diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json
index a92c3b4e3..1a3b3938d 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Agent Flow.json	
@@ -1037,6 +1037,7 @@
                 "input_types": [],
                 "name": "agent_llm",
                 "options": [
+                  "Amazon Bedrock",
                   "Anthropic",
                   "Azure OpenAI",
                   "Groq",
@@ -1089,7 +1090,7 @@
                 "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 ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\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=\"Add tool 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 = self.get_llm()\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}\") 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                    inputs = provider_info.get(\"inputs\")\n                    prefix = provider_info.get(\"prefix\", \"\")\n                    return self._build_llm_model(component_class, inputs, prefix)\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\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        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\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\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\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\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 ALL_PROVIDER_FIELDS, MODEL_PROVIDERS_DICT\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.components.memories.memory import MemoryComponent\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=\"Add tool 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 = 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}\") 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 self._build_llm_model(component_class, inputs, prefix), display_name\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        if field_name == \"agent_llm\":\n            # Define provider configurations as (fields_to_add, fields_to_delete)\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\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\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\n        return build_config\n"
               },
               "handle_parsing_errors": {
                 "_input_type": "BoolInput",
@@ -1483,7 +1484,7 @@
                 "list": true,
                 "name": "tools",
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "trace_as_metadata": true,
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json
index 4fcd9013c..ccac3d66c 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Complex Agent.json	
@@ -683,7 +683,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from crewai import Crew, Process\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n    display_name: str = \"Hierarchical Crew\"\n    description: str = (\n        \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n    )\n    documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n    icon = \"CrewAI\"\n\n    inputs = [\n        *BaseCrewComponent._base_inputs,\n        HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n        HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n        HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n        HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n    ]\n\n    def build_crew(self) -> Crew:\n        tasks, agents = self.get_tasks_and_agents()\n        return Crew(\n            agents=agents,\n            tasks=tasks,\n            process=Process.hierarchical,\n            verbose=self.verbose,\n            memory=self.memory,\n            cache=self.use_cache,\n            max_rpm=self.max_rpm,\n            share_crew=self.share_crew,\n            function_calling_llm=self.function_calling_llm,\n            manager_agent=self.manager_agent,\n            manager_llm=self.manager_llm,\n            step_callback=self.get_step_callback(),\n            task_callback=self.get_task_callback(),\n        )\n"
+                "value": "import os\n\nfrom crewai import Crew, Process\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput, SecretStrInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n    display_name: str = \"Hierarchical Crew\"\n    description: str = (\n        \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n    )\n    documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n    icon = \"CrewAI\"\n\n    inputs = [\n        *BaseCrewComponent._base_inputs,\n        HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n        HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n        HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n        HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            value=\"OPENAI_API_KEY\",\n        ),\n    ]\n\n    def build_crew(self) -> Crew:\n        tasks, agents = self.get_tasks_and_agents()\n\n        # Set the OpenAI API Key\n        if self.openai_api_key:\n            os.environ[\"OPENAI_API_KEY\"] = self.openai_api_key\n\n        return Crew(\n            agents=agents,\n            tasks=tasks,\n            process=Process.hierarchical,\n            verbose=self.verbose,\n            memory=self.memory,\n            cache=self.use_cache,\n            max_rpm=self.max_rpm,\n            share_crew=self.share_crew,\n            function_calling_llm=self.function_calling_llm,\n            manager_agent=self.manager_agent,\n            manager_llm=self.manager_llm,\n            step_callback=self.get_step_callback(),\n            task_callback=self.get_task_callback(),\n        )\n"
               },
               "function_calling_llm": {
                 "advanced": true,
@@ -769,6 +769,25 @@
                 "type": "bool",
                 "value": false
               },
+              "openai_api_key": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "The OpenAI API Key to use for the OpenAI model.",
+                "input_types": [
+                  "Message"
+                ],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OPENAI_API_KEY"
+              },
               "share_crew": {
                 "advanced": true,
                 "display_name": "Share Crew",
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json b/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json
index c52228aa7..76c0f4187 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Hierarchical Agent.json	
@@ -380,7 +380,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from crewai import Crew, Process\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n    display_name: str = \"Hierarchical Crew\"\n    description: str = (\n        \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n    )\n    documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n    icon = \"CrewAI\"\n\n    inputs = [\n        *BaseCrewComponent._base_inputs,\n        HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n        HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n        HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n        HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n    ]\n\n    def build_crew(self) -> Crew:\n        tasks, agents = self.get_tasks_and_agents()\n        return Crew(\n            agents=agents,\n            tasks=tasks,\n            process=Process.hierarchical,\n            verbose=self.verbose,\n            memory=self.memory,\n            cache=self.use_cache,\n            max_rpm=self.max_rpm,\n            share_crew=self.share_crew,\n            function_calling_llm=self.function_calling_llm,\n            manager_agent=self.manager_agent,\n            manager_llm=self.manager_llm,\n            step_callback=self.get_step_callback(),\n            task_callback=self.get_task_callback(),\n        )\n"
