diff --git a/src/backend/base/langflow/components/needle/needle.py b/src/backend/base/langflow/components/needle/needle.py
index 11a6037c9..4fdba34a6 100644
--- a/src/backend/base/langflow/components/needle/needle.py
+++ b/src/backend/base/langflow/components/needle/needle.py
@@ -1,16 +1,14 @@
-from langchain.chains import ConversationalRetrievalChain
from langchain_community.retrievers.needle import NeedleRetriever
-from langchain_openai import ChatOpenAI
from langflow.custom.custom_component.component import Component
-from langflow.io import DropdownInput, Output, SecretStrInput, StrInput
+from langflow.io import IntInput, MessageTextInput, Output, SecretStrInput
from langflow.schema.message import Message
from langflow.utils.constants import MESSAGE_SENDER_AI
class NeedleComponent(Component):
display_name = "Needle Retriever"
- description = "A retriever that uses the Needle API to search collections and generates responses using OpenAI."
+ description = "A retriever that uses the Needle API to search collections."
documentation = "https://docs.needle-ai.com"
icon = "Needle"
name = "needle"
@@ -22,30 +20,24 @@ class NeedleComponent(Component):
info="Your Needle API key.",
required=True,
),
- SecretStrInput(
- name="openai_api_key",
- display_name="OpenAI API Key",
- info="Your OpenAI API key.",
- required=True,
- ),
- StrInput(
+ MessageTextInput(
name="collection_id",
display_name="Collection ID",
info="The ID of the Needle collection.",
required=True,
),
- StrInput(
+ MessageTextInput(
name="query",
display_name="User Query",
- info="Enter your question here.",
+ info="Enter your question here. In tool mode, you can also specify top_k parameter (min: 20).",
required=True,
+ tool_mode=True,
),
- DropdownInput(
- name="output_type",
- display_name="Output Type",
- info="Return either the answer or the chunks.",
- options=["answer", "chunks"],
- value="answer",
+ IntInput(
+ name="top_k",
+ display_name="Top K Results",
+ info="Number of search results to return (min: 20).",
+ value=20,
required=True,
),
]
@@ -53,74 +45,57 @@ class NeedleComponent(Component):
outputs = [Output(display_name="Result", name="result", type_="Message", method="run")]
def run(self) -> Message:
- needle_api_key = self.needle_api_key or ""
- openai_api_key = self.openai_api_key or ""
- collection_id = self.collection_id
- query = self.query
- output_type = self.output_type
+ # Extract query and top_k
+ query_input = self.query
+ actual_query = query_input.get("query", "") if isinstance(query_input, dict) else query_input
- # Define error messages
- needle_api_key = "The Needle API key cannot be empty."
- openai_api_key = "The OpenAI API key cannot be empty."
- collection_id_error = "The Collection ID cannot be empty."
- query_error = "The query cannot be empty."
+ # Parse top_k from tool input or use default, always enforcing minimum of 20
+ try:
+ if isinstance(query_input, dict) and "top_k" in query_input:
+ agent_top_k = query_input.get("top_k")
+ # Check if agent_top_k is not None before converting to int
+ top_k = max(20, int(agent_top_k)) if agent_top_k is not None else max(20, self.top_k)
+ else:
+ top_k = max(20, self.top_k)
+ except (ValueError, TypeError):
+ top_k = max(20, self.top_k)
- # Validate inputs
- if not needle_api_key.strip():
- raise ValueError(needle_api_key)
- if not openai_api_key.strip():
- raise ValueError(openai_api_key)
- if not collection_id.strip():
- raise ValueError(collection_id_error)
- if not query.strip():
- raise ValueError(query_error)
-
- # Handle output_type if it's somehow a list
- if isinstance(output_type, list):
- output_type = output_type[0]
+ # Validate required inputs
+ if not self.needle_api_key or not self.needle_api_key.strip():
+ error_msg = "The Needle API key cannot be empty."
+ raise ValueError(error_msg)
+ if not self.collection_id or not self.collection_id.strip():
+ error_msg = "The Collection ID cannot be empty."
