europe/Paris","Asia/Tokyo","Australia/Sydney","America/Sao_Paulo","America/Cuiaba"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"timezone","value":"UTC","display_name":"Timezone","advanced":false,"dynamic":false,"info":"Select the timezone for the current date and 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Date","method":"get_current_date","value":"__UNDEFINED__","cache":true}],"field_order":["timezone"],"beta":false,"edited":true,"official":false,"lf_version":"1.0.17"},"id":"CurrentDateComponent-NSNQ8","showNode":false},"selected":false,"width":96,"height":96,"dragging":false,"positionAbsolute":{"x":1092.5108512311297,"y":868.3249850335523}}],"edges":[{"source":"ChatInput-bcq6D","target":"ToolCallingAgent-50Gcd","sourceHandle":"{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bcq6Dœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","id":"reactflow__edge-ChatInput-bcq6D{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bcq6Dœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œinput_valueœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"input_value","id":"ToolCallingAgent-50Gcd","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"ChatInput","id":"ChatInput-bcq6D","name":"message","output_types":["Message"]}},"selected":false,"className":""},{"source":"ToolCallingAgent-50Gcd","target":"ChatOutput-TSCup","sourceHandle":"{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-50Gcdœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TSCupœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","id":"reactflow__edge-ToolCallingAgent-50Gcd{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-50Gcdœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-TSCup{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TSCupœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"input_value","id":"ChatOutput-TSCup","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"ToolCallingAgent","id":"ToolCallingAgent-50Gcd","name":"response","output_types":["Message"]}},"selected":false,"className":""},{"source":"ToolkitComponent-2lNG0","target":"ToolCallingAgent-50Gcd","sourceHandle":"{œdataTypeœ:œToolkitComponentœ,œidœ:œToolkitComponent-2lNG0œ,œnameœ:œgenerated_toolsœ,œoutput_typesœ:[œBaseToolœ]}","targetHandle":"{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}","id":"reactflow__edge-ToolkitComponent-2lNG0{œdataTypeœ:œToolkitComponentœ,œidœ:œToolkitComponent-2lNG0œ,œnameœ:œgenerated_toolsœ,œoutput_typesœ:[œBaseToolœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"tools","id":"ToolCallingAgent-50Gcd","inputTypes":["Tool","BaseTool"],"type":"other"},"sourceHandle":{"dataType":"ToolkitComponent","id":"ToolkitComponent-2lNG0","name":"generated_tools","output_types":["BaseTool"]}},"selected":false,"className":""},{"source":"NotionPageUpdate-6FyYd","sourceHandle":"{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-6FyYdœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}","target":"ToolkitComponent-2lNG0","targetHandle":"{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"tools","id":"ToolkitComponent-2lNG0","inputTypes":["Tool"],"type":"other"},"sourceHandle":{"dataType":"NotionPageUpdate","id":"NotionPageUpdate-6FyYd","name":"example_tool_output","output_types":["Tool"]}},"id":"reactflow__edge-NotionPageUpdate-6FyYd{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-6FyYdœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}","className":"","selected":false},{"source":"NotionPageCreator-6SCB5","sourceHandle":"{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-6SCB5œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}","target":"ToolkitComponent-2lNG0","targetHandle":"{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"tools","id":"ToolkitComponent-2lNG0","inputTypes":["Tool"],"type":"other"},"sourceHandle":{"dataType":"NotionPageCreator","id":"NotionPageCreator-6SCB5","name":"example_tool_output","output_types":["Tool"]}},"id":"reactflow__edge-NotionPageCreator-6SCB5{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-6SCB5œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}","className":"","selected":false},{"source":"AddContentToPage-ZezUn","sourceHandle":"{œdataTypeœ:œAddContentToPageœ,œidœ:œAddContentToPage-ZezUnœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}","target":"ToolkitComponent-2lNG0","targetHandle":"{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"tools","id":"ToolkitComponent-2lNG0","inputTypes":["Tool"],"type":"other"},"sourceHandl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+{
+    "id": "e070f0be-edc4-4512-bb0f-e53307062a26",
+    "data": {
+        "nodes": [
+            {
+                "id": "AddContentToPage-ZezUn",
+                "type": "genericNode",
+                "position": {
+                    "x": 1416.217259177943,
+                    "y": 1709.6205867919527
+                },
+                "data": {
+                    "type": "AddContentToPage",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "block_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "block_id",
+                                "value": "",
+                                "display_name": "Page/Block ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the page/block to add the content.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nfrom typing import Dict, Any, Union\nfrom markdown import markdown\nfrom bs4 import BeautifulSoup\nimport requests\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\n\nclass AddContentToPage(LCToolComponent):\n    display_name: str = \"Add Content to Page \"\n    description: str = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\n    documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        MultilineInput(\n            name=\"markdown_text\",\n            display_name=\"Markdown Text\",\n            info=\"The markdown text to convert to Notion blocks.\",\n        ),\n        StrInput(\n            name=\"block_id\",\n            display_name=\"Page/Block ID\",\n            info=\"The ID of the page/block to add the content.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class AddContentToPageSchema(BaseModel):\n        markdown_text: str = Field(..., description=\"The markdown text to convert to Notion blocks.\")\n        block_id: str = Field(..., description=\"The ID of the page/block to add the content.\")\n\n    def run_model(self) -> Data:\n        result = self._add_content_to_page(self.markdown_text, self.block_id)\n        return Data(data=result, text=json.dumps(result))\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"add_content_to_notion_page\",\n            description=\"Convert markdown text to Notion blocks and append them to a Notion page.\",\n            func=self._add_content_to_page,\n            args_schema=self.AddContentToPageSchema,\n        )\n\n    def _add_content_to_page(self, markdown_text: str, block_id: str) -> Union[Dict[str, Any], str]:\n        try:\n            html_text = markdown(markdown_text)\n            soup = BeautifulSoup(html_text, \"html.parser\")\n            blocks = self.process_node(soup)\n\n            url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\n            headers = {\n                \"Authorization\": f\"Bearer {self.notion_secret}\",\n                \"Content-Type\": \"application/json\",\n                \"Notion-Version\": \"2022-06-28\",\n            }\n\n            data = {\n                \"children\": blocks,\n            }\n\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n\n            return response.json()\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to add content to Notion page. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while adding content to Notion page. {str(e)}\"\n\n    def process_node(self, node):\n        blocks = []\n        if isinstance(node, str):\n            text = node.strip()\n            if text:\n                if text.startswith(\"#\"):\n                    heading_level = text.count(\"#\", 0, 6)\n                    heading_text = text[heading_level:].strip()\n                    if heading_level == 1:\n                        blocks.append(self.create_block(\"heading_1\", heading_text))\n                    elif heading_level == 2:\n                        blocks.append(self.create_block(\"heading_2\", heading_text))\n                    elif heading_level == 3:\n                        blocks.append(self.create_block(\"heading_3\", heading_text))\n                else:\n                    blocks.append(self.create_block(\"paragraph\", text))\n        elif node.name == \"h1\":\n            blocks.append(self.create_block(\"heading_1\", node.get_text(strip=True)))\n        elif node.name == \"h2\":\n            blocks.append(self.create_block(\"heading_2\", node.get_text(strip=True)))\n        elif node.name == \"h3\":\n            blocks.append(self.create_block(\"heading_3\", node.get_text(strip=True)))\n        elif node.name == \"p\":\n            code_node = node.find(\"code\")\n            if code_node:\n                code_text = code_node.get_text()\n                language, code = self.extract_language_and_code(code_text)\n                blocks.append(self.create_block(\"code\", code, language=language))\n            elif self.is_table(str(node)):\n                blocks.extend(self.process_table(node))\n            else:\n                blocks.append(self.create_block(\"paragraph\", node.get_text(strip=True)))\n        elif node.name == \"ul\":\n            blocks.extend(self.process_list(node, \"bulleted_list_item\"))\n        elif node.name == \"ol\":\n            blocks.extend(self.process_list(node, \"numbered_list_item\"))\n        elif node.name == \"blockquote\":\n            blocks.append(self.create_block(\"quote\", node.get_text(strip=True)))\n        elif node.name == \"hr\":\n            blocks.append(self.create_block(\"divider\", \"\"))\n        elif node.name == \"img\":\n            blocks.append(self.create_block(\"image\", \"\", image_url=node.get(\"src\")))\n        elif node.name == \"a\":\n            blocks.append(self.create_block(\"bookmark\", node.get_text(strip=True), link_url=node.get(\"href\")))\n        elif node.name == \"table\":\n            blocks.extend(self.process_table(node))\n\n        for child in node.children:\n            if isinstance(child, str):\n                continue\n            blocks.extend(self.process_node(child))\n\n        return blocks\n\n    def extract_language_and_code(self, code_text):\n        lines = code_text.split(\"\\n\")\n        language = lines[0].strip()\n        code = \"\\n\".join(lines[1:]).strip()\n        return language, code\n\n    def is_code_block(self, text):\n        return text.startswith(\"```\")\n\n    def extract_code_block(self, text):\n        lines = text.split(\"\\n\")\n        language = lines[0].strip(\"`\").strip()\n        code = \"\\n\".join(lines[1:]).strip(\"`\").strip()\n        return language, code\n\n    def is_table(self, text):\n        rows = text.split(\"\\n\")\n        if len(rows) < 2:\n            return False\n\n        has_separator = False\n        for i, row in enumerate(rows):\n            if \"|\" in row:\n                cells = [cell.strip() for cell in row.split(\"|\")]\n                cells = [cell for cell in cells if cell]  # Remove empty cells\n                if i == 1 and all(set(cell) <= set(\"-|\") for cell in cells):\n                    has_separator = True\n                elif not cells:\n                    return False\n\n        return has_separator and len(rows) >= 3\n\n    def process_list(self, node, list_type):\n        blocks = []\n        for item in node.find_all(\"li\"):\n            item_text = item.get_text(strip=True)\n            checked = item_text.startswith(\"[x]\")\n            is_checklist = item_text.startswith(\"[ ]\") or checked\n\n            if is_checklist:\n                item_text = item_text.replace(\"[x]\", \"\").replace(\"[ ]\", \"\").strip()\n                blocks.append(self.create_block(\"to_do\", item_text, checked=checked))\n            else:\n                blocks.append(self.create_block(list_type, item_text))\n        return blocks\n\n    def process_table(self, node):\n        blocks = []\n        header_row = node.find(\"thead\").find(\"tr\") if node.find(\"thead\") else None\n        body_rows = node.find(\"tbody\").find_all(\"tr\") if node.find(\"tbody\") else []\n\n        if header_row or body_rows:\n            table_width = max(\n                len(header_row.find_all([\"th\", \"td\"])) if header_row else 0,\n                max(len(row.find_all([\"th\", \"td\"])) for row in body_rows),\n            )\n\n            table_block = self.create_block(\"table\", \"\", table_width=table_width, has_column_header=bool(header_row))\n            blocks.append(table_block)\n\n            if header_row:\n                header_cells = [cell.get_text(strip=True) for cell in header_row.find_all([\"th\", \"td\"])]\n                header_row_block = self.create_block(\"table_row\", header_cells)\n                blocks.append(header_row_block)\n\n            for row in body_rows:\n                cells = [cell.get_text(strip=True) for cell in row.find_all([\"th\", \"td\"])]\n                row_block = self.create_block(\"table_row\", cells)\n                blocks.append(row_block)\n\n        return blocks\n\n    def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\n        block: dict[str, Any] = {\n            \"object\": \"block\",\n            \"type\": block_type,\n            block_type: {},\n        }\n\n        if block_type in [\n            \"paragraph\",\n            \"heading_1\",\n            \"heading_2\",\n            \"heading_3\",\n            \"bulleted_list_item\",\n            \"numbered_list_item\",\n            \"quote\",\n        ]:\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n        elif block_type == \"to_do\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"checked\"] = kwargs.get(\"checked\", False)\n        elif block_type == \"code\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"language\"] = kwargs.get(\"language\", \"plain text\")\n        elif block_type == \"image\":\n            block[block_type] = {\"type\": \"external\", \"external\": {\"url\": kwargs.get(\"image_url\", \"\")}}\n        elif block_type == \"divider\":\n            pass\n        elif block_type == \"bookmark\":\n            block[block_type][\"url\"] = kwargs.get(\"link_url\", \"\")\n        elif block_type == \"table\":\n            block[block_type][\"table_width\"] = kwargs.get(\"table_width\", 0)\n            block[block_type][\"has_column_header\"] = kwargs.get(\"has_column_header\", False)\n            block[block_type][\"has_row_header\"] = kwargs.get(\"has_row_header\", False)\n        elif block_type == \"table_row\":\n            block[block_type][\"cells\"] = [[{\"type\": \"text\", \"text\": {\"content\": cell}} for cell in content]]\n\n        return block\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "markdown_text": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "markdown_text",
+                                "value": "",
+                                "display_name": "Markdown Text",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The markdown text to convert to Notion blocks.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Convert markdown text to Notion blocks and append them to a Notion page.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Add Content to Page ",
+                        "documentation": "https://developers.notion.com/reference/patch-block-children",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "markdown_text",
+                            "block_id",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "AddContentToPage-ZezUn",
+                    "description": "Convert markdown text to Notion blocks and append them to a Notion page.",
+                    "display_name": "Add Content to Page "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 330,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1416.217259177943,
+                    "y": 1709.6205867919527
+                }
+            },
+            {
+                "id": "NotionPageCreator-6SCB5",
+                "type": "genericNode",
+                "position": {
+                    "x": 1413.9782390799146,
+                    "y": 2051.645785494985
+                },
+                "data": {
+                    "type": "NotionPageCreator",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nfrom typing import Dict, Any, Union\nimport requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageCreator(LCToolComponent):\n    display_name: str = \"Create Page \"\n    description: str = \"A component for creating Notion pages.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"properties_json\",\n            display_name=\"Properties (JSON)\",\n            info=\"The properties of the new page as a JSON string.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageCreatorSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n        properties_json: str = Field(..., description=\"The properties of the new page as a JSON string.\")\n\n    def run_model(self) -> Data:\n        result = self._create_notion_page(self.database_id, self.properties_json)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the created page data\n            output = \"Created page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"create_notion_page\",\n            description=\"Create a new page in a Notion database. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._create_notion_page,\n            args_schema=self.NotionPageCreatorSchema,\n        )\n\n    def _create_notion_page(self, database_id: str, properties_json: str) -> Union[Dict[str, Any], str]:\n        if not database_id or not properties_json:\n            return \"Invalid input. Please provide 'database_id' and 'properties_json'.\"\n\n        try:\n            properties = json.loads(properties_json)\n        except json.JSONDecodeError as e:\n            return f\"Invalid properties format. Please provide a valid JSON string. Error: {str(e)}\"\n\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"parent\": {\"database_id\": database_id},\n            \"properties\": properties,\n        }\n\n        try:\n            response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\n            response.raise_for_status()\n            result = response.json()\n            return result\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Failed to create Notion page. Error: {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._create_notion_page(*args, **kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "database_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "database_id",
+                                "value": "",
+                                "display_name": "Database ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion database.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "properties_json": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "properties_json",
+                                "value": "",
+                                "display_name": "Properties (JSON)",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The properties of the new page as a JSON string.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "A component for creating Notion pages.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Create Page ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/page-create",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "database_id",
+                            "notion_secret",
+                            "properties_json"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionPageCreator-6SCB5",
+                    "description": "A component for creating Notion pages.",
+                    "display_name": "Create Page "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1413.9782390799146,
+                    "y": 2051.645785494985
+                }
+            },
+            {
+                "id": "NotionDatabaseProperties-aeWil",
+                "type": "genericNode",
+                "position": {
+                    "x": 1004.5753613670959,
+                    "y": 1713.914531491452
+                },
+                "data": {
+                    "type": "NotionDatabaseProperties",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import Dict, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionDatabaseProperties(LCToolComponent):\n    display_name: str = \"List Database Properties \"\n    description: str = \"Retrieve properties of a Notion database.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionDatabasePropertiesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n\n    def run_model(self) -> Data:\n        result = self._fetch_database_properties(self.database_id)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the properties\n            return Data(text=str(result), data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_database_properties\",\n            description=\"Retrieve properties of a Notion database. Input should include the database ID.\",\n            func=self._fetch_database_properties,\n            args_schema=self.NotionDatabasePropertiesSchema,\n        )\n\n    def _fetch_database_properties(self, database_id: str) -> Union[Dict, str]:\n        url = f\"https://api.notion.com/v1/databases/{database_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n        try:\n            response = requests.get(url, headers=headers)\n            response.raise_for_status()\n            data = response.json()\n            properties = data.get(\"properties\", {})\n            return properties\n        except requests.exceptions.RequestException as e:\n            return f\"Error fetching Notion database properties: {str(e)}\"\n        except ValueError as e:\n            return f\"Error parsing Notion API response: {str(e)}\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "database_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "database_id",
+                                "value": "",
+                                "display_name": "Database ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion database.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Retrieve properties of a Notion database.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Database Properties ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-database-properties",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "database_id",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionDatabaseProperties-aeWil",
+                    "description": "Retrieve properties of a Notion database.",
+                    "display_name": "List Database Properties "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1004.5753613670959,
+                    "y": 1713.914531491452
+                }
+            },
+            {
+                "id": "NotionListPages-znA3w",
+                "type": "genericNode",
+                "position": {
+                    "x": 1006.1848442547046,
+                    "y": 2022.7880909242833
+                },
+                "data": {
+                    "type": "NotionListPages",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nimport json\nfrom typing import Dict, Any, List, Optional\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionListPages(LCToolComponent):\n    display_name: str = \"List Pages \"\n    description: str = (\n        \"Query a Notion database with filtering and sorting. \"\n        \"The input should be a JSON string containing the 'filter' and 'sorts' objects. \"\n        \"Example input:\\n\"\n        '{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}'\n    )\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-pages\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database to query.\",\n        ),\n        MultilineInput(\n            name=\"query_json\",\n            display_name=\"Database query (JSON)\",\n            info=\"A JSON string containing the filters and sorts that will be used for querying the database. Leave empty for no filters or sorts.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n    class NotionListPagesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database to query.\")\n        query_json: Optional[str] = Field(\n            default=\"\",\n            description=\"A JSON string containing the filters and sorts for querying the database. Leave empty for no filters or sorts.\",\n        )\n\n    def run_model(self) -> List[Data]:\n        result = self._query_notion_database(self.database_id, self.query_json)\n\n        if isinstance(result, str):\n            # An error occurred, return it as a single record\n            return [Data(text=result)]\n\n        records = []\n        combined_text = f\"Pages found: {len(result)}\\n\\n\"\n\n        for page in result:\n            page_data = {\n                \"id\": page[\"id\"],\n                \"url\": page[\"url\"],\n                \"created_time\": page[\"created_time\"],\n                \"last_edited_time\": page[\"last_edited_time\"],\n                \"properties\": page[\"properties\"],\n            }\n\n            text = (\n                f\"id: {page['id']}\\n\"\n                f\"url: {page['url']}\\n\"\n                f\"created_time: {page['created_time']}\\n\"\n                f\"last_edited_time: {page['last_edited_time']}\\n\"\n                f\"properties: {json.dumps(page['properties'], indent=2)}\\n\\n\"\n            )\n\n            combined_text += text\n            records.append(Data(text=text, **page_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_pages\",\n            description=self.description,\n            func=self._query_notion_database,\n            args_schema=self.NotionListPagesSchema,\n        )\n\n    def _query_notion_database(self, database_id: str, query_json: Optional[str] = None) -> List[Dict[str, Any]] | str:\n        url = f\"https://api.notion.com/v1/databases/{database_id}/query\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        query_payload = {}\n        if query_json and query_json.strip():\n            try:\n                query_payload = json.loads(query_json)\n            except json.JSONDecodeError as e:\n                return f\"Invalid JSON format for query: {str(e)}\"\n\n        try:\n            response = requests.post(url, headers=headers, json=query_payload)\n            response.raise_for_status()\n            results = response.json()\n            return results[\"results\"]\n        except requests.exceptions.RequestException as e:\n            return f\"Error querying Notion database: {str(e)}\"\n        except KeyError:\n            return \"Unexpected response format from Notion API\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "database_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "database_id",
+                                "value": "",
+                                "display_name": "Database ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion database to query.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "query_json": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "query_json",
+                                "value": "",
+                                "display_name": "Database query (JSON)",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "A JSON string containing the filters and sorts that will be used for querying the database. Leave empty for no filters or sorts.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Pages ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-pages",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret",
+                            "database_id",
+                            "query_json"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionListPages-znA3w",
+                    "description": "Query a Notion database with filtering and sorting. The input should be a JSON string containing the 'filter' and 'sorts' objects. Example input:\n{\"filter\": {\"property\": \"Status\", \"select\": {\"equals\": \"Done\"}}, \"sorts\": [{\"timestamp\": \"created_time\", \"direction\": \"descending\"}]}",
+                    "display_name": "List Pages "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 470,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1006.1848442547046,
+                    "y": 2022.7880909242833
+                }
+            },
+            {
+                "id": "NotionUserList-C3eGn",
+                "type": "genericNode",
+                "position": {
+                    "x": 2260.15497405973,
+                    "y": 1717.4551881467207
+                },
+                "data": {
+                    "type": "NotionUserList",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Retrieve users from Notion.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Users ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-users",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionUserList-C3eGn",
+                    "description": "Retrieve users from Notion.",
+                    "display_name": "List Users "
+                },
+                "selected": true,
+                "width": 384,
+                "height": 302,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 2260.15497405973,
+                    "y": 1717.4551881467207
+                }
+            },
+            {
+                "id": "NotionPageContent-SlL21",
+                "type": "genericNode",
+                "position": {
+                    "x": 1826.4242329724448,
+                    "y": 1715.6365113286927
+                },
+                "data": {
+                    "type": "NotionPageContent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageContent(LCToolComponent):\n    display_name = \"Page Content Viewer \"\n    description = \"Retrieve the content of a Notion page as plain text.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/page-content-viewer\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to retrieve.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageContentSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to retrieve.\")\n\n    def run_model(self) -> Data:\n        result = self._retrieve_page_content(self.page_id)\n        if isinstance(result, str) and result.startswith(\"Error:\"):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the content\n            return Data(text=result, data={\"content\": result})\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_page_content\",\n            description=\"Retrieve the content of a Notion page as plain text.\",\n            func=self._retrieve_page_content,\n            args_schema=self.NotionPageContentSchema,\n        )\n\n    def _retrieve_page_content(self, page_id: str) -> str:\n        blocks_url = f\"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n        try:\n            blocks_response = requests.get(blocks_url, headers=headers)\n            blocks_response.raise_for_status()\n            blocks_data = blocks_response.json()\n            return self.parse_blocks(blocks_data.get(\"results\", []))\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to retrieve Notion page content. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while retrieving Notion page content. {str(e)}\"\n\n    def parse_blocks(self, blocks: list) -> str:\n        content = \"\"\n        for block in blocks:\n            block_type = block.get(\"type\")\n            if block_type in [\"paragraph\", \"heading_1\", \"heading_2\", \"heading_3\", \"quote\"]:\n                content += self.parse_rich_text(block[block_type].get(\"rich_text\", [])) + \"\\n\\n\"\n            elif block_type in [\"bulleted_list_item\", \"numbered_list_item\"]:\n                content += self.parse_rich_text(block[block_type].get(\"rich_text\", [])) + \"\\n\"\n            elif block_type == \"to_do\":\n                content += self.parse_rich_text(block[\"to_do\"].get(\"rich_text\", [])) + \"\\n\"\n            elif block_type == \"code\":\n                content += self.parse_rich_text(block[\"code\"].get(\"rich_text\", [])) + \"\\n\\n\"\n            elif block_type == \"image\":\n                content += f\"[Image: {block['image'].get('external', {}).get('url', 'No URL')}]\\n\\n\"\n            elif block_type == \"divider\":\n                content += \"---\\n\\n\"\n        return content.strip()\n\n    def parse_rich_text(self, rich_text: list) -> str:\n        return \"\".join(segment.get(\"plain_text\", \"\") for segment in rich_text)\n\n    def __call__(self, *args, **kwargs):\n        return self._retrieve_page_content(*args, **kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "page_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "page_id",
+                                "value": "",
+                                "display_name": "Page ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion page to retrieve.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            }
+                        },
+                        "description": "Retrieve the content of a Notion page as plain text.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Page Content Viewer ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/page-content-viewer",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "page_id",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionPageContent-SlL21",
+                    "description": "Retrieve the content of a Notion page as plain text.",
+                    "display_name": "Page Content Viewer "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 330,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1826.4242329724448,
+                    "y": 1715.6365113286927
+                }
+            },
+            {
+                "id": "NotionSearch-VS2mI",
+                "type": "genericNode",
+                "position": {
+                    "x": 2258.1166047519732,
+                    "y": 2034.3959294952945
+                },
+                "data": {
+                    "type": "NotionSearch",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "filter_value": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "page",
+                                    "database"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "filter_value",
+                                "value": "page",
+                                "display_name": "Filter Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Limits the results to either only pages or only databases.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "query": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "query",
+                                "value": "",
+                                "display_name": "Search Query",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The text that the API compares page and database titles against.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "sort_direction": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "ascending",
+                                    "descending"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sort_direction",
+                                "value": "descending",
+                                "display_name": "Sort Direction",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The direction to sort the results.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Search ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/search",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret",
+                            "query",
+                            "filter_value",
+                            "sort_direction"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionSearch-VS2mI",
+                    "description": "Searches all pages and databases that have been shared with an integration.",
+                    "display_name": "Search "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 386,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 2258.1166047519732,
+                    "y": 2034.3959294952945
+                }
+            },
+            {
+                "id": "NotionPageUpdate-6FyYd",
+                "type": "genericNode",
+                "position": {
+                    "x": 1827.0574354713603,
+                    "y": 2055.9948126656136
+                },
+                "data": {
+                    "type": "NotionPageUpdate",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nimport requests\nfrom typing import Dict, Any, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom loguru import logger\nfrom langflow.io import Output\n\nclass NotionPageUpdate(LCToolComponent):\n    display_name: str = \"Update Page Property \"\n    description: str = \"Update the properties of a Notion page.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to update.\",\n        ),\n        MultilineInput(\n            name=\"properties\",\n            display_name=\"Properties\",\n            info=\"The properties to update on the page (as a JSON string or a dictionary).\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageUpdateSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to update.\")\n        properties: Union[str, Dict[str, Any]] = Field(\n            ..., description=\"The properties to update on the page (as a JSON string or a dictionary).\"\n        )\n\n    def run_model(self) -> Data:\n        result = self._update_notion_page(self.page_id, self.properties)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the updated page data\n            output = \"Updated page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"update_notion_page\",\n            description=\"Update the properties of a Notion page. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._update_notion_page,\n            args_schema=self.NotionPageUpdateSchema,\n        )\n\n    def _update_notion_page(self, page_id: str, properties: Union[str, Dict[str, Any]]) -> Union[Dict[str, Any], str]:\n        url = f\"https://api.notion.com/v1/pages/{page_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n\n        # Parse properties if it's a string\n        if isinstance(properties, str):\n            try:\n                parsed_properties = json.loads(properties)\n            except json.JSONDecodeError as e:\n                error_message = f\"Invalid JSON format for properties: {str(e)}\"\n                logger.error(error_message)\n                return error_message\n\n        else:\n            parsed_properties = properties\n\n        data = {\"properties\": parsed_properties}\n\n        try:\n            logger.info(f\"Sending request to Notion API: URL: {url}, Data: {json.dumps(data)}\")\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n            updated_page = response.json()\n\n            logger.info(f\"Successfully updated Notion page. Response: {json.dumps(updated_page)}\")\n            return updated_page\n        except requests.exceptions.HTTPError as e:\n            error_message = f\"HTTP Error occurred: {str(e)}\"\n            if e.response is not None:\n                error_message += f\"\\nStatus code: {e.response.status_code}\"\n                error_message += f\"\\nResponse body: {e.response.text}\"\n            logger.error(error_message)\n            return error_message\n        except requests.exceptions.RequestException as e:\n            error_message = f\"An error occurred while making the request: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n        except Exception as e:\n            error_message = f\"An unexpected error occurred: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._update_notion_page(*args, **kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "page_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "page_id",
+                                "value": "",
+                                "display_name": "Page ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion page to update.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "properties": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "properties",
+                                "value": "",
+                                "display_name": "Properties",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The properties to update on the page (as a JSON string or a dictionary).",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Update the properties of a Notion page.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Update Page Property ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/page-update",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "page_id",
+                            "properties",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionPageUpdate-6FyYd",
+                    "description": "Update the properties of a Notion page.",
+                    "display_name": "Update Page Property "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1827.0574354713603,
+                    "y": 2055.9948126656136
+                }
+            },
+            {
+                "id": "ToolCallingAgent-50Gcd",
+                "type": "genericNode",
+                "position": {
+                    "x": 2186.0530739759893,
+                    "y": 612.1744804997304
+                },
+                "data": {
+                    "type": "ToolCallingAgent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "chat_history": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "chat_history",
+                                "value": "",
+                                "display_name": "Chat History",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "llm": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "llm",
+                                "value": "",
+                                "display_name": "Language Model",
+                                "advanced": false,
+                                "input_types": [
+                                    "LanguageModel"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "tools": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "tools",
+                                "value": "",
+                                "display_name": "Tools",
+                                "advanced": false,
+                                "input_types": [
+                                    "Tool",
+                                    "BaseTool"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "handle_parsing_errors": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "handle_parsing_errors",
+                                "value": true,
+                                "display_name": "Handle Parse Errors",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Input",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "max_iterations": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_iterations",
+                                "value": 15,
+                                "display_name": "Max Iterations",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "system_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_prompt",
+                                "value": "",
+                                "display_name": "System Prompt",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System prompt for the agent.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "user_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "user_prompt",
+                                "value": "{input}",
+                                "display_name": "Prompt",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "This prompt must contain 'input' key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "verbose": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "verbose",
+                                "value": true,
+                                "display_name": "Verbose",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Agent that uses tools",
+                        "icon": "LangChain",
+                        "base_classes": [
+                            "AgentExecutor",
+                            "Message"
+                        ],
+                        "display_name": "Tool Calling Agent",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "AgentExecutor"
+                                ],
+                                "selected": "AgentExecutor",
+                                "name": "agent",
+                                "display_name": "Agent",
+                                "method": "build_agent",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "response",
+                                "display_name": "Response",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "handle_parsing_errors",
+                            "verbose",
+                            "max_iterations",
+                            "tools",
+                            "llm",
+                            "system_prompt",
+                            "user_prompt",
+                            "chat_history"
+                        ],
+                        "beta": true,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ToolCallingAgent-50Gcd"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 532,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 2186.0530739759893,
+                    "y": 612.1744804997304
+                }
+            },
+            {
+                "id": "ChatOutput-TSCup",
+                "type": "genericNode",
+                "position": {
+                    "x": 2649.190603849412,
+                    "y": 841.0466487848925
+                },
+                "data": {
+                    "type": "ChatOutput",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "data_template": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data_template",
+                                "value": "{text}",
+                                "display_name": "Data Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Text",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Message to be passed as output.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "sender": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender",
+                                "value": "Machine",
+                                "display_name": "Sender Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Type of sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender_name": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender_name",
+                                "value": "AI",
+                                "display_name": "Sender Name",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Name of the sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "session_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "session_id",
+                                "value": "",
+                                "display_name": "Session ID",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "should_store_message": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "should_store_message",
+                                "value": true,
+                                "display_name": "Store Messages",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Store the message in the history.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Display a chat message in the Playground.",
+                        "icon": "ChatOutput",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Chat Output",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "message",
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "should_store_message",
+                            "sender",
+                            "sender_name",
+                            "session_id",
+                            "data_template"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ChatOutput-TSCup"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 2649.190603849412,
+                    "y": 841.0466487848925
+                },
+                "dragging": false
+            },
+            {
+                "id": "ChatInput-bcq6D",
+                "type": "genericNode",
+                "position": {
+                    "x": 557.6262725075026,
+                    "y": 724.8518930903978
+                },
+                "data": {
+                    "type": "ChatInput",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "files": {
+                                "trace_as_metadata": true,
+                                "file_path": "",
+                                "fileTypes": [
+                                    "txt",
+                                    "md",
+                                    "mdx",
+                                    "csv",
+                                    "json",
+                                    "yaml",
+                                    "yml",
+                                    "xml",
+                                    "html",
+                                    "htm",
+                                    "pdf",
+                                    "docx",
+                                    "py",
+                                    "sh",
+                                    "sql",
+                                    "js",
+                                    "ts",
+                                    "tsx",
+                                    "jpg",
+                                    "jpeg",
+                                    "png",
+                                    "bmp",
+                                    "image"
+                                ],
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "files",
+                                "value": "",
+                                "display_name": "Files",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Files to be sent with the message.",
+                                "title_case": false,
+                                "type": "file",
+                                "_input_type": "FileInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, MESSAGE_SENDER_NAME_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "list users",
+                                "display_name": "Text",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Message to be passed as input.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "sender": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender",
+                                "value": "User",
+                                "display_name": "Sender Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Type of sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender_name": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender_name",
+                                "value": "User",
+                                "display_name": "Sender Name",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Name of the sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "session_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "session_id",
+                                "value": "",
+                                "display_name": "Session ID",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "should_store_message": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "should_store_message",
+                                "value": true,
+                                "display_name": "Store Messages",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Store the message in the history.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Get chat inputs from the Playground.",
+                        "icon": "ChatInput",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Chat Input",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "message",
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "should_store_message",
+                            "sender",
+                            "sender_name",
+                            "session_id",
+                            "files"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ChatInput-bcq6D"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 557.6262725075026,
+                    "y": 724.8518930903978
+                },
+                "dragging": false
+            },
+            {
+                "id": "ToolkitComponent-2lNG0",
+                "type": "genericNode",
+                "position": {
+                    "x": 1731.8884789245508,
+                    "y": 1378.7846304343796
+                },
+                "data": {
+                    "type": "ToolkitComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "tools": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "tools",
+                                "value": "",
+                                "display_name": "Tools",
+                                "advanced": false,
+                                "input_types": [
+                                    "Tool"
+                                ],
+                                "dynamic": false,
+                                "info": "List of tools to combine.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from typing import List\r\nfrom langflow.custom import Component\r\nfrom langflow.inputs import HandleInput, MessageTextInput\r\nfrom langflow.template import Output\r\nfrom langflow.field_typing import Tool, Embeddings\r\nfrom langchain.tools.base import BaseTool, StructuredTool\r\nfrom langflow.schema import Data\r\n\r\nclass ToolkitComponent(Component):\r\n    display_name = \"Toolkit\"\r\n    description = \"Combines multiple tools into a single list of tools.\"\r\n    icon = \"pocket-knife\"\r\n\r\n    inputs = [\r\n        HandleInput(\r\n            name=\"tools\",\r\n            display_name=\"Tools\",\r\n            input_types=[\"Tool\"],\r\n            info=\"List of tools to combine.\",\r\n            is_list=True,\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Tools\", name=\"generated_tools\", method=\"generate_toolkit\"),\r\n        Output(display_name=\"Tool Data\", name=\"tool_data\", method=\"generate_tool_data\"),\r\n    ]\r\n\r\n    def generate_toolkit(self) -> List[BaseTool]:\r\n        combined_tools = []\r\n        name_count = {}\r\n        for index, tool in enumerate(self.tools):\r\n            self.log(f\"Processing tool {index}: {type(tool)}\")\r\n            if isinstance(tool, (BaseTool, StructuredTool)):\r\n                processed_tool = tool\r\n            elif hasattr(tool, 'build_tool'):\r\n                processed_tool = tool.build_tool()\r\n            else:\r\n                self.log(f\"Unsupported tool type: {type(tool)}. Attempting to process anyway.\")\r\n                processed_tool = tool\r\n\r\n            original_name = getattr(processed_tool, 'name', f\"UnnamedTool_{index}\")\r\n            self.log(f\"Original tool name: {original_name}\")\r\n\r\n            if original_name not in name_count:\r\n                name_count[original_name] = 0\r\n                final_name = original_name\r\n            else:\r\n                name_count[original_name] += 1\r\n                final_name = f\"{original_name}_{name_count[original_name]}\"\r\n\r\n            if hasattr(processed_tool, 'name'):\r\n                processed_tool.name = final_name\r\n\r\n            self.log(f\"Final tool name: {final_name}\")\r\n\r\n            if isinstance(processed_tool, StructuredTool) and hasattr(processed_tool, 'args_schema'):\r\n                processed_tool.args_schema.name = f\"{final_name}_Schema\"\r\n\r\n            combined_tools.append(processed_tool)\r\n\r\n        debug_info = \"\\n\".join([f\"Tool {i}: {getattr(tool, 'name', f'UnnamedTool_{i}')} (Original: {getattr(tool, '_original_name', 'N/A')}) - Type: {type(tool)}\" for i, tool in enumerate(combined_tools)])\r\n        self.log(\"Final toolkit composition:\")\r\n        self.log(debug_info)\r\n\r\n\r\n        self.status = combined_tools\r\n        return combined_tools\r\n\r\n    def generate_tool_data(self) -> List[Data]:\r\n        tool_data = []\r\n        for tool in self.generate_toolkit():\r\n            tool_data.append(Data(\r\n                data={\r\n                    \"name\": getattr(tool, 'name', 'Unnamed Tool'),\r\n                    \"description\": getattr(tool, 'description', 'No description available')\r\n                }\r\n            ))\r\n        return tool_data",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            }