+                "value": "import os\n\nfrom crewai import Crew, Process\n\nfrom langflow.base.agents.crewai.crew import BaseCrewComponent\nfrom langflow.io import HandleInput, SecretStrInput\n\n\nclass HierarchicalCrewComponent(BaseCrewComponent):\n    display_name: str = \"Hierarchical Crew\"\n    description: str = (\n        \"Represents a group of agents, defining how they should collaborate and the tasks they should perform.\"\n    )\n    documentation: str = \"https://docs.crewai.com/how-to/Hierarchical/\"\n    icon = \"CrewAI\"\n\n    inputs = [\n        *BaseCrewComponent._base_inputs,\n        HandleInput(name=\"agents\", display_name=\"Agents\", input_types=[\"Agent\"], is_list=True),\n        HandleInput(name=\"tasks\", display_name=\"Tasks\", input_types=[\"HierarchicalTask\"], is_list=True),\n        HandleInput(name=\"manager_llm\", display_name=\"Manager LLM\", input_types=[\"LanguageModel\"], required=False),\n        HandleInput(name=\"manager_agent\", display_name=\"Manager Agent\", input_types=[\"Agent\"], required=False),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            value=\"OPENAI_API_KEY\",\n        ),\n    ]\n\n    def build_crew(self) -> Crew:\n        tasks, agents = self.get_tasks_and_agents()\n\n        # Set the OpenAI API Key\n        if self.openai_api_key:\n            os.environ[\"OPENAI_API_KEY\"] = self.openai_api_key\n\n        return Crew(\n            agents=agents,\n            tasks=tasks,\n            process=Process.hierarchical,\n            verbose=self.verbose,\n            memory=self.memory,\n            cache=self.use_cache,\n            max_rpm=self.max_rpm,\n            share_crew=self.share_crew,\n            function_calling_llm=self.function_calling_llm,\n            manager_agent=self.manager_agent,\n            manager_llm=self.manager_llm,\n            step_callback=self.get_step_callback(),\n            task_callback=self.get_task_callback(),\n        )\n"
               },
               "function_calling_llm": {
                 "advanced": true,
@@ -466,6 +466,25 @@
                 "type": "bool",
                 "value": false
               },
+              "openai_api_key": {
+                "_input_type": "SecretStrInput",
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "The OpenAI API Key to use for the OpenAI model.",
+                "input_types": [
+                  "Message"
+                ],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "OPENAI_API_KEY"
+              },
               "share_crew": {
                 "advanced": true,
                 "display_name": "Share Crew",
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 24a0c74f2..2e7bfa93f 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	
@@ -718,7 +718,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\n\nimport orjson\nfrom astrapy.admin import parse_api_endpoint\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\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\n\n\nclass AstraVectorStoreComponent(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    VECTORIZE_PROVIDERS_MAPPING = {\n        \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\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    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        ),\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        ),\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n            is_list=True,\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"embedding_service\",\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\",\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            is_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            is_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        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        DictInput(\n            name=\"search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\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_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"embedding_service\":\n            if field_value == \"Astra Vectorize\":\n                for field in [\"embedding\"]:\n                    if field in build_config:\n                        del build_config[field]\n\n                new_parameter = DropdownInput(\n                    name=\"provider\",\n                    display_name=\"Vectorize Provider\",\n                    options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n            else:\n                for field in [\n                    \"provider\",\n                    \"z_00_model_name\",\n                    \"z_01_model_parameters\",\n                    \"z_02_api_key_name\",\n                    \"z_03_provider_api_key\",\n                    \"z_04_authentication\",\n                ]:\n                    if field in build_config:\n                        del build_config[field]\n\n                new_parameter = HandleInput(\n                    name=\"embedding\",\n                    display_name=\"Embedding Model\",\n                    input_types=[\"Embeddings\"],\n                    info=\"Allows an embedding model configuration.\",\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n        elif field_name == \"provider\":\n            for field in [\n                \"z_00_model_name\",\n                \"z_01_model_parameters\",\n                \"z_02_api_key_name\",\n                \"z_03_provider_api_key\",\n                \"z_04_authentication\",\n            ]:\n                if field in build_config:\n                    del build_config[field]\n\n            model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n            new_parameter_0 = DropdownInput(\n                name=\"z_00_model_name\",\n                display_name=\"Model Name\",\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                placeholder=\"Select a model\",\n                value=model_options[0],\n                required=True,\n            ).to_dict()\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                is_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                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                is_list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"provider\",\n                {\n                    \"z_00_model_name\": new_parameter_0,\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            \"provider\",\n            \"z_00_model_name\",\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_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n        model_name = self.z_00_model_name or kwargs.get(\"z_00_model_name\")\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\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        if self.embedding_service == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Fetch values from kwargs if any self.