+ raise ValueError(error_msg)
+ if not actual_query or not actual_query.strip():
+ error_msg = "The query cannot be empty."
+ raise ValueError(error_msg)
try:
- # Initialize the retriever
+ # Initialize the retriever and get documents
retriever = NeedleRetriever(
- needle_api_key=needle_api_key,
- collection_id=collection_id,
+ needle_api_key=self.needle_api_key,
+ collection_id=self.collection_id,
+ top_k=top_k,
)
- # Create the chain
- llm = ChatOpenAI(
- temperature=0.7,
- api_key=openai_api_key,
- )
+ docs = retriever.get_relevant_documents(actual_query)
- qa_chain = ConversationalRetrievalChain.from_llm(
- llm=llm,
- retriever=retriever,
- return_source_documents=True,
- )
-
- # Process the query
- result = qa_chain({"question": query, "chat_history": []})
-
- # Format content based on output type
- if str(output_type).lower().strip() == "chunks":
- # If chunks selected, include full context and answer
- docs = result["source_documents"]
- context = "\n\n".join([f"Document {i + 1}:\n{doc.page_content}" for i, doc in enumerate(docs)])
- text_content = f"Question: {query}\n\nContext:\n{context}\n\nAnswer: {result['answer']}"
+ # Format the response
+ if not docs:
+ text_content = "No relevant documents found for the query."
else:
- # If answer selected, only include the answer
- text_content = result["answer"]
+ context = "\n\n".join([f"Document {i + 1}:\n{doc.page_content}" for i, doc in enumerate(docs)])
+ text_content = f"Question: {actual_query}\n\nContext:\n{context}"
- # Create a Message object following chat.py pattern
+ # Return formatted message
return Message(
text=text_content,
type="assistant",
sender=MESSAGE_SENDER_AI,
additional_kwargs={
- "source_documents": [
- {"page_content": doc.page_content, "metadata": doc.metadata}
- for doc in result["source_documents"]
- ]
+ "source_documents": [{"page_content": doc.page_content, "metadata": doc.metadata} for doc in docs],
+ "top_k_used": top_k,
},
)
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json
new file mode 100644
index 000000000..19a4bdb54
--- /dev/null
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json
@@ -0,0 +1,1935 @@
+{
+ "data": {
+ "edges": [
+ {
+ "animated": false,
+ "className": "",
+ "data": {
+ "sourceHandle": {
+ "dataType": "Prompt",
+ "id": "Prompt-WRaed",
+ "name": "prompt",
+ "output_types": [
+ "Message"
+ ]
+ },
+ "targetHandle": {
+ "fieldName": "system_prompt",
+ "id": "Agent-IDDA1",
+ "inputTypes": [
+ "Message"
+ ],
+ "type": "str"
+ }
+ },
+ "id": "reactflow__edge-Prompt-WRaed{œdataTypeœ:œPromptœ,œidœ:œPrompt-WRaedœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-Agent-IDDA1{œfieldNameœ:œsystem_promptœ,œidœ:œAgent-IDDA1œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+ "selected": false,
+ "source": "Prompt-WRaed",
+ "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-WRaedœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+ "target": "Agent-IDDA1",
+ "targetHandle": "{œfieldNameœ: œsystem_promptœ, œidœ: œAgent-IDDA1œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+ },
+ {
+ "animated": false,
+ "className": "",
+ "data": {
+ "sourceHandle": {
+ "dataType": "needle",
+ "id": "needle-nk3Lq",
+ "name": "component_as_tool",
+ "output_types": [
+ "Tool"
+ ]
+ },
+ "targetHandle": {
+ "fieldName": "tools",
+ "id": "Agent-IDDA1",
+ "inputTypes": [
+ "Tool"
+ ],
+ "type": "other"
+ }
+ },
+ "id": "reactflow__edge-needle-nk3Lq{œdataTypeœ:œneedleœ,œidœ:œneedle-nk3Lqœ,œnameœ:œcomponent_as_toolœ,œoutput_typesœ:[œToolœ]}-Agent-IDDA1{œfieldNameœ:œtoolsœ,œidœ:œAgent-IDDA1œ,œinputTypesœ:[œToolœ],œtypeœ:œotherœ}",
+ "selected": false,
+ "source": "needle-nk3Lq",
+ "sourceHandle": "{œdataTypeœ: œneedleœ, œidœ: œneedle-nk3Lqœ, œnameœ: œcomponent_as_toolœ, œoutput_typesœ: [œToolœ]}",
+ "target": "Agent-IDDA1",
+ "targetHandle": "{œfieldNameœ: œtoolsœ, œidœ: œAgent-IDDA1œ, œinputTypesœ: [œToolœ], œtypeœ: œotherœ}"