+                        },
+                        "description": "Combines multiple tools into a single list of tools.",
+                        "icon": "pocket-knife",
+                        "base_classes": [
+                            "BaseTool",
+                            "Data"
+                        ],
+                        "display_name": "Toolkit",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "BaseTool"
+                                ],
+                                "selected": "BaseTool",
+                                "name": "generated_tools",
+                                "display_name": "Tools",
+                                "method": "generate_toolkit",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            },
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "tool_data",
+                                "display_name": "Tool Data",
+                                "method": "generate_tool_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            }
+                        ],
+                        "field_order": [
+                            "tools"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ToolkitComponent-2lNG0"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 292,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1731.8884789245508,
+                    "y": 1378.7846304343796
+                }
+            },
+            {
+                "id": "OpenAIModel-BJWIg",
+                "type": "genericNode",
+                "position": {
+                    "x": 1718.9773974162958,
+                    "y": 603.4642741725065
+                },
+                "data": {
+                    "type": "OpenAIModel",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "api_key": {
+                                "load_from_db": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "api_key",
+                                "value": "",
+                                "display_name": "OpenAI API Key",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Input",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageInput"
+                            },
+                            "json_mode": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_mode",
+                                "value": false,
+                                "display_name": "JSON Mode",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "If True, it will output JSON regardless of passing a schema.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "max_tokens": {
+                                "trace_as_metadata": true,
+                                "range_spec": {
+                                    "step_type": "float",
+                                    "min": 0,
+                                    "max": 128000,
+                                    "step": 0.1
+                                },
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_tokens",
+                                "value": "",
+                                "display_name": "Max Tokens",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "model_kwargs": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_kwargs",
+                                "value": {},
+                                "display_name": "Model Kwargs",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "model_name": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "gpt-4o-mini",
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-4",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_name",
+                                "value": "gpt-4o",
+                                "display_name": "Model Name",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "openai_api_base": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "openai_api_base",
+                                "value": "",
+                                "display_name": "OpenAI API Base",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "output_schema": {
+                                "trace_as_input": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "output_schema",
+                                "value": {},
+                                "display_name": "Schema",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "seed": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "seed",
+                                "value": 1,
+                                "display_name": "Seed",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "stream": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "stream",
+                                "value": false,
+                                "display_name": "Stream",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "system_message": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_message",
+                                "value": "",
+                                "display_name": "System Message",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "temperature": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "temperature",
+                                "value": "0.2",
+                                "display_name": "Temperature",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "float",
+                                "_input_type": "FloatInput"
+                            }
+                        },
+                        "description": "Generates text using OpenAI LLMs.",
+                        "icon": "OpenAI",
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text_output",
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            },
+                            {
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "selected": "LanguageModel",
+                                "name": "model_output",
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "system_message",
+                            "stream",
+                            "max_tokens",
+                            "model_kwargs",
+                            "json_mode",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "api_key",
+                            "temperature",
+                            "seed"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "OpenAIModel-BJWIg"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 433,
+                "positionAbsolute": {
+                    "x": 1718.9773974162958,
+                    "y": 603.4642741725065
+                },
+                "dragging": false
+            },
+            {
+                "id": "Memory-CTQWu",
+                "type": "genericNode",
+                "position": {
+                    "x": 1240.7186213296432,
+                    "y": 1059.5754404393747
+                },
+                "data": {
+                    "type": "Memory",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "memory": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "memory",
+                                "value": "",
+                                "display_name": "External Memory",
+                                "advanced": true,
+                                "input_types": [
+                                    "BaseChatMessageHistory"
+                                ],
+                                "dynamic": false,
+                                "info": "Retrieve messages from an external memory. If empty, it will use the Langflow tables.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Messages (Data)\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n        Output(display_name=\"Memory\", name=\"lc_memory\", method=\"build_lc_memory\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        if self.memory:\n            chat_memory = self.memory\n        else:\n            chat_memory = LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "n_messages": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "n_messages",
+                                "value": 100,
+                                "display_name": "Number of Messages",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Number of messages to retrieve.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "order": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Ascending",
+                                    "Descending"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "order",
+                                "value": "Ascending",
+                                "display_name": "Order",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Order of the messages.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Machine",
+                                    "User",
+                                    "Machine and User"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender",
+                                "value": "Machine and User",
+                                "display_name": "Sender Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Filter by sender type.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender_name": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender_name",
+                                "value": "",
+                                "display_name": "Sender Name",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Filter by sender name.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "session_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "session_id",
+                                "value": "",
+                                "display_name": "Session ID",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{sender_name}: {text}",
+                                "display_name": "Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Retrieves stored chat messages from Langflow tables or an external memory.",
+                        "icon": "message-square-more",
+                        "base_classes": [
+                            "BaseChatMemory",
+                            "Data",
+                            "Message"
+                        ],
+                        "display_name": "Chat Memory",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "messages",
+                                "display_name": "Messages (Data)",
+                                "method": "retrieve_messages",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            },
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "messages_text",
+                                "display_name": "Messages (Text)",
+                                "method": "retrieve_messages_as_text",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "BaseChatMemory"
+                                ],
+                                "selected": "BaseChatMemory",
+                                "name": "lc_memory",
+                                "display_name": "Memory",
+                                "method": "build_lc_memory",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            }
+                        ],
+                        "field_order": [
+                            "memory",
+                            "sender",
+                            "sender_name",
+                            "n_messages",
+                            "session_id",
+                            "order",
+                            "template"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "Memory-CTQWu"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 244,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1240.7186213296432,
+                    "y": 1059.5754404393747
+                }
+            },
+            {
+                "id": "Prompt-0dWZu",
+                "type": "genericNode",
+                "position": {
+                    "x": 1227.4862876736101,
+                    "y": 616.3826667128244
+                },
+                "data": {
+                    "type": "Prompt",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "<Instructions>\nYou are a Notion Agent, an AI assistant designed to help users interact with their Notion workspace. Your role is to understand user requests, utilize the appropriate Notion tools to fulfill these requests, and communicate clearly with the user throughout the process.\n\nGeneral Guidelines:\n\n1. Carefully analyze each user request to determine which tool(s) you need to use.\n\n2. Before using any tool, ensure you have all the necessary information. If you need more details, ask the user clear and concise questions.\n\n3. When using a tool, provide a brief explanation to the user about what you're doing and why.\n\n4. After using a tool, interpret the results for the user in a clear, concise manner.\n\n5. If a task requires multiple steps, outline your plan to the user before proceeding.\n\n6. If you encounter an error or limitation, explain it to the user and suggest possible solutions or alternative approaches.\n\n7. Always maintain a helpful and professional tone in your interactions.\n\n8. Be proactive in offering suggestions or alternatives if the user's initial request can't be fulfilled exactly as stated.\n\n9. When providing information or results, focus on relevance and clarity. Summarize when necessary, but provide details when they're important.\n\n10. If a user's request is unclear or could be interpreted in multiple ways, ask for clarification before proceeding.\n\n11. After completing a task, summarize what was accomplished and suggest any relevant next steps or additional actions the user might want to take.\n\n12. If a user asks about capabilities you don't have or tools you can't access, clearly explain your limitations and suggest alternative ways to assist if possible.\n\nRemember, your primary goal is to assist the user effectively with their Notion-related tasks using the provided tools. Always strive for clarity, accuracy, and helpfulness in your interactions. Adapt your communication style to the user's level of technical understanding and familiarity with Notion.\n\nNow, you're ready to assist the user\n\nToday is: {CURRENT_DATE}\n</Instructions>",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "prompt",
+                                "_input_type": "PromptInput"
+                            },
+                            "CURRENT_DATE": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "CURRENT_DATE",
+                                "display_name": "CURRENT_DATE",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "icon": "prompts",
+                        "is_input": null,
+                        "is_output": null,
+                        "is_composition": null,
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "name": "",
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "custom_fields": {
+                            "template": [
+                                "CURRENT_DATE"
+                            ]
+                        },
+                        "output_types": [],
+                        "full_path": null,
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "prompt",
+                                "hidden": null,
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "template"
+                        ],
+                        "beta": false,
+                        "error": null,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "Prompt-0dWZu"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 416,
+                "positionAbsolute": {
+                    "x": 1227.4862876736101,
+                    "y": 616.3826667128244
+                },
+                "dragging": false
+            },
+            {
+                "id": "CurrentDateComponent-NSNQ8",
+                "type": "genericNode",
+                "position": {
+                    "x": 1092.5108512311297,
+                    "y": 868.3249850335523
+                },
+                "data": {
+                    "type": "CurrentDateComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from datetime import datetime\r\nfrom zoneinfo import ZoneInfo\r\nfrom typing import List\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import DropdownInput, Output\r\nfrom langflow.schema.message import Message\r\n\r\nclass CurrentDateComponent(Component):\r\n    display_name = \"Current Date 🕰️\"\r\n    description = \"Returns the current date and time in the selected timezone.\"\r\n    icon = \"clock\"\r\n\r\n    inputs = [\r\n        DropdownInput(\r\n            name=\"timezone\",\r\n            display_name=\"Timezone\",\r\n            options=[\r\n                \"UTC\",\r\n                \"US/Eastern\",\r\n                \"US/Central\",\r\n                \"US/Mountain\",\r\n                \"US/Pacific\",\r\n                \"Europe/London\",\r\n                \"Europe/Paris\",\r\n                \"Asia/Tokyo\",\r\n                \"Australia/Sydney\",\r\n                \"America/Sao_Paulo\",\r\n                \"America/Cuiaba\",\r\n            ],\r\n            value=\"UTC\",\r\n            info=\"Select the timezone for the current date and time.\",\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Current Date\", name=\"current_date\", method=\"get_current_date\"),\r\n    ]\r\n\r\n    def get_current_date(self) -> Message:\r\n        try:\r\n            tz = ZoneInfo(self.timezone)\r\n            current_date = datetime.now(tz).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\r\n            result = f\"Current date and time in {self.timezone}: {current_date}\"\r\n            self.status = result\r\n            return Message(text=result)\r\n        except Exception as e:\r\n            error_message = f\"Error: {str(e)}\"\r\n            self.status = error_message\r\n            return Message(text=error_message)",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "timezone": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "UTC",
+                                    "US/Eastern",
+                                    "US/Central",
+                                    "US/Mountain",
+                                    "US/Pacific",
+                                    "Europe/London",
+                                    "Europe/Paris",
+                                    "Asia/Tokyo",
+                                    "Australia/Sydney",
+                                    "America/Sao_Paulo",
+                                    "America/Cuiaba"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "timezone",
+                                "value": "UTC",
+                                "display_name": "Timezone",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Select the timezone for the current date and time.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "Returns the current date and time in the selected timezone.",
+                        "icon": "clock",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Current Date",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "current_date",
+                                "display_name": "Current Date",
+                                "method": "get_current_date",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "timezone"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "official": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "CurrentDateComponent-NSNQ8",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1092.5108512311297,
+                    "y": 868.3249850335523
+                }
+            }
+        ],
+        "edges": [
+            {
+                "source": "ChatInput-bcq6D",
+                "target": "ToolCallingAgent-50Gcd",
+                "sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bcq6Dœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "id": "reactflow__edge-ChatInput-bcq6D{œdataTypeœ:œChatInputœ,œidœ:œChatInput-bcq6Dœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œinput_valueœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ChatInput",
+                        "id": "ChatInput-bcq6D",
+                        "name": "message",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ToolCallingAgent-50Gcd",
+                "target": "ChatOutput-TSCup",
+                "sourceHandle": "{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-50Gcdœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TSCupœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "id": "reactflow__edge-ToolCallingAgent-50Gcd{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-50Gcdœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-TSCup{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-TSCupœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ChatOutput-TSCup",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ToolCallingAgent",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "name": "response",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ToolkitComponent-2lNG0",
+                "target": "ToolCallingAgent-50Gcd",
+                "sourceHandle": "{œdataTypeœ:œToolkitComponentœ,œidœ:œToolkitComponent-2lNG0œ,œnameœ:œgenerated_toolsœ,œoutput_typesœ:[œBaseToolœ]}",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "id": "reactflow__edge-ToolkitComponent-2lNG0{œdataTypeœ:œToolkitComponentœ,œidœ:œToolkitComponent-2lNG0œ,œnameœ:œgenerated_toolsœ,œoutput_typesœ:[œBaseToolœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ToolkitComponent",
+                        "id": "ToolkitComponent-2lNG0",
+                        "name": "generated_tools",
+                        "output_types": [
+                            "BaseTool"
+                        ]
+                    }
+                },
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionPageUpdate-6FyYd",
+                "sourceHandle": "{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-6FyYdœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionPageUpdate",
+                        "id": "NotionPageUpdate-6FyYd",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionPageUpdate-6FyYd{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-6FyYdœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionPageCreator-6SCB5",
+                "sourceHandle": "{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-6SCB5œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionPageCreator",
+                        "id": "NotionPageCreator-6SCB5",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionPageCreator-6SCB5{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-6SCB5œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "AddContentToPage-ZezUn",
+                "sourceHandle": "{œdataTypeœ:œAddContentToPageœ,œidœ:œAddContentToPage-ZezUnœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "AddContentToPage",
+                        "id": "AddContentToPage-ZezUn",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-AddContentToPage-ZezUn{œdataTypeœ:œAddContentToPageœ,œidœ:œAddContentToPage-ZezUnœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionDatabaseProperties-aeWil",
+                "sourceHandle": "{œdataTypeœ:œNotionDatabasePropertiesœ,œidœ:œNotionDatabaseProperties-aeWilœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionDatabaseProperties",
+                        "id": "NotionDatabaseProperties-aeWil",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionDatabaseProperties-aeWil{œdataTypeœ:œNotionDatabasePropertiesœ,œidœ:œNotionDatabaseProperties-aeWilœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionListPages-znA3w",
+                "sourceHandle": "{œdataTypeœ:œNotionListPagesœ,œidœ:œNotionListPages-znA3wœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionListPages",
+                        "id": "NotionListPages-znA3w",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionListPages-znA3w{œdataTypeœ:œNotionListPagesœ,œidœ:œNotionListPages-znA3wœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionPageContent-SlL21",
+                "sourceHandle": "{œdataTypeœ:œNotionPageContentœ,œidœ:œNotionPageContent-SlL21œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionPageContent",
+                        "id": "NotionPageContent-SlL21",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionPageContent-SlL21{œdataTypeœ:œNotionPageContentœ,œidœ:œNotionPageContent-SlL21œ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionUserList-C3eGn",
+                "sourceHandle": "{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-C3eGnœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionUserList",
+                        "id": "NotionUserList-C3eGn",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionUserList-C3eGn{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-C3eGnœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "NotionSearch-VS2mI",
+                "sourceHandle": "{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-VS2mIœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolkitComponent-2lNG0",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolkitComponent-2lNG0",
+                        "inputTypes": [
+                            "Tool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionSearch",
+                        "id": "NotionSearch-VS2mI",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionSearch-VS2mI{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-VS2mIœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolkitComponent-2lNG0{œfieldNameœ:œtoolsœ,œidœ:œToolkitComponent-2lNG0œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "OpenAIModel-BJWIg",
+                "sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-BJWIgœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}",
+                "target": "ToolCallingAgent-50Gcd",
+                "targetHandle": "{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "llm",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "inputTypes": [
+                            "LanguageModel"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-BJWIg",
+                        "name": "model_output",
+                        "output_types": [
+                            "LanguageModel"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-OpenAIModel-BJWIg{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-BJWIgœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "Memory-CTQWu",
+                "sourceHandle": "{œdataTypeœ:œMemoryœ,œidœ:œMemory-CTQWuœ,œnameœ:œmessagesœ,œoutput_typesœ:[œDataœ]}",
+                "target": "ToolCallingAgent-50Gcd",
+                "targetHandle": "{œfieldNameœ:œchat_historyœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "chat_history",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Memory",
+                        "id": "Memory-CTQWu",
+                        "name": "messages",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Memory-CTQWu{œdataTypeœ:œMemoryœ,œidœ:œMemory-CTQWuœ,œnameœ:œmessagesœ,œoutput_typesœ:[œDataœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œchat_historyœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "Prompt-0dWZu",
+                "sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-0dWZuœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "ToolCallingAgent-50Gcd",
+                "targetHandle": "{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "system_prompt",
+                        "id": "ToolCallingAgent-50Gcd",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-0dWZu",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Prompt-0dWZu{œdataTypeœ:œPromptœ,œidœ:œPrompt-0dWZuœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-ToolCallingAgent-50Gcd{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-50Gcdœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "className": "",
+                "selected": false
+            },
+            {
+                "source": "CurrentDateComponent-NSNQ8",
+                "sourceHandle": "{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-NSNQ8œ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-0dWZu",
+                "targetHandle": "{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-0dWZuœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "CURRENT_DATE",
+                        "id": "Prompt-0dWZu",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "CurrentDateComponent",
+                        "id": "CurrentDateComponent-NSNQ8",
+                        "name": "current_date",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-CurrentDateComponent-NSNQ8{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-NSNQ8œ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}-Prompt-0dWZu{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-0dWZuœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "className": "",
+                "selected": false
+            }
+        ],
+        "viewport": {
+            "x": 97.72528949998423,
+            "y": -211.85229348429561,
+            "zoom": 0.41621432461249197
+        }
+    },
+    "description": "This flow creates an AI assistant that interacts with your Notion workspace. It understands natural language requests, performs actions in Notion (like creating pages or searching for information), and provides helpful responses. To use it, simply start a conversation by asking the agent to perform a Notion-related task, and it will guide you through the process, making it easy to manage your Notion workspace through chat.",
+    "name": "Conversational Notion Agent",
+    "last_tested_version": "1.0.17",
+    "endpoint_name": null,
+    "is_component": false
+}
\ No newline at end of file
diff --git a/docs/docs/Integrations/Notion/Meeting_Notes_Agent.json b/docs/docs/Integrations/Notion/Meeting_Notes_Agent.json
index 56d8e74e7..e567567ba 100644
--- a/docs/docs/Integrations/Notion/Meeting_Notes_Agent.json
+++ b/docs/docs/Integrations/Notion/Meeting_Notes_Agent.json
@@ -1 +1,4150 @@
-{"id":"b6de0fdb-31a2-40bf-b921-719bc0890a0e","data":{"nodes":[{"id":"TextInput-iJPEJ","type":"genericNode","position":{"x":94.43614181571661,"y":387.24602783243165},"data":{"type":"TextInput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"Good morning. Thanks for joining this project review meeting. We've got quite a few tasks to discuss, especially some Notion-related ones. Shall we get started?\n\nMorning, Felipe. Absolutely, let's dive in. I see we have several projects and tasks on our plate.\n\nGreat. Let's begin with the AI Content Gen project. I'm currently working on \"Montar base agente seletor de cortes.\" It's in progress, and I'm aiming to complete it by June 14th. Have you had a chance to look at this task, Cezar?\n\nI haven't been directly involved with that one. Can you give me an overview of what it entails?\n\nOf course. Essentially, we're building a base agent that can intelligently select and edit content. It's part of our larger AI-driven content generation initiative. The challenge is creating an algorithm that can understand context and make smart editing decisions.\n\nInteresting. How's the progress so far?\n\nIt's coming along. I've set up the basic framework, but fine-tuning the selection criteria is proving to be more complex than initially anticipated. I might need an extra day or two beyond the June 14th deadline.\n\nUnderstood, Felipe. Keep me posted if you need any resources or if the deadline needs to be adjusted. By the way, I've been meaning to ask - have you had a chance to look into that new NLP library I mentioned last week? I think it could be useful for this project.\n\nActually, Cezar, I haven't gotten to that yet. Should we add it as a new task? Maybe \"Evaluate NLP library for content selection\"?\n\nGood idea. Let's add that to our task list with a due date of next Friday. Now, moving on to the next task in this project - \"Create Notion Task Automation.\" It's assigned to you and set for June 19th, but you haven't started it yet, right? This is where I'd like to focus our discussion today.\n\nThat's correct. So, the goal is to streamline our workflow by automating certain tasks within Notion. I'm thinking we could create scripts or use Notion's API to automatically create, assign, and update tasks based on certain triggers or schedules.\n\nThat sounds like it could save us a lot of time. What specific automations are you considering?\n\nI'm glad you asked, Cezar. I'm thinking of a few key areas:\n1. Automatic task creation based on project milestones\n2. Assigning tasks to team members based on their expertise and current workload\n3. Updating task statuses based on linked database entries\n4. Generating weekly progress reports\n5. Setting up reminders for overdue tasks\n\nThose all sound valuable. Have you looked into the technical requirements for implementing these?\n\nI've done some initial research. Notion's API seems robust enough to handle these automations. We'll likely need to use a combination of Notion's API and a server to run our scripts. I'm thinking of using Node.js for this.\n\nGood thinking. Do you foresee any challenges?\n\nThe main challenge will be ensuring our automations are flexible enough to handle different project structures and team dynamics. We'll need to build in some configurability.\n\nAgreed. Let's make sure we involve the team in defining these automations. Their input will be crucial for making this truly useful. Oh, and speaking of team input, I think we should add a task for \"Conduct team survey on Notion pain points.\" This could help us prioritize which automations to tackle first.\n\nThat's an excellent idea, Cezar. I'll create that task and aim to complete the survey by next Wednesday. Now, I see we have another Notion-related task: \"Subir Notion Agent no Langflow Prod.\" Can you remind me what this entails?\n\nYes, this task is about deploying our Notion integration agent to the Langflow production environment. It's not started yet, but it's a crucial step in making our Notion automations available to the whole team.\n\nI see. What's the timeline for this?\n\nWe haven't set a specific deadline yet, but I think we should aim to complete this shortly after the automation task. Let's tentatively say by the end of June?\n\nSounds reasonable. Make sure to coordinate with the DevOps team for a smooth deployment. And while we're on the topic of deployment, we should probably add a task for \"Create documentation for Notion Agent usage.\" We want to make sure the team knows how to use these new tools once they're available.\n\nYou're right, Felipe. I'll add that to our task list. Now, switching gears a bit, let's talk about the Internal Projects. I see you're working on \"Crypto Links\" - it's in progress.\n\nAh yes, our blockchain initiative. It's moving forward. I'm researching various blockchain platforms and their potential applications for our projects. I'm particularly interested in smart contract capabilities.\n\nInteresting. Keep me updated on any promising findings. By the way, have you considered reaching out to any blockchain experts for consultation? It might be worth adding a task for \"Schedule blockchain expert consultation.\"\n\nThat's a great suggestion, Cezar. I'll add it to my to-do list. Now, for the Internal Tasks, I see you're assigned to \"Revisar modos do Charlinho, preparar para open source.\" What's the status on that?\n\nI haven't started yet, but it's on my radar. The deadline is June 7th, so I'll be diving into it this week. Essentially, we need to review and refine Charlinho's modes before we open-source the project.\n\nSounds good. Let me know if you need any assistance with that. Oh, and don't forget we need to add a task for \"Prepare Charlinho documentation for open source.\" We want to make sure our project is well-documented when we release it.\n\nYou're right, Felipe. I'll make sure to include that in our task list. Now, I see you have several tasks assigned to you in the Internal Tasks section. Can you give me a quick rundown?\n\nOf course. I'm working on finding a freelancer to create flows in ComfyUI - that's in progress and due May 28th. I'm also handling the conception of the Agent UI, due May 30th. Both are moving along well.\n\nThere's also a task to \"Check, install and test Gladia to use a bot in Google Meet.\" That's in progress, and I'm collaborating with C on it.\n\nThat's quite a workload. How are you managing all these tasks?\n\nIt's challenging, but I'm prioritizing based on deadlines and dependencies. The Notion automation project is a high priority because it'll help us manage tasks more efficiently in the long run.\n\nGood strategy, Felipe. Is there anything you need from me or the team to help move these tasks forward?\n\nActually, yes. For the \"pegar os arquivos necessários para tentarmos montar um stinger com ffmpeg\" task, I could use some input on which files are critical for this. It's a low-priority task due June 2nd, but any insights would be helpful.\n\nI'll review our asset library and send you a list of potential files by tomorrow. Oh, and let's add a task for \"Create ffmpeg stinger tutorial\" once we figure out the process. It could be useful for the team in the future.\n\nGreat idea, Cezar. I'll add that to our backlog. Anything else we should discuss?\n\nI think we've covered the major points. Oh, one last thing - for the \"Create Notion Task Automation\" project, I was thinking of setting up a series of short daily meetings next week to keep everyone aligned. What do you think?\n\nThat's a good idea. Maybe 15-minute stand-ups? We can use those to address any roadblocks quickly. And let's add a task for \"Set up Notion Automation progress tracking board\" to help visualize our progress during these stand-ups.\n\nPerfect. I'll send out calendar invites this afternoon and create that tracking board task. Any final thoughts or concerns, Cezar?\n\nNot from my side. I think we have a clear path forward, especially with the Notion-related tasks and the new items we've added to our list.\n\nAgreed. Let's plan to reconvene next week to check on progress, particularly for the Notion automation project and these new tasks we've discussed. Thanks for the comprehensive update, Felipe.\n\nThank you, Cezar. I'll send out a summary of our discussion and action items shortly, including all the new tasks we've identified during this meeting.\n","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Text to be passed as input.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Get text inputs from the Playground.","icon":"type","base_classes":["Message"],"display_name":"Meeting Transcript","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"TextInput-iJPEJ"},"selected":false,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":94.43614181571661,"y":387.24602783243165}},{"id":"NotionUserList-TvIKS","type":"genericNode","position":{"x":80.49204196902156,"y":741.0568511678105},"data":{"type":"NotionUserList","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Retrieve users from Notion.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Users ","documentation":"https://docs.langflow.org/integrations/notion/list-users","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true,"hidden":true}],"field_order":["notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionUserList-TvIKS","description":"Retrieve users from Notion.","display_name":"List Users "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":80.49204196902156,"y":741.0568511678105},"dragging":false},{"id":"NotionSearch-M66HF","type":"genericNode","position":{"x":1095.6934863134345,"y":407.8718765800806},"data":{"type":"NotionSearch","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"filter_value":{"trace_as_metadata":true,"options":["page","database"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"filter_value","value":"database","display_name":"Filter Type","advanced":true,"dynamic":false,"info":"Limits the results to either only pages or only databases.","title_case":false,"type":"str","_input_type":"DropdownInput"},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"query":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"query","value":"","display_name":"Search Query","advanced":true,"dynamic":false,"info":"The text that the API compares page and database titles against.","title_case":false,"type":"str","_input_type":"StrInput"},"sort_direction":{"trace_as_metadata":true,"options":["ascending","descending"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sort_direction","value":"descending","display_name":"Sort Direction","advanced":true,"dynamic":false,"info":"The direction to sort the results.","title_case":false,"type":"str","_input_type":"DropdownInput"}},"description":"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Search ","documentation":"https://docs.langflow.org/integrations/notion/search","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true,"hidden":false}],"field_order":["notion_secret","query","filter_value","sort_direction"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionSearch-M66HF","description":"Searches all pages and databases that have been shared with an integration.","display_name":"Search "},"selected":false,"width":384,"height":386,"positionAbsolute":{"x":1095.6934863134345,"y":407.8718765800806},"dragging":false},{"id":"Prompt-19rub","type":"genericNode","position":{"x":688.7954025956392,"y":456.4686463487848},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"<Instructions>\nYou are an AI assistant specialized in analyzing meeting transcripts and identifying tasks. Your goal is to extract relevant tasks from the given transcript, search for related existing tasks in Notion, and provide a comprehensive list of tasks with their current status and any needed updates.\n\nYou have access to the following input:\n\n<transcript>\n{TRANSCRIPT}\n</transcript>\n\n<users>\n{USERS}\n</users>\n\nFollow these steps to complete your task:\n\n1. Carefully read through the transcript and identify any mentioned tasks, action items, or follow-ups.\n\n2. For each identified task:\n   a. Use the notion_search tool to find if there's an existing related task in Notion.\n   b. If a related task is found, note its ID and current status.\n   c. If no related task is found, mark it as a new task.\n\n3. For each task (existing or new), determine:\n   a. The task name or description\n   b. The assigned person (if mentioned)\n   c. The current status (for existing tasks) or suggested status (for new tasks)\n   d. Any updates or changes mentioned in the transcript\n\n4. Compile your findings into a list of tasks using the following format:\n\n<task_list>\n<task>\n<id>[Notion page ID if existing, or \"NEW\" if new task]</id>\n<name>[Task name or description]</name>\n<assignee>[Assigned person, if mentioned]</assignee>\n<status>[Current status for existing tasks, or suggested status for new tasks]</status>\n<updates>[Any updates or changes mentioned in the transcript]</updates>\n</task>\n</task_list>\n\nRemember to focus on tasks that are directly related to the meeting discussion. Do not include general conversation topics or unrelated mentions as tasks.\n\nProvide your final output in the format specified above, with each task enclosed in its own <task></task> tags within the overall <task_list></task_list> structure.\n\nToday is: {CURRENT_DATE}\n</Instructions>\n\n\n","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","_input_type":"PromptInput"},"TRANSCRIPT":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"TRANSCRIPT","display_name":"TRANSCRIPT","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"USERS":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"USERS","display_name":"USERS","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"CURRENT_DATE":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"CURRENT_DATE","display_name":"CURRENT_DATE","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","is_input":null,"is_output":null,"is_composition":null,"base_classes":["Message"],"name":"","display_name":"Prompt","documentation":"","custom_fields":{"template":["TRANSCRIPT","USERS","CURRENT_DATE"]},"output_types":[],"full_path":null,"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","hidden":null,"display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"error":null,"edited":false,"lf_version":"1.0.17"},"id":"Prompt-19rub"},"selected":false,"width":384,"height":588,"positionAbsolute":{"x":688.7954025956392,"y":456.4686463487848},"dragging":false},{"id":"ParseData-aNk1v","type":"genericNode","position":{"x":540.4151030255898,"y":834.2819856588019},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_metadata":true,"list":false,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"data","value":"","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sep","value":"\n","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{text}","display_name":"Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true}],"field_order":["data","template","sep"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ParseData-aNk1v","showNode":false},"selected":false,"width":96,"height":96,"dragging":false,"positionAbsolute":{"x":540.4151030255898,"y":834.2819856588019}},{"id":"ToolCallingAgent-rVWeq","type":"genericNode","position":{"x":1566.291217492157,"y":583.6687094567968},"data":{"type":"ToolCallingAgent","node":{"template":{"_type":"Component","chat_history":{"trace_as_metadata":true,"list":true,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"chat_history","value":"","display_name":"Chat History","advanced":true,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"DataInput"},"llm":{"trace_as_metadata":true,"list":false,"required":true,"placeholder":"","show":true,"name":"llm","value":"","display_name":"Language Model","advanced":false,"input_types":["LanguageModel"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"tools":{"trace_as_metadata":true,"list":true,"required":false,"placeholder":"","show":true,"name":"tools","value":"","display_name":"Tools","advanced":false,"input_types":["Tool","BaseTool"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"handle_parsing_errors":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"handle_parsing_errors","value":true,"display_name":"Handle Parse Errors","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"Analyze this meeting","display_name":"Input","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"},"max_iterations":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"max_iterations","value":15,"display_name":"Max Iterations","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"int","_input_type":"IntInput"},"system_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_prompt","value":"","display_name":"System Prompt","advanced":false,"input_types":["Message"],"dynamic":false,"info":"System prompt for the agent.","title_case":false,"type":"str","_input_type":"MultilineInput"},"user_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"user_prompt","value":"{input}","display_name":"Prompt","advanced":true,"input_types":["Message"],"dynamic":false,"info":"This prompt must contain 'input' key.","title_case":false,"type":"str","_input_type":"MultilineInput"},"verbose":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"verbose","value":true,"display_name":"Verbose","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Agent that uses tools","icon":"LangChain","base_classes":["AgentExecutor","Message"],"display_name":"Tool Calling Agent","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["AgentExecutor"],"selected":"AgentExecutor","name":"agent","display_name":"Agent","method":"build_agent","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Message"],"selected":"Message","name":"response","display_name":"Response","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","handle_parsing_errors","verbose","max_iterations","tools","llm","system_prompt","user_prompt","chat_history"],"beta":true,"edited":false,"lf_version":"1.0.17"},"id":"ToolCallingAgent-rVWeq"},"selected":false,"width":384,"height":398,"positionAbsolute":{"x":1566.291217492157,"y":583.6687094567968},"dragging":false},{"id":"OpenAIModel-Ht8xI","type":"genericNode","position":{"x":1097.0545781920632,"y":805.60631548423},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"name":"api_key","value":"","display_name":"OpenAI API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Input","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"json_mode","value":false,"display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"name":"max_tokens","value":"","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"model_kwargs","value":{},"display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model_name","value":"gpt-4o","display_name":"Model Name","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"openai_api_base","value":"","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"name":"output_schema","value":{},"display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"seed","value":1,"display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"stream","value":false,"display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_message","value":"","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"temperature","value":0.1,"display_name":"Temperature","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"OpenAIModel-Ht8xI"},"selected":false,"width":384,"height":302,"dragging":false,"positionAbsolute":{"x":1097.0545781920632,"y":805.60631548423}},{"id":"Prompt-Lbxk6","type":"genericNode","position":{"x":3042.6844997246735,"y":416.83992118486856},"data":{"type":"Prompt","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"<Instructions>\nYou are an AI assistant responsible for updating tasks in Notion based on the information provided from a meeting analysis. Your goal is to create new tasks and update existing ones using the Notion API tools available to you, and then provide a summary in a simple markdown format suitable for a chat interface.\n\nYou have access to the following inputs:\n\n<task_list>\n{TASK_LIST}\n</task_list>\n\n<databases>\n{DATABASES}\n</databases>\n\n<users>\n{USERS}\n</users>\n\nFollow these steps to update the tasks in Notion and generate a markdown summary:\n\n1. Identify the Task database ID from the provided <databases> list.\n\n2. Before processing any tasks, retrieve the database properties for the Task database:\n   a. Use the notion_database_properties and carefully review the properties, their types, and any options for select or multi-select properties.\n   b. Pay attention to the properties format for further usage.\n\n3. For each task in the task list:\n   a. If the task ID is \"NEW\", create a new task using the create_notion_page tool.\n   b. If the task has an existing ID, update the task using the update_notion_page tool.\n   c. Remember to use the properties from the DB retrieved from the notion_database_properties tool\n\n4. When creating a new task:\n   a. Use the create_notion_page tool.\n   b. Include the task name, assignee (if available), status, and any other relevant properties based on the database structure.\n   c. Ensure that the property names and types match exactly with what you retrieved from the notion_database_properties call.\n\n5. When updating an existing task:\n   a. Use the update_notion_page tool.\n   b. Update the status, assignee, or any other relevant properties mentioned in the <updates> field.\n   c. Ensure that the property names and types match exactly with what you retrieved from the notion_database_properties call.\n\n6. After each function call, wait for the <function_result> before proceeding to the next task.\n\n7. If you encounter any errors during the process, note them and continue with the next task.\n\n8. Provide a summary of your actions for each task in a simple markdown format. Use the following structure:\n   # Task Update Summary\n\n   ## Created Tasks\n   - **[Task Name]**: Assigned to [Assignee], Status: [Status]\n     - Details: [Brief description of the new task]\n\n   ## Updated Tasks\n   - **[Task Name]** (ID: [Notion Page ID])\n     - Changes: [Brief description of changes]\n     - Status: [Success/Error]\n\n   ## Errors\n   - **[Task Name or ID]**: [Description of the error encountered]\n\n\nRemember to use the exact property names, types, and options as specified in the Notion database properties you retrieved at the beginning. This is crucial for ensuring that all updates and creations are done correctly.\n\nIf you encounter any errors or uncertainties, include them in the Errors section of the markdown summary. With enough detail to the user understand the issues.\n\nProvide your final output as a complete markdown document containing all the tasks you've processed, whether they were created, updated, or encountered errors. Use only basic markdown formatting (headers, bold, lists) to ensure compatibility with chat interfaces. Do not include any XML tags or complex formatting in your final output.\n\nToday is: {CURRENT_DATE}\n\n</Instructions>","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","_input_type":"PromptInput"},"TASK_LIST":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"TASK_LIST","display_name":"TASK_LIST","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"DATABASES":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"DATABASES","display_name":"DATABASES","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"USERS":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"USERS","display_name":"USERS","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"},"CURRENT_DATE":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"CURRENT_DATE","display_name":"CURRENT_DATE","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","is_input":null,"is_output":null,"is_composition":null,"base_classes":["Message"],"name":"","display_name":"Prompt","documentation":"","custom_fields":{"template":["TASK_LIST","DATABASES","USERS","CURRENT_DATE"]},"output_types":[],"full_path":null,"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","hidden":null,"display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"error":null,"edited":false},"id":"Prompt-Lbxk6"},"selected":false,"width":384,"height":674,"positionAbsolute":{"x":3042.6844997246735,"y":416.83992118486856},"dragging":false},{"id":"ToolCallingAgent-GurdE","type":"genericNode","position":{"x":3974.1377259893243,"y":867.4647271037014},"data":{"type":"ToolCallingAgent","node":{"template":{"_type":"Component","chat_history":{"trace_as_metadata":true,"list":true,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"chat_history","value":"","display_name":"Chat History","advanced":true,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"DataInput"},"llm":{"trace_as_metadata":true,"list":false,"required":true,"placeholder":"","show":true,"name":"llm","value":"","display_name":"Language Model","advanced":false,"input_types":["LanguageModel"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"tools":{"trace_as_metadata":true,"list":true,"required":false,"placeholder":"","show":true,"name":"tools","value":"","display_name":"Tools","advanced":false,"input_types":["Tool","BaseTool"],"dynamic":false,"info":"","title_case":false,"type":"other","_input_type":"HandleInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"handle_parsing_errors":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"handle_parsing_errors","value":true,"display_name":"Handle Parse Errors","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"Do your task.","display_name":"Input","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageTextInput"},"max_iterations":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"max_iterations","value":15,"display_name":"Max Iterations","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"int","_input_type":"IntInput"},"system_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_prompt","value":"","display_name":"System Prompt","advanced":false,"input_types":["Message"],"dynamic":false,"info":"System prompt for the agent.","title_case":false,"type":"str","_input_type":"MultilineInput"},"user_prompt":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"user_prompt","value":"{input}","display_name":"Prompt","advanced":true,"input_types":["Message"],"dynamic":false,"info":"This prompt must contain 'input' key.","title_case":false,"type":"str","_input_type":"MultilineInput"},"verbose":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"verbose","value":true,"display_name":"Verbose","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Agent that uses tools","icon":"LangChain","base_classes":["AgentExecutor","Message"],"display_name":"Tool Calling Agent","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["AgentExecutor"],"selected":"AgentExecutor","name":"agent","display_name":"Agent","method":"build_agent","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Message"],"selected":"Message","name":"response","display_name":"Response","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","handle_parsing_errors","verbose","max_iterations","tools","llm","system_prompt","user_prompt","chat_history"],"beta":true,"edited":false,"lf_version":"1.0.17"},"id":"ToolCallingAgent-GurdE"},"selected":false,"width":384,"height":398,"positionAbsolute":{"x":3974.1377259893243,"y":867.4647271037014},"dragging":false},{"id":"OpenAIModel-OTfnt","type":"genericNode","position":{"x":3513.5648778762093,"y":710.2099422974287},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"name":"api_key","value":"","display_name":"OpenAI API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Input","advanced":true,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"json_mode","value":false,"display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"name":"max_tokens","value":"","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"model_kwargs","value":{},"display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model_name","value":"gpt-4o","display_name":"Model Name","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"openai_api_base","value":"","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"name":"output_schema","value":{},"display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"seed","value":1,"display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"stream","value":false,"display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_message","value":"","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"temperature","value":0.1,"display_name":"Temperature","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"OpenAIModel-OTfnt"},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":3513.5648778762093,"y":710.2099422974287},"dragging":false},{"id":"AddContentToPage-vrAvx","type":"genericNode","position":{"x":2649.2991466550634,"y":1050.6250104897197},"data":{"type":"AddContentToPage","node":{"template":{"_type":"Component","block_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"block_id","value":"","display_name":"Page/Block ID","advanced":true,"dynamic":false,"info":"The ID of the page/block to add the content.","title_case":false,"type":"str","_input_type":"StrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nfrom typing import Dict, Any, Union\nfrom markdown import markdown\nfrom bs4 import BeautifulSoup\nimport requests\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\n\nclass AddContentToPage(LCToolComponent):\n    display_name: str = \"Add Content to Page \"\n    description: str = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\n    documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        MultilineInput(\n            name=\"markdown_text\",\n            display_name=\"Markdown Text\",\n            info=\"The markdown text to convert to Notion blocks.