* attributes are None\n            dict_options = vectorize_options or self.build_vectorize_options()\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        try:\n            vector_store = AstraDBVectorStore(\n                collection_name=self.collection_name,\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.namespace or None,\n                environment=parse_api_endpoint(self.api_endpoint).environment if self.api_endpoint else None,\n                metric=self.metric or None,\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                **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        args = {\n            \"k\": self.number_of_results,\n            \"score_threshold\": self.search_score_threshold,\n        }\n\n        if self.search_filter:\n            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n            if len(clean_filter) > 0:\n                args[\"filter\"] = clean_filter\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        if not vector_store:\n            vector_store = 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        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                search_type = self._map_search_type()\n                search_args = self._build_search_args()\n\n                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n            except Exception as e:\n                msg = f\"Error performing search 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        self.log(\"No search input provided. Skipping search.\")\n        return []\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\n\nimport orjson\nfrom astrapy.admin import parse_api_endpoint\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\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\n\n\nclass AstraVectorStoreComponent(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    VECTORIZE_PROVIDERS_MAPPING = {\n        \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\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    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        ),\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        ),\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n            is_list=True,\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\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\",\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            is_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            is_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        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        DictInput(\n            name=\"search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            is_list=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_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                self.del_fields(build_config, [\"embedding\"])\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=self.VECTORIZE_PROVIDERS_MAPPING.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                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                new_parameter = HandleInput(\n                    name=\"embedding\",\n                    display_name=\"Embedding Model\",\n                    input_types=[\"Embeddings\"],\n                    info=\"Allows an embedding model configuration.\",\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding\": new_parameter})\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            model_options = self.VECTORIZE_PROVIDERS_MAPPING[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                is_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                is_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_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.embedding_provider, [None])[0] or kwargs.get(\n            \"embedding_provider\"\n        )\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\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        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Fetch values from kwargs if any self.* attributes are None\n            dict_options = vectorize_options or self.build_vectorize_options()\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        try:\n            vector_store = AstraDBVectorStore(\n                collection_name=self.collection_name,\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.namespace or None,\n                environment=parse_api_endpoint(self.api_endpoint).environment if self.api_endpoint else None,\n                metric=self.metric or None,\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                **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        args = {\n            \"k\": self.number_of_results,\n            \"score_threshold\": self.search_score_threshold,\n        }\n\n        if self.search_filter:\n            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n            if len(clean_filter) > 0:\n                args[\"filter\"] = clean_filter\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        if not vector_store:\n            vector_store = 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        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                search_type = self._map_search_type()\n                search_args = self._build_search_args()\n\n                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n            except Exception as e:\n                msg = f\"Error performing search 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        self.log(\"No search input provided. Skipping search.\")\n        return []\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": {
                 "advanced": true,
@@ -770,14 +770,14 @@
                 "type": "other",
                 "value": ""
               },
-              "embedding_service": {
+              "embedding_choice": {
                 "_input_type": "DropdownInput",
                 "advanced": false,
                 "combobox": false,
                 "display_name": "Embedding Model or Astra Vectorize",
                 "dynamic": false,
                 "info": "Determines whether to use Astra Vectorize for the collection.",
-                "name": "embedding_service",
+                "name": "embedding_choice",
                 "options": [
                   "Embedding Model",
                   "Astra Vectorize"
@@ -787,6 +787,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
                 "value": "Embedding Model"