+ },
+ {
+ "animated": false,
+ "className": "",
+ "data": {
+ "sourceHandle": {
+ "dataType": "ChatInput",
+ "id": "ChatInput-V0cjZ",
+ "name": "message",
+ "output_types": [
+ "Message"
+ ]
+ },
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "Agent-IDDA1",
+ "inputTypes": [
+ "Message"
+ ],
+ "type": "str"
+ }
+ },
+ "id": "reactflow__edge-ChatInput-V0cjZ{œdataTypeœ:œChatInputœ,œidœ:œChatInput-V0cjZœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Agent-IDDA1{œfieldNameœ:œinput_valueœ,œidœ:œAgent-IDDA1œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+ "selected": false,
+ "source": "ChatInput-V0cjZ",
+ "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-V0cjZœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+ "target": "Agent-IDDA1",
+ "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œAgent-IDDA1œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+ },
+ {
+ "data": {
+ "sourceHandle": {
+ "dataType": "Agent",
+ "id": "Agent-IDDA1",
+ "name": "response",
+ "output_types": [
+ "Message"
+ ]
+ },
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "ChatOutput-Ftr5v",
+ "inputTypes": [
+ "Data",
+ "DataFrame",
+ "Message"
+ ],
+ "type": "str"
+ }
+ },
+ "id": "xy-edge__Agent-IDDA1{œdataTypeœ:œAgentœ,œidœ:œAgent-IDDA1œ,œnameœ:œresponseœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-Ftr5v{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Ftr5vœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œstrœ}",
+ "source": "Agent-IDDA1",
+ "sourceHandle": "{œdataTypeœ: œAgentœ, œidœ: œAgent-IDDA1œ, œnameœ: œresponseœ, œoutput_typesœ: [œMessageœ]}",
+ "target": "ChatOutput-Ftr5v",
+ "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-Ftr5vœ, œinputTypesœ: [œDataœ, œDataFrameœ, œMessageœ], œtypeœ: œstrœ}"
+ }
+ ],
+ "nodes": [
+ {
+ "data": {
+ "id": "note-JlPa7",
+ "node": {
+ "description": "# Invoice Summarizer\n\nLeverage the **Needle Search API** and an **Agent** to gather and summarize your invoice data quickly and accurately.\n\n## Prerequisites\n\n* A **Collection** and an **API Key** from your [Needle.ai](https://needle-ai.com) deployment\n* An [OpenAI API key](https://platform.openai.com/)\n\n## Quickstart\n\n1. Load your invoices into your Needle Collection. \n\n2. In the **Needle Search** tool, add your **Needle Collection ID** and **Needle API Key**.\n\n3. In the **Agent** component, add your **OpenAI Key**.\n\n4. Open the **Playground** and query your invoices. The **Agent** component determines the correct query and search size for data retrieval.\n",
+ "display_name": "",
+ "documentation": "",
+ "template": {
+ "backgroundColor": "emerald"
+ }
+ },
+ "type": "note"
+ },
+ "dragging": false,
+ "height": 632,
+ "id": "note-JlPa7",
+ "measured": {
+ "height": 632,
+ "width": 583
+ },
+ "position": {
+ "x": -153.15585970276706,
+ "y": -405.10874004814775
+ },
+ "resizing": false,
+ "selected": false,
+ "type": "noteNode",
+ "width": 582
+ },
+ {
+ "data": {
+ "id": "Prompt-WRaed",
+ "node": {
+ "base_classes": [
+ "Message"
+ ],
+ "beta": false,
+ "conditional_paths": [],
+ "custom_fields": {
+ "template": []
+ },
+ "description": "Create a prompt template with dynamic variables.",
+ "display_name": "Prompt",
+ "documentation": "",
+ "edited": false,
+ "error": null,
+ "field_order": [
+ "template",
+ "tool_placeholder"
+ ],
+ "frozen": false,
+ "full_path": null,
+ "icon": "prompts",
+ "is_composition": null,
+ "is_input": null,
+ "is_output": null,
+ "legacy": false,
+ "metadata": {},
+ "minimized": false,
+ "name": "",
+ "output_types": [],
+ "outputs": [
+ {
+ "allows_loop": false,
+ "cache": true,
+ "display_name": "Prompt Message",
+ "method": "build_prompt",
+ "name": "prompt",
+ "selected": "Message",
+ "tool_mode": true,
+ "types": [
+ "Message"
+ ],
+ "value": "__UNDEFINED__"