\",\n        ),\n        StrInput(\n            name=\"block_id\",\n            display_name=\"Page/Block ID\",\n            info=\"The ID of the page/block to add the content.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class AddContentToPageSchema(BaseModel):\n        markdown_text: str = Field(..., description=\"The markdown text to convert to Notion blocks.\")\n        block_id: str = Field(..., description=\"The ID of the page/block to add the content.\")\n\n    def run_model(self) -> Data:\n        result = self._add_content_to_page(self.markdown_text, self.block_id)\n        return Data(data=result, text=json.dumps(result))\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"add_content_to_notion_page\",\n            description=\"Convert markdown text to Notion blocks and append them to a Notion page.\",\n            func=self._add_content_to_page,\n            args_schema=self.AddContentToPageSchema,\n        )\n\n    def _add_content_to_page(self, markdown_text: str, block_id: str) -> Union[Dict[str, Any], str]:\n        try:\n            html_text = markdown(markdown_text)\n            soup = BeautifulSoup(html_text, \"html.parser\")\n            blocks = self.process_node(soup)\n\n            url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\n            headers = {\n                \"Authorization\": f\"Bearer {self.notion_secret}\",\n                \"Content-Type\": \"application/json\",\n                \"Notion-Version\": \"2022-06-28\",\n            }\n\n            data = {\n                \"children\": blocks,\n            }\n\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n\n            return response.json()\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to add content to Notion page. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while adding content to Notion page. {str(e)}\"\n\n    def process_node(self, node):\n        blocks = []\n        if isinstance(node, str):\n            text = node.strip()\n            if text:\n                if text.startswith(\"#\"):\n                    heading_level = text.count(\"#\", 0, 6)\n                    heading_text = text[heading_level:].strip()\n                    if heading_level == 1:\n                        blocks.append(self.create_block(\"heading_1\", heading_text))\n                    elif heading_level == 2:\n                        blocks.append(self.create_block(\"heading_2\", heading_text))\n                    elif heading_level == 3:\n                        blocks.append(self.create_block(\"heading_3\", heading_text))\n                else:\n                    blocks.append(self.create_block(\"paragraph\", text))\n        elif node.name == \"h1\":\n            blocks.append(self.create_block(\"heading_1\", node.get_text(strip=True)))\n        elif node.name == \"h2\":\n            blocks.append(self.create_block(\"heading_2\", node.get_text(strip=True)))\n        elif node.name == \"h3\":\n            blocks.append(self.create_block(\"heading_3\", node.get_text(strip=True)))\n        elif node.name == \"p\":\n            code_node = node.find(\"code\")\n            if code_node:\n                code_text = code_node.get_text()\n                language, code = self.extract_language_and_code(code_text)\n                blocks.append(self.create_block(\"code\", code, language=language))\n            elif self.is_table(str(node)):\n                blocks.extend(self.process_table(node))\n            else:\n                blocks.append(self.create_block(\"paragraph\", node.get_text(strip=True)))\n        elif node.name == \"ul\":\n            blocks.extend(self.process_list(node, \"bulleted_list_item\"))\n        elif node.name == \"ol\":\n            blocks.extend(self.process_list(node, \"numbered_list_item\"))\n        elif node.name == \"blockquote\":\n            blocks.append(self.create_block(\"quote\", node.get_text(strip=True)))\n        elif node.name == \"hr\":\n            blocks.append(self.create_block(\"divider\", \"\"))\n        elif node.name == \"img\":\n            blocks.append(self.create_block(\"image\", \"\", image_url=node.get(\"src\")))\n        elif node.name == \"a\":\n            blocks.append(self.create_block(\"bookmark\", node.get_text(strip=True), link_url=node.get(\"href\")))\n        elif node.name == \"table\":\n            blocks.extend(self.process_table(node))\n\n        for child in node.children:\n            if isinstance(child, str):\n                continue\n            blocks.extend(self.process_node(child))\n\n        return blocks\n\n    def extract_language_and_code(self, code_text):\n        lines = code_text.split(\"\\n\")\n        language = lines[0].strip()\n        code = \"\\n\".join(lines[1:]).strip()\n        return language, code\n\n    def is_code_block(self, text):\n        return text.startswith(\"```\")\n\n    def extract_code_block(self, text):\n        lines = text.split(\"\\n\")\n        language = lines[0].strip(\"`\").strip()\n        code = \"\\n\".join(lines[1:]).strip(\"`\").strip()\n        return language, code\n\n    def is_table(self, text):\n        rows = text.split(\"\\n\")\n        if len(rows) < 2:\n            return False\n\n        has_separator = False\n        for i, row in enumerate(rows):\n            if \"|\" in row:\n                cells = [cell.strip() for cell in row.split(\"|\")]\n                cells = [cell for cell in cells if cell]  # Remove empty cells\n                if i == 1 and all(set(cell) <= set(\"-|\") for cell in cells):\n                    has_separator = True\n                elif not cells:\n                    return False\n\n        return has_separator and len(rows) >= 3\n\n    def process_list(self, node, list_type):\n        blocks = []\n        for item in node.find_all(\"li\"):\n            item_text = item.get_text(strip=True)\n            checked = item_text.startswith(\"[x]\")\n            is_checklist = item_text.startswith(\"[ ]\") or checked\n\n            if is_checklist:\n                item_text = item_text.replace(\"[x]\", \"\").replace(\"[ ]\", \"\").strip()\n                blocks.append(self.create_block(\"to_do\", item_text, checked=checked))\n            else:\n                blocks.append(self.create_block(list_type, item_text))\n        return blocks\n\n    def process_table(self, node):\n        blocks = []\n        header_row = node.find(\"thead\").find(\"tr\") if node.find(\"thead\") else None\n        body_rows = node.find(\"tbody\").find_all(\"tr\") if node.find(\"tbody\") else []\n\n        if header_row or body_rows:\n            table_width = max(\n                len(header_row.find_all([\"th\", \"td\"])) if header_row else 0,\n                max(len(row.find_all([\"th\", \"td\"])) for row in body_rows),\n            )\n\n            table_block = self.create_block(\"table\", \"\", table_width=table_width, has_column_header=bool(header_row))\n            blocks.append(table_block)\n\n            if header_row:\n                header_cells = [cell.get_text(strip=True) for cell in header_row.find_all([\"th\", \"td\"])]\n                header_row_block = self.create_block(\"table_row\", header_cells)\n                blocks.append(header_row_block)\n\n            for row in body_rows:\n                cells = [cell.get_text(strip=True) for cell in row.find_all([\"th\", \"td\"])]\n                row_block = self.create_block(\"table_row\", cells)\n                blocks.append(row_block)\n\n        return blocks\n\n    def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\n        block: dict[str, Any] = {\n            \"object\": \"block\",\n            \"type\": block_type,\n            block_type: {},\n        }\n\n        if block_type in [\n            \"paragraph\",\n            \"heading_1\",\n            \"heading_2\",\n            \"heading_3\",\n            \"bulleted_list_item\",\n            \"numbered_list_item\",\n            \"quote\",\n        ]:\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n        elif block_type == \"to_do\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"checked\"] = kwargs.get(\"checked\", False)\n        elif block_type == \"code\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"language\"] = kwargs.get(\"language\", \"plain text\")\n        elif block_type == \"image\":\n            block[block_type] = {\"type\": \"external\", \"external\": {\"url\": kwargs.get(\"image_url\", \"\")}}\n        elif block_type == \"divider\":\n            pass\n        elif block_type == \"bookmark\":\n            block[block_type][\"url\"] = kwargs.get(\"link_url\", \"\")\n        elif block_type == \"table\":\n            block[block_type][\"table_width\"] = kwargs.get(\"table_width\", 0)\n            block[block_type][\"has_column_header\"] = kwargs.get(\"has_column_header\", False)\n            block[block_type][\"has_row_header\"] = kwargs.get(\"has_row_header\", False)\n        elif block_type == \"table_row\":\n            block[block_type][\"cells\"] = [[{\"type\": \"text\", \"text\": {\"content\": cell}} for cell in content]]\n\n        return block\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"markdown_text":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"markdown_text","value":"","display_name":"Markdown Text","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The markdown text to convert to Notion blocks.","title_case":false,"type":"str","_input_type":"MultilineInput"},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Convert markdown text to Notion blocks and append them to a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Add Content to Page ","documentation":"https://developers.notion.com/reference/patch-block-children","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["markdown_text","block_id","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"AddContentToPage-vrAvx","description":"Convert markdown text to Notion blocks and append them to a Notion page.","display_name":"Add Content to Page "},"selected":false,"width":384,"height":330,"positionAbsolute":{"x":2649.2991466550634,"y":1050.6250104897197},"dragging":false},{"id":"NotionPageCreator-Exc7f","type":"genericNode","position":{"x":3050.8201437255634,"y":1391.0449862668834},"data":{"type":"NotionPageCreator","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nfrom typing import Dict, Any, Union\nimport requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageCreator(LCToolComponent):\n    display_name: str = \"Create Page \"\n    description: str = \"A component for creating Notion pages.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"properties_json\",\n            display_name=\"Properties (JSON)\",\n            info=\"The properties of the new page as a JSON string.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageCreatorSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n        properties_json: str = Field(..., description=\"The properties of the new page as a JSON string.\")\n\n    def run_model(self) -> Data:\n        result = self._create_notion_page(self.database_id, self.properties_json)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the created page data\n            output = \"Created page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"create_notion_page\",\n            description=\"Create a new page in a Notion database. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._create_notion_page,\n            args_schema=self.NotionPageCreatorSchema,\n        )\n\n    def _create_notion_page(self, database_id: str, properties_json: str) -> Union[Dict[str, Any], str]:\n        if not database_id or not properties_json:\n            return \"Invalid input. Please provide 'database_id' and 'properties_json'.\"\n\n        try:\n            properties = json.loads(properties_json)\n        except json.JSONDecodeError as e:\n            return f\"Invalid properties format. Please provide a valid JSON string. Error: {str(e)}\"\n\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"parent\": {\"database_id\": database_id},\n            \"properties\": properties,\n        }\n\n        try:\n            response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\n            response.raise_for_status()\n            result = response.json()\n            return result\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Failed to create Notion page. Error: {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._create_notion_page(*args, **kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"database_id","value":"","display_name":"Database ID","advanced":true,"dynamic":false,"info":"The ID of the Notion database.","title_case":false,"type":"str","_input_type":"StrInput"},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"properties_json":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"properties_json","value":"","display_name":"Properties (JSON)","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The properties of the new page as a JSON string.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"A component for creating Notion pages.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Create Page ","documentation":"https://docs.langflow.org/integrations/notion/page-create","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["database_id","notion_secret","properties_json"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionPageCreator-Exc7f","description":"A component for creating Notion pages.","display_name":"Create Page "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":3050.8201437255634,"y":1391.0449862668834},"dragging":false},{"id":"NotionDatabaseProperties-IjzLV","type":"genericNode","position":{"x":3053.0023230574693,"y":1061.535907149244},"data":{"type":"NotionDatabaseProperties","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import Dict, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionDatabaseProperties(LCToolComponent):\n    display_name: str = \"List Database Properties \"\n    description: str = \"Retrieve properties of a Notion database.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionDatabasePropertiesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n\n    def run_model(self) -> Data:\n        result = self._fetch_database_properties(self.database_id)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the properties\n            return Data(text=str(result), data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_database_properties\",\n            description=\"Retrieve properties of a Notion database. Input should include the database ID.\",\n            func=self._fetch_database_properties,\n            args_schema=self.NotionDatabasePropertiesSchema,\n        )\n\n    def _fetch_database_properties(self, database_id: str) -> Union[Dict, str]:\n        url = f\"https://api.notion.com/v1/databases/{database_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n        try:\n            response = requests.get(url, headers=headers)\n            response.raise_for_status()\n            data = response.json()\n            properties = data.get(\"properties\", {})\n            return properties\n        except requests.exceptions.RequestException as e:\n            return f\"Error fetching Notion database properties: {str(e)}\"\n        except ValueError as e:\n            return f\"Error parsing Notion API response: {str(e)}\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"database_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"database_id","value":"","display_name":"Database ID","advanced":true,"dynamic":false,"info":"The ID of the Notion database.","title_case":false,"type":"str","_input_type":"StrInput"},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Retrieve properties of a Notion database.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Database Properties ","documentation":"https://docs.langflow.org/integrations/notion/list-database-properties","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["database_id","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionDatabaseProperties-IjzLV","description":"Retrieve properties of a Notion database.","display_name":"List Database Properties "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":3053.0023230574693,"y":1061.535907149244},"dragging":false},{"id":"NotionPageUpdate-bexvy","type":"genericNode","position":{"x":2649.2991466550625,"y":1385.262204377853},"data":{"type":"NotionPageUpdate","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nimport requests\nfrom typing import Dict, Any, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom loguru import logger\nfrom langflow.io import Output\n\nclass NotionPageUpdate(LCToolComponent):\n    display_name: str = \"Update Page Property \"\n    description: str = \"Update the properties of a Notion page.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to update.\",\n        ),\n        MultilineInput(\n            name=\"properties\",\n            display_name=\"Properties\",\n            info=\"The properties to update on the page (as a JSON string or a dictionary).\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageUpdateSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to update.\")\n        properties: Union[str, Dict[str, Any]] = Field(\n            ..., description=\"The properties to update on the page (as a JSON string or a dictionary).\"\n        )\n\n    def run_model(self) -> Data:\n        result = self._update_notion_page(self.page_id, self.properties)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the updated page data\n            output = \"Updated page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"update_notion_page\",\n            description=\"Update the properties of a Notion page. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._update_notion_page,\n            args_schema=self.NotionPageUpdateSchema,\n        )\n\n    def _update_notion_page(self, page_id: str, properties: Union[str, Dict[str, Any]]) -> Union[Dict[str, Any], str]:\n        url = f\"https://api.notion.com/v1/pages/{page_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n\n        # Parse properties if it's a string\n        if isinstance(properties, str):\n            try:\n                parsed_properties = json.loads(properties)\n            except json.JSONDecodeError as e:\n                error_message = f\"Invalid JSON format for properties: {str(e)}\"\n                logger.error(error_message)\n                return error_message\n\n        else:\n            parsed_properties = properties\n\n        data = {\"properties\": parsed_properties}\n\n        try:\n            logger.info(f\"Sending request to Notion API: URL: {url}, Data: {json.dumps(data)}\")\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n            updated_page = response.json()\n\n            logger.info(f\"Successfully updated Notion page. Response: {json.dumps(updated_page)}\")\n            return updated_page\n        except requests.exceptions.HTTPError as e:\n            error_message = f\"HTTP Error occurred: {str(e)}\"\n            if e.response is not None:\n                error_message += f\"\\nStatus code: {e.response.status_code}\"\n                error_message += f\"\\nResponse body: {e.response.text}\"\n            logger.error(error_message)\n            return error_message\n        except requests.exceptions.RequestException as e:\n            error_message = f\"An error occurred while making the request: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n        except Exception as e:\n            error_message = f\"An unexpected error occurred: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._update_notion_page(*args, **kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"page_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"page_id","value":"","display_name":"Page ID","advanced":true,"dynamic":false,"info":"The ID of the Notion page to update.","title_case":false,"type":"str","_input_type":"StrInput"},"properties":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"properties","value":"","display_name":"Properties","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The properties to update on the page (as a JSON string or a dictionary).","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Update the properties of a Notion page.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"Update Page Property ","documentation":"https://docs.langflow.org/integrations/notion/page-update","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true}],"field_order":["page_id","properties","notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionPageUpdate-bexvy","description":"Update the properties of a Notion page.","display_name":"Update Page Property "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":2649.2991466550625,"y":1385.262204377853},"dragging":false},{"id":"NotionSearch-EdSJb","type":"genericNode","position":{"x":2435.4455721283834,"y":357.45573905064634},"data":{"type":"NotionSearch","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"filter_value":{"trace_as_metadata":true,"options":["page","database"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"filter_value","value":"database","display_name":"Filter Type","advanced":true,"dynamic":false,"info":"Limits the results to either only pages or only databases.","title_case":false,"type":"str","_input_type":"DropdownInput"},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"query":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"query","value":"","display_name":"Search Query","advanced":true,"dynamic":false,"info":"The text that the API compares page and database titles against.","title_case":false,"type":"str","_input_type":"StrInput"},"sort_direction":{"trace_as_metadata":true,"options":["ascending","descending"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sort_direction","value":"descending","display_name":"Sort Direction","advanced":true,"dynamic":false,"info":"The direction to sort the results.","title_case":false,"type":"str","_input_type":"DropdownInput"}},"description":"List All Databases","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Databases","documentation":"https://docs.langflow.org/integrations/notion/search","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true,"hidden":false},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true,"hidden":true}],"field_order":["notion_secret","query","filter_value","sort_direction"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionSearch-EdSJb","description":"Searches all pages and databases that have been shared with an integration.","display_name":"Search "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":2435.4455721283834,"y":357.45573905064634},"dragging":false},{"id":"ParseData-vYVwu","type":"genericNode","position":{"x":2871.5903532688335,"y":563.1965154816405},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_metadata":true,"list":false,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"data","value":"","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sep","value":"\n","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{text}","display_name":"Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true}],"field_order":["data","template","sep"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ParseData-vYVwu","showNode":false},"selected":false,"width":96,"height":96,"positionAbsolute":{"x":2871.5903532688335,"y":563.1965154816405},"dragging":false},{"id":"ChatOutput-zBv53","type":"genericNode","position":{"x":4429.812566227955,"y":940.6072472757681},"data":{"type":"ChatOutput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"data_template":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"data_template","value":"{text}","display_name":"Data Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as output.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sender","value":"Machine","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sender_name","value":"AI","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"session_id","value":"","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"should_store_message":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"should_store_message","value":true,"display_name":"Store Messages","advanced":true,"dynamic":false,"info":"Store the message in the history.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Display a chat message in the Playground.","icon":"ChatOutput","base_classes":["Message"],"display_name":"Chat Output","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"message","display_name":"Message","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","should_store_message","sender","sender_name","session_id","data_template"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ChatOutput-zBv53"},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":4429.812566227955,"y":940.6072472757681},"dragging":false},{"id":"NotionUserList-wFEb1","type":"genericNode","position":{"x":2390.6365450681037,"y":694.4867003504073},"data":{"type":"NotionUserList","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"notion_secret":{"load_from_db":false,"required":true,"placeholder":"","show":true,"name":"notion_secret","value":"","display_name":"Notion Secret","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The Notion integration token.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Retrieve users from Notion.","icon":"NotionDirectoryLoader","base_classes":["Data","Tool"],"display_name":"List Users ","documentation":"https://docs.langflow.org/integrations/notion/list-users","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"example_output","display_name":"Data","method":"run_model","value":"__UNDEFINED__","cache":true},{"types":["Tool"],"selected":"Tool","name":"example_tool_output","display_name":"Tool","method":"build_tool","value":"__UNDEFINED__","cache":true,"hidden":true}],"field_order":["notion_secret"],"beta":false,"edited":true,"lf_version":"1.0.17"},"id":"NotionUserList-wFEb1","description":"Retrieve users from Notion.","display_name":"List Users "},"selected":false,"width":384,"height":302,"positionAbsolute":{"x":2390.6365450681037,"y":694.4867003504073},"dragging":false},{"id":"ParseData-WKjW6","type":"genericNode","position":{"x":2877.571533084884,"y":856.8480898893301},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_metadata":true,"list":false,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"data","value":"","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sep","value":"\n","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{text}","display_name":"Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true}],"field_order":["data","template","sep"],"beta":false,"edited":false,"lf_version":"1.0.17"},"id":"ParseData-WKjW6","showNode":false},"selected":false,"width":96,"height":96,"positionAbsolute":{"x":2877.571533084884,"y":856.8480898893301},"dragging":false},{"id":"CurrentDateComponent-WOwNq","type":"genericNode","position":{"x":536.7929500860405,"y":617.6055631700241},"data":{"type":"CurrentDateComponent","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from datetime import datetime\r\nfrom zoneinfo import ZoneInfo\r\nfrom typing import List\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import DropdownInput, 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Output(display_name=\"Current Date\", name=\"current_date\", method=\"get_current_date\"),\r\n    ]\r\n\r\n    def get_current_date(self) -> Message:\r\n        try:\r\n            tz = ZoneInfo(self.timezone)\r\n            current_date = datetime.now(tz).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\r\n            result = f\"Current date and time in {self.timezone}: {current_date}\"\r\n            self.status = result\r\n            return Message(text=result)\r\n        except Exception as e:\r\n            error_message = f\"Error: {str(e)}\"\r\n            self.status = error_message\r\n            return 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dataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-WOwNqœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}","target":"Prompt-19rub","targetHandle":"{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"CURRENT_DATE","id":"Prompt-19rub","inputTypes":["Message","Text"],"type":"str"},"sourceHandle":{"dataType":"CurrentDateComponent","id":"CurrentDateComponent-WOwNq","name":"current_date","output_types":["Message"]}},"id":"reactflow__edge-CurrentDateComponent-WOwNq{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-WOwNqœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}-Prompt-19rub{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","className":""},{"source":"CurrentDateComponent-PZ8xJ","sourceHandle":"{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-PZ8xJœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}","target":"Prompt-Lbxk6","targetHandle":"{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"CURRENT_DATE","id":"Prompt-Lbxk6","inputTypes":["Message","Text"],"type":"str"},"sourceHandle":{"dataType":"CurrentDateComponent","id":"CurrentDateComponent-PZ8xJ","name":"current_date","output_types":["Message"]}},"id":"reactflow__edge-CurrentDateComponent-PZ8xJ{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-PZ8xJœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}-Prompt-Lbxk6{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","className":""}],"viewport":{"x":-65.48833753518215,"y":119.49034539812101,"zoom":0.5588906662759379}},"description":"The Notion Agent for Meeting Notes is an AI-powered tool that automatically processes meeting transcripts and updates your Notion workspace accordingly. It identifies tasks, action items, and key points from your meetings, then creates new tasks or updates existing ones in Notion without manual input.\n\nTo use it, simply add your API Keys and provide a meeting transcript. The agent will analyze it, interact with your Notion workspace to make necessary updates, and give you a summary of actions taken. This streamlines your workflow, ensuring important meeting outcomes are captured and organized in Notion effortlessly.","name":"Notion Agent - Meeting Notes ","last_tested_version":"1.0.17.dev8","endpoint_name":null,"is_component":false}
\ No newline at end of file
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+                    "node": {
+                        "template": {
+                            "_type": "Component",
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+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
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+                                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import MultilineInput, Output\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n    name = \"TextInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
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+                                "dynamic": true,
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+                                "title_case": false
+                            },
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+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
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+                                "name": "input_value",
+                                "value": "Good morning. Thanks for joining this project review meeting. We've got quite a few tasks to discuss, especially some Notion-related ones. Shall we get started?\n\nMorning, Felipe. Absolutely, let's dive in. I see we have several projects and tasks on our plate.\n\nGreat. Let's begin with the AI Content Gen project. I'm currently working on \"Montar base agente seletor de cortes.\" It's in progress, and I'm aiming to complete it by June 14th. Have you had a chance to look at this task, Cezar?\n\nI haven't been directly involved with that one. Can you give me an overview of what it entails?\n\nOf course. Essentially, we're building a base agent that can intelligently select and edit content. It's part of our larger AI-driven content generation initiative. The challenge is creating an algorithm that can understand context and make smart editing decisions.\n\nInteresting. How's the progress so far?\n\nIt's coming along. I've set up the basic framework, but fine-tuning the selection criteria is proving to be more complex than initially anticipated. I might need an extra day or two beyond the June 14th deadline.\n\nUnderstood, Felipe. Keep me posted if you need any resources or if the deadline needs to be adjusted. By the way, I've been meaning to ask - have you had a chance to look into that new NLP library I mentioned last week? I think it could be useful for this project.\n\nActually, Cezar, I haven't gotten to that yet. Should we add it as a new task? Maybe \"Evaluate NLP library for content selection\"?\n\nGood idea. Let's add that to our task list with a due date of next Friday. Now, moving on to the next task in this project - \"Create Notion Task Automation.\" It's assigned to you and set for June 19th, but you haven't started it yet, right? This is where I'd like to focus our discussion today.\n\nThat's correct. So, the goal is to streamline our workflow by automating certain tasks within Notion. I'm thinking we could create scripts or use Notion's API to automatically create, assign, and update tasks based on certain triggers or schedules.\n\nThat sounds like it could save us a lot of time. What specific automations are you considering?\n\nI'm glad you asked, Cezar. I'm thinking of a few key areas:\n1. Automatic task creation based on project milestones\n2. Assigning tasks to team members based on their expertise and current workload\n3. Updating task statuses based on linked database entries\n4. Generating weekly progress reports\n5. Setting up reminders for overdue tasks\n\nThose all sound valuable. Have you looked into the technical requirements for implementing these?\n\nI've done some initial research. Notion's API seems robust enough to handle these automations. We'll likely need to use a combination of Notion's API and a server to run our scripts. I'm thinking of using Node.js for this.\n\nGood thinking. Do you foresee any challenges?\n\nThe main challenge will be ensuring our automations are flexible enough to handle different project structures and team dynamics. We'll need to build in some configurability.\n\nAgreed. Let's make sure we involve the team in defining these automations. Their input will be crucial for making this truly useful. Oh, and speaking of team input, I think we should add a task for \"Conduct team survey on Notion pain points.\" This could help us prioritize which automations to tackle first.\n\nThat's an excellent idea, Cezar. I'll create that task and aim to complete the survey by next Wednesday. Now, I see we have another Notion-related task: \"Subir Notion Agent no Langflow Prod.\" Can you remind me what this entails?\n\nYes, this task is about deploying our Notion integration agent to the Langflow production environment. It's not started yet, but it's a crucial step in making our Notion automations available to the whole team.\n\nI see. What's the timeline for this?\n\nWe haven't set a specific deadline yet, but I think we should aim to complete this shortly after the automation task. Let's tentatively say by the end of June?\n\nSounds reasonable. Make sure to coordinate with the DevOps team for a smooth deployment. And while we're on the topic of deployment, we should probably add a task for \"Create documentation for Notion Agent usage.\" We want to make sure the team knows how to use these new tools once they're available.\n\nYou're right, Felipe. I'll add that to our task list. Now, switching gears a bit, let's talk about the Internal Projects. I see you're working on \"Crypto Links\" - it's in progress.\n\nAh yes, our blockchain initiative. It's moving forward. I'm researching various blockchain platforms and their potential applications for our projects. I'm particularly interested in smart contract capabilities.\n\nInteresting. Keep me updated on any promising findings. By the way, have you considered reaching out to any blockchain experts for consultation? It might be worth adding a task for \"Schedule blockchain expert consultation.\"\n\nThat's a great suggestion, Cezar. I'll add it to my to-do list. Now, for the Internal Tasks, I see you're assigned to \"Revisar modos do Charlinho, preparar para open source.\" What's the status on that?\n\nI haven't started yet, but it's on my radar. The deadline is June 7th, so I'll be diving into it this week. Essentially, we need to review and refine Charlinho's modes before we open-source the project.\n\nSounds good. Let me know if you need any assistance with that. Oh, and don't forget we need to add a task for \"Prepare Charlinho documentation for open source.\" We want to make sure our project is well-documented when we release it.\n\nYou're right, Felipe. I'll make sure to include that in our task list. Now, I see you have several tasks assigned to you in the Internal Tasks section. Can you give me a quick rundown?\n\nOf course. I'm working on finding a freelancer to create flows in ComfyUI - that's in progress and due May 28th. I'm also handling the conception of the Agent UI, due May 30th. Both are moving along well.\n\nThere's also a task to \"Check, install and test Gladia to use a bot in Google Meet.\" That's in progress, and I'm collaborating with C on it.\n\nThat's quite a workload. How are you managing all these tasks?\n\nIt's challenging, but I'm prioritizing based on deadlines and dependencies. The Notion automation project is a high priority because it'll help us manage tasks more efficiently in the long run.\n\nGood strategy, Felipe. Is there anything you need from me or the team to help move these tasks forward?\n\nActually, yes. For the \"pegar os arquivos necessários para tentarmos montar um stinger com ffmpeg\" task, I could use some input on which files are critical for this. It's a low-priority task due June 2nd, but any insights would be helpful.\n\nI'll review our asset library and send you a list of potential files by tomorrow. Oh, and let's add a task for \"Create ffmpeg stinger tutorial\" once we figure out the process. It could be useful for the team in the future.\n\nGreat idea, Cezar. I'll add that to our backlog. Anything else we should discuss?\n\nI think we've covered the major points. Oh, one last thing - for the \"Create Notion Task Automation\" project, I was thinking of setting up a series of short daily meetings next week to keep everyone aligned. What do you think?\n\nThat's a good idea. Maybe 15-minute stand-ups? We can use those to address any roadblocks quickly. And let's add a task for \"Set up Notion Automation progress tracking board\" to help visualize our progress during these stand-ups.\n\nPerfect. I'll send out calendar invites this afternoon and create that tracking board task. Any final thoughts or concerns, Cezar?\n\nNot from my side. I think we have a clear path forward, especially with the Notion-related tasks and the new items we've added to our list.\n\nAgreed. Let's plan to reconvene next week to check on progress, particularly for the Notion automation project and these new tasks we've discussed. Thanks for the comprehensive update, Felipe.\n\nThank you, Cezar. I'll send out a summary of our discussion and action items shortly, including all the new tasks we've identified during this meeting.\n",
+                                "display_name": "Text",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
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+                                "dynamic": false,
+                                "info": "Text to be passed as input.",
+                                "title_case": false,
+                                "type": "str",
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+                            }
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+                        "description": "Get text inputs from the Playground.",
+                        "icon": "type",
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+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
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+                                    "Message"
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+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
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+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Retrieve users from Notion.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Users ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-users",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionUserList-TvIKS",
+                    "description": "Retrieve users from Notion.",
+                    "display_name": "List Users "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 80.49204196902156,
+                    "y": 741.0568511678105
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionSearch-M66HF",
+                "type": "genericNode",
+                "position": {
+                    "x": 1095.6934863134345,
+                    "y": 407.8718765800806
+                },
+                "data": {
+                    "type": "NotionSearch",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "filter_value": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "page",
+                                    "database"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "filter_value",
+                                "value": "database",
+                                "display_name": "Filter Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Limits the results to either only pages or only databases.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "query": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "query",
+                                "value": "",
+                                "display_name": "Search Query",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The text that the API compares page and database titles against.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "sort_direction": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "ascending",
+                                    "descending"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sort_direction",
+                                "value": "descending",
+                                "display_name": "Sort Direction",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The direction to sort the results.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Search ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/search",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": false
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret",
+                            "query",
+                            "filter_value",
+                            "sort_direction"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionSearch-M66HF",
+                    "description": "Searches all pages and databases that have been shared with an integration.",
+                    "display_name": "Search "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 386,
+                "positionAbsolute": {
+                    "x": 1095.6934863134345,
+                    "y": 407.8718765800806
+                },
+                "dragging": false
+            },
+            {
+                "id": "Prompt-19rub",
+                "type": "genericNode",
+                "position": {
+                    "x": 688.7954025956392,
+                    "y": 456.4686463487848
+                },
+                "data": {
+                    "type": "Prompt",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "<Instructions>\nYou are an AI assistant specialized in analyzing meeting transcripts and identifying tasks. Your goal is to extract relevant tasks from the given transcript, search for related existing tasks in Notion, and provide a comprehensive list of tasks with their current status and any needed updates.\n\nYou have access to the following input:\n\n<transcript>\n{TRANSCRIPT}\n</transcript>\n\n<users>\n{USERS}\n</users>\n\nFollow these steps to complete your task:\n\n1. Carefully read through the transcript and identify any mentioned tasks, action items, or follow-ups.\n\n2. For each identified task:\n   a. Use the notion_search tool to find if there's an existing related task in Notion.\n   b. If a related task is found, note its ID and current status.\n   c. If no related task is found, mark it as a new task.\n\n3. For each task (existing or new), determine:\n   a. The task name or description\n   b. The assigned person (if mentioned)\n   c. The current status (for existing tasks) or suggested status (for new tasks)\n   d. Any updates or changes mentioned in the transcript\n\n4. Compile your findings into a list of tasks using the following format:\n\n<task_list>\n<task>\n<id>[Notion page ID if existing, or \"NEW\" if new task]</id>\n<name>[Task name or description]</name>\n<assignee>[Assigned person, if mentioned]</assignee>\n<status>[Current status for existing tasks, or suggested status for new tasks]</status>\n<updates>[Any updates or changes mentioned in the transcript]</updates>\n</task>\n</task_list>\n\nRemember to focus on tasks that are directly related to the meeting discussion. Do not include general conversation topics or unrelated mentions as tasks.\n\nProvide your final output in the format specified above, with each task enclosed in its own <task></task> tags within the overall <task_list></task_list> structure.\n\nToday is: {CURRENT_DATE}\n</Instructions>\n\n\n",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "prompt",
+                                "_input_type": "PromptInput"
+                            },
+                            "TRANSCRIPT": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "TRANSCRIPT",
+                                "display_name": "TRANSCRIPT",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            },
+                            "USERS": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "USERS",
+                                "display_name": "USERS",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            },
+                            "CURRENT_DATE": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "CURRENT_DATE",
+                                "display_name": "CURRENT_DATE",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "icon": "prompts",
+                        "is_input": null,
+                        "is_output": null,
+                        "is_composition": null,
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "name": "",
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "custom_fields": {
+                            "template": [
+                                "TRANSCRIPT",
+                                "USERS",
+                                "CURRENT_DATE"
+                            ]
+                        },
+                        "output_types": [],
+                        "full_path": null,
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "prompt",
+                                "hidden": null,
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "template"
+                        ],
+                        "beta": false,
+                        "error": null,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "Prompt-19rub"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 588,
+                "positionAbsolute": {
+                    "x": 688.7954025956392,
+                    "y": 456.4686463487848
+                },
+                "dragging": false
+            },
+            {
+                "id": "ParseData-aNk1v",
+                "type": "genericNode",
+                "position": {
+                    "x": 540.4151030255898,
+                    "y": 834.2819856588019
+                },
+                "data": {
+                    "type": "ParseData",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "data": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data",
+                                "value": "",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "sep": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sep",
+                                "value": "\n",
+                                "display_name": "Separator",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{text}",
+                                "display_name": "Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Convert Data into plain text following a specified template.",
+                        "icon": "braces",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ParseData-aNk1v",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 540.4151030255898,
+                    "y": 834.2819856588019
+                }
+            },
+            {
+                "id": "ToolCallingAgent-rVWeq",
+                "type": "genericNode",
+                "position": {
+                    "x": 1566.291217492157,
+                    "y": 583.6687094567968
+                },
+                "data": {
+                    "type": "ToolCallingAgent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "chat_history": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "chat_history",
+                                "value": "",
+                                "display_name": "Chat History",
+                                "advanced": true,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "llm": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "llm",
+                                "value": "",
+                                "display_name": "Language Model",
+                                "advanced": false,
+                                "input_types": [
+                                    "LanguageModel"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "tools": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "tools",
+                                "value": "",
+                                "display_name": "Tools",
+                                "advanced": false,
+                                "input_types": [
+                                    "Tool",
+                                    "BaseTool"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "handle_parsing_errors": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "handle_parsing_errors",
+                                "value": true,
+                                "display_name": "Handle Parse Errors",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "Analyze this meeting",
+                                "display_name": "Input",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "max_iterations": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_iterations",
+                                "value": 15,
+                                "display_name": "Max Iterations",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "system_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_prompt",
+                                "value": "",
+                                "display_name": "System Prompt",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System prompt for the agent.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "user_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "user_prompt",
+                                "value": "{input}",
+                                "display_name": "Prompt",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "This prompt must contain 'input' key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "verbose": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "verbose",
+                                "value": true,
+                                "display_name": "Verbose",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Agent that uses tools",
+                        "icon": "LangChain",
+                        "base_classes": [
+                            "AgentExecutor",
+                            "Message"
+                        ],
+                        "display_name": "Tool Calling Agent",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "AgentExecutor"
+                                ],
+                                "selected": "AgentExecutor",
+                                "name": "agent",
+                                "display_name": "Agent",
+                                "method": "build_agent",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "response",
+                                "display_name": "Response",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "handle_parsing_errors",
+                            "verbose",
+                            "max_iterations",
+                            "tools",
+                            "llm",
+                            "system_prompt",
+                            "user_prompt",
+                            "chat_history"
+                        ],
+                        "beta": true,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ToolCallingAgent-rVWeq"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 398,
+                "positionAbsolute": {
+                    "x": 1566.291217492157,
+                    "y": 583.6687094567968
+                },
+                "dragging": false
+            },
+            {
+                "id": "OpenAIModel-Ht8xI",