@@ -2054,7 +2055,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "import os\n\nimport orjson\nfrom astrapy.admin import parse_api_endpoint\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\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\n\n\nclass AstraVectorStoreComponent(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    VECTORIZE_PROVIDERS_MAPPING = {\n        \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\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    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        ),\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        ),\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n            is_list=True,\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"embedding_service\",\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\",\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            is_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            is_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        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        DictInput(\n            name=\"search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\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_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"embedding_service\":\n            if field_value == \"Astra Vectorize\":\n                for field in [\"embedding\"]:\n                    if field in build_config:\n                        del build_config[field]\n\n                new_parameter = DropdownInput(\n                    name=\"provider\",\n                    display_name=\"Vectorize Provider\",\n                    options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),\n                    value=\"\",\n                    required=True,\n                    real_time_refresh=True,\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_service\", {\"provider\": new_parameter})\n            else:\n                for field in [\n                    \"provider\",\n                    \"z_00_model_name\",\n                    \"z_01_model_parameters\",\n                    \"z_02_api_key_name\",\n                    \"z_03_provider_api_key\",\n                    \"z_04_authentication\",\n                ]:\n                    if field in build_config:\n                        del build_config[field]\n\n                new_parameter = HandleInput(\n                    name=\"embedding\",\n                    display_name=\"Embedding Model\",\n                    input_types=[\"Embeddings\"],\n                    info=\"Allows an embedding model configuration.\",\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_service\", {\"embedding\": new_parameter})\n\n        elif field_name == \"provider\":\n            for field in [\n                \"z_00_model_name\",\n                \"z_01_model_parameters\",\n                \"z_02_api_key_name\",\n                \"z_03_provider_api_key\",\n                \"z_04_authentication\",\n            ]:\n                if field in build_config:\n                    del build_config[field]\n\n            model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]\n\n            new_parameter_0 = DropdownInput(\n                name=\"z_00_model_name\",\n                display_name=\"Model Name\",\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                placeholder=\"Select a model\",\n                value=model_options[0],\n                required=True,\n            ).to_dict()\n\n            new_parameter_1 = DictInput(\n                name=\"z_01_model_parameters\",\n                display_name=\"Model Parameters\",\n                is_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                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                is_list=True,\n            ).to_dict()\n\n            self.insert_in_dict(\n                build_config,\n                \"provider\",\n                {\n                    \"z_00_model_name\": new_parameter_0,\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            \"provider\",\n            \"z_00_model_name\",\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_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.provider, [None])[0] or kwargs.get(\"provider\")\n        model_name = self.z_00_model_name or kwargs.get(\"z_00_model_name\")\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\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        if self.embedding_service == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Fetch values from kwargs if any self.* attributes are None\n            dict_options = vectorize_options or self.build_vectorize_options()\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        try:\n            vector_store = AstraDBVectorStore(\n                collection_name=self.collection_name,\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.namespace or None,\n                environment=parse_api_endpoint(self.api_endpoint).environment if self.api_endpoint else None,\n                metric=self.metric or None,\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                **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        args = {\n            \"k\": self.number_of_results,\n            \"score_threshold\": self.search_score_threshold,\n        }\n\n        if self.search_filter:\n            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n            if len(clean_filter) > 0:\n                args[\"filter\"] = clean_filter\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        if not vector_store:\n            vector_store = 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        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                search_type = self._map_search_type()\n                search_args = self._build_search_args()\n\n                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n            except Exception as e:\n                msg = f\"Error performing search 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        self.log(\"No search input provided. Skipping search.\")\n        return []\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\n\nimport orjson\nfrom astrapy.admin import parse_api_endpoint\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\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\n\n\nclass AstraVectorStoreComponent(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    VECTORIZE_PROVIDERS_MAPPING = {\n        \"Azure OpenAI\": [\"azureOpenAI\", [\"text-embedding-3-small\", \"text-embedding-3-large\", \"text-embedding-ada-002\"]],\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    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        ),\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        ),\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n            is_list=True,\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\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\",\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            is_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            is_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        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        DictInput(\n            name=\"search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            is_list=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_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):\n        if field_name == \"embedding_choice\":\n            if field_value == \"Astra Vectorize\":\n                self.del_fields(build_config, [\"embedding\"])\n\n                new_parameter = DropdownInput(\n                    name=\"embedding_provider\",\n                    display_name=\"Embedding Provider\",\n                    options=self.VECTORIZE_PROVIDERS_MAPPING.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                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                new_parameter = HandleInput(\n                    name=\"embedding\",\n                    display_name=\"Embedding Model\",\n                    input_types=[\"Embeddings\"],\n                    info=\"Allows an embedding model configuration.