+ }
+ ],
+ "pinned": false,
+ "template": {
+ "_type": "Component",
+ "code": {
+ "advanced": true,
+ "dynamic": true,
+ "fileTypes": [],
+ "file_path": "",
+ "info": "",
+ "list": false,
+ "load_from_db": false,
+ "multiline": true,
+ "name": "code",
+ "password": false,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "code",
+ "value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import MessageTextInput, Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n MessageTextInput(\n name=\"tool_placeholder\",\n display_name=\"Tool Placeholder\",\n tool_mode=True,\n advanced=True,\n info=\"A placeholder input for tool mode.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(self) -> Message:\n prompt = Message.from_template(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n )\n return frontend_node\n\n async def update_frontend_node(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"This function is called after the code validation is done.\"\"\"\n frontend_node = await super().update_frontend_node(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n # Kept it duplicated for backwards compatibility\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n"
+ },
+ "template": {
+ "_input_type": "PromptInput",
+ "advanced": false,
+ "display_name": "Template",
+ "dynamic": false,
+ "info": "",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "template",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "type": "prompt",
+ "value": "You are a meticulous expense analyst with expertise in financial data analysis and cost categorization. \nYour role is to analyze invoices, break down expenses, identify spending patterns, and provide actionable cost-saving insights. \nYou use available tools, such as a knowledge base search, to enhance your analysis. \nProvide structured and well-formatted responses.\n"
+ },
+ "tool_placeholder": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Tool Placeholder",
+ "dynamic": false,
+ "info": "A placeholder input for tool mode.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "tool_placeholder",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": true,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ }
+ },
+ "tool_mode": false
+ },
+ "showNode": true,
+ "type": "Prompt"
+ },
+ "dragging": false,
+ "id": "Prompt-WRaed",
+ "measured": {
+ "height": 256,
+ "width": 320
+ },
+ "position": {
+ "x": 531.8828954152592,
+ "y": -476.0431839408612
+ },
+ "selected": false,
+ "type": "genericNode"
+ },
+ {
+ "data": {
+ "id": "ChatOutput-Ftr5v",
+ "node": {
+ "base_classes": [
+ "Message"
+ ],
+ "beta": false,
+ "conditional_paths": [],
+ "custom_fields": {},
+ "description": "Display a chat message in the Playground.",
+ "display_name": "Chat Output",
+ "documentation": "",
+ "edited": false,
+ "field_order": [
+ "input_value",
+ "should_store_message",
+ "sender",
+ "sender_name",
+ "session_id",
+ "data_template",
+ "background_color",
+ "chat_icon",
+ "text_color"
+ ],
+ "frozen": false,
+ "icon": "MessagesSquare",
+ "legacy": false,
+ "lf_version": "1.1.5",
+ "metadata": {},
+ "minimized": true,
+ "output_types": [],
+ "outputs": [
+ {
+ "allows_loop": false,
+ "cache": true,
+ "display_name": "Message",
+ "method": "message_response",
+ "name": "message",
+ "selected": "Message",
+ "tool_mode": true,
+ "types": [
+ "Message"
+ ],
+ "value": "__UNDEFINED__"
+ }
+ ],
+ "pinned": false,
+ "template": {
+ "_type": "Component",
+ "background_color": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Background Color",
+ "dynamic": false,
+ "info": "The background color of the icon.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "background_color",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "chat_icon": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Icon",
+ "dynamic": false,