+                "type": "genericNode",
+                "position": {
+                    "x": 1097.0545781920632,
+                    "y": 805.60631548423
+                },
+                "data": {
+                    "type": "OpenAIModel",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "api_key": {
+                                "load_from_db": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "api_key",
+                                "value": "",
+                                "display_name": "OpenAI API Key",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Input",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageInput"
+                            },
+                            "json_mode": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_mode",
+                                "value": false,
+                                "display_name": "JSON Mode",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "If True, it will output JSON regardless of passing a schema.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "max_tokens": {
+                                "trace_as_metadata": true,
+                                "range_spec": {
+                                    "step_type": "float",
+                                    "min": 0,
+                                    "max": 128000,
+                                    "step": 0.1
+                                },
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_tokens",
+                                "value": "",
+                                "display_name": "Max Tokens",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "model_kwargs": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_kwargs",
+                                "value": {},
+                                "display_name": "Model Kwargs",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "model_name": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "gpt-4o-mini",
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-4",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_name",
+                                "value": "gpt-4o",
+                                "display_name": "Model Name",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "openai_api_base": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "openai_api_base",
+                                "value": "",
+                                "display_name": "OpenAI API Base",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "output_schema": {
+                                "trace_as_input": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "output_schema",
+                                "value": {},
+                                "display_name": "Schema",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "seed": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "seed",
+                                "value": 1,
+                                "display_name": "Seed",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "stream": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "stream",
+                                "value": false,
+                                "display_name": "Stream",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "system_message": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_message",
+                                "value": "",
+                                "display_name": "System Message",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "temperature": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "temperature",
+                                "value": 0.1,
+                                "display_name": "Temperature",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "float",
+                                "_input_type": "FloatInput"
+                            }
+                        },
+                        "description": "Generates text using OpenAI LLMs.",
+                        "icon": "OpenAI",
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text_output",
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "selected": "LanguageModel",
+                                "name": "model_output",
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "system_message",
+                            "stream",
+                            "max_tokens",
+                            "model_kwargs",
+                            "json_mode",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "api_key",
+                            "temperature",
+                            "seed"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "OpenAIModel-Ht8xI"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 1097.0545781920632,
+                    "y": 805.60631548423
+                }
+            },
+            {
+                "id": "Prompt-Lbxk6",
+                "type": "genericNode",
+                "position": {
+                    "x": 3042.6844997246735,
+                    "y": 416.83992118486856
+                },
+                "data": {
+                    "type": "Prompt",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "<Instructions>\nYou are an AI assistant responsible for updating tasks in Notion based on the information provided from a meeting analysis. Your goal is to create new tasks and update existing ones using the Notion API tools available to you, and then provide a summary in a simple markdown format suitable for a chat interface.\n\nYou have access to the following inputs:\n\n<task_list>\n{TASK_LIST}\n</task_list>\n\n<databases>\n{DATABASES}\n</databases>\n\n<users>\n{USERS}\n</users>\n\nFollow these steps to update the tasks in Notion and generate a markdown summary:\n\n1. Identify the Task database ID from the provided <databases> list.\n\n2. Before processing any tasks, retrieve the database properties for the Task database:\n   a. Use the notion_database_properties and carefully review the properties, their types, and any options for select or multi-select properties.\n   b. Pay attention to the properties format for further usage.\n\n3. For each task in the task list:\n   a. If the task ID is \"NEW\", create a new task using the create_notion_page tool.\n   b. If the task has an existing ID, update the task using the update_notion_page tool.\n   c. Remember to use the properties from the DB retrieved from the notion_database_properties tool\n\n4. When creating a new task:\n   a. Use the create_notion_page tool.\n   b. Include the task name, assignee (if available), status, and any other relevant properties based on the database structure.\n   c. Ensure that the property names and types match exactly with what you retrieved from the notion_database_properties call.\n\n5. When updating an existing task:\n   a. Use the update_notion_page tool.\n   b. Update the status, assignee, or any other relevant properties mentioned in the <updates> field.\n   c. Ensure that the property names and types match exactly with what you retrieved from the notion_database_properties call.\n\n6. After each function call, wait for the <function_result> before proceeding to the next task.\n\n7. If you encounter any errors during the process, note them and continue with the next task.\n\n8. Provide a summary of your actions for each task in a simple markdown format. Use the following structure:\n   # Task Update Summary\n\n   ## Created Tasks\n   - **[Task Name]**: Assigned to [Assignee], Status: [Status]\n     - Details: [Brief description of the new task]\n\n   ## Updated Tasks\n   - **[Task Name]** (ID: [Notion Page ID])\n     - Changes: [Brief description of changes]\n     - Status: [Success/Error]\n\n   ## Errors\n   - **[Task Name or ID]**: [Description of the error encountered]\n\n\nRemember to use the exact property names, types, and options as specified in the Notion database properties you retrieved at the beginning. This is crucial for ensuring that all updates and creations are done correctly.\n\nIf you encounter any errors or uncertainties, include them in the Errors section of the markdown summary. With enough detail to the user understand the issues.\n\nProvide your final output as a complete markdown document containing all the tasks you've processed, whether they were created, updated, or encountered errors. Use only basic markdown formatting (headers, bold, lists) to ensure compatibility with chat interfaces. Do not include any XML tags or complex formatting in your final output.\n\nToday is: {CURRENT_DATE}\n\n</Instructions>",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "prompt",
+                                "_input_type": "PromptInput"
+                            },
+                            "TASK_LIST": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "TASK_LIST",
+                                "display_name": "TASK_LIST",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            },
+                            "DATABASES": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "DATABASES",
+                                "display_name": "DATABASES",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            },
+                            "USERS": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "USERS",
+                                "display_name": "USERS",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            },
+                            "CURRENT_DATE": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "CURRENT_DATE",
+                                "display_name": "CURRENT_DATE",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "icon": "prompts",
+                        "is_input": null,
+                        "is_output": null,
+                        "is_composition": null,
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "name": "",
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "custom_fields": {
+                            "template": [
+                                "TASK_LIST",
+                                "DATABASES",
+                                "USERS",
+                                "CURRENT_DATE"
+                            ]
+                        },
+                        "output_types": [],
+                        "full_path": null,
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "prompt",
+                                "hidden": null,
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "template"
+                        ],
+                        "beta": false,
+                        "error": null,
+                        "edited": false
+                    },
+                    "id": "Prompt-Lbxk6"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 674,
+                "positionAbsolute": {
+                    "x": 3042.6844997246735,
+                    "y": 416.83992118486856
+                },
+                "dragging": false
+            },
+            {
+                "id": "ToolCallingAgent-GurdE",
+                "type": "genericNode",
+                "position": {
+                    "x": 3974.1377259893243,
+                    "y": 867.4647271037014
+                },
+                "data": {
+                    "type": "ToolCallingAgent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "chat_history": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "chat_history",
+                                "value": "",
+                                "display_name": "Chat History",
+                                "advanced": true,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "llm": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "llm",
+                                "value": "",
+                                "display_name": "Language Model",
+                                "advanced": false,
+                                "input_types": [
+                                    "LanguageModel"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "tools": {
+                                "trace_as_metadata": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "tools",
+                                "value": "",
+                                "display_name": "Tools",
+                                "advanced": false,
+                                "input_types": [
+                                    "Tool",
+                                    "BaseTool"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from typing import Optional, List\n\nfrom langchain.agents import create_tool_calling_agent\nfrom langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.inputs import MultilineInput\nfrom langflow.inputs.inputs import HandleInput, DataInput\nfrom langflow.schema import Data\n\n\nclass ToolCallingAgentComponent(LCToolsAgentComponent):\n    display_name: str = \"Tool Calling Agent\"\n    description: str = \"Agent that uses tools\"\n    icon = \"LangChain\"\n    beta = True\n    name = \"ToolCallingAgent\"\n\n    inputs = LCToolsAgentComponent._base_inputs + [\n        HandleInput(name=\"llm\", display_name=\"Language Model\", input_types=[\"LanguageModel\"], required=True),\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"System Prompt\",\n            info=\"System prompt for the agent.\",\n            value=\"You are a helpful assistant\",\n        ),\n        MultilineInput(\n            name=\"user_prompt\", display_name=\"Prompt\", info=\"This prompt must contain 'input' key.\", value=\"{input}\"\n        ),\n        DataInput(name=\"chat_history\", display_name=\"Chat History\", is_list=True, advanced=True),\n    ]\n\n    def get_chat_history_data(self) -> Optional[List[Data]]:\n        return self.chat_history\n\n    def create_agent_runnable(self):\n        if \"input\" not in self.user_prompt:\n            raise ValueError(\"Prompt must contain 'input' key.\")\n        messages = [\n            (\"system\", self.system_prompt),\n            (\"placeholder\", \"{chat_history}\"),\n            HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=[\"input\"], template=self.user_prompt)),\n            (\"placeholder\", \"{agent_scratchpad}\"),\n        ]\n        prompt = ChatPromptTemplate.from_messages(messages)\n        return create_tool_calling_agent(self.llm, self.tools, prompt)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "handle_parsing_errors": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "handle_parsing_errors",
+                                "value": true,
+                                "display_name": "Handle Parse Errors",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "Do your task.",
+                                "display_name": "Input",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "max_iterations": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_iterations",
+                                "value": 15,
+                                "display_name": "Max Iterations",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "system_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_prompt",
+                                "value": "",
+                                "display_name": "System Prompt",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System prompt for the agent.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "user_prompt": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "user_prompt",
+                                "value": "{input}",
+                                "display_name": "Prompt",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "This prompt must contain 'input' key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "verbose": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "verbose",
+                                "value": true,
+                                "display_name": "Verbose",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Agent that uses tools",
+                        "icon": "LangChain",
+                        "base_classes": [
+                            "AgentExecutor",
+                            "Message"
+                        ],
+                        "display_name": "Tool Calling Agent",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "AgentExecutor"
+                                ],
+                                "selected": "AgentExecutor",
+                                "name": "agent",
+                                "display_name": "Agent",
+                                "method": "build_agent",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "response",
+                                "display_name": "Response",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "handle_parsing_errors",
+                            "verbose",
+                            "max_iterations",
+                            "tools",
+                            "llm",
+                            "system_prompt",
+                            "user_prompt",
+                            "chat_history"
+                        ],
+                        "beta": true,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ToolCallingAgent-GurdE"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 398,
+                "positionAbsolute": {
+                    "x": 3974.1377259893243,
+                    "y": 867.4647271037014
+                },
+                "dragging": false
+            },
+            {
+                "id": "OpenAIModel-OTfnt",
+                "type": "genericNode",
+                "position": {
+                    "x": 3513.5648778762093,
+                    "y": 710.2099422974287
+                },
+                "data": {
+                    "type": "OpenAIModel",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "api_key": {
+                                "load_from_db": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "api_key",
+                                "value": "",
+                                "display_name": "OpenAI API Key",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = LCModelComponent._base_inputs + [\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})  # type: ignore\n\n        return output  # type: ignore\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Input",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageInput"
+                            },
+                            "json_mode": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_mode",
+                                "value": false,
+                                "display_name": "JSON Mode",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "If True, it will output JSON regardless of passing a schema.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "max_tokens": {
+                                "trace_as_metadata": true,
+                                "range_spec": {
+                                    "step_type": "float",
+                                    "min": 0,
+                                    "max": 128000,
+                                    "step": 0.1
+                                },
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_tokens",
+                                "value": "",
+                                "display_name": "Max Tokens",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "model_kwargs": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_kwargs",
+                                "value": {},
+                                "display_name": "Model Kwargs",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "model_name": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "gpt-4o-mini",
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-4",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_name",
+                                "value": "gpt-4o",
+                                "display_name": "Model Name",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "openai_api_base": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "openai_api_base",
+                                "value": "",
+                                "display_name": "OpenAI API Base",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "output_schema": {
+                                "trace_as_input": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "output_schema",
+                                "value": {},
+                                "display_name": "Schema",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "seed": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "seed",
+                                "value": 1,
+                                "display_name": "Seed",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "stream": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "stream",
+                                "value": false,
+                                "display_name": "Stream",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "system_message": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_message",
+                                "value": "",
+                                "display_name": "System Message",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "temperature": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "temperature",
+                                "value": 0.1,
+                                "display_name": "Temperature",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "float",
+                                "_input_type": "FloatInput"
+                            }
+                        },
+                        "description": "Generates text using OpenAI LLMs.",
+                        "icon": "OpenAI",
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text_output",
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "selected": "LanguageModel",
+                                "name": "model_output",
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "system_message",
+                            "stream",
+                            "max_tokens",
+                            "model_kwargs",
+                            "json_mode",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "api_key",
+                            "temperature",
+                            "seed"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "OpenAIModel-OTfnt"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 3513.5648778762093,
+                    "y": 710.2099422974287
+                },
+                "dragging": false
+            },
+            {
+                "id": "AddContentToPage-vrAvx",
+                "type": "genericNode",
+                "position": {
+                    "x": 2649.2991466550634,
+                    "y": 1050.6250104897197
+                },
+                "data": {
+                    "type": "AddContentToPage",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "block_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "block_id",
+                                "value": "",
+                                "display_name": "Page/Block ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the page/block to add the content.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nfrom typing import Dict, Any, Union\nfrom markdown import markdown\nfrom bs4 import BeautifulSoup\nimport requests\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom pydantic import BaseModel, Field\n\n\nclass AddContentToPage(LCToolComponent):\n    display_name: str = \"Add Content to Page \"\n    description: str = \"Convert markdown text to Notion blocks and append them to a Notion page.\"\n    documentation: str = \"https://developers.notion.com/reference/patch-block-children\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        MultilineInput(\n            name=\"markdown_text\",\n            display_name=\"Markdown Text\",\n            info=\"The markdown text to convert to Notion blocks.\",\n        ),\n        StrInput(\n            name=\"block_id\",\n            display_name=\"Page/Block ID\",\n            info=\"The ID of the page/block to add the content.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class AddContentToPageSchema(BaseModel):\n        markdown_text: str = Field(..., description=\"The markdown text to convert to Notion blocks.\")\n        block_id: str = Field(..., description=\"The ID of the page/block to add the content.\")\n\n    def run_model(self) -> Data:\n        result = self._add_content_to_page(self.markdown_text, self.block_id)\n        return Data(data=result, text=json.dumps(result))\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"add_content_to_notion_page\",\n            description=\"Convert markdown text to Notion blocks and append them to a Notion page.\",\n            func=self._add_content_to_page,\n            args_schema=self.AddContentToPageSchema,\n        )\n\n    def _add_content_to_page(self, markdown_text: str, block_id: str) -> Union[Dict[str, Any], str]:\n        try:\n            html_text = markdown(markdown_text)\n            soup = BeautifulSoup(html_text, \"html.parser\")\n            blocks = self.process_node(soup)\n\n            url = f\"https://api.notion.com/v1/blocks/{block_id}/children\"\n            headers = {\n                \"Authorization\": f\"Bearer {self.notion_secret}\",\n                \"Content-Type\": \"application/json\",\n                \"Notion-Version\": \"2022-06-28\",\n            }\n\n            data = {\n                \"children\": blocks,\n            }\n\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n\n            return response.json()\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Error: Failed to add content to Notion page. {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n        except Exception as e:\n            return f\"Error: An unexpected error occurred while adding content to Notion page. {str(e)}\"\n\n    def process_node(self, node):\n        blocks = []\n        if isinstance(node, str):\n            text = node.strip()\n            if text:\n                if text.startswith(\"#\"):\n                    heading_level = text.count(\"#\", 0, 6)\n                    heading_text = text[heading_level:].strip()\n                    if heading_level == 1:\n                        blocks.append(self.create_block(\"heading_1\", heading_text))\n                    elif heading_level == 2:\n                        blocks.append(self.create_block(\"heading_2\", heading_text))\n                    elif heading_level == 3:\n                        blocks.append(self.create_block(\"heading_3\", heading_text))\n                else:\n                    blocks.append(self.create_block(\"paragraph\", text))\n        elif node.name == \"h1\":\n            blocks.append(self.create_block(\"heading_1\", node.get_text(strip=True)))\n        elif node.name == \"h2\":\n            blocks.append(self.create_block(\"heading_2\", node.get_text(strip=True)))\n        elif node.name == \"h3\":\n            blocks.append(self.create_block(\"heading_3\", node.get_text(strip=True)))\n        elif node.name == \"p\":\n            code_node = node.find(\"code\")\n            if code_node:\n                code_text = code_node.get_text()\n                language, code = self.extract_language_and_code(code_text)\n                blocks.append(self.create_block(\"code\", code, language=language))\n            elif self.is_table(str(node)):\n                blocks.extend(self.process_table(node))\n            else:\n                blocks.append(self.create_block(\"paragraph\", node.get_text(strip=True)))\n        elif node.name == \"ul\":\n            blocks.extend(self.process_list(node, \"bulleted_list_item\"))\n        elif node.name == \"ol\":\n            blocks.extend(self.process_list(node, \"numbered_list_item\"))\n        elif node.name == \"blockquote\":\n            blocks.append(self.create_block(\"quote\", node.get_text(strip=True)))\n        elif node.name == \"hr\":\n            blocks.append(self.create_block(\"divider\", \"\"))\n        elif node.name == \"img\":\n            blocks.append(self.create_block(\"image\", \"\", image_url=node.get(\"src\")))\n        elif node.name == \"a\":\n            blocks.append(self.create_block(\"bookmark\", node.get_text(strip=True), link_url=node.get(\"href\")))\n        elif node.name == \"table\":\n            blocks.extend(self.process_table(node))\n\n        for child in node.children:\n            if isinstance(child, str):\n                continue\n            blocks.extend(self.process_node(child))\n\n        return blocks\n\n    def extract_language_and_code(self, code_text):\n        lines = code_text.split(\"\\n\")\n        language = lines[0].strip()\n        code = \"\\n\".join(lines[1:]).strip()\n        return language, code\n\n    def is_code_block(self, text):\n        return text.startswith(\"```\")\n\n    def extract_code_block(self, text):\n        lines = text.split(\"\\n\")\n        language = lines[0].strip(\"`\").strip()\n        code = \"\\n\".join(lines[1:]).strip(\"`\").strip()\n        return language, code\n\n    def is_table(self, text):\n        rows = text.split(\"\\n\")\n        if len(rows) < 2:\n            return False\n\n        has_separator = False\n        for i, row in enumerate(rows):\n            if \"|\" in row:\n                cells = [cell.strip() for cell in row.split(\"|\")]\n                cells = [cell for cell in cells if cell]  # Remove empty cells\n                if i == 1 and all(set(cell) <= set(\"-|\") for cell in cells):\n                    has_separator = True\n                elif not cells:\n                    return False\n\n        return has_separator and len(rows) >= 3\n\n    def process_list(self, node, list_type):\n        blocks = []\n        for item in node.find_all(\"li\"):\n            item_text = item.get_text(strip=True)\n            checked = item_text.startswith(\"[x]\")\n            is_checklist = item_text.startswith(\"[ ]\") or checked\n\n            if is_checklist:\n                item_text = item_text.replace(\"[x]\", \"\").replace(\"[ ]\", \"\").strip()\n                blocks.append(self.create_block(\"to_do\", item_text, checked=checked))\n            else:\n                blocks.append(self.create_block(list_type, item_text))\n        return blocks\n\n    def process_table(self, node):\n        blocks = []\n        header_row = node.find(\"thead\").find(\"tr\") if node.find(\"thead\") else None\n        body_rows = node.find(\"tbody\").find_all(\"tr\") if node.find(\"tbody\") else []\n\n        if header_row or body_rows:\n            table_width = max(\n                len(header_row.find_all([\"th\", \"td\"])) if header_row else 0,\n                max(len(row.find_all([\"th\", \"td\"])) for row in body_rows),\n            )\n\n            table_block = self.create_block(\"table\", \"\", table_width=table_width, has_column_header=bool(header_row))\n            blocks.append(table_block)\n\n            if header_row:\n                header_cells = [cell.get_text(strip=True) for cell in header_row.find_all([\"th\", \"td\"])]\n                header_row_block = self.create_block(\"table_row\", header_cells)\n                blocks.append(header_row_block)\n\n            for row in body_rows:\n                cells = [cell.get_text(strip=True) for cell in row.find_all([\"th\", \"td\"])]\n                row_block = self.create_block(\"table_row\", cells)\n                blocks.append(row_block)\n\n        return blocks\n\n    def create_block(self, block_type: str, content: str, **kwargs) -> Dict[str, Any]:\n        block: dict[str, Any] = {\n            \"object\": \"block\",\n            \"type\": block_type,\n            block_type: {},\n        }\n\n        if block_type in [\n            \"paragraph\",\n            \"heading_1\",\n            \"heading_2\",\n            \"heading_3\",\n            \"bulleted_list_item\",\n            \"numbered_list_item\",\n            \"quote\",\n        ]:\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n        elif block_type == \"to_do\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"checked\"] = kwargs.get(\"checked\", False)\n        elif block_type == \"code\":\n            block[block_type][\"rich_text\"] = [\n                {\n                    \"type\": \"text\",\n                    \"text\": {\n                        \"content\": content,\n                    },\n                }\n            ]\n            block[block_type][\"language\"] = kwargs.get(\"language\", \"plain text\")\n        elif block_type == \"image\":\n            block[block_type] = {\"type\": \"external\", \"external\": {\"url\": kwargs.get(\"image_url\", \"\")}}\n        elif block_type == \"divider\":\n            pass\n        elif block_type == \"bookmark\":\n            block[block_type][\"url\"] = kwargs.get(\"link_url\", \"\")\n        elif block_type == \"table\":\n            block[block_type][\"table_width\"] = kwargs.get(\"table_width\", 0)\n            block[block_type][\"has_column_header\"] = kwargs.get(\"has_column_header\", False)\n            block[block_type][\"has_row_header\"] = kwargs.get(\"has_row_header\", False)\n        elif block_type == \"table_row\":\n            block[block_type][\"cells\"] = [[{\"type\": \"text\", \"text\": {\"content\": cell}} for cell in content]]\n\n        return block\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "markdown_text": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "markdown_text",
+                                "value": "",
+                                "display_name": "Markdown Text",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The markdown text to convert to Notion blocks.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Convert markdown text to Notion blocks and append them to a Notion page.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Add Content to Page ",
+                        "documentation": "https://developers.notion.com/reference/patch-block-children",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "markdown_text",
+                            "block_id",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "AddContentToPage-vrAvx",
+                    "description": "Convert markdown text to Notion blocks and append them to a Notion page.",
+                    "display_name": "Add Content to Page "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 330,
+                "positionAbsolute": {
+                    "x": 2649.2991466550634,
+                    "y": 1050.6250104897197
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionPageCreator-Exc7f",
+                "type": "genericNode",
+                "position": {
+                    "x": 3050.8201437255634,
+                    "y": 1391.0449862668834
+                },
+                "data": {
+                    "type": "NotionPageCreator",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nfrom typing import Dict, Any, Union\nimport requests\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionPageCreator(LCToolComponent):\n    display_name: str = \"Create Page \"\n    description: str = \"A component for creating Notion pages.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-create\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"properties_json\",\n            display_name=\"Properties (JSON)\",\n            info=\"The properties of the new page as a JSON string.\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageCreatorSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n        properties_json: str = Field(..., description=\"The properties of the new page as a JSON string.\")\n\n    def run_model(self) -> Data:\n        result = self._create_notion_page(self.database_id, self.properties_json)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the created page data\n            output = \"Created page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"create_notion_page\",\n            description=\"Create a new page in a Notion database. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._create_notion_page,\n            args_schema=self.NotionPageCreatorSchema,\n        )\n\n    def _create_notion_page(self, database_id: str, properties_json: str) -> Union[Dict[str, Any], str]:\n        if not database_id or not properties_json:\n            return \"Invalid input. Please provide 'database_id' and 'properties_json'.\"\n\n        try:\n            properties = json.loads(properties_json)\n        except json.JSONDecodeError as e:\n            return f\"Invalid properties format. Please provide a valid JSON string. Error: {str(e)}\"\n\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"parent\": {\"database_id\": database_id},\n            \"properties\": properties,\n        }\n\n        try:\n            response = requests.post(\"https://api.notion.com/v1/pages\", headers=headers, json=data)\n            response.raise_for_status()\n            result = response.json()\n            return result\n        except requests.exceptions.RequestException as e:\n            error_message = f\"Failed to create Notion page. Error: {str(e)}\"\n            if hasattr(e, \"response\") and e.response is not None:\n                error_message += f\" Status code: {e.response.status_code}, Response: {e.response.text}\"\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._create_notion_page(*args, **kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "database_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "database_id",
+                                "value": "",
+                                "display_name": "Database ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion database.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "properties_json": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "properties_json",
+                                "value": "",
+                                "display_name": "Properties (JSON)",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The properties of the new page as a JSON string.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "A component for creating Notion pages.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Create Page ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/page-create",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "database_id",
+                            "notion_secret",
+                            "properties_json"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionPageCreator-Exc7f",
+                    "description": "A component for creating Notion pages.",
+                    "display_name": "Create Page "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 3050.8201437255634,
+                    "y": 1391.0449862668834
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionDatabaseProperties-IjzLV",
+                "type": "genericNode",
+                "position": {
+                    "x": 3053.0023230574693,
+                    "y": 1061.535907149244
+                },
+                "data": {
+                    "type": "NotionDatabaseProperties",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import Dict, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom langflow.io import Output\n\nclass NotionDatabaseProperties(LCToolComponent):\n    display_name: str = \"List Database Properties \"\n    description: str = \"Retrieve properties of a Notion database.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/list-database-properties\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"database_id\",\n            display_name=\"Database ID\",\n            info=\"The ID of the Notion database.\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionDatabasePropertiesSchema(BaseModel):\n        database_id: str = Field(..., description=\"The ID of the Notion database.\")\n\n    def run_model(self) -> Data:\n        result = self._fetch_database_properties(self.database_id)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the properties\n            return Data(text=str(result), data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_database_properties\",\n            description=\"Retrieve properties of a Notion database. Input should include the database ID.\",\n            func=self._fetch_database_properties,\n            args_schema=self.NotionDatabasePropertiesSchema,\n        )\n\n    def _fetch_database_properties(self, database_id: str) -> Union[Dict, str]:\n        url = f\"https://api.notion.com/v1/databases/{database_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n        try:\n            response = requests.get(url, headers=headers)\n            response.raise_for_status()\n            data = response.json()\n            properties = data.get(\"properties\", {})\n            return properties\n        except requests.exceptions.RequestException as e:\n            return f\"Error fetching Notion database properties: {str(e)}\"\n        except ValueError as e:\n            return f\"Error parsing Notion API response: {str(e)}\"\n        except Exception as e:\n            return f\"An unexpected error occurred: {str(e)}\"\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "database_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "database_id",
+                                "value": "",
+                                "display_name": "Database ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion database.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Retrieve properties of a Notion database.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Database Properties ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-database-properties",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "database_id",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionDatabaseProperties-IjzLV",
+                    "description": "Retrieve properties of a Notion database.",
+                    "display_name": "List Database Properties "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 3053.0023230574693,
+                    "y": 1061.535907149244
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionPageUpdate-bexvy",
+                "type": "genericNode",
+                "position": {
+                    "x": 2649.2991466550625,
+                    "y": 1385.262204377853
+                },
+                "data": {
+                    "type": "NotionPageUpdate",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nimport requests\nfrom typing import Dict, Any, Union\nfrom pydantic import BaseModel, Field\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, MultilineInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\nfrom loguru import logger\nfrom langflow.io import Output\n\nclass NotionPageUpdate(LCToolComponent):\n    display_name: str = \"Update Page Property \"\n    description: str = \"Update the properties of a Notion page.\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/page-update\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        StrInput(\n            name=\"page_id\",\n            display_name=\"Page ID\",\n            info=\"The ID of the Notion page to update.\",\n        ),\n        MultilineInput(\n            name=\"properties\",\n            display_name=\"Properties\",\n            info=\"The properties to update on the page (as a JSON string or a dictionary).\",\n        ),\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionPageUpdateSchema(BaseModel):\n        page_id: str = Field(..., description=\"The ID of the Notion page to update.\")\n        properties: Union[str, Dict[str, Any]] = Field(\n            ..., description=\"The properties to update on the page (as a JSON string or a dictionary).\"\n        )\n\n    def run_model(self) -> Data:\n        result = self._update_notion_page(self.page_id, self.properties)\n        if isinstance(result, str):\n            # An error occurred, return it as text\n            return Data(text=result)\n        else:\n            # Success, return the updated page data\n            output = \"Updated page properties:\\n\"\n            for prop_name, prop_value in result.get(\"properties\", {}).items():\n                output += f\"{prop_name}: {prop_value}\\n\"\n            return Data(text=output, data=result)\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"update_notion_page\",\n            description=\"Update the properties of a Notion page. IMPORTANT: Use the tool to check the Database properties for more details before using this tool.\",\n            func=self._update_notion_page,\n            args_schema=self.NotionPageUpdateSchema,\n        )\n\n    def _update_notion_page(self, page_id: str, properties: Union[str, Dict[str, Any]]) -> Union[Dict[str, Any], str]:\n        url = f\"https://api.notion.com/v1/pages/{page_id}\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",  # Use the latest supported version\n        }\n\n        # Parse properties if it's a string\n        if isinstance(properties, str):\n            try:\n                parsed_properties = json.loads(properties)\n            except json.JSONDecodeError as e:\n                error_message = f\"Invalid JSON format for properties: {str(e)}\"\n                logger.error(error_message)\n                return error_message\n\n        else:\n            parsed_properties = properties\n\n        data = {\"properties\": parsed_properties}\n\n        try:\n            logger.info(f\"Sending request to Notion API: URL: {url}, Data: {json.dumps(data)}\")\n            response = requests.patch(url, headers=headers, json=data)\n            response.raise_for_status()\n            updated_page = response.json()\n\n            logger.info(f\"Successfully updated Notion page. Response: {json.dumps(updated_page)}\")\n            return updated_page\n        except requests.exceptions.HTTPError as e:\n            error_message = f\"HTTP Error occurred: {str(e)}\"\n            if e.response is not None:\n                error_message += f\"\\nStatus code: {e.response.status_code}\"\n                error_message += f\"\\nResponse body: {e.response.text}\"\n            logger.error(error_message)\n            return error_message\n        except requests.exceptions.RequestException as e:\n            error_message = f\"An error occurred while making the request: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n        except Exception as e:\n            error_message = f\"An unexpected error occurred: {str(e)}\"\n            logger.error(error_message)\n            return error_message\n\n    def __call__(self, *args, **kwargs):\n        return self._update_notion_page(*args, **kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "page_id": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "page_id",
+                                "value": "",
+                                "display_name": "Page ID",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The ID of the Notion page to update.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "properties": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "properties",
+                                "value": "",
+                                "display_name": "Properties",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The properties to update on the page (as a JSON string or a dictionary).",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Update the properties of a Notion page.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "Update Page Property ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/page-update",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "page_id",
+                            "properties",
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionPageUpdate-bexvy",
+                    "description": "Update the properties of a Notion page.",
+                    "display_name": "Update Page Property "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 2649.2991466550625,
+                    "y": 1385.262204377853
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionSearch-EdSJb",
+                "type": "genericNode",
+                "position": {
+                    "x": 2435.4455721283834,
+                    "y": 357.45573905064634
+                },
+                "data": {
+                    "type": "NotionSearch",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import Dict, Any, List\nfrom pydantic import BaseModel, Field\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput, StrInput, DropdownInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionSearch(LCToolComponent):\n    display_name: str = \"Search \"\n    description: str = \"Searches all pages and databases that have been shared with an integration. The search field can be an empty value to show all values from that search\"\n    documentation: str = \"https://docs.langflow.org/integrations/notion/search\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n        StrInput(\n            name=\"query\",\n            display_name=\"Search Query\",\n            info=\"The text that the API compares page and database titles against.\",\n        ),\n        DropdownInput(\n            name=\"filter_value\",\n            display_name=\"Filter Type\",\n            info=\"Limits the results to either only pages or only databases.\",\n            options=[\"page\", \"database\"],\n            value=\"page\",\n        ),\n        DropdownInput(\n            name=\"sort_direction\",\n            display_name=\"Sort Direction\",\n            info=\"The direction to sort the results.\",\n            options=[\"ascending\", \"descending\"],\n            value=\"descending\",\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionSearchSchema(BaseModel):\n        query: str = Field(..., description=\"The search query text.\")\n        filter_value: str = Field(default=\"page\", description=\"Filter type: 'page' or 'database'.\")\n        sort_direction: str = Field(default=\"descending\", description=\"Sort direction: 'ascending' or 'descending'.\")\n\n    def run_model(self) -> List[Data]:\n        results = self._search_notion(self.query, self.filter_value, self.sort_direction)\n        records = []\n        combined_text = f\"Results found: {len(results)}\\n\\n\"\n\n        for result in results:\n            result_data = {\n                \"id\": result[\"id\"],\n                \"type\": result[\"object\"],\n                \"last_edited_time\": result[\"last_edited_time\"],\n            }\n\n            if result[\"object\"] == \"page\":\n                result_data[\"title_or_url\"] = result[\"url\"]\n                text = f\"id: {result['id']}\\ntitle_or_url: {result['url']}\\n\"\n            elif result[\"object\"] == \"database\":\n                if \"title\" in result and isinstance(result[\"title\"], list) and len(result[\"title\"]) > 0:\n                    result_data[\"title_or_url\"] = result[\"title\"][0][\"plain_text\"]\n                    text = f\"id: {result['id']}\\ntitle_or_url: {result['title'][0]['plain_text']}\\n\"\n                else:\n                    result_data[\"title_or_url\"] = \"N/A\"\n                    text = f\"id: {result['id']}\\ntitle_or_url: N/A\\n\"\n\n            text += f\"type: {result['object']}\\nlast_edited_time: {result['last_edited_time']}\\n\\n\"\n            combined_text += text\n            records.append(Data(text=text, data=result_data))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_search\",\n            description=\"Search Notion pages and databases. Input should include the search query and optionally filter type and sort direction.\",\n            func=self._search_notion,\n            args_schema=self.NotionSearchSchema,\n        )\n\n    def _search_notion(\n        self, query: str, filter_value: str = \"page\", sort_direction: str = \"descending\"\n    ) -> List[Dict[str, Any]]:\n        url = \"https://api.notion.com/v1/search\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Content-Type\": \"application/json\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        data = {\n            \"query\": query,\n            \"filter\": {\"value\": filter_value, \"property\": \"object\"},\n            \"sort\": {\"direction\": sort_direction, \"timestamp\": \"last_edited_time\"},\n        }\n\n        response = requests.post(url, headers=headers, json=data)\n        response.raise_for_status()\n\n        results = response.json()\n        return results[\"results\"]\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "filter_value": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "page",
+                                    "database"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "filter_value",
+                                "value": "database",
+                                "display_name": "Filter Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Limits the results to either only pages or only databases.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "query": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "query",
+                                "value": "",
+                                "display_name": "Search Query",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The text that the API compares page and database titles against.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "sort_direction": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "ascending",
+                                    "descending"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sort_direction",
+                                "value": "descending",
+                                "display_name": "Sort Direction",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The direction to sort the results.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "List All Databases",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Databases",
+                        "documentation": "https://docs.langflow.org/integrations/notion/search",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": false
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret",
+                            "query",
+                            "filter_value",
+                            "sort_direction"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionSearch-EdSJb",
+                    "description": "Searches all pages and databases that have been shared with an integration.",
+                    "display_name": "Search "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 2435.4455721283834,
+                    "y": 357.45573905064634
+                },
+                "dragging": false
+            },
+            {
+                "id": "ParseData-vYVwu",
+                "type": "genericNode",
+                "position": {
+                    "x": 2871.5903532688335,
+                    "y": 563.1965154816405
+                },
+                "data": {
+                    "type": "ParseData",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "data": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data",
+                                "value": "",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "sep": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sep",
+                                "value": "\n",
+                                "display_name": "Separator",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{text}",
+                                "display_name": "Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Convert Data into plain text following a specified template.",
+                        "icon": "braces",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ParseData-vYVwu",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "positionAbsolute": {
+                    "x": 2871.5903532688335,
+                    "y": 563.1965154816405
+                },
+                "dragging": false
+            },
+            {
+                "id": "ChatOutput-zBv53",
+                "type": "genericNode",
+                "position": {
+                    "x": 4429.812566227955,
+                    "y": 940.6072472757681
+                },
+                "data": {
+                    "type": "ChatOutput",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "data_template": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data_template",
+                                "value": "{text}",