\",\n                ).to_dict()\n\n                self.insert_in_dict(build_config, \"embedding_choice\", {\"embedding\": new_parameter})\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            model_options = self.VECTORIZE_PROVIDERS_MAPPING[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                is_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                is_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_value = self.VECTORIZE_PROVIDERS_MAPPING.get(self.embedding_provider, [None])[0] or kwargs.get(\n            \"embedding_provider\"\n        )\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\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        if self.embedding_choice == \"Embedding Model\":\n            embedding_dict = {\"embedding\": self.embedding}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            # Fetch values from kwargs if any self.* attributes are None\n            dict_options = vectorize_options or self.build_vectorize_options()\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        try:\n            vector_store = AstraDBVectorStore(\n                collection_name=self.collection_name,\n                token=self.token,\n                api_endpoint=self.api_endpoint,\n                namespace=self.namespace or None,\n                environment=parse_api_endpoint(self.api_endpoint).environment if self.api_endpoint else None,\n                metric=self.metric or None,\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                **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        args = {\n            \"k\": self.number_of_results,\n            \"score_threshold\": self.search_score_threshold,\n        }\n\n        if self.search_filter:\n            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n            if len(clean_filter) > 0:\n                args[\"filter\"] = clean_filter\n        return args\n\n    def search_documents(self, vector_store=None) -> list[Data]:\n        if not vector_store:\n            vector_store = 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        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                search_type = self._map_search_type()\n                search_args = self._build_search_args()\n\n                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n            except Exception as e:\n                msg = f\"Error performing search 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        self.log(\"No search input provided. Skipping search.\")\n        return []\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": {
                 "advanced": true,
@@ -2106,14 +2107,14 @@
                 "type": "other",
                 "value": ""
               },
-              "embedding_service": {
+              "embedding_choice": {
                 "_input_type": "DropdownInput",
                 "advanced": false,
                 "combobox": false,
                 "display_name": "Embedding Model or Astra Vectorize",
                 "dynamic": false,
                 "info": "Determines whether to use Astra Vectorize for the collection.",
-                "name": "embedding_service",
+                "name": "embedding_choice",
                 "options": [
                   "Embedding Model",
                   "Astra Vectorize"
@@ -2123,6 +2124,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
+                "tool_mode": false,
                 "trace_as_metadata": true,
                 "type": "str",
                 "value": "Embedding Model"
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index 19c5693dc..dd64d3d24 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -835,7 +835,6 @@
     },
     "node_modules/@clack/prompts/node_modules/is-unicode-supported": {
       "version": "1.3.0",
-      "extraneous": true,
       "inBundle": true,
       "license": "MIT",
       "engines": {
diff --git a/src/frontend/src/components/folderSidebarComponent/components/sideBarFolderButtons/index.tsx b/src/frontend/src/components/folderSidebarComponent/components/sideBarFolderButtons/index.tsx
index f1ba93a77..f7031f8bc 100644
--- a/src/frontend/src/components/folderSidebarComponent/components/sideBarFolderButtons/index.tsx
+++ b/src/frontend/src/components/folderSidebarComponent/components/sideBarFolderButtons/index.tsx
@@ -70,8 +70,6 @@ const SideBarFoldersButtonsComponent = ({
   const urlWithoutPath =
     pathname.split("/").length < (ENABLE_CUSTOM_PARAM ? 5 : 4);
   const myCollectionId = useFolderStore((state) => state.myCollectionId);
-  const folderIdDragging = useFolderStore((state) => state.folderIdDragging);
-
   const checkPathName = (itemId: string) => {
     if (urlWithoutPath && itemId === myCollectionId) {
       return true;
diff --git a/src/frontend/src/components/folderSidebarComponent/index.tsx b/src/frontend/src/components/folderSidebarComponent/index.tsx
index 51feb3196..e975c96be 100644
--- a/src/frontend/src/components/folderSidebarComponent/index.tsx
+++ b/src/frontend/src/components/folderSidebarComponent/index.tsx
@@ -1,5 +1,3 @@
-import { useFolderStore } from "@/stores/foldersStore";
-import { useIsFetching } from "@tanstack/react-query";
 import { useLocation } from "react-router-dom";
 import { FolderType } from "../../pages/MainPage/entities";
 import SideBarFoldersButtonsComponent from "./components/sideBarFolderButtons";
@@ -17,13 +15,6 @@ export default function FolderSidebarNav({
   ...props
 }: SidebarNavProps) {
   const location = useLocation();
-  const pathname = location.pathname;
-  const folders = useFolderStore((state) => state.folders);
-
-  const isPending = !!useIsFetching({
-    queryKey: ["useGetFolders"],
-    exact: false,
-  });
 
   return (
     <SideBarFoldersButtonsComponent
diff --git a/src/frontend/src/components/inputComponent/components/popover/index.tsx b/src/frontend/src/components/inputComponent/components/popover/index.tsx
index 9f3834a6b..ca1aa1cb6 100644
--- a/src/frontend/src/components/inputComponent/components/popover/index.tsx
+++ b/src/frontend/src/components/inputComponent/components/popover/index.tsx
@@ -68,7 +68,7 @@ const CustomInputPopover = ({
         <div
           data-testid={`anchor-${id}`}
           className={cn(
-            "primary-input border-1 flex h-full min-h-[2.375rem] flex-wrap items-center px-3",
+            "primary-input noflow nowheel nopan nodelete nodrag border-1 flex h-full min-h-[2.375rem] cursor-default flex-wrap items-center px-3",
             editNode && "min-h-7 p-0",
             editNode && disabled && "min-h-5 border-muted p-0",
             disabled && "bg-muted text-muted",
diff --git a/src/frontend/src/components/pageLayout/index.tsx b/src/frontend/src/components/pageLayout/index.tsx
index 72bde250e..5e9152de2 100644
--- a/src/frontend/src/components/pageLayout/index.tsx
+++ b/src/frontend/src/components/pageLayout/index.tsx
@@ -22,9 +22,9 @@ export default function PageLayout({
   const navigate = useCustomNavigate();
 
   return (
-    <div className="flex w-full flex-1 flex-col justify-between overflow-auto bg-background px-6 pt-10">
+    <div className="flex w-full flex-1 flex-col justify-between overflow-auto overflow-x-hidden bg-background">
       <div className="mx-auto flex w-full max-w-[1440px] flex-1 flex-col">
-        <div className="flex flex-col gap-4">