+ "info": "The icon of the message.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "chat_icon",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "clean_data": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Basic Clean Data",
+ "dynamic": false,
+ "info": "Whether to clean the data",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "clean_data",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ },
+ "code": {
+ "advanced": true,
+ "dynamic": true,
+ "fileTypes": [],
+ "file_path": "",
+ "info": "",
+ "list": false,
+ "load_from_db": false,
+ "multiline": true,
+ "name": "code",
+ "password": false,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "code",
+ "value": "from collections.abc import Generator\nfrom typing import Any\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.inputs.inputs import HandleInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.schema.properties import Source\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def _safe_convert(self, data: Any) -> str:\n \"\"\"Safely convert input data to string.\"\"\"\n try:\n if isinstance(data, str):\n return data\n if isinstance(data, Message):\n return data.get_text()\n if isinstance(data, Data):\n if data.get_text() is None:\n msg = \"Empty Data object\"\n raise ValueError(msg)\n return data.get_text()\n if isinstance(data, DataFrame):\n if self.clean_data:\n # Remove empty rows\n data = data.dropna(how=\"all\")\n # Remove empty lines in each cell\n data = data.replace(r\"^\\s*$\", \"\", regex=True)\n # Replace multiple newlines with a single newline\n data = data.replace(r\"\\n+\", \"\\n\", regex=True)\n\n # Replace pipe characters to avoid markdown table issues\n processed_data = data.replace(r\"\\|\", r\"\\\\|\", regex=True)\n\n processed_data = processed_data.map(\n lambda x: str(x).replace(\"\\n\", \"
\") if isinstance(x, str) else x\n )\n\n return processed_data.to_markdown(index=False)\n return str(data)\n except (ValueError, TypeError, AttributeError) as e:\n msg = f\"Error converting data: {e!s}\"\n raise ValueError(msg) from e\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([self._safe_convert(item) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return self._safe_convert(self.input_value)\n"
+ },
+ "data_template": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Data Template",
+ "dynamic": false,
+ "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "data_template",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "{text}"
+ },
+ "input_value": {
+ "_input_type": "MessageInput",
+ "advanced": false,
+ "display_name": "Text",
+ "dynamic": false,
+ "info": "Message to be passed as output.",
+ "input_types": [
+ "Data",
+ "DataFrame",
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "input_value",
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "sender": {
+ "_input_type": "DropdownInput",
+ "advanced": true,
+ "combobox": false,
+ "dialog_inputs": {},
+ "display_name": "Sender Type",
+ "dynamic": false,
+ "info": "Type of sender.",
+ "name": "sender",
+ "options": [
+ "Machine",
+ "User"
+ ],
+ "options_metadata": [],
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "Machine"
+ },
+ "sender_name": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Sender Name",
+ "dynamic": false,
+ "info": "Name of the sender.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "sender_name",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "AI"
+ },
+ "session_id": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Session ID",
+ "dynamic": false,
+ "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "session_id",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "should_store_message": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Store Messages",
+ "dynamic": false,