+                                "display_name": "Data Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Text",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Message to be passed as output.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "sender": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender",
+                                "value": "Machine",
+                                "display_name": "Sender Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Type of sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender_name": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender_name",
+                                "value": "AI",
+                                "display_name": "Sender Name",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Name of the sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "session_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "session_id",
+                                "value": "",
+                                "display_name": "Session ID",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "should_store_message": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "should_store_message",
+                                "value": true,
+                                "display_name": "Store Messages",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Store the message in the history.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Display a chat message in the Playground.",
+                        "icon": "ChatOutput",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Chat Output",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "message",
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "should_store_message",
+                            "sender",
+                            "sender_name",
+                            "session_id",
+                            "data_template"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ChatOutput-zBv53"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 4429.812566227955,
+                    "y": 940.6072472757681
+                },
+                "dragging": false
+            },
+            {
+                "id": "NotionUserList-wFEb1",
+                "type": "genericNode",
+                "position": {
+                    "x": 2390.6365450681037,
+                    "y": 694.4867003504073
+                },
+                "data": {
+                    "type": "NotionUserList",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import requests\nfrom typing import List, Dict\nfrom pydantic import BaseModel\nfrom langflow.io import Output\nfrom langflow.base.langchain_utilities.model import LCToolComponent\nfrom langflow.inputs import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.field_typing import Tool\nfrom langchain.tools import StructuredTool\n\n\nclass NotionUserList(LCToolComponent):\n    display_name = \"List Users \"\n    description = \"Retrieve users from Notion.\"\n    documentation = \"https://docs.langflow.org/integrations/notion/list-users\"\n    icon = \"NotionDirectoryLoader\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"notion_secret\",\n            display_name=\"Notion Secret\",\n            info=\"The Notion integration token.\",\n            required=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"example_output\", display_name=\"Data\", method=\"run_model\"),\n        Output(name=\"example_tool_output\", display_name=\"Tool\", method=\"build_tool\"),\n    ]\n\n    class NotionUserListSchema(BaseModel):\n        pass\n\n    def run_model(self) -> List[Data]:\n        users = self._list_users()\n        records = []\n        combined_text = \"\"\n\n        for user in users:\n            output = \"User:\\n\"\n            for key, value in user.items():\n                output += f\"{key.replace('_', ' ').title()}: {value}\\n\"\n            output += \"________________________\\n\"\n\n            combined_text += output\n            records.append(Data(text=output, data=user))\n\n        self.status = records\n        return records\n\n    def build_tool(self) -> Tool:\n        return StructuredTool.from_function(\n            name=\"notion_list_users\",\n            description=\"Retrieve users from Notion.\",\n            func=self._list_users,\n            args_schema=self.NotionUserListSchema,\n        )\n\n    def _list_users(self) -> List[Dict]:\n        url = \"https://api.notion.com/v1/users\"\n        headers = {\n            \"Authorization\": f\"Bearer {self.notion_secret}\",\n            \"Notion-Version\": \"2022-06-28\",\n        }\n\n        response = requests.get(url, headers=headers)\n        response.raise_for_status()\n\n        data = response.json()\n        results = data[\"results\"]\n\n        users = []\n        for user in results:\n            user_data = {\n                \"id\": user[\"id\"],\n                \"type\": user[\"type\"],\n                \"name\": user.get(\"name\", \"\"),\n                \"avatar_url\": user.get(\"avatar_url\", \"\"),\n            }\n            users.append(user_data)\n\n        return users\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "notion_secret": {
+                                "load_from_db": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "notion_secret",
+                                "value": "",
+                                "display_name": "Notion Secret",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The Notion integration token.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Retrieve users from Notion.",
+                        "icon": "NotionDirectoryLoader",
+                        "base_classes": [
+                            "Data",
+                            "Tool"
+                        ],
+                        "display_name": "List Users ",
+                        "documentation": "https://docs.langflow.org/integrations/notion/list-users",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "example_output",
+                                "display_name": "Data",
+                                "method": "run_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            },
+                            {
+                                "types": [
+                                    "Tool"
+                                ],
+                                "selected": "Tool",
+                                "name": "example_tool_output",
+                                "display_name": "Tool",
+                                "method": "build_tool",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": true
+                            }
+                        ],
+                        "field_order": [
+                            "notion_secret"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "NotionUserList-wFEb1",
+                    "description": "Retrieve users from Notion.",
+                    "display_name": "List Users "
+                },
+                "selected": false,
+                "width": 384,
+                "height": 302,
+                "positionAbsolute": {
+                    "x": 2390.6365450681037,
+                    "y": 694.4867003504073
+                },
+                "dragging": false
+            },
+            {
+                "id": "ParseData-WKjW6",
+                "type": "genericNode",
+                "position": {
+                    "x": 2877.571533084884,
+                    "y": 856.8480898893301
+                },
+                "data": {
+                    "type": "ParseData",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "data": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data",
+                                "value": "",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "sep": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sep",
+                                "value": "\n",
+                                "display_name": "Separator",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{text}",
+                                "display_name": "Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Convert Data into plain text following a specified template.",
+                        "icon": "braces",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "lf_version": "1.0.17"
+                    },
+                    "id": "ParseData-WKjW6",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "positionAbsolute": {
+                    "x": 2877.571533084884,
+                    "y": 856.8480898893301
+                },
+                "dragging": false
+            },
+            {
+                "id": "CurrentDateComponent-WOwNq",
+                "type": "genericNode",
+                "position": {
+                    "x": 536.7929500860405,
+                    "y": 617.6055631700241
+                },
+                "data": {
+                    "type": "CurrentDateComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from datetime import datetime\r\nfrom zoneinfo import ZoneInfo\r\nfrom typing import List\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import DropdownInput, Output\r\nfrom langflow.schema.message import Message\r\n\r\nclass CurrentDateComponent(Component):\r\n    display_name = \"Current Date 🕰️\"\r\n    description = \"Returns the current date and time in the selected timezone.\"\r\n    icon = \"clock\"\r\n\r\n    inputs = [\r\n        DropdownInput(\r\n            name=\"timezone\",\r\n            display_name=\"Timezone\",\r\n            options=[\r\n                \"UTC\",\r\n                \"US/Eastern\",\r\n                \"US/Central\",\r\n                \"US/Mountain\",\r\n                \"US/Pacific\",\r\n                \"Europe/London\",\r\n                \"Europe/Paris\",\r\n                \"Asia/Tokyo\",\r\n                \"Australia/Sydney\",\r\n                \"America/Sao_Paulo\",\r\n                \"America/Cuiaba\",\r\n            ],\r\n            value=\"UTC\",\r\n            info=\"Select the timezone for the current date and time.\",\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Current Date\", name=\"current_date\", method=\"get_current_date\"),\r\n    ]\r\n\r\n    def get_current_date(self) -> Message:\r\n        try:\r\n            tz = ZoneInfo(self.timezone)\r\n            current_date = datetime.now(tz).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\r\n            result = f\"Current date and time in {self.timezone}: {current_date}\"\r\n            self.status = result\r\n            return Message(text=result)\r\n        except Exception as e:\r\n            error_message = f\"Error: {str(e)}\"\r\n            self.status = error_message\r\n            return Message(text=error_message)",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "timezone": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "UTC",
+                                    "US/Eastern",
+                                    "US/Central",
+                                    "US/Mountain",
+                                    "US/Pacific",
+                                    "Europe/London",
+                                    "Europe/Paris",
+                                    "Asia/Tokyo",
+                                    "Australia/Sydney",
+                                    "America/Sao_Paulo",
+                                    "America/Cuiaba"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "timezone",
+                                "value": "UTC",
+                                "display_name": "Timezone",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Select the timezone for the current date and time.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "Returns the current date and time in the selected timezone.",
+                        "icon": "clock",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Current Date",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "current_date",
+                                "display_name": "Current Date",
+                                "method": "get_current_date",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "timezone"
+                        ],
+                        "beta": false,
+                        "edited": true
+                    },
+                    "id": "CurrentDateComponent-WOwNq",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 536.7929500860405,
+                    "y": 617.6055631700241
+                }
+            },
+            {
+                "id": "CurrentDateComponent-PZ8xJ",
+                "type": "genericNode",
+                "position": {
+                    "x": 2871.6341688682833,
+                    "y": 453.3374434097356
+                },
+                "data": {
+                    "type": "CurrentDateComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from datetime import datetime\r\nfrom zoneinfo import ZoneInfo\r\nfrom typing import List\r\n\r\nfrom langflow.custom import Component\r\nfrom langflow.io import DropdownInput, Output\r\nfrom langflow.schema.message import Message\r\n\r\nclass CurrentDateComponent(Component):\r\n    display_name = \"Current Date 🕰️\"\r\n    description = \"Returns the current date and time in the selected timezone.\"\r\n    icon = \"clock\"\r\n\r\n    inputs = [\r\n        DropdownInput(\r\n            name=\"timezone\",\r\n            display_name=\"Timezone\",\r\n            options=[\r\n                \"UTC\",\r\n                \"US/Eastern\",\r\n                \"US/Central\",\r\n                \"US/Mountain\",\r\n                \"US/Pacific\",\r\n                \"Europe/London\",\r\n                \"Europe/Paris\",\r\n                \"Asia/Tokyo\",\r\n                \"Australia/Sydney\",\r\n                \"America/Sao_Paulo\",\r\n                \"America/Cuiaba\",\r\n            ],\r\n            value=\"UTC\",\r\n            info=\"Select the timezone for the current date and time.\",\r\n        ),\r\n    ]\r\n\r\n    outputs = [\r\n        Output(display_name=\"Current Date\", name=\"current_date\", method=\"get_current_date\"),\r\n    ]\r\n\r\n    def get_current_date(self) -> Message:\r\n        try:\r\n            tz = ZoneInfo(self.timezone)\r\n            current_date = datetime.now(tz).strftime(\"%Y-%m-%d %H:%M:%S %Z\")\r\n            result = f\"Current date and time in {self.timezone}: {current_date}\"\r\n            self.status = result\r\n            return Message(text=result)\r\n        except Exception as e:\r\n            error_message = f\"Error: {str(e)}\"\r\n            self.status = error_message\r\n            return Message(text=error_message)",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "timezone": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "UTC",
+                                    "US/Eastern",
+                                    "US/Central",
+                                    "US/Mountain",
+                                    "US/Pacific",
+                                    "Europe/London",
+                                    "Europe/Paris",
+                                    "Asia/Tokyo",
+                                    "Australia/Sydney",
+                                    "America/Sao_Paulo",
+                                    "America/Cuiaba"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "timezone",
+                                "value": "UTC",
+                                "display_name": "Timezone",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Select the timezone for the current date and time.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            }
+                        },
+                        "description": "Returns the current date and time in the selected timezone.",
+                        "icon": "clock",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Current Date",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "current_date",
+                                "display_name": "Current Date",
+                                "method": "get_current_date",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "timezone"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "official": false
+                    },
+                    "id": "CurrentDateComponent-PZ8xJ",
+                    "showNode": false
+                },
+                "selected": false,
+                "width": 96,
+                "height": 96,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 2871.6341688682833,
+                    "y": 453.3374434097356
+                }
+            }
+        ],
+        "edges": [
+            {
+                "source": "TextInput-iJPEJ",
+                "sourceHandle": "{œdataTypeœ:œTextInputœ,œidœ:œTextInput-iJPEJœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-19rub",
+                "targetHandle": "{œfieldNameœ:œTRANSCRIPTœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "TRANSCRIPT",
+                        "id": "Prompt-19rub",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "TextInput",
+                        "id": "TextInput-iJPEJ",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-TextInput-iJPEJ{œdataTypeœ:œTextInputœ,œidœ:œTextInput-iJPEJœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-19rub{œfieldNameœ:œTRANSCRIPTœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionUserList-TvIKS",
+                "sourceHandle": "{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-TvIKSœ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}",
+                "target": "ParseData-aNk1v",
+                "targetHandle": "{œfieldNameœ:œdataœ,œidœ:œParseData-aNk1vœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-aNk1v",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionUserList",
+                        "id": "NotionUserList-TvIKS",
+                        "name": "example_output",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionUserList-TvIKS{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-TvIKSœ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}-ParseData-aNk1v{œfieldNameœ:œdataœ,œidœ:œParseData-aNk1vœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ParseData-aNk1v",
+                "sourceHandle": "{œdataTypeœ:œParseDataœ,œidœ:œParseData-aNk1vœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-19rub",
+                "targetHandle": "{œfieldNameœ:œUSERSœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "USERS",
+                        "id": "Prompt-19rub",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-aNk1v",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ParseData-aNk1v{œdataTypeœ:œParseDataœ,œidœ:œParseData-aNk1vœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-19rub{œfieldNameœ:œUSERSœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "Prompt-19rub",
+                "sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-19rubœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "ToolCallingAgent-rVWeq",
+                "targetHandle": "{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "system_prompt",
+                        "id": "ToolCallingAgent-rVWeq",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-19rub",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Prompt-19rub{œdataTypeœ:œPromptœ,œidœ:œPrompt-19rubœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-ToolCallingAgent-rVWeq{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionSearch-M66HF",
+                "sourceHandle": "{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-M66HFœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolCallingAgent-rVWeq",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-rVWeq",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionSearch",
+                        "id": "NotionSearch-M66HF",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionSearch-M66HF{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-M66HFœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolCallingAgent-rVWeq{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "OpenAIModel-Ht8xI",
+                "sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Ht8xIœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}",
+                "target": "ToolCallingAgent-rVWeq",
+                "targetHandle": "{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "llm",
+                        "id": "ToolCallingAgent-rVWeq",
+                        "inputTypes": [
+                            "LanguageModel"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-Ht8xI",
+                        "name": "model_output",
+                        "output_types": [
+                            "LanguageModel"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-OpenAIModel-Ht8xI{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Ht8xIœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}-ToolCallingAgent-rVWeq{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-rVWeqœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ToolCallingAgent-rVWeq",
+                "sourceHandle": "{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-rVWeqœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-Lbxk6",
+                "targetHandle": "{œfieldNameœ:œTASK_LISTœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "TASK_LIST",
+                        "id": "Prompt-Lbxk6",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ToolCallingAgent",
+                        "id": "ToolCallingAgent-rVWeq",
+                        "name": "response",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ToolCallingAgent-rVWeq{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-rVWeqœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-Prompt-Lbxk6{œfieldNameœ:œTASK_LISTœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "OpenAIModel-OTfnt",
+                "sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-OTfntœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "llm",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "LanguageModel"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-OTfnt",
+                        "name": "model_output",
+                        "output_types": [
+                            "LanguageModel"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-OpenAIModel-OTfnt{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-OTfntœ,œnameœ:œmodel_outputœ,œoutput_typesœ:[œLanguageModelœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œllmœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œLanguageModelœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "Prompt-Lbxk6",
+                "sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-Lbxk6œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "system_prompt",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-Lbxk6",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Prompt-Lbxk6{œdataTypeœ:œPromptœ,œidœ:œPrompt-Lbxk6œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œsystem_promptœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "AddContentToPage-vrAvx",
+                "sourceHandle": "{œdataTypeœ:œAddContentToPageœ,œidœ:œAddContentToPage-vrAvxœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "AddContentToPage",
+                        "id": "AddContentToPage-vrAvx",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-AddContentToPage-vrAvx{œdataTypeœ:œAddContentToPageœ,œidœ:œAddContentToPage-vrAvxœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionPageCreator-Exc7f",
+                "sourceHandle": "{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-Exc7fœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionPageCreator",
+                        "id": "NotionPageCreator-Exc7f",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionPageCreator-Exc7f{œdataTypeœ:œNotionPageCreatorœ,œidœ:œNotionPageCreator-Exc7fœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionDatabaseProperties-IjzLV",
+                "sourceHandle": "{œdataTypeœ:œNotionDatabasePropertiesœ,œidœ:œNotionDatabaseProperties-IjzLVœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionDatabaseProperties",
+                        "id": "NotionDatabaseProperties-IjzLV",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionDatabaseProperties-IjzLV{œdataTypeœ:œNotionDatabasePropertiesœ,œidœ:œNotionDatabaseProperties-IjzLVœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionPageUpdate-bexvy",
+                "sourceHandle": "{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-bexvyœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}",
+                "target": "ToolCallingAgent-GurdE",
+                "targetHandle": "{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "tools",
+                        "id": "ToolCallingAgent-GurdE",
+                        "inputTypes": [
+                            "Tool",
+                            "BaseTool"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionPageUpdate",
+                        "id": "NotionPageUpdate-bexvy",
+                        "name": "example_tool_output",
+                        "output_types": [
+                            "Tool"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionPageUpdate-bexvy{œdataTypeœ:œNotionPageUpdateœ,œidœ:œNotionPageUpdate-bexvyœ,œnameœ:œexample_tool_outputœ,œoutput_typesœ:[œToolœ]}-ToolCallingAgent-GurdE{œfieldNameœ:œtoolsœ,œidœ:œToolCallingAgent-GurdEœ,œinputTypesœ:[œToolœ,œBaseToolœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionSearch-EdSJb",
+                "sourceHandle": "{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-EdSJbœ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}",
+                "target": "ParseData-vYVwu",
+                "targetHandle": "{œfieldNameœ:œdataœ,œidœ:œParseData-vYVwuœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-vYVwu",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionSearch",
+                        "id": "NotionSearch-EdSJb",
+                        "name": "example_output",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionSearch-EdSJb{œdataTypeœ:œNotionSearchœ,œidœ:œNotionSearch-EdSJbœ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}-ParseData-vYVwu{œfieldNameœ:œdataœ,œidœ:œParseData-vYVwuœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ParseData-vYVwu",
+                "sourceHandle": "{œdataTypeœ:œParseDataœ,œidœ:œParseData-vYVwuœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-Lbxk6",
+                "targetHandle": "{œfieldNameœ:œDATABASESœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "DATABASES",
+                        "id": "Prompt-Lbxk6",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-vYVwu",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ParseData-vYVwu{œdataTypeœ:œParseDataœ,œidœ:œParseData-vYVwuœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-Lbxk6{œfieldNameœ:œDATABASESœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "ToolCallingAgent-GurdE",
+                "sourceHandle": "{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-GurdEœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "ChatOutput-zBv53",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-zBv53œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ChatOutput-zBv53",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ToolCallingAgent",
+                        "id": "ToolCallingAgent-GurdE",
+                        "name": "response",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ToolCallingAgent-GurdE{œdataTypeœ:œToolCallingAgentœ,œidœ:œToolCallingAgent-GurdEœ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-zBv53{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-zBv53œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "selected": false,
+                "className": ""
+            },
+            {
+                "source": "NotionUserList-wFEb1",
+                "sourceHandle": "{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-wFEb1œ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}",
+                "target": "ParseData-WKjW6",
+                "targetHandle": "{œfieldNameœ:œdataœ,œidœ:œParseData-WKjW6œ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-WKjW6",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "NotionUserList",
+                        "id": "NotionUserList-wFEb1",
+                        "name": "example_output",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-NotionUserList-wFEb1{œdataTypeœ:œNotionUserListœ,œidœ:œNotionUserList-wFEb1œ,œnameœ:œexample_outputœ,œoutput_typesœ:[œDataœ]}-ParseData-WKjW6{œfieldNameœ:œdataœ,œidœ:œParseData-WKjW6œ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "className": ""
+            },
+            {
+                "source": "ParseData-WKjW6",
+                "sourceHandle": "{œdataTypeœ:œParseDataœ,œidœ:œParseData-WKjW6œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-Lbxk6",
+                "targetHandle": "{œfieldNameœ:œUSERSœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "USERS",
+                        "id": "Prompt-Lbxk6",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-WKjW6",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ParseData-WKjW6{œdataTypeœ:œParseDataœ,œidœ:œParseData-WKjW6œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-Lbxk6{œfieldNameœ:œUSERSœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "className": ""
+            },
+            {
+                "source": "CurrentDateComponent-WOwNq",
+                "sourceHandle": "{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-WOwNqœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-19rub",
+                "targetHandle": "{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "CURRENT_DATE",
+                        "id": "Prompt-19rub",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "CurrentDateComponent",
+                        "id": "CurrentDateComponent-WOwNq",
+                        "name": "current_date",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-CurrentDateComponent-WOwNq{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-WOwNqœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}-Prompt-19rub{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-19rubœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "className": ""
+            },
+            {
+                "source": "CurrentDateComponent-PZ8xJ",
+                "sourceHandle": "{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-PZ8xJœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-Lbxk6",
+                "targetHandle": "{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "CURRENT_DATE",
+                        "id": "Prompt-Lbxk6",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "CurrentDateComponent",
+                        "id": "CurrentDateComponent-PZ8xJ",
+                        "name": "current_date",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-CurrentDateComponent-PZ8xJ{œdataTypeœ:œCurrentDateComponentœ,œidœ:œCurrentDateComponent-PZ8xJœ,œnameœ:œcurrent_dateœ,œoutput_typesœ:[œMessageœ]}-Prompt-Lbxk6{œfieldNameœ:œCURRENT_DATEœ,œidœ:œPrompt-Lbxk6œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "className": ""
+            }
+        ],
+        "viewport": {
+            "x": -65.48833753518215,
+            "y": 119.49034539812101,
+            "zoom": 0.5588906662759379
+        }
+    },
+    "description": "The Notion Agent for Meeting Notes is an AI-powered tool that automatically processes meeting transcripts and updates your Notion workspace accordingly. It identifies tasks, action items, and key points from your meetings, then creates new tasks or updates existing ones in Notion without manual input.\n\nTo use it, simply add your API Keys and provide a meeting transcript. The agent will analyze it, interact with your Notion workspace to make necessary updates, and give you a summary of actions taken. This streamlines your workflow, ensuring important meeting outcomes are captured and organized in Notion effortlessly.",
+    "name": "Notion Agent - Meeting Notes ",
+    "last_tested_version": "1.0.17.dev8",
+    "endpoint_name": null,
+    "is_component": false
+}
\ No newline at end of file
diff --git a/docs/static/files/Google_Drive_Docs_Translations_Example.json b/docs/static/files/Google_Drive_Docs_Translations_Example.json
index 77e6fbda1..ca3c6dc7b 100644
--- a/docs/static/files/Google_Drive_Docs_Translations_Example.json
+++ b/docs/static/files/Google_Drive_Docs_Translations_Example.json
@@ -1 +1,1580 @@
-{"id":"bc2cbf16-ed92-4ef6-a4a5-1526af21d044","data":{"nodes":[{"id":"ParseData-LFq8b","type":"genericNode","position":{"x":1068.0112494649502,"y":237.00462961615915},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_metadata":true,"list":false,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"data","value":"","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sep","value":"\n","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{data}","display_name":"Template","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true}],"field_order":["data","template","sep"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"id":"ParseData-LFq8b"},"selected":false,"width":384,"height":353,"positionAbsolute":{"x":1068.0112494649502,"y":237.00462961615915},"dragging":false},{"id":"GoogleDriveComponent-x1jHz","type":"genericNode","position":{"x":1919.003246541804,"y":230.60766717987485},"data":{"type":"GoogleDriveComponent","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nfrom json.decoder import JSONDecodeError\n\nfrom google.auth.exceptions import RefreshError\nfrom google.oauth2.credentials import Credentials\nfrom langchain_google_community import GoogleDriveLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.inputs import MessageTextInput\nfrom langflow.io import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass GoogleDriveComponent(Component):\n    display_name = \"Google Drive Loader\"\n    description = \"Loads documents from Google Drive using provided credentials.\"\n    icon = \"Google\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"json_string\",\n            display_name=\"JSON String of the Service Account Token\",\n            info=\"JSON string containing OAuth 2.0 access token information for service account access\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"document_id\", display_name=\"Document ID\", info=\"Single Google Drive document ID\", required=True\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Loaded Documents\", name=\"docs\", method=\"load_documents\"),\n    ]\n\n    def load_documents(self) -> Data:\n        class CustomGoogleDriveLoader(GoogleDriveLoader):\n            creds: Credentials | None = None\n            \"\"\"Credentials object to be passed directly.\"\"\"\n\n            def _load_credentials(self):\n                \"\"\"Load credentials from the provided creds attribute or fallback to the original method.\"\"\"\n                if self.creds:\n                    return self.creds\n                msg = \"No credentials provided.\"\n                raise ValueError(msg)\n\n            class Config:\n                arbitrary_types_allowed = True\n\n        json_string = self.json_string\n\n        document_ids = [self.document_id]\n        if len(document_ids) != 1:\n            msg = \"Expected a single document ID\"\n            raise ValueError(msg)\n\n        # TODO: Add validation to check if the document ID is valid\n\n        # Load the token information from the JSON string\n        try:\n            token_info = json.loads(json_string)\n        except JSONDecodeError as e:\n            msg = \"Invalid JSON string\"\n            raise ValueError(msg) from e\n\n        # Initialize the custom loader with the provided credentials and document IDs\n        loader = CustomGoogleDriveLoader(\n            creds=Credentials.from_authorized_user_info(token_info), document_ids=document_ids\n        )\n\n        # Load the documents\n        try:\n            docs = loader.load()\n        # catch google.auth.exceptions.RefreshError\n        except RefreshError as e:\n            msg = \"Authentication error: Unable to refresh authentication token. Please try to reauthenticate.\"\n            raise ValueError(msg) from e\n        except Exception as e:\n            msg = f\"Error loading documents: {e}\"\n            raise ValueError(msg) from e\n\n        assert len(docs) == 1, \"Expected a single document to be loaded.\"\n\n        data = docs_to_data(docs)\n        # Return the loaded documents\n        self.status = data\n        return Data(data={\"text\": data})\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"document_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":true,"placeholder":"","show":true,"name":"document_id","value":"YOUR-DOCUMENT-ID-HERE","display_name":"Document ID","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Single Google Drive document ID","title_case":false,"type":"str","_input_type":"MessageTextInput"},"json_string":{"load_from_db":true,"required":true,"placeholder":"","show":true,"name":"json_string","value":"","display_name":"JSON String of the Service Account Token","advanced":false,"input_types":["Message"],"dynamic":false,"info":"JSON string containing OAuth 2.0 access token information for service account access","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"}},"description":"Loads documents from Google Drive using provided credentials.","icon":"Google","base_classes":["Data"],"display_name":"Google Drive Loader","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"docs","display_name":"Loaded Documents","method":"load_documents","value":"__UNDEFINED__","cache":true}],"field_order":["json_string","document_id"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"id":"GoogleDriveComponent-x1jHz","description":"Loads documents from Google Drive using provided credentials.","display_name":"Google Drive Loader"},"selected":false,"width":384,"height":389,"positionAbsolute":{"x":1919.003246541804,"y":230.60766717987485},"dragging":false},{"id":"ParseData-7qhmP","type":"genericNode","position":{"x":2341.9094676839277,"y":249.73963276782303},"data":{"type":"ParseData","node":{"template":{"_type":"Component","data":{"trace_as_metadata":true,"list":false,"trace_as_input":true,"required":false,"placeholder":"","show":true,"name":"data","value":"","display_name":"Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"The data to convert to text.","title_case":false,"type":"other","_input_type":"DataInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"sep":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sep","value":"\n","display_name":"Separator","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"StrInput"},"template":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{text}","display_name":"Template","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Convert Data into plain text following a specified template.","icon":"braces","base_classes":["Message"],"display_name":"Parse Data","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text","display_name":"Text","method":"parse_data","value":"__UNDEFINED__","cache":true}],"field_order":["data","template","sep"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"id":"ParseData-7qhmP"},"selected":false,"width":384,"height":353,"positionAbsolute":{"x":2341.9094676839277,"y":249.73963276782303},"dragging":false},{"id":"OpenAIModel-YUM1a","type":"genericNode","position":{"x":3231.963340176361,"y":94.70653241512102},"data":{"type":"OpenAIModel","node":{"template":{"_type":"Component","output_parser":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"output_parser","value":"","display_name":"Output Parser","advanced":true,"input_types":["OutputParser"],"dynamic":false,"info":"The parser to use to parse the output of the model","title_case":false,"type":"other","_input_type":"HandleInput"},"api_key":{"load_from_db":true,"required":false,"placeholder":"","show":true,"name":"api_key","value":"","display_name":"OpenAI API Key","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","title_case":false,"password":true,"type":"str","_input_type":"SecretStrInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"MessageInput"},"json_mode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"json_mode","value":false,"display_name":"JSON Mode","advanced":true,"dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","title_case":false,"type":"bool","_input_type":"BoolInput"},"max_tokens":{"trace_as_metadata":true,"range_spec":{"step_type":"float","min":0,"max":128000,"step":0.1},"list":false,"required":false,"placeholder":"","show":true,"name":"max_tokens","value":"","display_name":"Max Tokens","advanced":true,"dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","title_case":false,"type":"int","_input_type":"IntInput"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"model_kwargs","value":{},"display_name":"Model Kwargs","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict","_input_type":"DictInput"},"model_name":{"trace_as_metadata":true,"options":["gpt-4o-mini","gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"model_name","value":"gpt-4o-mini","display_name":"Model Name","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str","_input_type":"DropdownInput"},"openai_api_base":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"openai_api_base","value":"","display_name":"OpenAI API Base","advanced":true,"dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","title_case":false,"type":"str","_input_type":"StrInput"},"output_schema":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"name":"output_schema","value":{},"display_name":"Schema","advanced":true,"dynamic":false,"info":"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.","title_case":false,"type":"dict","_input_type":"DictInput"},"seed":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"seed","value":1,"display_name":"Seed","advanced":true,"dynamic":false,"info":"The seed controls the reproducibility of the job.","title_case":false,"type":"int","_input_type":"IntInput"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"stream","value":false,"display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool","_input_type":"BoolInput"},"system_message":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"system_message","value":"","display_name":"System Message","advanced":true,"input_types":["Message"],"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"temperature","value":0.1,"display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float","_input_type":"FloatInput"}},"description":"Generates text using OpenAI LLMs.","icon":"OpenAI","base_classes":["LanguageModel","Message"],"display_name":"OpenAI","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"text_output","display_name":"Text","method":"text_response","value":"__UNDEFINED__","cache":true,"required_inputs":["input_value","stream","system_message"]},{"types":["LanguageModel"],"selected":"LanguageModel","name":"model_output","display_name":"Language Model","method":"build_model","value":"__UNDEFINED__","cache":true,"required_inputs":["api_key","json_mode","max_tokens","model_kwargs","model_name","openai_api_base","output_schema","seed","temperature"]}],"field_order":["input_value","system_message","stream","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","api_key","temperature","seed","output_parser"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"id":"OpenAIModel-YUM1a","description":"Generates text using OpenAI LLMs.","display_name":"OpenAI"},"selected":false,"width":384,"height":587,"positionAbsolute":{"x":3231.963340176361,"y":94.70653241512102},"dragging":false},{"id":"ChatOutput-c4k0L","type":"genericNode","position":{"x":3669.141671875754,"y":248.4257650261032},"data":{"type":"ChatOutput","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"data_template":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"data_template","value":"{text}","display_name":"Data Template","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"input_value","value":"","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as output.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"combobox":false,"required":false,"placeholder":"","show":true,"name":"sender","value":"Machine","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str","_input_type":"DropdownInput"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"sender_name","value":"AI","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"name":"session_id","value":"","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"The session ID of the chat. If empty, the current session ID parameter will be used.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"should_store_message":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"should_store_message","value":true,"display_name":"Store Messages","advanced":true,"dynamic":false,"info":"Store the message in the history.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Display a chat message in the Playground.","icon":"ChatOutput","base_classes":["Message"],"display_name":"Chat Output","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"message","display_name":"Message","method":"message_response","value":"__UNDEFINED__","cache":true}],"field_order":["input_value","should_store_message","sender","sender_name","session_id","data_template"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"id":"ChatOutput-c4k0L","description":"Display a chat message in the Playground.","display_name":"Chat Output"},"selected":false,"width":384,"height":289,"positionAbsolute":{"x":3669.141671875754,"y":248.4257650261032},"dragging":false},{"id":"note-9ynW0","type":"noteNode","position":{"x":-4,"y":38},"data":{"node":{"description":"**Google Drive Example Scopes**\n\n**Langflow - OAuth Integration Documentation**\n\n```\ndocs.langflow.org/integrations-setup-google-oauth-langflow\n```\n\n**Drive API Documentation**\n\nhttps://developers.google.com/drive/api/guides/api-specific-auth\n\n**Scope Used in This Example**\n\nPermission to view and download all your Drive files.\n\n```\nhttps://www.googleapis.com/auth/drive.readonly\n```\n\n**Example of How to Enter Scopes**\n\n```\nhttps://www.googleapis.com/auth/drive.apps.readonly, https://www.googleapis.com/auth/drive, https://www.googleapis.com/auth/drive.readonly, https://www.googleapis.com/auth/drive.activity\n```","display_name":"","documentation":"","template":{"backgroundColor":"indigo"}},"type":"note","id":"note-9ynW0"},"selected":false,"width":584,"height":673,"dragging":false,"style":{"width":584,"height":673},"resizing":false},{"id":"GoogleOAuthToken-piWFF","type":"genericNode","position":{"x":609.7629463431374,"y":218.39474366635045},"data":{"node":{"template":{"_type":"Component","oauth_credentials":{"trace_as_metadata":true,"file_path":"bc2cbf16-ed92-4ef6-a4a5-1526af21d044/2024-11-06_15-06-14_client_secret_998884801917-hsjq3alo0vfqih74fe635k01khqi74d3.apps.googleusercontent.com.json","fileTypes":["json"],"list":false,"required":true,"placeholder":"","show":true,"name":"oauth_credentials","value":"","display_name":"Credentials File","advanced":false,"dynamic":false,"info":"Input OAuth Credentials file (e.g. credentials.json).","title_case":false,"type":"file","_input_type":"FileInput"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nimport re\nfrom pathlib import Path\n\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\n\nfrom langflow.custom import Component\nfrom langflow.io import FileInput, MultilineInput, Output\nfrom langflow.schema import Data\n\n\nclass GoogleOAuthToken(Component):\n    display_name = \"Google OAuth Token\"\n    description = \"Generates a JSON string with your Google OAuth token.\"\n    documentation: str = \"https://developers.google.com/identity/protocols/oauth2/web-server?hl=pt-br#python_1\"\n    icon = \"Google\"\n    name = \"GoogleOAuthToken\"\n\n    inputs = [\n        MultilineInput(\n            name=\"scopes\",\n            display_name=\"Scopes\",\n            info=\"Input scopes for your application.\",\n            required=True,\n        ),\n        FileInput(\n            name=\"oauth_credentials\",\n            display_name=\"Credentials File\",\n            info=\"Input OAuth Credentials file (e.g. credentials.json).\",\n            file_types=[\"json\"],\n            required=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n    ]\n\n    def validate_scopes(self, scopes):\n        pattern = (\n            r\"^(https://www\\.googleapis\\.com/auth/[\\w\\.\\-]+\"\n            r\"|mail\\.google\\.com/\"\n            r\"|www\\.google\\.com/calendar/feeds\"\n            r\"|www\\.google\\.com/m8/feeds)\"\n            r\"(,\\s*https://www\\.googleapis\\.com/auth/[\\w\\.\\-]+\"\n            r\"|mail\\.google\\.com/\"\n            r\"|www\\.google\\.com/calendar/feeds\"\n            r\"|www\\.google\\.com/m8/feeds)*$\"\n        )\n        if not re.match(pattern, scopes):\n            error_message = \"Invalid scope format.\"\n            raise ValueError(error_message)\n\n    def build_output(self) -> Data:\n        self.validate_scopes(self.scopes)\n\n        user_scopes = [scope.strip() for scope in self.scopes.split(\",\")]\n        if self.scopes:\n            scopes = user_scopes\n        else:\n            error_message = \"Incorrect scope, check the scopes field.\"\n            raise ValueError(error_message)\n\n        creds = None\n        token_path = Path(\"token.json\")\n\n        if token_path.exists():\n            with token_path.open(mode=\"r\", encoding=\"utf-8\") as token_file:\n                creds = Credentials.from_authorized_user_file(\n                    str(token_path), scopes)\n\n        if not creds or not creds.valid:\n            if creds and creds.expired and creds.refresh_token:\n                creds.refresh(Request())\n            else:\n                if self.oauth_credentials:\n                    client_secret_file = self.oauth_credentials\n                else:\n                    error_message = \"OAuth 2.0 Credentials file not provided.\"\n                    raise ValueError(error_message)\n\n                flow = InstalledAppFlow.from_client_secrets_file(\n                    client_secret_file, scopes)\n                creds = flow.run_local_server(port=0)\n\n            with token_path.open(mode=\"w\", encoding=\"utf-8\") as token_file:\n                token_file.write(creds.to_json())\n\n        creds_json = json.loads(creds.to_json())\n\n        return Data(data=creds_json)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"scopes":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":true,"placeholder":"","show":true,"name":"scopes","value":"https://www.googleapis.com/auth/drive.readonly","display_name":"Scopes","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Input scopes for your application.","title_case":false,"type":"str","_input_type":"MultilineInput"}},"description":"Generates a JSON string with your Google OAuth token.","icon":"Google","base_classes":["Data"],"display_name":"Google OAuth Token","documentation":"https://developers.google.com/identity/protocols/oauth2/web-server?hl=pt-br#python_1","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"output","display_name":"Output","method":"build_output","value":"__UNDEFINED__","cache":true}],"field_order":["scopes","oauth_credentials"],"beta":false,"edited":true,"metadata":{},"lf_version":"1.0.19.post2"},"type":"GoogleOAuthToken","id":"GoogleOAuthToken-piWFF","description":"A component to generate a json string containing your Google OAuth token.","display_name":"Google OAuth Token"},"selected":true,"width":384,"height":391,"dragging":false,"positionAbsolute":{"x":609.7629463431374,"y":218.39474366635045}},{"id":"JSONCleaner-N4p7F","type":"genericNode","position":{"x":1484.3316811088714,"y":174.88328534754476},"data":{"node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"import json\nimport re\nimport unicodedata\n\nfrom langflow.custom import Component\nfrom langflow.inputs import BoolInput, MessageTextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass JSONCleaner(Component):\n    display_name = \"JSON Cleaner\"\n    description = (\n        \"Cleans the messy and sometimes incorrect JSON strings produced by LLMs \"\n        \"so that they are fully compliant with the JSON spec.\"\n    )\n    icon = \"custom_components\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"json_str\", display_name=\"JSON String\", info=\"The JSON string to be cleaned.\", required=True\n        ),\n        BoolInput(\n            name=\"remove_control_chars\",\n            display_name=\"Remove Control Characters\",\n            info=\"Remove control characters from the JSON string.\",\n            required=False,\n        ),\n        BoolInput(\n            name=\"normalize_unicode\",\n            display_name=\"Normalize Unicode\",\n            info=\"Normalize Unicode characters in the JSON string.\",\n            required=False,\n        ),\n        BoolInput(\n            name=\"validate_json\",\n            display_name=\"Validate JSON\",\n            info=\"Validate the JSON string to ensure it is well-formed.\",\n            required=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Cleaned JSON String\", name=\"output\", method=\"clean_json\"),\n    ]\n\n    def clean_json(self) -> Message:\n        try:\n            from json_repair import repair_json\n        except ImportError as e:\n            msg = \"Could not import the json_repair package. Please install it with `pip install json_repair`.\"\n            raise ImportError(msg) from e\n\n        \"\"\"Clean the input JSON string based on provided options and return the cleaned JSON string.\"\"\"\n        json_str = self.json_str\n        remove_control_chars = self.remove_control_chars\n        normalize_unicode = self.normalize_unicode\n        validate_json = self.validate_json\n\n        try:\n            start = json_str.find(\"{\")\n            end = json_str.rfind(\"}\")\n            if start == -1 or end == -1:\n                msg = \"Invalid JSON string: Missing '{' or '}'\"\n                raise ValueError(msg)\n            json_str = json_str[start : end + 1]\n\n            if remove_control_chars:\n                json_str = self._remove_control_characters(json_str)\n            if normalize_unicode:\n                json_str = self._normalize_unicode(json_str)\n            if validate_json:\n                json_str = self._validate_json(json_str)\n\n            cleaned_json_str = repair_json(json_str)\n            result = str(cleaned_json_str)\n\n            self.status = result\n            return Message(text=result)\n        except Exception as e:\n            msg = f\"Error cleaning JSON string: {e}\"\n            raise ValueError(msg) from e\n\n    def _remove_control_characters(self, s: str) -> str:\n        \"\"\"Remove control characters from the string.\"\"\"\n        return re.sub(r\"[\\x00-\\x1F\\x7F]\", \"\", s)\n\n    def _normalize_unicode(self, s: str) -> str:\n        \"\"\"Normalize Unicode characters in the string.\"\"\"\n        return unicodedata.normalize(\"NFC\", s)\n\n    def _validate_json(self, s: str) -> str:\n        \"\"\"Validate the JSON string.\"\"\"\n        try:\n            json.loads(s)\n            return s\n        except json.JSONDecodeError as e:\n            msg = f\"Invalid JSON string: {e}\"\n            raise ValueError(msg) from e\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"json_str":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":true,"placeholder":"","show":true,"name":"json_str","value":"","display_name":"JSON String","advanced":false,"input_types":["Message"],"dynamic":false,"info":"The JSON string to be cleaned.","title_case":false,"type":"str","_input_type":"MessageTextInput"},"normalize_unicode":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"normalize_unicode","value":true,"display_name":"Normalize Unicode","advanced":false,"dynamic":false,"info":"Normalize Unicode characters in the JSON string.","title_case":false,"type":"bool","_input_type":"BoolInput","load_from_db":false},"remove_control_chars":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"remove_control_chars","value":false,"display_name":"Remove Control Characters","advanced":false,"dynamic":false,"info":"Remove control characters from the JSON string.","title_case":false,"type":"bool","_input_type":"BoolInput"},"validate_json":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"name":"validate_json","value":false,"display_name":"Validate JSON","advanced":false,"dynamic":false,"info":"Validate the JSON string to ensure it is well-formed.","title_case":false,"type":"bool","_input_type":"BoolInput"}},"description":"Cleans the messy and sometimes incorrect JSON strings produced by LLMs so that they are fully compliant with the JSON spec.","icon":"custom_components","base_classes":["Message"],"display_name":"JSON Cleaner","documentation":"","custom_fields":{},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"output","display_name":"Cleaned JSON String","method":"clean_json","value":"__UNDEFINED__","cache":true}],"field_order":["json_str","remove_control_chars","normalize_unicode","validate_json"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"type":"JSONCleaner","id":"JSONCleaner-N4p7F","description":"Cleans the messy and sometimes incorrect JSON strings produced by LLMs so that they are fully compliant with the JSON spec.","display_name":"JSON Cleaner"},"selected":false,"width":384,"height":537,"positionAbsolute":{"x":1484.3316811088714,"y":174.88328534754476},"dragging":false},{"id":"Prompt-uk0Fr","type":"genericNode","position":{"x":2784.1158749493534,"y":225.01053782589702},"data":{"node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"template":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"name":"template","value":"{context}\n\nTranslate the text you receive into Spanish!","display_name":"Template","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"prompt","_input_type":"PromptInput","load_from_db":false},"context":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","name":"context","display_name":"context","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","base_classes":["Message"],"display_name":"Prompt","documentation":"","custom_fields":{"template":["context"]},"output_types":[],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Message"],"selected":"Message","name":"prompt","display_name":"Prompt Message","method":"build_prompt","value":"__UNDEFINED__","cache":true}],"field_order":["template"],"beta":false,"edited":false,"metadata":{},"lf_version":"1.0.19.post2"},"type":"Prompt","id":"Prompt-uk0Fr","description":"Create a prompt template with dynamic variables.","display_name":"Prompt"},"selected":false,"width":384,"height":391,"positionAbsolute":{"x":2784.1158749493534,"y":225.01053782589702},"dragging":false}],"edges":[{"source":"GoogleDriveComponent-x1jHz","target":"ParseData-7qhmP","sourceHandle":"{œdataTypeœ:œGoogleDriveComponentœ,œidœ:œGoogleDriveComponent-x1jHzœ,œnameœ:œdocsœ,œoutput_typesœ:[œDataœ]}","targetHandle":"{œfieldNameœ:œdataœ,œidœ:œParseData-7qhmPœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}","id":"reactflow__edge-GoogleDriveComponent-x1jHz{œdataTypeœ:œGoogleDriveComponentœ,œidœ:œGoogleDriveComponent-x1jHzœ,œnameœ:œdocsœ,œoutput_typesœ:[œDataœ]}-ParseData-7qhmP{œfieldNameœ:œdataœ,œidœ:œParseData-7qhmPœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"data","id":"ParseData-7qhmP","inputTypes":["Data"],"type":"other"},"sourceHandle":{"dataType":"GoogleDriveComponent","id":"GoogleDriveComponent-x1jHz","name":"docs","output_types":["Data"]}},"selected":false,"animated":false,"className":""},{"source":"OpenAIModel-YUM1a","target":"ChatOutput-c4k0L","sourceHandle":"{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-YUM1aœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-c4k0Lœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","id":"reactflow__edge-OpenAIModel-YUM1a{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-YUM1aœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-c4k0L{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-c4k0Lœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"input_value","id":"ChatOutput-c4k0L","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"OpenAIModel","id":"OpenAIModel-YUM1a","name":"text_output","output_types":["Message"]}},"selected":false,"animated":false,"className":""},{"source":"GoogleOAuthToken-piWFF","sourceHandle":"{œdataTypeœ:œGoogleOAuthTokenœ,œidœ:œGoogleOAuthToken-piWFFœ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}","target":"ParseData-LFq8b","targetHandle":"{œfieldNameœ:œdataœ,œidœ:œParseData-LFq8bœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}","data":{"targetHandle":{"fieldName":"data","id":"ParseData-LFq8b","inputTypes":["Data"],"type":"other"},"sourceHandle":{"dataType":"GoogleOAuthToken","id":"GoogleOAuthToken-piWFF","name":"output","output_types":["Data"]}},"id":"reactflow__edge-GoogleOAuthToken-piWFF{œdataTypeœ:œGoogleOAuthTokenœ,œidœ:œGoogleOAuthToken-piWFFœ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}-ParseData-LFq8b{œfieldNameœ:œdataœ,œidœ:œParseData-LFq8bœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}","animated":false,"className":""},{"source":"ParseData-LFq8b","sourceHandle":"{œdataTypeœ:œParseDataœ,œidœ:œParseData-LFq8bœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}","target":"JSONCleaner-N4p7F","targetHandle":"{œfieldNameœ:œjson_strœ,œidœ:œJSONCleaner-N4p7Fœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"json_str","id":"JSONCleaner-N4p7F","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"ParseData","id":"ParseData-LFq8b","name":"text","output_types":["Message"]}},"id":"reactflow__edge-ParseData-LFq8b{œdataTypeœ:œParseDataœ,œidœ:œParseData-LFq8bœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-JSONCleaner-N4p7F{œfieldNameœ:œjson_strœ,œidœ:œJSONCleaner-N4p7Fœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","animated":false,"className":""},{"source":"JSONCleaner-N4p7F","sourceHandle":"{œdataTypeœ:œJSONCleanerœ,œidœ:œJSONCleaner-N4p7Fœ,œnameœ:œoutputœ,œoutput_typesœ:[œMessageœ]}","target":"GoogleDriveComponent-x1jHz","targetHandle":"{œfieldNameœ:œjson_stringœ,œidœ:œGoogleDriveComponent-x1jHzœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"json_string","id":"GoogleDriveComponent-x1jHz","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"JSONCleaner","id":"JSONCleaner-N4p7F","name":"output","output_types":["Message"]}},"id":"reactflow__edge-JSONCleaner-N4p7F{œdataTypeœ:œJSONCleanerœ,œidœ:œJSONCleaner-N4p7Fœ,œnameœ:œoutputœ,œoutput_typesœ:[œMessageœ]}-GoogleDriveComponent-x1jHz{œfieldNameœ:œjson_stringœ,œidœ:œGoogleDriveComponent-x1jHzœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","animated":false,"className":""},{"source":"ParseData-7qhmP","sourceHandle":"{œdataTypeœ:œParseDataœ,œidœ:œParseData-7qhmPœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}","target":"Prompt-uk0Fr","targetHandle":"{œfieldNameœ:œcontextœ,œidœ:œPrompt-uk0Frœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"context","id":"Prompt-uk0Fr","inputTypes":["Message","Text"],"type":"str"},"sourceHandle":{"dataType":"ParseData","id":"ParseData-7qhmP","name":"text","output_types":["Message"]}},"id":"reactflow__edge-ParseData-7qhmP{œdataTypeœ:œParseDataœ,œidœ:œParseData-7qhmPœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-uk0Fr{œfieldNameœ:œcontextœ,œidœ:œPrompt-uk0Frœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","animated":false,"className":""},{"source":"Prompt-uk0Fr","sourceHandle":"{œdataTypeœ:œPromptœ,œidœ:œPrompt-uk0Frœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}","target":"OpenAIModel-YUM1a","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-YUM1aœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"input_value","id":"OpenAIModel-YUM1a","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"Prompt","id":"Prompt-uk0Fr","name":"prompt","output_types":["Message"]}},"id":"reactflow__edge-Prompt-uk0Fr{œdataTypeœ:œPromptœ,œidœ:œPrompt-uk0Frœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-YUM1a{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-YUM1aœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","animated":false,"className":""}],"viewport":{"x":-1019.4126433636247,"y":197.09015595775952,"zoom":0.6352002867729776}},"description":"An example of a flow that connects to Google Drive to access a text document, reads the content, translates it into the desired language, and returns the translated text in the chat, allowing for quick and efficient automation of the Google Docs file translation process.","name":"Google Drive Docs Translations Example","last_tested_version":"1.0.19.post2","endpoint_name":null,"is_component":false}