+        <div className="flex flex-col gap-4 p-6 pt-10">
           <CustomBanner />
           <div className="flex w-full items-center justify-between gap-4 space-y-0.5 py-2">
             <div className="flex w-full flex-col">
@@ -55,8 +55,10 @@ export default function PageLayout({
             <div className="flex-shrink-0">{button && button}</div>
           </div>
         </div>
-        <Separator className="my-6 flex" />
-        {children}
+        <div className="flex shrink-0 px-6">
+          <Separator className="flex" />
+        </div>
+        <div className="flex flex-1 p-6 pt-7">{children}</div>
       </div>
     </div>
   );
diff --git a/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx b/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx
deleted file mode 100644
index 128d80fa7..000000000
--- a/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx
+++ /dev/null
@@ -1,43 +0,0 @@
-import { CustomLink } from "@/customization/components/custom-link";
-import { cn } from "../../../../utils/utils";
-import { buttonVariants } from "../../../ui/button";
-
-type SideBarButtonsComponentProps = {
-  items: {
-    href?: string;
-    title: string;
-    icon: React.ReactNode;
-  }[];
-  pathname: string;
-  handleOpenNewFolderModal?: () => void;
-};
-const SideBarButtonsComponent = ({
-  items,
-  pathname,
-}: SideBarButtonsComponentProps) => {
-  return (
-    <div className="flex gap-2 overflow-auto lg:h-[70vh] lg:flex-col">
-      {items.map((item, index) => (
-        <CustomLink key={index} to={item.href!}>
-          <div
-            key={item.title}
-            data-testid={`sidebar-nav-${item.title}`}
-            className={cn(
-              buttonVariants({ variant: "ghost" }),
-              item.href && pathname.endsWith(item.href)
-                ? "border border-border bg-muted hover:bg-muted"
-                : "border border-transparent hover:border-border hover:bg-transparent",
-              "flex w-full shrink-0 justify-start gap-4",
-            )}
-          >
-            {item.icon}
-            <span className="block max-w-full truncate opacity-100">
-              {item.title}
-            </span>
-          </div>
-        </CustomLink>
-      ))}
-    </div>
-  );
-};
-export default SideBarButtonsComponent;
diff --git a/src/frontend/src/components/sidebarComponent/index.tsx b/src/frontend/src/components/sidebarComponent/index.tsx
index 7b53aa1da..542c6b2f4 100644
--- a/src/frontend/src/components/sidebarComponent/index.tsx
+++ b/src/frontend/src/components/sidebarComponent/index.tsx
@@ -1,30 +1,62 @@
+import { CustomLink } from "@/customization/components/custom-link";
+import { useIsMobile } from "@/hooks/use-mobile";
 import { useLocation } from "react-router-dom";
-import { cn } from "../../utils/utils";
-import HorizontalScrollFadeComponent from "../horizontalScrollFadeComponent";
-import SideBarButtonsComponent from "./components/sideBarButtons";
+import {
+  Sidebar,
+  SidebarContent,
+  SidebarGroup,
+  SidebarGroupContent,
+  SidebarMenu,
+  SidebarMenuButton,
+  SidebarMenuItem,
+} from "../ui/sidebar";
 
-type SidebarNavProps = {
+type SideBarButtonsComponentProps = {
   items: {
     href?: string;
     title: string;
     icon: React.ReactNode;
   }[];
-  className?: string;
+  handleOpenNewFolderModal?: () => void;
 };
 
-export default function SidebarNav({
-  className,
-  items,
-  ...props
-}: SidebarNavProps) {
+const SideBarButtonsComponent = ({ items }: SideBarButtonsComponentProps) => {
   const location = useLocation();
   const pathname = location.pathname;
 
+  const isMobile = useIsMobile();
+
   return (
-    <nav className={cn(className)} {...props}>
-      <HorizontalScrollFadeComponent>
-        <SideBarButtonsComponent items={items} pathname={pathname} />
-      </HorizontalScrollFadeComponent>
-    </nav>
+    <Sidebar collapsible={isMobile ? "icon" : "none"} className="border-none">
+      <SidebarContent className="pr-6">
+        <SidebarGroup>
+          <SidebarGroupContent>
+            <SidebarMenu>
+              {items.map((item, index) => (
+                <SidebarMenuItem key={index}>
+                  <CustomLink to={item.href!}>
+                    <SidebarMenuButton
+                      size="md"
+                      isActive={
+                        item.href ? pathname.endsWith(item.href) : false
+                      }
+                      data-testid={`sidebar-nav-${item.title}`}
+                      tooltip={item.title}
+                    >
+                      {item.icon}
+                      <span className="block max-w-full truncate">
+                        {item.title}
+                      </span>
+                    </SidebarMenuButton>
+                  </CustomLink>
+                </SidebarMenuItem>
+              ))}
+            </SidebarMenu>
+          </SidebarGroupContent>
+        </SidebarGroup>
+      </SidebarContent>
+    </Sidebar>
   );
-}
+};
+
+export default SideBarButtonsComponent;
diff --git a/src/frontend/src/components/ui/sidebar.tsx b/src/frontend/src/components/ui/sidebar.tsx
index 4330c6583..cace7499a 100644
--- a/src/frontend/src/components/ui/sidebar.tsx
+++ b/src/frontend/src/components/ui/sidebar.tsx
@@ -28,6 +28,7 @@ type SidebarContext = {
   open: boolean;
   setOpen: (open: boolean) => void;
   toggleSidebar: () => void;
+  defaultOpen: boolean;
 };
 
 const SidebarContext = React.createContext<SidebarContext | null>(null);
@@ -114,8 +115,9 @@ const SidebarProvider = React.forwardRef<
         open,
         setOpen,
         toggleSidebar,
+        defaultOpen,
       }),
-      [state, open, setOpen, toggleSidebar],
+      [state, open, setOpen, toggleSidebar, defaultOpen],
     );
 
     return (
@@ -164,29 +166,30 @@ const Sidebar = React.forwardRef<
     },
     ref,
   ) => {
-    const { state, setOpen } = useSidebar();
+    const { state, setOpen, defaultOpen } = useSidebar();
 
     React.useEffect(() => {
       if (collapsible === "none") {
-        console.log("collapsible === none");
         setOpen(true);
+      } else {
+        setOpen(defaultOpen);
       }
     }, [collapsible]);
 
     if (collapsible === "none") {
       return (
         <div
-          className={cn(
-            "group flex h-full w-[--sidebar-width] flex-col bg-background text-foreground",
-            className,
-          )}
+          className={cn("group flex h-full flex-col")}
           data-side={side}
           ref={ref}
           {...props}
         >
           <div
             data-sidebar="sidebar"
-            className="flex h-full w-full flex-col group-data-[side=left]:border-r group-data-[side=right]:border-l"
+            className={cn(
+              "group flex h-full w-[--sidebar-width] flex-col bg-background text-foreground group-data-[side=left]:border-r group-data-[side=right]:border-l",
+              className,
+            )}
           >
             {children}
           </div>
diff --git a/src/frontend/src/pages/MainPage/oldPages/mainPage/index.tsx b/src/frontend/src/pages/MainPage/oldPages/mainPage/index.tsx
index 589d75dfa..54d1bea81 100644
--- a/src/frontend/src/pages/MainPage/oldPages/mainPage/index.tsx
+++ b/src/frontend/src/pages/MainPage/oldPages/mainPage/index.tsx
@@ -1,4 +1,5 @@
 import FolderSidebarNav from "@/components/folderSidebarComponent";
+import { SidebarProvider } from "@/components/ui/sidebar";
 import { useDeleteFolders } from "@/controllers/API/queries/folders";
 import { useCustomNavigate } from "@/customization/hooks/use-custom-navigate";
 import { track } from "@/customization/utils/analytics";
@@ -80,41 +81,43 @@ export default function OldHomePage(): JSX.Element {
   return (
     <>
       <div className="flex h-full w-full space-y-8 md:flex-col lg:flex-row lg:space-y-0">
-        <aside className="hidden h-full w-fit flex-col space-y-6 border-r px-4 lg:flex">
-          <FolderSidebarNav
-            handleChangeFolder={(id: string) => {
-              navigate(`all/folder/${id}`);
-            }}
-            handleDeleteFolder={(item) => {
-              setFolderToEdit(item);
-              setOpenDeleteFolderModal(true);
-            }}
-            className="w-[20vw] max-w-[288px]"
-          />
-        </aside>
-        <PageLayout
-          title={USER_PROJECTS_HEADER}
-          description={MY_COLLECTION_DESC}
-          button={
-            <div className="flex gap-2">
-              <DropdownButton
-                firstButtonName="New Project"
-                onFirstBtnClick={() => {
-                  setOpenModal(true);
-                  track("New Project Button Clicked");
-                }}
-                options={dropdownOptions}
-                plusButton={true}
-                dropdownOptions={false}
-                isFetchingFolders={isLoadingFolder}
-              />
+        <SidebarProvider>
+          <aside className="hidden h-full w-fit flex-col space-y-6 px-4 lg:flex">
+            <FolderSidebarNav
+              handleChangeFolder={(id: string) => {
+                navigate(`all/folder/${id}`);
+              }}
+              handleDeleteFolder={(item) => {
+                setFolderToEdit(item);
+                setOpenDeleteFolderModal(true);
+              }}
+              className="w-[20vw] max-w-[288px]"
+            />
+          </aside>
+          <PageLayout
+            title={USER_PROJECTS_HEADER}
+            description={MY_COLLECTION_DESC}
+            button={
+              <div className="flex gap-2">
+                <DropdownButton
+                  firstButtonName="New Project"
+                  onFirstBtnClick={() => {
+                    setOpenModal(true);
+                    track("New Project Button Clicked");
+                  }}
+                  options={dropdownOptions}
+                  plusButton={true}
+                  dropdownOptions={false}
+                  isFetchingFolders={isLoadingFolder}
+                />
+              </div>
+            }
+          >
+            <div className="relative h-full w-full flex-1">
+              <Outlet />
             </div>
-          }
-        >
-          <div className="relative h-full w-full flex-1">
-            <Outlet />
-          </div>
-        </PageLayout>
+          </PageLayout>
+        </SidebarProvider>
       </div>
       <ModalsComponent
         openModal={openModal}
diff --git a/src/frontend/src/pages/MainPage/pages/homePage/index.tsx b/src/frontend/src/pages/MainPage/pages/homePage/index.tsx
index 4d54351b4..e0404705e 100644
--- a/src/frontend/src/pages/MainPage/pages/homePage/index.tsx
+++ b/src/frontend/src/pages/MainPage/pages/homePage/index.tsx
@@ -86,94 +86,116 @@ const HomePage = ({ type }) => {
         className="flex h-full w-full flex-col overflow-y-auto"
         data-testid="cards-wrapper"
       >
-        {ENABLE_DATASTAX_LANGFLOW && <CustomBanner />}
-
-        {/* mt-10 to mt-8 for Datastax LF */}
-        <div className="flex flex-1 flex-col justify-start px-5 pt-10">
-          <div className="flex flex-col justify-start xl:container">
-            <HeaderComponent
-              folderName={folderName}
-              flowType={flowType}
-              setFlowType={setFlowType}
-              view={view}
-              setView={setView}
-              setNewProjectModal={setNewProjectModal}
-              setSearch={onSearch}
-              isEmptyFolder={isEmptyFolder}
-            />
-            {isEmptyFolder ? (
-              <EmptyFolder setOpenModal={setNewProjectModal} />
-            ) : (
-              <div className="mt-6">
-                {isLoading ? (
-                  view === "grid" ? (
-                    <div className="mt-1 grid grid-cols-1 gap-3 md:grid-cols-2 lg:grid-cols-3">
-                      <GridSkeleton />
-                      <GridSkeleton />
-                    </div>
-                  ) : (
-                    <div className="flex flex-col">
-                      <ListSkeleton />
-                      <ListSkeleton />
-                    </div>
-                  )
-                ) : data && data.pagination.total > 0 ? (
-                  view === "grid" ? (
-                    <div className="mt-1 grid grid-cols-1 gap-3 md:grid-cols-2 lg:grid-cols-3">
-                      {data.flows.map((flow) => (
-                        <GridComponent key={flow.id} flowData={flow} />
-                      ))}
-                    </div>
-                  ) : (
-                    <div className="flex flex-col">
-                      {data.flows.map((flow) => (
-                        <ListComponent key={flow.id} flowData={flow} />
-                      ))}
-                    </div>
-                  )
-                ) : flowType === "flows" ? (
-                  <div className="pt-2 text-center text-sm text-secondary-foreground">
-                    No flows in this folder.{" "}
-                    <a
-                      onClick={() => setNewProjectModal(true)}
-                      className="cursor-pointer underline"
-                    >
-                      Create a new flow
-                    </a>
-                    , or browse the store.
-                  </div>
-                ) : (
-                  <div className="pt-2 text-center text-sm text-secondary-foreground">
-                    No saved or custom components. Learn more about{" "}
-                    <a
-                      href="https://docs.langflow.org/components-custom-components"
-                      target="_blank"
-                      rel="noreferrer"
-                      className="underline"
-                    >
-                      creating custom components
-                    </a>
-                    , or browse the store.
-                  </div>
-                )}
-              </div>
-            )}
+        <div className="flex h-full w-full flex-col xl:container">
+          {/* TODO: Move to Datastax LF and update Icon */}
+          {/* <div className="mx-4 mt-10 flex flex-row items-center rounded-lg border border-purple-300 bg-purple-50 p-4 dark:border-purple-700 dark:bg-purple-950">
+          <ForwardedIconComponent
+            name="info"
+            className="mr-4 h-5 w-5 text-purple-500 dark:text-purple-400"
+          />
+          <div className="text-sm">
+            DataStax Langflow is in public preview and is not suitable for
+            production. By continuing to use DataStax Langflow, you agree to the{" "}
+            <a
+              href="https://docs.shortlang.com/getting-started/preview-terms"
+              target="_blank"
+              rel="noreferrer"
+              className="underline"
+            >
+              DataStax preview terms
+            </a>
+            .