+{
+    "id": "bc2cbf16-ed92-4ef6-a4a5-1526af21d044",
+    "data": {
+        "nodes": [
+            {
+                "id": "ParseData-LFq8b",
+                "type": "genericNode",
+                "position": {
+                    "x": 1068.0112494649502,
+                    "y": 237.00462961615915
+                },
+                "data": {
+                    "type": "ParseData",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "data": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data",
+                                "value": "",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "sep": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sep",
+                                "value": "\n",
+                                "display_name": "Separator",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{data}",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Convert Data into plain text following a specified template.",
+                        "icon": "braces",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "id": "ParseData-LFq8b"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 353,
+                "positionAbsolute": {
+                    "x": 1068.0112494649502,
+                    "y": 237.00462961615915
+                },
+                "dragging": false
+            },
+            {
+                "id": "GoogleDriveComponent-x1jHz",
+                "type": "genericNode",
+                "position": {
+                    "x": 1919.003246541804,
+                    "y": 230.60766717987485
+                },
+                "data": {
+                    "type": "GoogleDriveComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nfrom json.decoder import JSONDecodeError\n\nfrom google.auth.exceptions import RefreshError\nfrom google.oauth2.credentials import Credentials\nfrom langchain_google_community import GoogleDriveLoader\n\nfrom langflow.custom import Component\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.inputs import MessageTextInput\nfrom langflow.io import SecretStrInput\nfrom langflow.schema import Data\nfrom langflow.template import Output\n\n\nclass GoogleDriveComponent(Component):\n    display_name = \"Google Drive Loader\"\n    description = \"Loads documents from Google Drive using provided credentials.\"\n    icon = \"Google\"\n\n    inputs = [\n        SecretStrInput(\n            name=\"json_string\",\n            display_name=\"JSON String of the Service Account Token\",\n            info=\"JSON string containing OAuth 2.0 access token information for service account access\",\n            required=True,\n        ),\n        MessageTextInput(\n            name=\"document_id\", display_name=\"Document ID\", info=\"Single Google Drive document ID\", required=True\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Loaded Documents\", name=\"docs\", method=\"load_documents\"),\n    ]\n\n    def load_documents(self) -> Data:\n        class CustomGoogleDriveLoader(GoogleDriveLoader):\n            creds: Credentials | None = None\n            \"\"\"Credentials object to be passed directly.\"\"\"\n\n            def _load_credentials(self):\n                \"\"\"Load credentials from the provided creds attribute or fallback to the original method.\"\"\"\n                if self.creds:\n                    return self.creds\n                msg = \"No credentials provided.\"\n                raise ValueError(msg)\n\n            class Config:\n                arbitrary_types_allowed = True\n\n        json_string = self.json_string\n\n        document_ids = [self.document_id]\n        if len(document_ids) != 1:\n            msg = \"Expected a single document ID\"\n            raise ValueError(msg)\n\n        # TODO: Add validation to check if the document ID is valid\n\n        # Load the token information from the JSON string\n        try:\n            token_info = json.loads(json_string)\n        except JSONDecodeError as e:\n            msg = \"Invalid JSON string\"\n            raise ValueError(msg) from e\n\n        # Initialize the custom loader with the provided credentials and document IDs\n        loader = CustomGoogleDriveLoader(\n            creds=Credentials.from_authorized_user_info(token_info), document_ids=document_ids\n        )\n\n        # Load the documents\n        try:\n            docs = loader.load()\n        # catch google.auth.exceptions.RefreshError\n        except RefreshError as e:\n            msg = \"Authentication error: Unable to refresh authentication token. Please try to reauthenticate.\"\n            raise ValueError(msg) from e\n        except Exception as e:\n            msg = f\"Error loading documents: {e}\"\n            raise ValueError(msg) from e\n\n        assert len(docs) == 1, \"Expected a single document to be loaded.\"\n\n        data = docs_to_data(docs)\n        # Return the loaded documents\n        self.status = data\n        return Data(data={\"text\": data})\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "document_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "document_id",
+                                "value": "YOUR-DOCUMENT-ID-HERE",
+                                "display_name": "Document ID",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Single Google Drive document ID",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "json_string": {
+                                "load_from_db": true,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_string",
+                                "value": "",
+                                "display_name": "JSON String of the Service Account Token",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "JSON string containing OAuth 2.0 access token information for service account access",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            }
+                        },
+                        "description": "Loads documents from Google Drive using provided credentials.",
+                        "icon": "Google",
+                        "base_classes": [
+                            "Data"
+                        ],
+                        "display_name": "Google Drive Loader",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "docs",
+                                "display_name": "Loaded Documents",
+                                "method": "load_documents",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "json_string",
+                            "document_id"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "id": "GoogleDriveComponent-x1jHz",
+                    "description": "Loads documents from Google Drive using provided credentials.",
+                    "display_name": "Google Drive Loader"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 389,
+                "positionAbsolute": {
+                    "x": 1919.003246541804,
+                    "y": 230.60766717987485
+                },
+                "dragging": false
+            },
+            {
+                "id": "ParseData-7qhmP",
+                "type": "genericNode",
+                "position": {
+                    "x": 2341.9094676839277,
+                    "y": 249.73963276782303
+                },
+                "data": {
+                    "type": "ParseData",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "data": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "trace_as_input": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data",
+                                "value": "",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "DataInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n    name = \"ParseData\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "sep": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sep",
+                                "value": "\n",
+                                "display_name": "Separator",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{text}",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Convert Data into plain text following a specified template.",
+                        "icon": "braces",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text",
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "id": "ParseData-7qhmP"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 353,
+                "positionAbsolute": {
+                    "x": 2341.9094676839277,
+                    "y": 249.73963276782303
+                },
+                "dragging": false
+            },
+            {
+                "id": "OpenAIModel-YUM1a",
+                "type": "genericNode",
+                "position": {
+                    "x": 3231.963340176361,
+                    "y": 94.70653241512102
+                },
+                "data": {
+                    "type": "OpenAIModel",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "output_parser": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "output_parser",
+                                "value": "",
+                                "display_name": "Output Parser",
+                                "advanced": true,
+                                "input_types": [
+                                    "OutputParser"
+                                ],
+                                "dynamic": false,
+                                "info": "The parser to use to parse the output of the model",
+                                "title_case": false,
+                                "type": "other",
+                                "_input_type": "HandleInput"
+                            },
+                            "api_key": {
+                                "load_from_db": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "api_key",
+                                "value": "",
+                                "display_name": "OpenAI API Key",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "title_case": false,
+                                "password": true,
+                                "type": "str",
+                                "_input_type": "SecretStrInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Input",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageInput"
+                            },
+                            "json_mode": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_mode",
+                                "value": false,
+                                "display_name": "JSON Mode",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "If True, it will output JSON regardless of passing a schema.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "max_tokens": {
+                                "trace_as_metadata": true,
+                                "range_spec": {
+                                    "step_type": "float",
+                                    "min": 0,
+                                    "max": 128000,
+                                    "step": 0.1
+                                },
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "max_tokens",
+                                "value": "",
+                                "display_name": "Max Tokens",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "model_kwargs": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_kwargs",
+                                "value": {},
+                                "display_name": "Model Kwargs",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "model_name": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "gpt-4o-mini",
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-4",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "model_name",
+                                "value": "gpt-4o-mini",
+                                "display_name": "Model Name",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "openai_api_base": {
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "openai_api_base",
+                                "value": "",
+                                "display_name": "OpenAI API Base",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "StrInput"
+                            },
+                            "output_schema": {
+                                "trace_as_input": true,
+                                "list": true,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "output_schema",
+                                "value": {},
+                                "display_name": "Schema",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "title_case": false,
+                                "type": "dict",
+                                "_input_type": "DictInput"
+                            },
+                            "seed": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "seed",
+                                "value": 1,
+                                "display_name": "Seed",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "title_case": false,
+                                "type": "int",
+                                "_input_type": "IntInput"
+                            },
+                            "stream": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "stream",
+                                "value": false,
+                                "display_name": "Stream",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "system_message": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "system_message",
+                                "value": "",
+                                "display_name": "System Message",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "temperature": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "temperature",
+                                "value": 0.1,
+                                "display_name": "Temperature",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "float",
+                                "_input_type": "FloatInput"
+                            }
+                        },
+                        "description": "Generates text using OpenAI LLMs.",
+                        "icon": "OpenAI",
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "text_output",
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "required_inputs": [
+                                    "input_value",
+                                    "stream",
+                                    "system_message"
+                                ]
+                            },
+                            {
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "selected": "LanguageModel",
+                                "name": "model_output",
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "required_inputs": [
+                                    "api_key",
+                                    "json_mode",
+                                    "max_tokens",
+                                    "model_kwargs",
+                                    "model_name",
+                                    "openai_api_base",
+                                    "output_schema",
+                                    "seed",
+                                    "temperature"
+                                ]
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "system_message",
+                            "stream",
+                            "max_tokens",
+                            "model_kwargs",
+                            "json_mode",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "api_key",
+                            "temperature",
+                            "seed",
+                            "output_parser"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "id": "OpenAIModel-YUM1a",
+                    "description": "Generates text using OpenAI LLMs.",
+                    "display_name": "OpenAI"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 587,
+                "positionAbsolute": {
+                    "x": 3231.963340176361,
+                    "y": 94.70653241512102
+                },
+                "dragging": false
+            },
+            {
+                "id": "ChatOutput-c4k0L",
+                "type": "genericNode",
+                "position": {
+                    "x": 3669.141671875754,
+                    "y": 248.4257650261032
+                },
+                "data": {
+                    "type": "ChatOutput",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if (\n            self.session_id\n            and isinstance(message, Message)\n            and isinstance(message.text, str)\n            and self.should_store_message\n        ):\n            store_message(\n                message,\n                flow_id=self.graph.flow_id,\n            )\n            self.message.value = message\n\n        self.status = message\n        return message\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "data_template": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "data_template",
+                                "value": "{text}",
+                                "display_name": "Data Template",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "input_value": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "input_value",
+                                "value": "",
+                                "display_name": "Text",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Message to be passed as output.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "sender": {
+                                "trace_as_metadata": true,
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "combobox": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender",
+                                "value": "Machine",
+                                "display_name": "Sender Type",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Type of sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "DropdownInput"
+                            },
+                            "sender_name": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "sender_name",
+                                "value": "AI",
+                                "display_name": "Sender Name",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Name of the sender.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "session_id": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "session_id",
+                                "value": "",
+                                "display_name": "Session ID",
+                                "advanced": true,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "should_store_message": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "should_store_message",
+                                "value": true,
+                                "display_name": "Store Messages",
+                                "advanced": true,
+                                "dynamic": false,
+                                "info": "Store the message in the history.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Display a chat message in the Playground.",
+                        "icon": "ChatOutput",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Chat Output",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "message",
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value",
+                            "should_store_message",
+                            "sender",
+                            "sender_name",
+                            "session_id",
+                            "data_template"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "id": "ChatOutput-c4k0L",
+                    "description": "Display a chat message in the Playground.",
+                    "display_name": "Chat Output"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 289,
+                "positionAbsolute": {
+                    "x": 3669.141671875754,
+                    "y": 248.4257650261032
+                },
+                "dragging": false
+            },
+            {
+                "id": "note-9ynW0",
+                "type": "noteNode",
+                "position": {
+                    "x": -4,
+                    "y": 38
+                },
+                "data": {
+                    "node": {
+                        "description": "**Google Drive Example Scopes**\n\n**Langflow - OAuth Integration Documentation**\n\n```\ndocs.langflow.org/integrations-setup-google-oauth-langflow\n```\n\n**Drive API Documentation**\n\nhttps://developers.google.com/drive/api/guides/api-specific-auth\n\n**Scope Used in This Example**\n\nPermission to view and download all your Drive files.\n\n```\nhttps://www.googleapis.com/auth/drive.readonly\n```\n\n**Example of How to Enter Scopes**\n\n```\nhttps://www.googleapis.com/auth/drive.apps.readonly, https://www.googleapis.com/auth/drive, https://www.googleapis.com/auth/drive.readonly, https://www.googleapis.com/auth/drive.activity\n```",
+                        "display_name": "",
+                        "documentation": "",
+                        "template": {
+                            "backgroundColor": "indigo"
+                        }
+                    },
+                    "type": "note",
+                    "id": "note-9ynW0"
+                },
+                "selected": false,
+                "width": 584,
+                "height": 673,
+                "dragging": false,
+                "style": {
+                    "width": 584,
+                    "height": 673
+                },
+                "resizing": false
+            },
+            {
+                "id": "GoogleOAuthToken-piWFF",
+                "type": "genericNode",
+                "position": {
+                    "x": 609.7629463431374,
+                    "y": 218.39474366635045
+                },
+                "data": {
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "oauth_credentials": {
+                                "trace_as_metadata": true,
+                                "file_path": "bc2cbf16-ed92-4ef6-a4a5-1526af21d044/2024-11-06_15-06-14_client_secret_998884801917-hsjq3alo0vfqih74fe635k01khqi74d3.apps.googleusercontent.com.json",
+                                "fileTypes": [
+                                    "json"
+                                ],
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "oauth_credentials",
+                                "value": "",
+                                "display_name": "Credentials File",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Input OAuth Credentials file (e.g. credentials.json).",
+                                "title_case": false,
+                                "type": "file",
+                                "_input_type": "FileInput"
+                            },
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nimport re\nfrom pathlib import Path\n\nfrom google.auth.transport.requests import Request\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\n\nfrom langflow.custom import Component\nfrom langflow.io import FileInput, MultilineInput, Output\nfrom langflow.schema import Data\n\n\nclass GoogleOAuthToken(Component):\n    display_name = \"Google OAuth Token\"\n    description = \"Generates a JSON string with your Google OAuth token.\"\n    documentation: str = \"https://developers.google.com/identity/protocols/oauth2/web-server?hl=pt-br#python_1\"\n    icon = \"Google\"\n    name = \"GoogleOAuthToken\"\n\n    inputs = [\n        MultilineInput(\n            name=\"scopes\",\n            display_name=\"Scopes\",\n            info=\"Input scopes for your application.\",\n            required=True,\n        ),\n        FileInput(\n            name=\"oauth_credentials\",\n            display_name=\"Credentials File\",\n            info=\"Input OAuth Credentials file (e.g. credentials.json).\",\n            file_types=[\"json\"],\n            required=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n    ]\n\n    def validate_scopes(self, scopes):\n        pattern = (\n            r\"^(https://www\\.googleapis\\.com/auth/[\\w\\.\\-]+\"\n            r\"|mail\\.google\\.com/\"\n            r\"|www\\.google\\.com/calendar/feeds\"\n            r\"|www\\.google\\.com/m8/feeds)\"\n            r\"(,\\s*https://www\\.googleapis\\.com/auth/[\\w\\.\\-]+\"\n            r\"|mail\\.google\\.com/\"\n            r\"|www\\.google\\.com/calendar/feeds\"\n            r\"|www\\.google\\.com/m8/feeds)*$\"\n        )\n        if not re.match(pattern, scopes):\n            error_message = \"Invalid scope format.\"\n            raise ValueError(error_message)\n\n    def build_output(self) -> Data:\n        self.validate_scopes(self.scopes)\n\n        user_scopes = [scope.strip() for scope in self.scopes.split(\",\")]\n        if self.scopes:\n            scopes = user_scopes\n        else:\n            error_message = \"Incorrect scope, check the scopes field.\"\n            raise ValueError(error_message)\n\n        creds = None\n        token_path = Path(\"token.json\")\n\n        if token_path.exists():\n            with token_path.open(mode=\"r\", encoding=\"utf-8\") as token_file:\n                creds = Credentials.from_authorized_user_file(\n                    str(token_path), scopes)\n\n        if not creds or not creds.valid:\n            if creds and creds.expired and creds.refresh_token:\n                creds.refresh(Request())\n            else:\n                if self.oauth_credentials:\n                    client_secret_file = self.oauth_credentials\n                else:\n                    error_message = \"OAuth 2.0 Credentials file not provided.\"\n                    raise ValueError(error_message)\n\n                flow = InstalledAppFlow.from_client_secrets_file(\n                    client_secret_file, scopes)\n                creds = flow.run_local_server(port=0)\n\n            with token_path.open(mode=\"w\", encoding=\"utf-8\") as token_file:\n                token_file.write(creds.to_json())\n\n        creds_json = json.loads(creds.to_json())\n\n        return Data(data=creds_json)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "scopes": {
+                                "trace_as_input": true,
+                                "multiline": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "scopes",
+                                "value": "https://www.googleapis.com/auth/drive.readonly",
+                                "display_name": "Scopes",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Input scopes for your application.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MultilineInput"
+                            }
+                        },
+                        "description": "Generates a JSON string with your Google OAuth token.",
+                        "icon": "Google",
+                        "base_classes": [
+                            "Data"
+                        ],
+                        "display_name": "Google OAuth Token",
+                        "documentation": "https://developers.google.com/identity/protocols/oauth2/web-server?hl=pt-br#python_1",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "output",
+                                "display_name": "Output",
+                                "method": "build_output",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "scopes",
+                            "oauth_credentials"
+                        ],
+                        "beta": false,
+                        "edited": true,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "type": "GoogleOAuthToken",
+                    "id": "GoogleOAuthToken-piWFF",
+                    "description": "A component to generate a json string containing your Google OAuth token.",
+                    "display_name": "Google OAuth Token"
+                },
+                "selected": true,
+                "width": 384,
+                "height": 391,
+                "dragging": false,
+                "positionAbsolute": {
+                    "x": 609.7629463431374,
+                    "y": 218.39474366635045
+                }
+            },
+            {
+                "id": "JSONCleaner-N4p7F",
+                "type": "genericNode",
+                "position": {
+                    "x": 1484.3316811088714,
+                    "y": 174.88328534754476
+                },
+                "data": {
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\nimport re\nimport unicodedata\n\nfrom langflow.custom import Component\nfrom langflow.inputs import BoolInput, MessageTextInput\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass JSONCleaner(Component):\n    display_name = \"JSON Cleaner\"\n    description = (\n        \"Cleans the messy and sometimes incorrect JSON strings produced by LLMs \"\n        \"so that they are fully compliant with the JSON spec.\"\n    )\n    icon = \"custom_components\"\n\n    inputs = [\n        MessageTextInput(\n            name=\"json_str\", display_name=\"JSON String\", info=\"The JSON string to be cleaned.\", required=True\n        ),\n        BoolInput(\n            name=\"remove_control_chars\",\n            display_name=\"Remove Control Characters\",\n            info=\"Remove control characters from the JSON string.\",\n            required=False,\n        ),\n        BoolInput(\n            name=\"normalize_unicode\",\n            display_name=\"Normalize Unicode\",\n            info=\"Normalize Unicode characters in the JSON string.\",\n            required=False,\n        ),\n        BoolInput(\n            name=\"validate_json\",\n            display_name=\"Validate JSON\",\n            info=\"Validate the JSON string to ensure it is well-formed.\",\n            required=False,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Cleaned JSON String\", name=\"output\", method=\"clean_json\"),\n    ]\n\n    def clean_json(self) -> Message:\n        try:\n            from json_repair import repair_json\n        except ImportError as e:\n            msg = \"Could not import the json_repair package. Please install it with `pip install json_repair`.\"\n            raise ImportError(msg) from e\n\n        \"\"\"Clean the input JSON string based on provided options and return the cleaned JSON string.\"\"\"\n        json_str = self.json_str\n        remove_control_chars = self.remove_control_chars\n        normalize_unicode = self.normalize_unicode\n        validate_json = self.validate_json\n\n        try:\n            start = json_str.find(\"{\")\n            end = json_str.rfind(\"}\")\n            if start == -1 or end == -1:\n                msg = \"Invalid JSON string: Missing '{' or '}'\"\n                raise ValueError(msg)\n            json_str = json_str[start : end + 1]\n\n            if remove_control_chars:\n                json_str = self._remove_control_characters(json_str)\n            if normalize_unicode:\n                json_str = self._normalize_unicode(json_str)\n            if validate_json:\n                json_str = self._validate_json(json_str)\n\n            cleaned_json_str = repair_json(json_str)\n            result = str(cleaned_json_str)\n\n            self.status = result\n            return Message(text=result)\n        except Exception as e:\n            msg = f\"Error cleaning JSON string: {e}\"\n            raise ValueError(msg) from e\n\n    def _remove_control_characters(self, s: str) -> str:\n        \"\"\"Remove control characters from the string.\"\"\"\n        return re.sub(r\"[\\x00-\\x1F\\x7F]\", \"\", s)\n\n    def _normalize_unicode(self, s: str) -> str:\n        \"\"\"Normalize Unicode characters in the string.\"\"\"\n        return unicodedata.normalize(\"NFC\", s)\n\n    def _validate_json(self, s: str) -> str:\n        \"\"\"Validate the JSON string.\"\"\"\n        try:\n            json.loads(s)\n            return s\n        except json.JSONDecodeError as e:\n            msg = f\"Invalid JSON string: {e}\"\n            raise ValueError(msg) from e\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "json_str": {
+                                "trace_as_input": true,
+                                "trace_as_metadata": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": true,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "json_str",
+                                "value": "",
+                                "display_name": "JSON String",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "The JSON string to be cleaned.",
+                                "title_case": false,
+                                "type": "str",
+                                "_input_type": "MessageTextInput"
+                            },
+                            "normalize_unicode": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "normalize_unicode",
+                                "value": true,
+                                "display_name": "Normalize Unicode",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Normalize Unicode characters in the JSON string.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput",
+                                "load_from_db": false
+                            },
+                            "remove_control_chars": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "remove_control_chars",
+                                "value": false,
+                                "display_name": "Remove Control Characters",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Remove control characters from the JSON string.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            },
+                            "validate_json": {
+                                "trace_as_metadata": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "validate_json",
+                                "value": false,
+                                "display_name": "Validate JSON",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "Validate the JSON string to ensure it is well-formed.",
+                                "title_case": false,
+                                "type": "bool",
+                                "_input_type": "BoolInput"
+                            }
+                        },
+                        "description": "Cleans the messy and sometimes incorrect JSON strings produced by LLMs so that they are fully compliant with the JSON spec.",
+                        "icon": "custom_components",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "JSON Cleaner",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "output",
+                                "display_name": "Cleaned JSON String",
+                                "method": "clean_json",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "json_str",
+                            "remove_control_chars",
+                            "normalize_unicode",
+                            "validate_json"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "type": "JSONCleaner",
+                    "id": "JSONCleaner-N4p7F",
+                    "description": "Cleans the messy and sometimes incorrect JSON strings produced by LLMs so that they are fully compliant with the JSON spec.",
+                    "display_name": "JSON Cleaner"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 537,
+                "positionAbsolute": {
+                    "x": 1484.3316811088714,
+                    "y": 174.88328534754476
+                },
+                "dragging": false
+            },
+            {
+                "id": "Prompt-uk0Fr",
+                "type": "genericNode",
+                "position": {
+                    "x": 2784.1158749493534,
+                    "y": 225.01053782589702
+                },
+                "data": {
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "template": {
+                                "trace_as_input": true,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "name": "template",
+                                "value": "{context}\n\nTranslate the text you receive into Spanish!",
+                                "display_name": "Template",
+                                "advanced": false,
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "prompt",
+                                "_input_type": "PromptInput",
+                                "load_from_db": false
+                            },
+                            "context": {
+                                "field_type": "str",
+                                "required": false,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "name": "context",
+                                "display_name": "context",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "icon": "prompts",
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "custom_fields": {
+                            "template": [
+                                "context"
+                            ]
+                        },
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Message"
+                                ],
+                                "selected": "Message",
+                                "name": "prompt",
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "template"
+                        ],
+                        "beta": false,
+                        "edited": false,
+                        "metadata": {},
+                        "lf_version": "1.0.19.post2"
+                    },
+                    "type": "Prompt",
+                    "id": "Prompt-uk0Fr",
+                    "description": "Create a prompt template with dynamic variables.",
+                    "display_name": "Prompt"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 391,
+                "positionAbsolute": {
+                    "x": 2784.1158749493534,
+                    "y": 225.01053782589702
+                },
+                "dragging": false
+            }
+        ],
+        "edges": [
+            {
+                "source": "GoogleDriveComponent-x1jHz",
+                "target": "ParseData-7qhmP",
+                "sourceHandle": "{œdataTypeœ:œGoogleDriveComponentœ,œidœ:œGoogleDriveComponent-x1jHzœ,œnameœ:œdocsœ,œoutput_typesœ:[œDataœ]}",
+                "targetHandle": "{œfieldNameœ:œdataœ,œidœ:œParseData-7qhmPœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "id": "reactflow__edge-GoogleDriveComponent-x1jHz{œdataTypeœ:œGoogleDriveComponentœ,œidœ:œGoogleDriveComponent-x1jHzœ,œnameœ:œdocsœ,œoutput_typesœ:[œDataœ]}-ParseData-7qhmP{œfieldNameœ:œdataœ,œidœ:œParseData-7qhmPœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-7qhmP",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "GoogleDriveComponent",
+                        "id": "GoogleDriveComponent-x1jHz",
+                        "name": "docs",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "selected": false,
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "OpenAIModel-YUM1a",
+                "target": "ChatOutput-c4k0L",
+                "sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-YUM1aœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-c4k0Lœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "id": "reactflow__edge-OpenAIModel-YUM1a{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-YUM1aœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-c4k0L{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-c4k0Lœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ChatOutput-c4k0L",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-YUM1a",
+                        "name": "text_output",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "selected": false,
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "GoogleOAuthToken-piWFF",
+                "sourceHandle": "{œdataTypeœ:œGoogleOAuthTokenœ,œidœ:œGoogleOAuthToken-piWFFœ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}",
+                "target": "ParseData-LFq8b",
+                "targetHandle": "{œfieldNameœ:œdataœ,œidœ:œParseData-LFq8bœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-LFq8b",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    },
+                    "sourceHandle": {
+                        "dataType": "GoogleOAuthToken",
+                        "id": "GoogleOAuthToken-piWFF",
+                        "name": "output",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-GoogleOAuthToken-piWFF{œdataTypeœ:œGoogleOAuthTokenœ,œidœ:œGoogleOAuthToken-piWFFœ,œnameœ:œoutputœ,œoutput_typesœ:[œDataœ]}-ParseData-LFq8b{œfieldNameœ:œdataœ,œidœ:œParseData-LFq8bœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "ParseData-LFq8b",
+                "sourceHandle": "{œdataTypeœ:œParseDataœ,œidœ:œParseData-LFq8bœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "JSONCleaner-N4p7F",
+                "targetHandle": "{œfieldNameœ:œjson_strœ,œidœ:œJSONCleaner-N4p7Fœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "json_str",
+                        "id": "JSONCleaner-N4p7F",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-LFq8b",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ParseData-LFq8b{œdataTypeœ:œParseDataœ,œidœ:œParseData-LFq8bœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-JSONCleaner-N4p7F{œfieldNameœ:œjson_strœ,œidœ:œJSONCleaner-N4p7Fœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "JSONCleaner-N4p7F",
+                "sourceHandle": "{œdataTypeœ:œJSONCleanerœ,œidœ:œJSONCleaner-N4p7Fœ,œnameœ:œoutputœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "GoogleDriveComponent-x1jHz",
+                "targetHandle": "{œfieldNameœ:œjson_stringœ,œidœ:œGoogleDriveComponent-x1jHzœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "json_string",
+                        "id": "GoogleDriveComponent-x1jHz",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "JSONCleaner",
+                        "id": "JSONCleaner-N4p7F",
+                        "name": "output",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-JSONCleaner-N4p7F{œdataTypeœ:œJSONCleanerœ,œidœ:œJSONCleaner-N4p7Fœ,œnameœ:œoutputœ,œoutput_typesœ:[œMessageœ]}-GoogleDriveComponent-x1jHz{œfieldNameœ:œjson_stringœ,œidœ:œGoogleDriveComponent-x1jHzœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "ParseData-7qhmP",
+                "sourceHandle": "{œdataTypeœ:œParseDataœ,œidœ:œParseData-7qhmPœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "Prompt-uk0Fr",
+                "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-uk0Frœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "context",
+                        "id": "Prompt-uk0Fr",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-7qhmP",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-ParseData-7qhmP{œdataTypeœ:œParseDataœ,œidœ:œParseData-7qhmPœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-uk0Fr{œfieldNameœ:œcontextœ,œidœ:œPrompt-uk0Frœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "animated": false,
+                "className": ""
+            },
+            {
+                "source": "Prompt-uk0Fr",
+                "sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-uk0Frœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
+                "target": "OpenAIModel-YUM1a",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-YUM1aœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "OpenAIModel-YUM1a",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-uk0Fr",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Prompt-uk0Fr{œdataTypeœ:œPromptœ,œidœ:œPrompt-uk0Frœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-YUM1a{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-YUM1aœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "animated": false,
+                "className": ""
+            }
+        ],
+        "viewport": {
+            "x": -1019.4126433636247,
+            "y": 197.09015595775952,
+            "zoom": 0.6352002867729776
+        }
+    },
+    "description": "An example of a flow that connects to Google Drive to access a text document, reads the content, translates it into the desired language, and returns the translated text in the chat, allowing for quick and efficient automation of the Google Docs file translation process.",
+    "name": "Google Drive Docs Translations Example",
+    "last_tested_version": "1.0.19.post2",
+    "endpoint_name": null,
+    "is_component": false
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json
index d955faa3c..5128a60a7 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json	
@@ -252,7 +252,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -271,6 +271,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are a seasoned business analyst with a strong background in tech product development and market research. Your analytical skills are unparalleled, allowing you to dissect product concepts and evaluate their market viability with precision. You have a keen eye for identifying potential challenges and opportunities that others might overlook. Your insights have been crucial in shaping successful product strategies for numerous tech companies.\n\nYour task is to:\n\n1. Evaluate the concept in terms of market potential and technical feasibility\n2. Identify two potential challenges for developing this product\n3. Suggest one improvement or expansion to the concept\n\n\nPlease structure your response as follows:\n\nConcept Evaluation:\n[concept_evaluation]\n\nPotential Challenges:\n1. [challenge_1]\n2. [challenge_2]\n...\n\nImprovement Suggestion:\n[improvement_suggestion]\n\nProvide an objective and well-founded analysis, considering market and technological factors in your evaluation.\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -399,7 +421,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -692,7 +714,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -919,7 +941,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -938,6 +960,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are an accomplished product manager with a track record of bringing innovative tech products from concept to market. Your strategic thinking and ability to balance technical feasibility with market demands have resulted in several successful product launches. You excel at distilling complex ideas into clear, actionable plans and have a talent for identifying the most critical features that will drive product adoption and success.\n\nBased on the analysis of the innovative product, create a simplified development plan that includes:\n\n1. Product overview (1-2 sentences)\n2. Three main features to be developed\n3. A basic market launch strategy\n\n\nPlease structure your plan as follows:\n\nProduct Overview:\n[product_overview]\n\nMain Features:\n1. [feature_1]\n2. [feature_2]\n3. [feature_3]\n...\n\nLaunch Strategy:\n[launch_strategy]\n\nYour plan should be concise, realistic, and aligned with the information provided in the previous steps.\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1061,7 +1105,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1408,7 +1452,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1712,7 +1756,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -1731,6 +1775,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are a visionary product innovator at a cutting-edge tech startup. Your expertise lies in identifying emerging market trends and translating them into groundbreaking product concepts. Your creative thinking and deep understanding of technology allow you to envision products that not only meet current needs but also anticipate future demands. Your ideas often challenge conventional thinking and push the boundaries of what's possible with current technology.\n\nPlease create a product concept, providing:\n\n1. Product name\n2. Brief description (2-3 sentences)\n3. Main innovative feature\n4. Target audience\n\nStructure your response like this:\n\nProduct Name: [product_name]\n\nDescription: [product_description]\n\nMain Innovation: [main_innovation]\n\nTarget Audience: [target_audience]\n\nBe creative and bold in your idea, but keep it realistic and aligned with the provided market trend."
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1920,7 +1986,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json
index ee373f7aa..155ff5f79 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json	
@@ -187,7 +187,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -423,7 +423,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -442,6 +442,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh."