           </div>
+        </div> */}
+          {ENABLE_DATASTAX_LANGFLOW && <CustomBanner />}
+
+          {/* mt-10 to mt-8 for Datastax LF */}
+          <div className="flex flex-1 flex-col justify-start px-5 pt-10">
+            <div className="flex flex-col justify-start">
+              <HeaderComponent
+                folderName={folderName}
+                flowType={flowType}
+                setFlowType={setFlowType}
+                view={view}
+                setView={setView}
+                setNewProjectModal={setNewProjectModal}
+                setSearch={onSearch}
+                isEmptyFolder={isEmptyFolder}
+              />
+              {isEmptyFolder ? (
+                <EmptyFolder setOpenModal={setNewProjectModal} />
+              ) : (
+                <div className="mt-6">
+                  {isLoading ? (
+                    view === "grid" ? (
+                      <div className="mt-1 grid grid-cols-1 gap-3 md:grid-cols-2 lg:grid-cols-3">
+                        <GridSkeleton />
+                        <GridSkeleton />
+                      </div>
+                    ) : (
+                      <div className="flex flex-col">
+                        <ListSkeleton />
+                        <ListSkeleton />
+                      </div>
+                    )
+                  ) : data && data.pagination.total > 0 ? (
+                    view === "grid" ? (
+                      <div className="mt-1 grid grid-cols-1 gap-3 md:grid-cols-2 lg:grid-cols-3">
+                        {data.flows.map((flow) => (
+                          <GridComponent key={flow.id} flowData={flow} />
+                        ))}
+                      </div>
+                    ) : (
+                      <div className="flex flex-col">
+                        {data.flows.map((flow) => (
+                          <ListComponent key={flow.id} flowData={flow} />
+                        ))}
+                      </div>
+                    )
+                  ) : flowType === "flows" ? (
+                    <div className="pt-2 text-center text-sm text-secondary-foreground">
+                      No flows in this folder.{" "}
+                      <a
+                        onClick={() => setNewProjectModal(true)}
+                        className="cursor-pointer underline"
+                      >
+                        Create a new flow
+                      </a>
+                      , or browse the store.
+                    </div>
+                  ) : (
+                    <div className="pt-2 text-center text-sm text-secondary-foreground">
+                      No saved or custom components. Learn more about{" "}
+                      <a
+                        href="https://docs.langflow.org/components-custom-components"
+                        target="_blank"
+                        rel="noreferrer"
+                        className="underline"
+                      >
+                        creating custom components
+                      </a>
+                      , or browse the store.
+                    </div>
+                  )}
+                </div>
+              )}
+            </div>
+          </div>
+
+          {!isLoading && !isEmptyFolder && data.pagination.total >= 10 && (
+            <div className="flex justify-end px-3 py-4">
+              <PaginatorComponent
+                pageIndex={data.pagination.page}
+                pageSize={data.pagination.size}
+                rowsCount={[10, 20, 50, 100]}
+                totalRowsCount={data.pagination.total}
+                paginate={handlePageChange}
+                pages={data.pagination.pages}
+                isComponent={flowType === "components"}
+              />
+            </div>
+          )}
         </div>
-
-        {!isLoading && !isEmptyFolder && data.pagination.total >= 10 && (
-          <div className="flex justify-end px-3 py-4">
-            <PaginatorComponent
-              pageIndex={data.pagination.page}
-              pageSize={data.pagination.size}
-              rowsCount={[10, 20, 50, 100]}
-              totalRowsCount={data.pagination.total}
-              paginate={handlePageChange}
-              pages={data.pagination.pages}
-              isComponent={flowType === "components"}
-            />
-          </div>
-        )}
       </div>
 
       <ModalsComponent
diff --git a/src/frontend/src/pages/SettingsPage/index.tsx b/src/frontend/src/pages/SettingsPage/index.tsx
index 294562e9d..9b5e6cb0a 100644
--- a/src/frontend/src/pages/SettingsPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/index.tsx
@@ -1,10 +1,11 @@
+import SideBarButtonsComponent from "@/components/sidebarComponent";
+import { SidebarProvider } from "@/components/ui/sidebar";
 import { ENABLE_PROFILE_ICONS } from "@/customization/feature-flags";
 import useAuthStore from "@/stores/authStore";
 import { useStoreStore } from "@/stores/storeStore";
 import { Outlet } from "react-router-dom";
 import ForwardedIconComponent from "../../components/genericIconComponent";
 import PageLayout from "../../components/pageLayout";
-import SidebarNav from "../../components/sidebarComponent";
 
 export default function SettingsPage(): JSX.Element {
   const autoLogin = useAuthStore((state) => state.autoLogin);
@@ -80,16 +81,14 @@ export default function SettingsPage(): JSX.Element {
       title="Settings"
       description="Manage the general settings for Langflow."
     >
-      <div className="flex w-full flex-1 space-y-8 overflow-hidden lg:flex-row lg:space-x-8 lg:space-y-0">
-        <aside className="flex h-full shrink-0 flex-col space-y-6 lg:w-[20vw]">
-          <SidebarNav items={sidebarNavItems} />
-        </aside>
-        <div className="flex h-full w-full flex-1 flex-col">
-          <div className="flex-1 pb-8">
+      <SidebarProvider width="15rem" defaultOpen={false}>
+        <SideBarButtonsComponent items={sidebarNavItems} />
+        <main className="flex flex-1 overflow-hidden">
+          <div className="flex flex-1 flex-col overflow-x-hidden pt-1">
             <Outlet />
           </div>
-        </div>
-      </div>
+        </main>
+      </SidebarProvider>
     </PageLayout>
   );
 }
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
index e5cbe5bef..0730be5b2 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
@@ -142,10 +142,10 @@ export const GeneralPage = () => {
   }
 
   return (
-    <div className="flex h-full w-full flex-col gap-6">
+    <div className="flex h-full w-full flex-col gap-6 overflow-x-hidden">
       <GeneralPageHeaderComponent />
 
-      <div className="grid gap-6">
+      <div className="flex w-full flex-col gap-6">
         {ENABLE_PROFILE_ICONS && (
           <ProfilePictureFormComponent
             profilePicture={profilePicture}