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -629,7 +651,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -980,7 +1002,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json
index de81ecd3f..7bff175c2 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json	
@@ -481,7 +481,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "instructions": {
                 "advanced": false,
@@ -546,6 +546,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Reference 1:\n\n{references}\n\n---\n\n{instructions}\n\nBlog: \n\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             }
           },
@@ -770,7 +792,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "advanced": true,
@@ -1027,7 +1049,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "advanced": false,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json
index 92754e9a6..3f4faf94b 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Custom Component Maker.json	
@@ -308,7 +308,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -573,7 +573,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
+                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
@@ -920,7 +920,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -939,6 +939,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "<Instructions>\nYou are an AI assistant specialized in creating Langflow components based on user requirements. Your task is to generate the code for a custom Langflow component according to the user's specifications.\n\nFirst, review the following code snippets for reference:\n\n<base_component>\n{BASE_COMPONENT_CODE}\n</base_component>\n\n<custom_component>\n{CUSTOM_COMPONENT_CODE}\n</custom_component>\n\n<example_components>\n{EXAMPLE_COMPONENTS}\n</example_components>\n\nNow, follow these steps to create a custom Langflow component:\n\n1. Analyze the user's input to determine the requirements for the component.\n2. Use an <inner_monologue> section to plan out the component structure and features based on the user's requirements.\n3. Generate the code for the custom component, using the provided code snippets as reference and inspiration.\n4. Provide a brief explanation of the component's functionality and how to use it.\n\nHere's the chat history and user input:\n\n<ChatHistory>\n{CHAT_HISTORY}\n</ChatHistory>\n\n<UserInput>\n{USER_INPUT}\n</UserInput>\n\nBased on the user's input, create a custom Langflow component that meets their requirements. Your response should include:\n\n1. <inner_monologue>\n   Use this section to analyze the user's requirements and plan the component structure.\n</inner_monologue>\n\n2. <component_code>\n   Generate the complete code for the custom Langflow component here.\n</component_code>\n\n3. <explanation>\n   Provide a brief explanation of the component's functionality and how to use it.\n</explanation>\n\nRemember to:\n- Use the provided code snippets as a reference, but create a unique component tailored to the user's needs.\n- Include all necessary imports and class definitions.\n- Implement the required inputs, outputs, and any additional features specified by the user.\n- Use clear and descriptive variable names and comments to enhance code readability.\n- Ensure that the component follows Langflow best practices and conventions.\n\nIf the user's input is unclear or lacks specific details, make reasonable assumptions based on the context and explain these assumptions in your response.\n\n</Instructions>"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1068,7 +1090,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json
index a6af28f3d..0dd7b8047 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json	
@@ -238,7 +238,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -525,7 +525,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -968,7 +968,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1341,11 +1341,12 @@
                 "advanced": false,
                 "display_name": "Server File Path",
                 "dynamic": false,
-                "info": "Data object with a 'file_path' property pointing to server file. Supercedes 'Path'. ",
+                "info": "Data object with a 'file_path' property pointing to server file or a Message object with a path to the file. Supercedes 'Path' but supports same file types.",
                 "input_types": [
-                  "Data"
+                  "Data",
+                  "Message"
                 ],
-                "list": false,
+                "list": true,
                 "name": "file_path",
                 "placeholder": "",
                 "required": false,
@@ -1571,7 +1572,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "advanced": false,
@@ -1588,6 +1589,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json
index 0f96cdfe6..c4963c2e5 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json	
@@ -272,7 +272,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -571,7 +571,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -831,7 +831,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.field_typing.constants import LanguageModel\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field \" \"(e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        _output_model = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[_output_model], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = _output_model\n        try:\n            llm_with_structured_output = cast(LanguageModel, self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
+                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            value=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field \" \"(e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
@@ -1252,7 +1252,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1556,7 +1556,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -1575,6 +1575,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Classify the image into neutral, negative or positive. "
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json
index cbad097ab..1305bee17 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json	
@@ -386,7 +386,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -632,7 +632,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "context": {
                 "advanced": false,
@@ -697,6 +697,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Based on the following context: \n\n{context} \n\n\nFollow these guidelines: \n\n{guidelines}"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -918,7 +940,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1224,7 +1246,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "post": {
                 "advanced": false,
@@ -1266,6 +1288,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Based on the following post: \n\n{post} \n\nCraft a compelling prompt for image generator involving a blend of specificity, creativity, and clarity. Begin with a clear, concise description of the subject or scene you envision, incorporating specific details such as the setting, mood, and any key elements that are crucial to your vision. It's important to use descriptive language that conveys not just the visual aspects but also the emotional tone or atmosphere you wish to capture. Modifiers that specify the style, technique, or artistic influences can greatly enhance the prompt, guiding the AI to produce results that align closely with your expectations. Additionally, consider including any particular textures, lighting styles, or perspectives that will help refine the image to your liking. The goal is to provide Leonardo AI with a well-rounded, detailed description that leaves little room for ambiguity, enabling it to generate an image that closely matches your request.\n\nA good prompt should read like a brief to an artist, containing all the necessary information but leaving enough creative freedom for the AI to work effectively. It's a delicate balance between being overly prescriptive and too vague. The inclusion of what to avoid, using negative prompts, can also be helpful in steering the AI away from undesired outcomes. Remember, the effectiveness of a prompt often improves with experimentation and iteration, refining your approach based on the results you receive.\n\nExample 1: \"Create a digital painting of a serene lakeside at dusk, reflecting the vibrant hues of the sunset. The scene should be framed by weeping willows, with a lone wooden rowboat gently bobbing on the water's surface. Aim for a realistic style with a touch of impressionism, focusing on the interplay of light and shadow.\"\n\nExample 2: \"Illustrate a bustling medieval marketplace scene, vibrant and full of life, set within a walled city. Include diverse merchants, from a blacksmith to a spice trader, and townsfolk in period attire. The artwork should capture the dynamic energy of the market, with attention to historical accuracy and rich, earthy colors. Opt for a detailed, digital illustration style that brings out the textures of fabrics, metals, and natural elements.\""
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1395,7 +1439,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1730,7 +1774,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -2513,7 +2557,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -2820,7 +2864,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "image_description": {
                 "advanced": false,
@@ -2885,6 +2929,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "{post}\n \n\n{image_description} "
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
index 99b4cec42..21367631c 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Market Research.json	
@@ -280,7 +280,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -573,7 +573,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1137,7 +1137,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.field_typing.constants import LanguageModel\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            table_schema=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field \" \"(e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        _output_model = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[_output_model], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = _output_model\n        try:\n            llm_with_structured_output = cast(LanguageModel, self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
+                "value": "from typing import TYPE_CHECKING, cast\n\nfrom pydantic import BaseModel, Field, create_model\n\nfrom langflow.base.models.chat_result import get_chat_result\nfrom langflow.custom import Component\nfrom langflow.helpers.base_model import build_model_from_schema\nfrom langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput\nfrom langflow.schema.data import Data\n\nif TYPE_CHECKING:\n    from langflow.field_typing.constants import LanguageModel\n\n\nclass StructuredOutputComponent(Component):\n    display_name = \"Structured Output\"\n    description = (\n        \"Transforms LLM responses into **structured data formats**. Ideal for extracting specific information \"\n        \"or creating consistent outputs.\"\n    )\n    icon = \"braces\"\n\n    inputs = [\n        HandleInput(\n            name=\"llm\",\n            display_name=\"Language Model\",\n            info=\"The language model to use to generate the structured output.\",\n            input_types=[\"LanguageModel\"],\n        ),\n        MessageTextInput(name=\"input_value\", display_name=\"Input message\"),\n        StrInput(\n            name=\"schema_name\",\n            display_name=\"Schema Name\",\n            info=\"Provide a name for the output data schema.\",\n        ),\n        TableInput(\n            name=\"output_schema\",\n            display_name=\"Output Schema\",\n            info=\"Define the structure and data types for the model's output.\",\n            value=[\n                {\n                    \"name\": \"name\",\n                    \"display_name\": \"Name\",\n                    \"type\": \"str\",\n                    \"description\": \"Specify the name of the output field.\",\n                },\n                {\n                    \"name\": \"description\",\n                    \"display_name\": \"Description\",\n                    \"type\": \"str\",\n                    \"description\": \"Describe the purpose of the output field.\",\n                },\n                {\n                    \"name\": \"type\",\n                    \"display_name\": \"Type\",\n                    \"type\": \"str\",\n                    \"description\": (\n                        \"Indicate the data type of the output field \" \"(e.g., str, int, float, bool, list, dict).\"\n                    ),\n                    \"default\": \"text\",\n                },\n                {\n                    \"name\": \"multiple\",\n                    \"display_name\": \"Multiple\",\n                    \"type\": \"boolean\",\n                    \"description\": \"Set to True if this output field should be a list of the specified type.\",\n                    \"default\": \"False\",\n                },\n            ],\n        ),\n        BoolInput(\n            name=\"multiple\",\n            display_name=\"Generate Multiple\",\n            info=\"Set to True if the model should generate a list of outputs instead of a single output.\",\n        ),\n    ]\n\n    outputs = [\n        Output(name=\"structured_output\", display_name=\"Structured Output\", method=\"build_structured_output\"),\n    ]\n\n    def build_structured_output(self) -> Data:\n        if not hasattr(self.llm, \"with_structured_output\"):\n            msg = \"Language model does not support structured output.\"\n            raise TypeError(msg)\n        if not self.output_schema:\n            msg = \"Output schema cannot be empty\"\n            raise ValueError(msg)\n\n        output_model_ = build_model_from_schema(self.output_schema)\n        if self.multiple:\n            output_model = create_model(\n                self.schema_name,\n                objects=(list[output_model_], Field(description=f\"A list of {self.schema_name}.\")),  # type: ignore[valid-type]\n            )\n        else:\n            output_model = output_model_\n        try:\n            llm_with_structured_output = cast(\"LanguageModel\", self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]\n\n        except NotImplementedError as exc:\n            msg = f\"{self.llm.__class__.__name__} does not support structured output.\"\n            raise TypeError(msg) from exc\n        config_dict = {\n            \"run_name\": self.display_name,\n            \"project_name\": self.get_project_name(),\n            \"callbacks\": self.get_langchain_callbacks(),\n        }\n        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)\n        if isinstance(output, BaseModel):\n            output_dict = output.model_dump()\n        else:\n            msg = f\"Output should be a Pydantic BaseModel, got {type(output)} ({output})\"\n            raise TypeError(msg)\n        return Data(data=output_dict)\n"
               },
               "input_value": {
                 "_input_type": "MessageTextInput",
@@ -1455,7 +1455,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1992,7 +1992,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -2018,7 +2018,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json
index 89917b4e3..195e37a31 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json	
@@ -219,7 +219,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -518,7 +518,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -845,7 +845,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1163,7 +1163,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = self.memory.messages\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = get_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
+                "value": "from langchain.memory import ConversationBufferMemory\n\nfrom langflow.custom import Component\nfrom langflow.field_typing import BaseChatMemory\nfrom langflow.helpers.data import data_to_text\nfrom langflow.inputs import HandleInput\nfrom langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import LCBuiltinChatMemory, aget_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER\n\n\nclass MemoryComponent(Component):\n    display_name = \"Message History\"\n    description = \"Retrieves stored chat messages from Langflow tables or an external memory.\"\n    icon = \"message-square-more\"\n    name = \"Memory\"\n\n    inputs = [\n        HandleInput(\n            name=\"memory\",\n            display_name=\"External Memory\",\n            input_types=[\"BaseChatMessageHistory\"],\n            info=\"Retrieve messages from an external memory. If empty, it will use the Langflow tables.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Filter by sender type.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Filter by sender name.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n            tool_mode=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. \"\n            \"It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Text\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    async def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        if self.memory:\n            # override session_id\n            self.memory.session_id = session_id\n\n            stored = await self.memory.aget_messages()\n            # langchain memories are supposed to return messages in ascending order\n            if order == \"DESC\":\n                stored = stored[::-1]\n            if n_messages:\n                stored = stored[:n_messages]\n            stored = [Message.from_lc_message(m) for m in stored]\n            if sender:\n                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER\n                stored = [m for m in stored if m.type == expected_type]\n        else:\n            stored = await aget_messages(\n                sender=sender,\n                sender_name=sender_name,\n                session_id=session_id,\n                limit=n_messages,\n                order=order,\n            )\n        self.status = stored\n        return stored\n\n    async def retrieve_messages_as_text(self) -> Message:\n        stored_text = data_to_text(self.template, await self.retrieve_messages())\n        self.status = stored_text\n        return Message(text=stored_text)\n\n    def build_lc_memory(self) -> BaseChatMemory:\n        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)\n        return ConversationBufferMemory(chat_memory=chat_memory)\n"
               },
               "memory": {
                 "_input_type": "HandleInput",
@@ -1395,7 +1395,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "memory": {
                 "advanced": false,
@@ -1436,6 +1436,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are a helpful assistant that answer questions.\n\nUse markdown to format your answer, properly embedding images and urls.\n\nHistory: \n\n{memory}\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json
index efd1a95ed..2a50bcf51 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json	
@@ -346,7 +346,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "previous_response": {
                 "advanced": false,
@@ -388,6 +388,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "\n\nRESEARCH PLAN: {previous_response}\n\nUse Tavily Search to investigate the queries and analyze the findings.\nFocus on academic and reliable sources.\n\nSteps:\n1. Search using provided queries\n2. Analyze search results\n3. Verify source credibility\n4. Extract key findings\n\nFormat findings as:\n\nSEARCH RESULTS:\n[Key findings from searches]\n\nSOURCE ANALYSIS:\n[Credibility assessment]\n\nMAIN INSIGHTS:\n[Critical discoveries]\n\nEVIDENCE QUALITY:\n[Evaluation of findings]"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -516,7 +538,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -809,7 +831,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1039,7 +1061,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1104,6 +1126,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "RESEARCH FINDINGS: {search_results}\nORIGINAL QUERY: {input_value}\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1463,7 +1507,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1810,7 +1854,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -2230,7 +2274,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -2256,7 +2300,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -2760,7 +2804,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -2779,6 +2823,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are an expert research assistant.\n\nCreate a focused research plan that will guide our search.\n\nFormat your response exactly as:\n\nRESEARCH OBJECTIVE:\n[Clear statement of research goal]\n\nKEY SEARCH QUERIES:\n1. [Primary academic search query]\n2. [Secondary search query]\n3. [Alternative search approach]\n\nSEARCH PRIORITIES:\n- [What types of sources to focus on]\n- [Key aspects to investigate]\n- [Specific areas to explore]"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -2859,7 +2925,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -2878,6 +2944,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are a research synthesis expert.\n\nCreate a comprehensive synthesis and report of our findings.\n\nFormat your response as:\n\nEXECUTIVE SUMMARY:\n[Key findings and implications]\n\nMETHODOLOGY:\n- Search Strategy Used\n- Sources Analyzed\n- Quality Assessment\n\nFINDINGS & ANALYSIS:\n[Detailed discussion of discoveries]\n\nCONCLUSIONS:\n[Main takeaways and insights]\n\nFUTURE DIRECTIONS:\n[Suggested next steps]\n\nIMPORTANT: For each major point or finding, include the relevant source link in square brackets at the end of the sentence or paragraph. For example: \"Harvard has developed a solid-state battery that charges in minutes. [Source: https://example.com/article]\"\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json
index 17995c65a..72f45c0c9 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json	
@@ -153,7 +153,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "current_solutions": {
                 "advanced": false,
@@ -310,6 +310,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "Product:\n{product}\n \nPain Points:\n{pain_points}\n \nGoals:\n{goals}\n \nCurrent Solutions:\n{current_solutions}\n \nSpecific Target Audience:\n{target_audience}\n\nExpertise Level:\n{expertise_level}\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -421,7 +443,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -440,6 +462,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "You are a digital marketing strategist specialized in generating highly relevant, optimized keywords for a product’s specific target audience. Your task is to create a list of keywords that are not only attractive and impactful but also resonate with the needs and desires of the customers, capturing the core motivations driving them to seek a solution.\n\nBelow are details about the product, including its target audience, the pain points faced by this audience, and the current solutions they consider or use. Use this information to generate precise keywords that connect directly with the unique value of the product and with the customers' goals. Consider factors like the customer’s level of expertise and major market trends to create a powerful and well-grounded keyword list.\n\n### Product Information:\n- **Product:** – A brief description of the product, including what sets it apart in the market.\n- **Customer Pain Points:** – Specific pain points that the audience faces and that the product aims to address.\n- **Customer Goals:** – The primary goals and aspirations of the target audience that the product helps to achieve.\n- **Current Solutions Used:** – How the audience currently tries to address these pain points, including competitor solutions or alternatives.\n- **Specific Target Audience:** – A detailed description of the target audience, including demographics, interests, lifestyle, and behavioral profile.\n- **Customer Expertise Level:**– The level of familiarity or experience the audience has with similar or related solutions.\n\n### Guidelines for Keyword Generation:\n1. **Focus on Pain Points and Solutions**: Generate keywords that accurately reflect the customers’ pain points, clearly conveying how the product offers an effective and unique solution.\n2. **Emphasize Goals and Benefits**: Highlight keywords aligned with customer goals, emphasizing the positive impact and achievable results of the product.\n3. **Consider Competition and Differentiators**: Think about existing solutions and how the product stands out. Create keywords that emphasize differentiators and help the product stand out in a competitive landscape.\n4. **Tailor to Target Audience**: Use terms and phrases that resonate directly with the target audience’s profile, utilizing language and themes most appealing to this segment.\n5. **Customize to Expertise Level**: Adjust the complexity of the keywords according to the audience’s experience level, ensuring they are appealing and accessible.\n6. **Incorporate Market Trends**: Where possible, include keywords that reflect the latest trends in the sector, increasing the content’s relevance and timeliness.\n\n### Example Keyword Suggestions:\n- **For customer pain points:**  – Use keywords that reinforce customer pain points, making it clear how the product can be a solution.\n- **For goals and aspirations:**  – Keywords that symbolize the outcomes and goals desired by customers, such as ‘stress relief,’ ‘productivity boost.’\n- **For product differentiators:** – Keywords that contrast the product with current solutions, highlighting its unique advantages.\n\nFor each keyword generated, provide a brief explanation of how it connects with the product details and the target audience, ensuring the final list is powerful, strategic, and well-founded for maximum market impact."
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -570,7 +614,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -840,7 +884,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json
index 2838abc54..e381644f8 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json	
@@ -151,7 +151,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "continuous_development_cost": {
                 "advanced": false,
@@ -285,6 +285,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "To calculate the monthly subscription price of the software based on the following data:\n\nMonthly infrastructure costs: ${monthly_infrastructure_costs}\nCustomer support: ${customer_support_cost}\nContinuous development: {continuous_development_cost}\nDesired profit margin: {desired_profit_margin}%\nEstimated number of subscribers: {estimated_subscribers}\n\nFollow the step to formulate the answer:\nFixed costs:\nProfit margin:\nTotal amount needed:\nPrice per subscriber:\nThe minimum subscription price per subscriber is:"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -415,7 +437,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -756,7 +778,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -782,7 +804,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json
index 3999a4fd7..a7c99c8b4 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents .json	
@@ -417,7 +417,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -727,7 +727,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -753,7 +753,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1340,7 +1340,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -1366,7 +1366,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1876,7 +1876,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -1895,6 +1895,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "# Expert Research Agent Protocol\n\n[Previous content remains the same, but adding this critical section about image handling:]\n\n## Image and Visual Data Handling\nWhen using Tavily Search with images enabled:\n\n1. Image Collection\n   - Always enable include_images in Tavily search\n   - Collect relevant stock charts, product images, and news photos\n   - Save image URLs from reliable sources\n   - Focus on recent, high-quality images\n\n2. Image Categories to Collect\n   - Product showcase images\n   - Stock performance charts\n   - Company facilities\n   - Key executive photos\n   - Recent event images\n   - Market share visualizations\n\n3. Image Documentation\n   - Include full image URL\n   - Add clear descriptions\n   - Note image source and date\n   - Explain image relevance\n\n4. Image Presentation in Output\n   ```markdown\n   ![Image Description](image_url)\n   - Source: [Source Name]\n   - Date: [Image Date]\n   - Context: [Brief explanation of image relevance]\n   ```\n\n## Output Structure\nPresent your findings in this format:\n\n### Company Overview\n[Comprehensive overview based on search results]\n\n### Recent Developments\n[Latest news and announcements with dates]\n\n### Market Context\n[Industry trends and competitive position]\n\n### Visual Insights\n[Reference relevant images from search]\n\n### Key Risk Factors\n[Identified risks and challenges]\n\n### Sources\n[List of key sources consulted]\n\nRemember to:\n- Use Markdown formatting for clear structure\n- Include dates for all time-sensitive information\n- Quote significant statistics and statements\n- Reference any included images\n- Highlight conflicting information or viewpoints\n- Pass all gathered data to the Finance Agent for detailed financial analysis"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1981,7 +2003,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -2000,6 +2022,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "# Financial Analysis Expert Protocol\n\nYou are an elite financial analyst with access to Yahoo Finance tools. Your role is to perform comprehensive financial analysis based on the research provided and the data available through Yahoo Finance methods.\n\n## CRITICAL: Stock Symbol Usage\n- Always use correct stock ticker symbols in UPPERCASE format\n- Examples of valid symbols:\n  * AAPL (Apple Inc.)\n  * MSFT (Microsoft)\n  * NVDA (NVIDIA)\n  * GOOGL (Alphabet/Google)\n  * TSLA (Tesla)\n- Invalid formats to avoid:\n  * ❌ Apple (company name instead of symbol)\n  * ❌ aapl (lowercase)\n  * ❌ $AAPL (with dollar sign)\n  * ❌ AAPL.US (with extension)\n\n## Data Collection Strategy\n\n1. Initial Symbol Verification\n   - Confirm valid stock symbol format before any analysis\n   - Use get_info first to verify symbol validity\n   - Cross-reference with get_fast_info to ensure data availability\n   - If symbol is invalid, immediately report the error\n\n2. Core Company Analysis\n   - Get basic info (get_info): Full company details\n   - Fast metrics (get_fast_info): Quick market data\n   - Earnings data (get_earnings): Performance history\n   - Calendar events (get_calendar): Upcoming events\n\n3. Financial Statement Analysis\n   - Income statements (get_income_stmt)\n   - Balance sheets (get_balance_sheet)\n   - Cash flow statements (get_cashflow)\n\n4. Market Intelligence\n   - Latest recommendations (get_recommendations)\n   - Recommendation trends (get_recommendations_summary)\n   - Recent rating changes (get_upgrades_downgrades)\n   - Breaking news (get_news, specify number of articles needed)\n\n5. Ownership Structure\n   - Institutional holdings (get_institutional_holders)\n   - Major stakeholders (get_major_holders)\n   - Fund ownership (get_mutualfund_holders)\n   - Insider activity:\n     * Recent purchases (get_insider_purchases)\n     * Transaction history (get_insider_transactions)\n     * Insider roster (get_insider_roster_holders)\n\n6. Historical Patterns\n   - Corporate actions (get_actions)\n   - Dividend history (get_dividends)\n   - Split history (get_splits)\n   - Capital gains (get_capital_gains)\n   - Regulatory filings (get_sec_filings)\n   - ESG metrics (get_sustainability)\n\n## Analysis Framework\n\n1. Profitability Metrics\n   - Revenue trends\n   - Margin analysis\n   - Efficiency ratios\n   - Return metrics\n\n2. Financial Health\n   - Liquidity ratios\n   - Debt analysis\n   - Working capital\n   - Cash flow quality\n\n3. Growth Assessment\n   - Historical rates\n   - Future projections\n   - Market opportunity\n   - Expansion plans\n\n4. Risk Evaluation\n   - Financial risks\n   - Market position\n   - Operational challenges\n   - Competitive threats\n\n## Output Structure\n\n### Symbol Information\n[Confirm stock symbol and basic company information]\n\n### Financial Overview\n[Key metrics summary with actual numbers]\n\n### Profitability Analysis\n[Detailed profit metrics with comparisons]\n\n### Balance Sheet Review\n[Asset and liability analysis]\n\n### Cash Flow Assessment\n[Cash generation and usage patterns]\n\n### Market Sentiment\n[Analyst views and institutional activity]\n\n### Growth Analysis\n[Historical and projected growth]\n\n### Risk Factors\n[Comprehensive risk assessment]\n\nRemember to:\n- ALWAYS verify stock symbol validity first\n- Use exact numbers from the data\n- Compare with industry standards\n- Highlight significant trends\n- Flag data anomalies\n- Identify key risks\n- Provide metric context\n- Focus on material information\n\nPass your comprehensive financial analysis to the Analysis & Editor Agent for final synthesis and recommendations. Include any invalid symbol errors or data availability issues in your report."
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -2089,7 +2133,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "finance_agent_output": {
                 "advanced": false,
@@ -2154,6 +2198,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "# Investment Analysis & Editorial Protocol\n\nYou are an elite financial analyst and editorial expert responsible for creating the final investment analysis report. Your role is to synthesize research and financial data into a visually appealing, data-rich investment analysis using proper markdown formatting.\n\n## Input Processing\n1. Research Agent Input (Visual + Market Research):\n   - Market research and news\n   - Industry trends\n   - Competitive analysis\n   - Images and charts\n   - News sentiment\n   - {research_agent_output}\n\n2. Finance Agent Input (Quantitative Data):\n   - Detailed financial metrics\n   - Stock statistics\n   - Analyst ratings\n   - Growth metrics\n   - Risk factors\n   - {finance_agent_output}\n\n## Output Format Requirements\n\n1. Header Format\n   Use single # for main title, increment for subsections\n   \n2. Image Placement\n   - Place images immediately after relevant sections\n   - Use proper markdown format: ![Alt Text](url)\n   - Always include source and context\n   - Use *italics* for image captions\n\n3. Table Formatting\n   - Use standard markdown tables\n   - Align numbers right, text left\n   - Include header separators\n   - Keep consistent column widths\n\n4. Data Presentation\n   - Use bold (**) for key metrics\n   - Include percentage changes\n   - Show comparisons\n   - Include trends (↑/↓)\n\n## Report Structure\n\n# Investment Analysis Report: [Company Name] ($SYMBOL)\n*Generated: [Date] | Type: Comprehensive Evaluation*\n\n[Executive Summary - 3 paragraphs max]\n\n## Quick Take\n- **Recommendation**: [BUY/HOLD/SELL]\n- **Target Price**: $XXX\n- **Risk Level**: [LOW/MEDIUM/HIGH]\n- **Investment Horizon**: [SHORT/MEDIUM/LONG]-term\n\n## Market Analysis\n[Insert most relevant market image here]\n*Source: [Name] - [Context]*\n\n### Industry Position\n- Market share data\n- Competitive analysis\n- Recent developments\n\n## Financial Health\n| Metric | Value | YoY Change | Industry Avg |\n|:-------|------:|-----------:|-------------:|\n| Revenue | $XXX | XX% | $XXX |\n[Additional metrics]\n\n### Key Performance Indicators\n- **Revenue Growth**: XX%\n- **Profit Margin**: XX%\n- **ROE**: XX%\n\n## Growth Drivers\n1. Short-term Catalysts\n2. Long-term Opportunities\n3. Innovation Pipeline\n\n## Risk Assessment\n| Risk Factor | Severity | Probability | Impact |\n|:------------|:---------|:------------|:-------|\n| [Risk 1] | HIGH/MED/LOW | H/M/L | Details |\n\n## Technical Analysis\n[Insert technical chart]\n*Source: [Name] - Analysis of key technical indicators*\n\n## Investment Strategy\n### Long-term (18+ months)\n- Entry points\n- Position sizing\n- Risk management\n\n### Medium-term (6-18 months)\n- Technical levels\n- Catalysts timeline\n\n### Short-term (0-6 months)\n- Support/Resistance\n- Trading parameters\n\n## Price Targets\n- **Bear Case**: $XXX (-XX%)\n- **Base Case**: $XXX\n- **Bull Case**: $XXX (+XX%)\n\n## Monitoring Checklist\n1. [Metric 1]\n2. [Metric 2]\n3. [Metric 3]\n\n## Visual Evidence\n[Insert additional relevant images]\n*Source: [Name] - [Specific context and analysis]*\n\n*Disclaimer: This analysis is for informational purposes only. Always conduct your own research before making investment decisions.*\n\n## Output Requirements\n\n1. Visual Excellence\n   - Strategic image placement\n   - Clear data visualization\n   - Consistent formatting\n   - Professional appearance\n\n2. Data Accuracy\n   - Cross-reference numbers\n   - Verify calculations\n   - Include trends\n   - Show comparisons\n\n3. Action Focus\n   - Clear recommendations\n   - Specific entry/exit points\n   - Risk management guidelines\n   - Monitoring triggers\n\n4. Professional Standards\n   - No spelling errors\n   - Consistent formatting\n   - Proper citations\n   - Clear attribution\n\nRemember:\n- Never use triple backticks\n- Include all images with proper markdown\n- Maintain consistent formatting\n- Provide specific, actionable insights\n- Use emojis sparingly and professionally\n- Cross-validate all data points"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -2282,7 +2348,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -3202,7 +3268,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -3228,7 +3294,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
index 398a7074a..641563e29 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json	
@@ -287,7 +287,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -839,7 +839,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -1138,7 +1138,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
index 2700c5fdc..1e033efa3 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json	
@@ -300,7 +300,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -593,7 +593,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1353,7 +1353,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -1379,7 +1379,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1966,7 +1966,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -1992,7 +1992,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -2579,7 +2579,7 @@
                 "input_types": [
                   "Message"
                 ],
-                "load_from_db": false,
+                "load_from_db": true,
                 "name": "api_key",
                 "password": true,
                 "placeholder": "",
@@ -2605,7 +2605,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = (\n        \"Define the agent's instructions, then enter a task to complete using tools.\"\n    )\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [\n        set_advanced_true(component_input)\n        for component_input in MemoryComponent().inputs\n    ]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [\n        Output(name=\"response\", display_name=\"Response\", method=\"message_response\")\n    ]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\")\n            for component_input in self.memory_inputs\n        }\n\n        return MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {\n            input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs\n        }\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(\n        self, build_config: dotdict, field_value: str, field_name: str | None = None\n    ) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(\n                    field in build_config for field in fields_to_add\n                ):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(\n                        build_config, field_value, field_name\n                    )\n\n        return build_config\n"
+                "value": "from langchain_core.tools import StructuredTool\n\nfrom langflow.base.agents.agent import LCToolsAgentComponent\nfrom langflow.base.models.model_input_constants import (\n    ALL_PROVIDER_FIELDS,\n    MODEL_PROVIDERS_DICT,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import (\n    ToolCallingAgentComponent,\n)\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.schema.dotdict import dotdict\nfrom langflow.schema.message import Message\n\n\ndef set_advanced_true(component_input):\n    component_input.advanced = True\n    return component_input\n\n\nclass AgentComponent(ToolCallingAgentComponent):\n    display_name: str = \"Agent\"\n    description: str = \"Define the agent's instructions, then enter a task to complete using tools.\"\n    icon = \"bot\"\n    beta = False\n    name = \"Agent\"\n\n    memory_inputs = [set_advanced_true(component_input) for component_input in MemoryComponent().inputs]\n\n    inputs = [\n        DropdownInput(\n            name=\"agent_llm\",\n            display_name=\"Model Provider\",\n            info=\"The provider of the language model that the agent will use to generate responses.\",\n            options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n            value=\"OpenAI\",\n            real_time_refresh=True,\n            input_types=[],\n        ),\n        *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n        MultilineInput(\n            name=\"system_prompt\",\n            display_name=\"Agent Instructions\",\n            info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n            value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n            advanced=False,\n        ),\n        *LCToolsAgentComponent._base_inputs,\n        *memory_inputs,\n        BoolInput(\n            name=\"add_current_date_tool\",\n            display_name=\"Current Date\",\n            advanced=True,\n            info=\"If true, will add a tool to the agent that returns the current date.\",\n            value=True,\n        ),\n    ]\n    outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n    async def message_response(self) -> Message:\n        llm_model, display_name = self.get_llm()\n        self.model_name = get_model_name(llm_model, display_name=display_name)\n        if llm_model is None:\n            msg = \"No language model selected\"\n            raise ValueError(msg)\n        self.chat_history = await self.get_memory_data()\n\n        if self.add_current_date_tool:\n            if not isinstance(self.tools, list):  # type: ignore[has-type]\n                self.tools = []\n            # Convert CurrentDateComponent to a StructuredTool\n            current_date_tool = CurrentDateComponent().to_toolkit()[0]\n            if isinstance(current_date_tool, StructuredTool):\n                self.tools.append(current_date_tool)\n            else:\n                msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n                raise ValueError(msg)\n\n        if not self.tools:\n            msg = \"Tools are required to run the agent.\"\n            raise ValueError(msg)\n        self.set(\n            llm=llm_model,\n            tools=self.tools,\n            chat_history=self.chat_history,\n            input_value=self.input_value,\n            system_prompt=self.system_prompt,\n        )\n        agent = self.create_agent_runnable()\n        return await self.run_agent(agent)\n\n    async def get_memory_data(self):\n        memory_kwargs = {\n            component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n        }\n\n        return await MemoryComponent().set(**memory_kwargs).retrieve_messages()\n\n    def get_llm(self):\n        if isinstance(self.agent_llm, str):\n            try:\n                provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n                if provider_info:\n                    component_class = provider_info.get(\"component_class\")\n                    display_name = component_class.display_name\n                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return (\n                        self._build_llm_model(component_class, inputs, prefix),\n                        display_name,\n                    )\n            except Exception as e:\n                msg = f\"Error building {self.agent_llm} language model\"\n                raise ValueError(msg) from e\n        return self.agent_llm, None\n\n    def _build_llm_model(self, component, inputs, prefix=\"\"):\n        model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n        return component.set(**model_kwargs).build_model()\n\n    def delete_fields(self, build_config: dotdict, fields: dict | list[str]) -> None:\n        \"\"\"Delete specified fields from build_config.\"\"\"\n        for field in fields:\n            build_config.pop(field, None)\n\n    def update_input_types(self, build_config: dotdict) -> dotdict:\n        \"\"\"Update input types for all fields in build_config.\"\"\"\n        for key, value in build_config.items():\n            if isinstance(value, dict):\n                if value.get(\"input_types\") is None:\n                    build_config[key][\"input_types\"] = []\n            elif hasattr(value, \"input_types\") and value.input_types is None:\n                value.input_types = []\n        return build_config\n\n    def update_build_config(self, build_config: dotdict, field_value: str, field_name: str | None = None) -> dotdict:\n        # Iterate over all providers in the MODEL_PROVIDERS_DICT\n        # Existing logic for updating build_config\n        if field_name == \"agent_llm\":\n            provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call the component class's update_build_config method\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n            provider_configs: dict[str, tuple[dict, list[dict]]] = {\n                provider: (\n                    MODEL_PROVIDERS_DICT[provider][\"fields\"],\n                    [\n                        MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n                        for other_provider in MODEL_PROVIDERS_DICT\n                        if other_provider != provider\n                    ],\n                )\n                for provider in MODEL_PROVIDERS_DICT\n            }\n            if field_value in provider_configs:\n                fields_to_add, fields_to_delete = provider_configs[field_value]\n\n                # Delete fields from other providers\n                for fields in fields_to_delete:\n                    self.delete_fields(build_config, fields)\n\n                # Add provider-specific fields\n                if field_value == \"OpenAI\" and not any(field in build_config for field in fields_to_add):\n                    build_config.update(fields_to_add)\n                else:\n                    build_config.update(fields_to_add)\n                # Reset input types for agent_llm\n                build_config[\"agent_llm\"][\"input_types\"] = []\n            elif field_value == \"Custom\":\n                # Delete all provider fields\n                self.delete_fields(build_config, ALL_PROVIDER_FIELDS)\n                # Update with custom component\n                custom_component = DropdownInput(\n                    name=\"agent_llm\",\n                    display_name=\"Language Model\",\n                    options=[*sorted(MODEL_PROVIDERS_DICT.keys()), \"Custom\"],\n                    value=\"Custom\",\n                    real_time_refresh=True,\n                    input_types=[\"LanguageModel\"],\n                )\n                build_config.update({\"agent_llm\": custom_component.to_dict()})\n            # Update input types for all fields\n            build_config = self.update_input_types(build_config)\n\n            # Validate required keys\n            default_keys = [\n                \"code\",\n                \"_type\",\n                \"agent_llm\",\n                \"tools\",\n                \"input_value\",\n                \"add_current_date_tool\",\n                \"system_prompt\",\n                \"agent_description\",\n                \"max_iterations\",\n                \"handle_parsing_errors\",\n                \"verbose\",\n            ]\n            missing_keys = [key for key in default_keys if key not in build_config]\n            if missing_keys:\n                msg = f\"Missing required keys in build_config: {missing_keys}\"\n                raise ValueError(msg)\n        if isinstance(self.agent_llm, str) and self.agent_llm in MODEL_PROVIDERS_DICT:\n            provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n            if provider_info:\n                component_class = provider_info.get(\"component_class\")\n                prefix = provider_info.get(\"prefix\")\n                if component_class and hasattr(component_class, \"update_build_config\"):\n                    # Call each component class's update_build_config method\n                    # remove the prefix from the field_name\n                    if isinstance(field_name, str) and isinstance(prefix, str):\n                        field_name = field_name.replace(prefix, \"\")\n                    build_config = component_class.update_build_config(build_config, field_value, field_name)\n\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json
index 16cdaff25..4e86d5efa 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json	
@@ -358,7 +358,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "_input_type": "FileInput",
@@ -742,7 +742,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1096,7 +1096,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -1991,7 +1991,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "template": {
                 "_input_type": "PromptInput",
@@ -2010,6 +2010,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "<Instructions Structure>\nIntroduce the task of generating tweets or tweet threads based on the provided inputs\n\nExplain each input variable:\n\n{{PROFILE_TYPE}}\n\n{{PROFILE_DETAILS}}\n\n{{CONTENT_GUIDELINES}}\n\n{{TONE_AND_STYLE}}\n\n{{CONTEXT}}\n\n{{OUTPUT_FORMAT}}\n\n{{OUTPUT_LANGUAGE}}\n\nProvide step-by-step instructions on how to analyze the inputs to determine if a single tweet or thread is appropriate\n\nGive guidance on generating tweet content that aligns with the profile, guidelines, tone, style, and context\n\nExplain how to format the output based on the {{OUTPUT_FORMAT}} value\n\nProvide tips for creating engaging, coherent tweet content\n\n</Instructions Structure>\n\n<Instructions>\nYou are an AI tweet generator that can create standalone tweets or multi-tweet threads based on a variety of inputs about the desired content. Here are the key inputs you will use to generate the tweet(s):\n\n<profile_type>\n\n{PROFILE_TYPE}\n\n</profile_type>\n\n<profile_details>\n\n{PROFILE_DETAILS}\n\n</profile_details>\n\n<content_guidelines>\n\n{CONTENT_GUIDELINES}\n\n</content_guidelines>\n\n<tone_and_style>\n\n{TONE_AND_STYLE}\n\n</tone_and_style>\n\n<output_format>\n\n{OUTPUT_FORMAT}\n\n</output_format>\n\n\n<output_language>\n\n{OUTPUT_LANGUAGE}\n\n</output_language>\n\nTo generate the appropriate tweet(s), follow these steps:\n\n<output_determination>\n\nCarefully analyze the {{PROFILE_TYPE}}, {{PROFILE_DETAILS}}, {{CONTENT_GUIDELINES}}, {{TONE_AND_STYLE}}, and {{CONTEXT}} to determine the depth and breadth of content needed.\n\nIf the {{OUTPUT_FORMAT}} is \"single_tweet\", plan to convey the key information in a concise, standalone tweet.\n\nIf the {{OUTPUT_FORMAT}} is \"thread\" or if the content seems too complex for a single tweet, outline a series of connected tweets that flow together to cover the topic.\n\n</output_determination>\n\n<content_generation>\n\nBrainstorm tweet content that aligns with the {{PROFILE_TYPE}} and {{PROFILE_DETAILS}}, adheres to the {{CONTENT_GUIDELINES}}, matches the {{TONE_AND_STYLE}}, and incorporates the {{CONTEXT}}.\n\nFor a single tweet, craft the most engaging, informative message possible within the 280 character limit.\n\nFor a thread, break down the content into distinct yet connected tweet-sized chunks. Ensure each tweet flows logically into the next to maintain reader engagement. Use transitional phrases as needed to link tweets.\n\n</content_generation>\n\n<formatting>\nFormat the output based on the {{OUTPUT_FORMAT}}:\n\nFor a single tweet, provide the content.\n\nFor a thread, include each tweet inside numbered markdown list.\n\n</formatting> <tips>\nFocus on creating original, engaging content that provides value to the intended audience.\n\nOptimize the tweet(s) for the 280 character limit. Be concise yet impactful.\n\nMaintain a consistent voice that matches the {{TONE_AND_STYLE}} throughout the tweet(s).\n\nInclude calls-to-action or questions to drive engagement when appropriate.\n\nDouble check that the final output aligns with the {{PROFILE_DETAILS}} and {{CONTENT_GUIDELINES}}.\n\n</tips>\n\nNow create a Tweet or Twitter Thread for this context:\n\n"
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json
index f7e7f40af..0b2b92dba 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json	
@@ -379,7 +379,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        _background_color = self.background_color\n        _text_color = self.text_color\n        _icon = self.chat_icon\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": _background_color, \"text_color\": _text_color, \"icon\": _icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_USER, MESSAGE_SENDER_USER\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_USER,\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_USER,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    async def message_response(self) -> Message:\n        background_color = self.background_color\n        text_color = self.text_color\n        icon = self.chat_icon\n\n        message = await Message.create(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n            properties={\"background_color\": background_color, \"text_color\": text_color, \"icon\": icon},\n        )\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "files": {
                 "advanced": true,
@@ -771,7 +771,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
+                "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n    trace_type = \"prompt\"\n    name = \"Prompt\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n        MessageTextInput(\n            name=\"tool_placeholder\",\n            display_name=\"Tool Placeholder\",\n            tool_mode=True,\n            advanced=True,\n            info=\"A placeholder input for tool mode.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(self) -> Message:\n        prompt = Message.from_template(**self._attributes)\n        self.status = prompt.text\n        return prompt\n\n    def _update_template(self, frontend_node: dict):\n        prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n        custom_fields = frontend_node[\"custom_fields\"]\n        frontend_node_template = frontend_node[\"template\"]\n        _ = process_prompt_template(\n            template=prompt_template,\n            name=\"template\",\n            custom_fields=custom_fields,\n            frontend_node_template=frontend_node_template,\n        )\n        return frontend_node\n\n    def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n        \"\"\"This function is called after the code validation is done.\"\"\"\n        frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\n        # Kept it duplicated for backwards compatibility\n        _ = process_prompt_template(\n            template=template,\n            name=\"template\",\n            custom_fields=frontend_node[\"custom_fields\"],\n            frontend_node_template=frontend_node[\"template\"],\n        )\n        # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n        return frontend_node\n\n    def _get_fallback_input(self, **kwargs):\n        return DefaultPromptField(**kwargs)\n"
               },
               "context": {
                 "advanced": false,
@@ -834,6 +834,28 @@
                 "trace_as_input": true,
                 "type": "prompt",
                 "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: "
+              },
+              "tool_placeholder": {
+                "_input_type": "MessageTextInput",
+                "advanced": true,
+                "display_name": "Tool Placeholder",
+                "dynamic": false,
+                "info": "A placeholder input for tool mode.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tool_placeholder",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "tool_mode": true,
+                "trace_as_input": true,
+                "trace_as_metadata": true,
+                "type": "str",
+                "value": ""
               }
             },
             "tool_mode": false
@@ -1170,7 +1192,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=1)),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs import BoolInput, DictInput, DropdownInput, IntInput, SecretStrInput, SliderInput, StrInput\nfrom langflow.inputs.inputs import HandleInput\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n    name = \"OpenAIModel\"\n\n    inputs = [\n        *LCModelComponent._base_inputs,\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            range_spec=RangeSpec(min=0, max=128000),\n        ),\n        DictInput(\n            name=\"model_kwargs\",\n            display_name=\"Model Kwargs\",\n            advanced=True,\n            info=\"Additional keyword arguments to pass to the model.\",\n        ),\n        BoolInput(\n            name=\"json_mode\",\n            display_name=\"JSON Mode\",\n            advanced=True,\n            info=\"If True, it will output JSON regardless of passing a schema.\",\n        ),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. \"\n            \"You must pass the word JSON in the prompt. \"\n            \"If left blank, JSON mode will be disabled. [DEPRECATED]\",\n        ),\n        DropdownInput(\n            name=\"model_name\",\n            display_name=\"Model Name\",\n            advanced=False,\n            options=OPENAI_MODEL_NAMES,\n            value=OPENAI_MODEL_NAMES[0],\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. \"\n            \"Defaults to https://api.openai.com/v1. \"\n            \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        SliderInput(\n            name=\"temperature\", display_name=\"Temperature\", value=0.1, range_spec=RangeSpec(min=0, max=2, step=0.01)\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n        HandleInput(\n            name=\"output_parser\",\n            display_name=\"Output Parser\",\n            info=\"The parser to use to parse the output of the model\",\n            advanced=True,\n            input_types=[\"OutputParser\"],\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schema is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict) or self.json_mode\n        seed = self.seed\n\n        api_key = SecretStr(openai_api_key).get_secret_value() if openai_api_key else None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature if temperature is not None else 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            if output_schema_dict:\n                output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n            else:\n                output = output.bind(response_format={\"type\": \"json_object\"})\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"Get a message from an OpenAI exception.\n\n        Args:\n            e (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return None\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")\n            if message:\n                return message\n        return None\n"
               },
               "input_value": {
                 "_input_type": "MessageInput",
@@ -1524,7 +1546,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, _id: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if _id:\n            source_dict[\"id\"] = _id\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    def message_response(self) -> Message:\n        _source, _icon, _display_name, _source_id = self.get_properties_from_source_component()\n        _background_color = self.background_color\n        _text_color = self.text_color\n        if self.chat_icon:\n            _icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(_source_id, _display_name, _source)\n        message.properties.icon = _icon\n        message.properties.background_color = _background_color\n        message.properties.text_color = _text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"MessagesSquare\"\n    name = \"ChatOutput\"\n\n    inputs = [\n        MessageInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        BoolInput(\n            name=\"should_store_message\",\n            display_name=\"Store Messages\",\n            info=\"Store the message in the history.\",\n            value=True,\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n            value=MESSAGE_SENDER_AI,\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=MESSAGE_SENDER_NAME_AI,\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n        MessageTextInput(\n            name=\"background_color\",\n            display_name=\"Background Color\",\n            info=\"The background color of the icon.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"chat_icon\",\n            display_name=\"Icon\",\n            info=\"The icon of the message.\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"text_color\",\n            display_name=\"Text Color\",\n            info=\"The text color of the name\",\n            advanced=True,\n        ),\n    ]\n    outputs = [\n        Output(\n            display_name=\"Message\",\n            name=\"message\",\n            method=\"message_response\",\n        ),\n    ]\n\n    def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n        source_dict = {}\n        if id_:\n            source_dict[\"id\"] = id_\n        if display_name:\n            source_dict[\"display_name\"] = display_name\n        if source:\n            source_dict[\"source\"] = source\n        return Source(**source_dict)\n\n    async def message_response(self) -> Message:\n        source, icon, display_name, source_id = self.get_properties_from_source_component()\n        background_color = self.background_color\n        text_color = self.text_color\n        if self.chat_icon:\n            icon = self.chat_icon\n        message = self.input_value if isinstance(self.input_value, Message) else Message(text=self.input_value)\n        message.sender = self.sender\n        message.sender_name = self.sender_name\n        message.session_id = self.session_id\n        message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n        message.properties.source = self._build_source(source_id, display_name, source)\n        message.properties.icon = icon\n        message.properties.background_color = background_color\n        message.properties.text_color = text_color\n        if self.session_id and isinstance(message, Message) and self.should_store_message:\n            stored_message = await self.send_message(\n                message,\n            )\n            self.message.value = stored_message\n            message = stored_message\n\n        self.status = message\n        return message\n"
               },
               "data_template": {
                 "_input_type": "MessageTextInput",
@@ -3077,18 +3099,6 @@
             "metadata": {},
             "output_types": [],
             "outputs": [
-              {
-                "cache": true,
-                "display_name": "Retriever",
-                "method": "build_base_retriever",
-                "name": "base_retriever",
-                "required_inputs": [],
-                "selected": "Retriever",
-                "types": [
-                  "Retriever"
-                ],
-                "value": "__UNDEFINED__"
-              },
               {
                 "cache": true,
                 "display_name": "Search Results",
@@ -3226,7 +3236,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom astrapy.exceptions import CollectionNotFoundException\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    def list_collections(self):\n        client = DataAPIClient(token=self.token)\n\n        database = client.get_database(\n            self.api_endpoint,\n            token=self.token,\n        )\n\n        return database.list_collections()\n\n    def _initialize_collection_options(self):\n        try:\n            collections = [collection.name for collection in self.list_collections()]\n        except (CollectionNotFoundException, ConnectionError, ValueError) as _:\n            collections = []\n\n        return [*collections, \"+ Create new collection\"]\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n            ],\n        },\n    )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\n            advanced=True,\n        ),\n    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        client = DataAPIClient(token=self.token)\n\n        database = client.get_database(\n            self.api_endpoint,\n            token=self.token,\n        )\n\n        collection = database.get_collection(self.get_collection_choice())\n\n        # Only get the options if the collection exists\n        try:\n            collection_options = collection.options()\n        except CollectionNotFoundException as e:\n            self.log(f\"Collection not found: {e}\")\n\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Write to a file in my home directory with field value and field name\n        with open(\"/Users/erichare/field_values.txt\", \"a\") as f:\n            f.write(f\"{field_name}: {field_value}\\n\")\n\n        # If the collection name is set to \"+ Create new collection\", show embedding choice\n        if field_name == \"collection_name\" and field_value == \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = False\n            build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n            build_config[\"embedding_model\"][\"advanced\"] = False\n\n            build_config[\"collection_name_new\"][\"advanced\"] = False\n            build_config[\"collection_name_new\"][\"required\"] = True\n\n        # But if it's not, hide embedding choice\n        elif field_name == \"collection_name\" and field_value != \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            build_config[\"collection_name_new\"][\"advanced\"] = True\n            build_config[\"collection_name_new\"][\"required\"] = False\n            build_config[\"collection_name_new\"][\"value\"] = \"\"\n\n        # Get the collection options\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                self.del_fields(build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ]\n                )\n\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_choice\"][\"value\"] = \"Astra Vectorize\"\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_provider\"][\"advanced\"] = False\n                build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        metric_value = self.metric or None\n        autodetect = False\n\n        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding_model}\n        # Use autodetect if the collection name is NOT set to \"+ Create new collection\"\n        elif self.collection_name != \"+ Create new collection\":\n            autodetect = True\n            metric_value = None\n            setup_mode_value = None\n            embedding_dict = {}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Grab the collection options if available\n            collection_options = self.get_collection_options()\n\n            # Ensure collection_options and its nested attributes are handled safely\n            authentication = getattr(self, \"z_04_authentication\", {}) or (\n                collection_options.service.authentication\n                if collection_options and collection_options.service and collection_options.service.authentication\n                else {}\n            )\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=(\n                    getattr(self, \"embedding_provider\", None)\n                    or (\n                        collection_options.service.provider\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                model=(\n                    getattr(self, \"model\", None)\n                    or (\n                        collection_options.service.model_name\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_01_model_parameters=(\n                    getattr(self, \"z_01_model_parameters\", None)\n                    or (\n                        collection_options.service.parameters\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_02_api_key_name=(\n                    getattr(self, \"z_02_api_key_name\", None)\n                    or (authentication.get(\"apiKey\") if authentication else None)\n                ),\n                z_03_provider_api_key=(\n                    getattr(self, \"z_03_provider_api_key\", None)\n                    or (authentication.get(\"providerKey\") if authentication else None)\n                ),\n                z_04_authentication=authentication,\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\":\n            langflow_prefix = \"ds-\"\n\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                environment=(\n                    parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n                    if getattr(self, \"api_endpoint\", None)\n                    else None\n                ),\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None,\n                metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None,\n                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)\n                if self.collection_indexing_policy\n                else None,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter or search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_input}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n            ],\n        },\n    )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\n            advanced=True,\n        ),\n    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # If the collection name is set to \"+ Create new collection\", show embedding choice\n        if field_name == \"collection_name\" and field_value == \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = False\n            build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n            build_config[\"embedding_model\"][\"advanced\"] = False\n\n            build_config[\"collection_name_new\"][\"advanced\"] = False\n            build_config[\"collection_name_new\"][\"required\"] = True\n\n        # But if it's not, hide embedding choice\n        elif field_name == \"collection_name\" and field_value != \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            build_config[\"collection_name_new\"][\"advanced\"] = True\n            build_config[\"collection_name_new\"][\"required\"] = False\n            build_config[\"collection_name_new\"][\"value\"] = \"\"\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_choice\"][\"value\"] = \"Astra Vectorize\"\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_provider\"][\"advanced\"] = False\n                build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        metric_value = self.metric or None\n        autodetect = False\n\n        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding_model}\n        # Use autodetect if the collection name is NOT set to \"+ Create new collection\"\n        elif self.collection_name != \"+ Create new collection\":\n            autodetect = True\n            metric_value = None\n            setup_mode_value = None\n            embedding_dict = {}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Grab the collection options if available\n            collection_options = self.get_collection_options()\n\n            # Ensure collection_options and its nested attributes are handled safely\n            authentication = getattr(self, \"z_04_authentication\", {}) or (\n                collection_options.service.authentication\n                if collection_options and collection_options.service and collection_options.service.authentication\n                else {}\n            )\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=(\n                    getattr(self, \"embedding_provider\", None)\n                    or (\n                        collection_options.service.provider\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                model=(\n                    getattr(self, \"model\", None)\n                    or (\n                        collection_options.service.model_name\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_01_model_parameters=(\n                    getattr(self, \"z_01_model_parameters\", None)\n                    or (\n                        collection_options.service.parameters\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_02_api_key_name=(\n                    getattr(self, \"z_02_api_key_name\", None)\n                    or (authentication.get(\"apiKey\") if authentication else None)\n                ),\n                z_03_provider_api_key=(\n                    getattr(self, \"z_03_provider_api_key\", None)\n                    or (authentication.get(\"providerKey\") if authentication else None)\n                ),\n                z_04_authentication=authentication,\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                environment=(\n                    parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n                    if getattr(self, \"api_endpoint\", None)\n                    else None\n                ),\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None,\n                metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None,\n                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)\n                if self.collection_indexing_policy\n                else None,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter or search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_input}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_indexing_policy": {
                 "_input_type": "StrInput",
@@ -3636,18 +3646,6 @@
             "metadata": {},
             "output_types": [],
             "outputs": [
-              {
-                "cache": true,
-                "display_name": "Retriever",
-                "method": "build_base_retriever",
-                "name": "base_retriever",
-                "required_inputs": [],
-                "selected": "Retriever",
-                "types": [
-                  "Retriever"
-                ],
-                "value": "__UNDEFINED__"
-              },
               {
                 "cache": true,
                 "display_name": "Search Results",
@@ -3785,7 +3783,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom astrapy.exceptions import CollectionNotFoundException\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    def list_collections(self):\n        client = DataAPIClient(token=self.token)\n\n        database = client.get_database(\n            self.api_endpoint,\n            token=self.token,\n        )\n\n        return database.list_collections()\n\n    def _initialize_collection_options(self):\n        try:\n            collections = [collection.name for collection in self.list_collections()]\n        except (CollectionNotFoundException, ConnectionError, ValueError) as _:\n            collections = []\n\n        return [*collections, \"+ Create new collection\"]\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n            ],\n        },\n    )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=False,\n            required=True,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\n            advanced=True,\n        ),\n    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        client = DataAPIClient(token=self.token)\n\n        database = client.get_database(\n            self.api_endpoint,\n            token=self.token,\n        )\n\n        collection = database.get_collection(self.get_collection_choice())\n\n        # Only get the options if the collection exists\n        try:\n            collection_options = collection.options()\n        except CollectionNotFoundException as e:\n            self.log(f\"Collection not found: {e}\")\n\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # Write to a file in my home directory with field value and field name\n        with open(\"/Users/erichare/field_values.txt\", \"a\") as f:\n            f.write(f\"{field_name}: {field_value}\\n\")\n\n        # If the collection name is set to \"+ Create new collection\", show embedding choice\n        if field_name == \"collection_name\" and field_value == \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = False\n            build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n            build_config[\"embedding_model\"][\"advanced\"] = False\n\n            build_config[\"collection_name_new\"][\"advanced\"] = False\n            build_config[\"collection_name_new\"][\"required\"] = True\n\n        # But if it's not, hide embedding choice\n        elif field_name == \"collection_name\" and field_value != \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            build_config[\"collection_name_new\"][\"advanced\"] = True\n            build_config[\"collection_name_new\"][\"required\"] = False\n            build_config[\"collection_name_new\"][\"value\"] = \"\"\n\n        # Get the collection options\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                self.del_fields(build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ]\n                )\n\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_choice\"][\"value\"] = \"Astra Vectorize\"\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_provider\"][\"advanced\"] = False\n                build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        metric_value = self.metric or None\n        autodetect = False\n\n        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding_model}\n        # Use autodetect if the collection name is NOT set to \"+ Create new collection\"\n        elif self.collection_name != \"+ Create new collection\":\n            autodetect = True\n            metric_value = None\n            setup_mode_value = None\n            embedding_dict = {}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Grab the collection options if available\n            collection_options = self.get_collection_options()\n\n            # Ensure collection_options and its nested attributes are handled safely\n            authentication = getattr(self, \"z_04_authentication\", {}) or (\n                collection_options.service.authentication\n                if collection_options and collection_options.service and collection_options.service.authentication\n                else {}\n            )\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=(\n                    getattr(self, \"embedding_provider\", None)\n                    or (\n                        collection_options.service.provider\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                model=(\n                    getattr(self, \"model\", None)\n                    or (\n                        collection_options.service.model_name\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_01_model_parameters=(\n                    getattr(self, \"z_01_model_parameters\", None)\n                    or (\n                        collection_options.service.parameters\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_02_api_key_name=(\n                    getattr(self, \"z_02_api_key_name\", None)\n                    or (authentication.get(\"apiKey\") if authentication else None)\n                ),\n                z_03_provider_api_key=(\n                    getattr(self, \"z_03_provider_api_key\", None)\n                    or (authentication.get(\"providerKey\") if authentication else None)\n                ),\n                z_04_authentication=authentication,\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\":\n            langflow_prefix = \"ds-\"\n\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                environment=(\n                    parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n                    if getattr(self, \"api_endpoint\", None)\n                    else None\n                ),\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None,\n                metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None,\n                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)\n                if self.collection_indexing_policy\n                else None,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter or search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_input}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
+                "value": "import os\nfrom collections import defaultdict\n\nimport orjson\nfrom astrapy import DataAPIClient\nfrom astrapy.admin import parse_api_endpoint\nfrom langchain_astradb import AstraDBVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput, MessageTextInput, NestedDictInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\nfrom langflow.utils.version import get_version_info\n\n\nclass AstraDBVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://docs.langflow.org/starter-projects-vector-store-rag\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vector_store: AstraDBVectorStore | None = None\n\n    VECTORIZE_PROVIDERS_MAPPING = defaultdict(\n        list,\n        {\n            \"Azure OpenAI\": [\n                \"azureOpenAI\",\n                [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"],\n            ],\n            \"Hugging Face - Dedicated\": [\"huggingfaceDedicated\", [\"endpoint-defined-model\"]],\n            \"Hugging Face - Serverless\": [\n                \"huggingface\",\n                [\n                    \"sentence-transformers/all-MiniLM-L6-v2\",\n                    \"intfloat/multilingual-e5-large\",\n                    \"intfloat/multilingual-e5-large-instruct\",\n                    \"BAAI/bge-small-en-v1.5\",\n                    \"BAAI/bge-base-en-v1.5\",\n                    \"BAAI/bge-large-en-v1.5\",\n                ],\n            ],\n            \"Jina AI\": [\n                \"jinaAI\",\n                [\n                    \"jina-embeddings-v2-base-en\",\n                    \"jina-embeddings-v2-base-de\",\n                    \"jina-embeddings-v2-base-es\",\n                    \"jina-embeddings-v2-base-code\",\n                    \"jina-embeddings-v2-base-zh\",\n                ],\n            ],\n            \"Mistral AI\": [\"mistral\", [\"mistral-embed\"]],\n            \"Nvidia\": [\"nvidia\", [\"NV-Embed-QA\"]],\n            \"OpenAI\": [\"openai\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\n            \"Upstage\": [\"upstageAI\", [\"solar-embedding-1-large\"]],\n            \"Voyage AI\": [\n                \"voyageAI\",\n                [\"voyage-large-2-instruct\", \"voyage-law-2\", \"voyage-code-2\", \"voyage-large-2\", \"voyage-2\"],\n            ],\n        },\n    )\n\n    inputs = [\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n            advanced=os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\",\n            real_time_refresh=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"Database\" if os.getenv(\"ASTRA_ENHANCED\", \"false\").lower() == \"true\" else \"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n            real_time_refresh=True,\n        ),\n        DropdownInput(\n            name=\"collection_name\",\n            display_name=\"Collection\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n            refresh_button=True,\n            real_time_refresh=True,\n            options=[\"+ Create new collection\"],\n            value=\"+ Create new collection\",\n        ),\n        StrInput(\n            name=\"collection_name_new\",\n            display_name=\"Collection Name\",\n            info=\"Name of the new collection to create.\",\n            advanced=os.getenv(\"LANGFLOW_HOST\") is not None,\n            required=os.getenv(\"LANGFLOW_HOST\") is None,\n        ),\n        StrInput(\n            name=\"keyspace\",\n            display_name=\"Keyspace\",\n            info=\"Optional keyspace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. \"\n            \"(when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        NestedDictInput(\n            name=\"advanced_search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n        ),\n        DictInput(\n            name=\"search_filter\",\n            display_name=\"[DEPRECATED] Search Metadata Filter\",\n            info=\"Deprecated: use advanced_search_filter. Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            list=True,\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n        ),\n        DropdownInput(\n            name=\"embedding_choice\",\n            display_name=\"Embedding Model or Astra Vectorize\",\n            info=\"Determines whether to use Astra Vectorize for the collection.\",\n            options=[\"Embedding Model\", \"Astra Vectorize\"],\n            real_time_refresh=True,\n            value=\"Embedding Model\",\n        ),\n        HandleInput(\n            name=\"embedding_model\",\n            display_name=\"Embedding Model\",\n            input_types=[\"Embeddings\"],\n            info=\"Allows an embedding model configuration.\",\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            value=\"cosine\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync' or 'Off'.\",\n            options=[\"Sync\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            list=True,\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info='Optional JSON string for the \"indexing\" field of the collection. '\n            \"See https://docs.datastax.com/en/astra-db-serverless/api-reference/collections.html#the-indexing-option\",\n            advanced=True,\n        ),\n    ]\n\n    def del_fields(self, build_config, field_list):\n        for field in field_list:\n            if field in build_config:\n                del build_config[field]\n\n        return build_config\n\n    def insert_in_dict(self, build_config, field_name, new_parameters):\n        # Insert the new key-value pair after the found key\n        for new_field_name, new_parameter in new_parameters.items():\n            # Get all the items as a list of tuples (key, value)\n            items = list(build_config.items())\n\n            # Find the index of the key to insert after\n            idx = len(items)\n            for i, (key, _) in enumerate(items):\n                if key == field_name:\n                    idx = i + 1\n                    break\n\n            items.insert(idx, (new_field_name, new_parameter))\n\n            # Clear the original dictionary and update with the modified items\n            build_config.clear()\n            build_config.update(items)\n\n        return build_config\n\n    def update_providers_mapping(self):\n        # If we don't have token or api_endpoint, we can't fetch the list of providers\n        if not self.token or not self.api_endpoint:\n            self.log(\"Astra DB token and API endpoint are required to fetch the list of Vectorize providers.\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n        try:\n            self.log(\"Dynamically updating list of Vectorize providers.\")\n\n            # Get the admin object\n            client = DataAPIClient(token=self.token)\n            admin = client.get_admin()\n\n            # Get the embedding providers\n            db_admin = admin.get_database_admin(self.api_endpoint)\n            embedding_providers = db_admin.find_embedding_providers().as_dict()\n\n            vectorize_providers_mapping = {}\n\n            # Map the provider display name to the provider key and models\n            for provider_key, provider_data in embedding_providers[\"embeddingProviders\"].items():\n                display_name = provider_data[\"displayName\"]\n                models = [model[\"name\"] for model in provider_data[\"models\"]]\n\n                vectorize_providers_mapping[display_name] = [provider_key, models]\n\n            # Sort the resulting dictionary\n            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching Vectorize providers: {e}\")\n\n            return self.VECTORIZE_PROVIDERS_MAPPING\n\n    def get_database(self):\n        try:\n            client = DataAPIClient(token=self.token)\n\n            return client.get_database(\n                self.api_endpoint,\n                token=self.token,\n            )\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error getting database: {e}\")\n\n            return None\n\n    def _initialize_collection_options(self):\n        database = self.get_database()\n        if database is None:\n            return [\"+ Create new collection\"]\n\n        try:\n            collections = [collection.name for collection in database.list_collections()]\n        except Exception as e:  # noqa: BLE001\n            self.log(f\"Error fetching collections: {e}\")\n\n            return [\"+ Create new collection\"]\n\n        return [*collections, \"+ Create new collection\"]\n\n    def get_collection_choice(self):\n        collection_name = self.collection_name\n        if collection_name == \"+ Create new collection\":\n            return self.collection_name_new\n\n        return collection_name\n\n    def get_collection_options(self):\n        # Only get the options if the collection exists\n        database = self.get_database()\n        if database is None:\n            return None\n\n        collection_name = self.get_collection_choice()\n\n        try:\n            collection = database.get_collection(collection_name)\n            collection_options = collection.options()\n        except Exception as _:  # noqa: BLE001\n            return None\n\n        return collection_options.vector\n\n    def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        # Refresh the collection name options\n        build_config[\"collection_name\"][\"options\"] = self._initialize_collection_options()\n\n        # If the collection name is set to \"+ Create new collection\", show embedding choice\n        if field_name == \"collection_name\" and field_value == \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = False\n            build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n            build_config[\"embedding_model\"][\"advanced\"] = False\n\n            build_config[\"collection_name_new\"][\"advanced\"] = False\n            build_config[\"collection_name_new\"][\"required\"] = True\n\n        # But if it's not, hide embedding choice\n        elif field_name == \"collection_name\" and field_value != \"+ Create new collection\":\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            build_config[\"collection_name_new\"][\"advanced\"] = True\n            build_config[\"collection_name_new\"][\"required\"] = False\n            build_config[\"collection_name_new\"][\"value\"] = \"\"\n\n        # Get the collection options for the selected collection\n        collection_options = self.get_collection_options()\n\n        # If the collection options are available (DB exists), show the advanced options\n        if collection_options:\n            build_config[\"embedding_choice\"][\"advanced\"] = True\n\n            if collection_options.service:\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n                build_config[\"embedding_model\"][\"advanced\"] = True\n                build_config[\"embedding_choice\"][\"value\"] = \"Astra Vectorize\"\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n                build_config[\"embedding_provider\"][\"advanced\"] = False\n                build_config[\"embedding_choice\"][\"value\"] = \"Embedding Model\"\n\n        elif field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                build_config[\"embedding_model\"][\"advanced\"] = True\n\n                # Update the providers mapping\n                vectorize_providers = self.update_providers_mapping()\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=vectorize_providers.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding_provider\": new_parameter})\n            else:\n                build_config[\"embedding_model\"][\"advanced\"] = False\n\n                self.del_fields(\n                    build_config,\n                    [\n                        \"embedding_provider\",\n                        \"model\",\n                        \"z_01_model_parameters\",\n                        \"z_02_api_key_name\",\n                        \"z_03_provider_api_key\",\n                        \"z_04_authentication\",\n                    ],\n                )\n\n        elif field_name == \"embedding_provider\":\n            self.del_fields(\n                build_config,\n                [\"model\", \"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            # Update the providers mapping\n            vectorize_providers = self.update_providers_mapping()\n            model_options = vectorize_providers[field_value][1]\n\n            new_parameter = DropdownInput(\n                name=\"model\",\n                display_name=\"Model\",\n                info=\"The embedding model to use for the selected provider. Each provider has a different set of \"\n                \"models available (full list at \"\n                \"https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\\n\\n\"\n                f\"{', '.join(model_options)}\",\n                options=model_options,\n                value=None,\n                required=True,\n                real_time_refresh=True,\n            ).to_dict()\n\n            self.insert_in_dict(build_config, \"embedding_provider\", {\"model\": new_parameter})\n\n        elif field_name == \"model\":\n            self.del_fields(\n                build_config,\n                [\"z_01_model_parameters\", \"z_02_api_key_name\", \"z_03_provider_api_key\", \"z_04_authentication\"],\n            )\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                list=True,\n            ).to_dict()\n\n            new_parameter_2 = MessageTextInput(\n                name=\"z_02_api_key_name\",\n                display_name=\"API Key Name\",\n                info=\"The name of the embeddings provider API key stored on Astra. \"\n                \"If set, it will override the 'ProviderKey' in the authentication parameters.\",\n            ).to_dict()\n\n            new_parameter_3 = SecretStrInput(\n                load_from_db=False,\n                name=\"z_03_provider_api_key\",\n                display_name=\"Provider API Key\",\n                info=\"An alternative to the Astra Authentication that passes an API key for the provider \"\n                \"with each request to Astra DB. \"\n                \"This may be used when Vectorize is configured for the collection, \"\n                \"but no corresponding provider secret is stored within Astra's key management system.\",\n            ).to_dict()\n\n            new_parameter_4 = DictInput(\n                name=\"z_04_authentication\",\n                display_name=\"Authentication Parameters\",\n                list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"model\",\n                {\n                    \"z_01_model_parameters\": new_parameter_1,\n                    \"z_02_api_key_name\": new_parameter_2,\n                    \"z_03_provider_api_key\": new_parameter_3,\n                    \"z_04_authentication\": new_parameter_4,\n                },\n            )\n\n        return build_config\n\n    def build_vectorize_options(self, **kwargs):\n        for attribute in [\n            \"embedding_provider\",\n            \"model\",\n            \"z_01_model_parameters\",\n            \"z_02_api_key_name\",\n            \"z_03_provider_api_key\",\n            \"z_04_authentication\",\n        ]:\n            if not hasattr(self, attribute):\n                setattr(self, attribute, None)\n\n        # Fetch values from kwargs if any self.* attributes are None\n        provider_mapping = self.update_providers_mapping()\n        provider_value = provider_mapping.get(self.embedding_provider, [None])[0] or kwargs.get(\"embedding_provider\")\n        model_name = self.model or kwargs.get(\"model\")\n        authentication = {**(self.z_04_authentication or {}), **kwargs.get(\"z_04_authentication\", {})}\n        parameters = self.z_01_model_parameters or kwargs.get(\"z_01_model_parameters\", {})\n\n        # Set the API key name if provided\n        api_key_name = self.z_02_api_key_name or kwargs.get(\"z_02_api_key_name\")\n        provider_key = self.z_03_provider_api_key or kwargs.get(\"z_03_provider_api_key\")\n        if api_key_name:\n            authentication[\"providerKey\"] = api_key_name\n        if authentication:\n            provider_key = None\n            authentication[\"providerKey\"] = authentication[\"providerKey\"].split(\".\")[0]\n\n        # Set authentication and parameters to None if no values are provided\n        if not authentication:\n            authentication = None\n        if not parameters:\n            parameters = None\n\n        return {\n            # must match astrapy.info.CollectionVectorServiceOptions\n            \"collection_vector_service_options\": {\n                \"provider\": provider_value,\n                \"modelName\": model_name,\n                \"authentication\": authentication,\n                \"parameters\": parameters,\n            },\n            \"collection_embedding_api_key\": provider_key,\n        }\n\n    @check_cached_vector_store\n    def build_vector_store(self, vectorize_options=None):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError as e:\n            msg = (\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n            raise ImportError(msg) from e\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError as e:\n            msg = f\"Invalid setup mode: {self.setup_mode}\"\n            raise ValueError(msg) from e\n\n        metric_value = self.metric or None\n        autodetect = False\n\n        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding_model}\n        # Use autodetect if the collection name is NOT set to \"+ Create new collection\"\n        elif self.collection_name != \"+ Create new collection\":\n            autodetect = True\n            metric_value = None\n            setup_mode_value = None\n            embedding_dict = {}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Grab the collection options if available\n            collection_options = self.get_collection_options()\n\n            # Ensure collection_options and its nested attributes are handled safely\n            authentication = getattr(self, \"z_04_authentication\", {}) or (\n                collection_options.service.authentication\n                if collection_options and collection_options.service and collection_options.service.authentication\n                else {}\n            )\n\n            # Build the vectorize options dictionary\n            dict_options = vectorize_options or self.build_vectorize_options(\n                embedding_provider=(\n                    getattr(self, \"embedding_provider\", None)\n                    or (\n                        collection_options.service.provider\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                model=(\n                    getattr(self, \"model\", None)\n                    or (\n                        collection_options.service.model_name\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_01_model_parameters=(\n                    getattr(self, \"z_01_model_parameters\", None)\n                    or (\n                        collection_options.service.parameters\n                        if collection_options and collection_options.service\n                        else None\n                    )\n                ),\n                z_02_api_key_name=(\n                    getattr(self, \"z_02_api_key_name\", None)\n                    or (authentication.get(\"apiKey\") if authentication else None)\n                ),\n                z_03_provider_api_key=(\n                    getattr(self, \"z_03_provider_api_key\", None)\n                    or (authentication.get(\"providerKey\") if authentication else None)\n                ),\n                z_04_authentication=authentication,\n            )\n\n            # Set the embedding dictionary\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(\n                    dict_options.get(\"collection_vector_service_options\")\n                ),\n                \"collection_embedding_api_key\": dict_options.get(\"collection_embedding_api_key\"),\n            }\n\n        # Get Langflow version and platform information\n        __version__ = get_version_info()[\"version\"]\n        langflow_prefix = \"\"\n        if os.getenv(\"LANGFLOW_HOST\") is not None:\n            langflow_prefix = \"ds-\"\n\n        try:\n            vector_store = AstraDBVectorStore(\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.keyspace or None,\n                collection_name=self.get_collection_choice(),\n                autodetect_collection=autodetect,\n                environment=(\n                    parse_api_endpoint(getattr(self, \"api_endpoint\", None)).environment\n                    if getattr(self, \"api_endpoint\", None)\n                    else None\n                ),\n                metric=metric_value,\n                batch_size=self.batch_size or None,\n                bulk_insert_batch_concurrency=self.bulk_insert_batch_concurrency or None,\n                bulk_insert_overwrite_concurrency=self.bulk_insert_overwrite_concurrency or None,\n                bulk_delete_concurrency=self.bulk_delete_concurrency or None,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=self.pre_delete_collection,\n                metadata_indexing_include=[s for s in self.metadata_indexing_include if s] or None,\n                metadata_indexing_exclude=[s for s in self.metadata_indexing_exclude if s] or None,\n                collection_indexing_policy=orjson.dumps(self.collection_indexing_policy)\n                if self.collection_indexing_policy\n                else None,\n                ext_callers=[(f\"{langflow_prefix}langflow\", __version__)],\n                **embedding_dict,\n            )\n        except Exception as e:\n            msg = f\"Error initializing AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store) -> None:\n        documents = []\n        for _input in self.ingest_data or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                msg = \"Vector Store Inputs must be Data objects.\"\n                raise TypeError(msg)\n\n        if documents:\n            self.log(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                msg = f\"Error adding documents to AstraDBVectorStore: {e}\"\n                raise ValueError(msg) from e\n        else:\n            self.log(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self) -> str:\n        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        if self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        return \"similarity\"\n\n    def _build_search_args(self):\n        query = self.search_input if isinstance(self.search_input, str) and self.search_input.strip() else None\n        search_filter = (\n            {k: v for k, v in self.search_filter.items() if k and v and k.strip()} if self.search_filter else None\n        )\n\n        if query:\n            args = {\n                \"query\": query,\n                \"search_type\": self._map_search_type(),\n                \"k\": self.number_of_results,\n                \"score_threshold\": self.search_score_threshold,\n            }\n        elif self.advanced_search_filter or search_filter:\n            args = {\n                \"n\": self.number_of_results,\n            }\n        else:\n            return {}\n\n        filter_arg = self.advanced_search_filter or {}\n\n        if search_filter:\n            self.log(self.log(f\"`search_filter` is deprecated. Use `advanced_search_filter`. Cleaned: {search_filter}\"))\n            filter_arg.update(search_filter)\n\n        if filter_arg:\n            args[\"filter\"] = filter_arg\n\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        vector_store = vector_store or self.build_vector_store()\n\n        self.log(f\"Search input: {self.search_input}\")\n        self.log(f\"Search type: {self.search_type}\")\n        self.log(f\"Number of results: {self.number_of_results}\")\n\n        try:\n            search_args = self._build_search_args()\n        except Exception as e:\n            msg = f\"Error in AstraDBVectorStore._build_search_args: {e}\"\n            raise ValueError(msg) from e\n\n        if not search_args:\n            self.log(\"No search input or filters provided. Skipping search.\")\n            return []\n\n        docs = []\n        search_method = \"search\" if \"query\" in search_args else \"metadata_search\"\n\n        try:\n            self.log(f\"Calling vector_store.{search_method} with args: {search_args}\")\n            docs = getattr(vector_store, search_method)(**search_args)\n        except Exception as e:\n            msg = f\"Error performing {search_method} in AstraDBVectorStore: {e}\"\n            raise ValueError(msg) from e\n\n        self.log(f\"Retrieved documents: {len(docs)}\")\n\n        data = docs_to_data(docs)\n        self.log(f\"Converted documents to data: {len(data)}\")\n        self.status = data\n        return data\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n"
               },
               "collection_indexing_policy": {
                 "_input_type": "StrInput",