+ "info": "Store the message in the history.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "should_store_message",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ },
+ "text_color": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Text Color",
+ "dynamic": false,
+ "info": "The text color of the name",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "text_color",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ }
+ },
+ "tool_mode": false
+ },
+ "showNode": false,
+ "type": "ChatOutput"
+ },
+ "dragging": false,
+ "id": "ChatOutput-Ftr5v",
+ "measured": {
+ "height": 66,
+ "width": 192
+ },
+ "position": {
+ "x": 1481.751052877705,
+ "y": 232.9723445431882
+ },
+ "selected": false,
+ "type": "genericNode"
+ },
+ {
+ "data": {
+ "id": "note-TRZfV",
+ "node": {
+ "description": "### 💡 Add your OpenAI API key here 👇",
+ "display_name": "",
+ "documentation": "",
+ "template": {
+ "backgroundColor": "transparent"
+ }
+ },
+ "type": "note"
+ },
+ "dragging": false,
+ "id": "note-TRZfV",
+ "measured": {
+ "height": 324,
+ "width": 324
+ },
+ "position": {
+ "x": 1076.1571598578398,
+ "y": -389.50649266044195
+ },
+ "selected": false,
+ "type": "noteNode"
+ },
+ {
+ "data": {
+ "id": "note-P7KNw",
+ "node": {
+ "description": "### 💡 Add your Needle Search API key here 👇",
+ "display_name": "",
+ "documentation": "",
+ "template": {
+ "backgroundColor": "transparent"
+ }
+ },
+ "type": "note"
+ },
+ "dragging": false,
+ "height": 324,
+ "id": "note-P7KNw",
+ "measured": {
+ "height": 324,
+ "width": 401
+ },
+ "position": {
+ "x": 500.88806568161635,
+ "y": -196.26232150672587
+ },
+ "resizing": false,
+ "selected": false,
+ "type": "noteNode",
+ "width": 402
+ },
+ {
+ "data": {
+ "id": "needle-nk3Lq",
+ "node": {
+ "base_classes": [
+ "Message"
+ ],
+ "beta": false,
+ "category": "needle",
+ "conditional_paths": [],
+ "custom_fields": {},
+ "description": "A retriever that uses the Needle API to search collections.",
+ "display_name": "Needle Retriever",
+ "documentation": "https://docs.needle-ai.com",
+ "edited": false,
+ "field_order": [
+ "needle_api_key",
+ "collection_id",
+ "query",
+ "top_k"
+ ],
+ "frozen": false,
+ "icon": "Needle",
+ "key": "needle",
+ "legacy": false,
+ "metadata": {},
+ "minimized": false,
+ "output_types": [],
+ "outputs": [
+ {
+ "allows_loop": false,
+ "cache": true,
+ "display_name": "Toolset",
+ "hidden": null,
+ "method": "to_toolkit",
+ "name": "component_as_tool",
+ "required_inputs": null,
+ "selected": "Tool",
+ "tool_mode": true,
+ "types": [
+ "Tool"
+ ],
+ "value": "__UNDEFINED__"
+ }
+ ],
+ "pinned": false,
+ "score": 7.568328950209746e-6,
+ "template": {
+ "_type": "Component",
+ "code": {
+ "advanced": true,
+ "dynamic": true,
+ "fileTypes": [],
+ "file_path": "",
+ "info": "",
+ "list": false,
+ "load_from_db": false,
+ "multiline": true,
+ "name": "code",
+ "password": false,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "code",
+ "value": "from langchain_community.retrievers.needle import NeedleRetriever\n\nfrom langflow.custom.custom_component.component import Component\nfrom langflow.io import IntInput, MessageTextInput, Output, SecretStrInput\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI\n\n\nclass NeedleComponent(Component):\n display_name = \"Needle Retriever\"\n description = \"A retriever that uses the Needle API to search collections.\"\n documentation = \"https://docs.needle-ai.com\"\n icon = \"Needle\"\n name = \"needle\"\n\n inputs = [\n SecretStrInput(\n name=\"needle_api_key\",\n display_name=\"Needle API Key\",\n info=\"Your Needle API key.\",\n required=True,\n ),\n MessageTextInput(\n name=\"collection_id\",\n display_name=\"Collection ID\",\n info=\"The ID of the Needle collection.\",\n required=True,\n ),\n MessageTextInput(\n name=\"query\",\n display_name=\"User Query\",\n info=\"Enter your question here. In tool mode, you can also specify top_k parameter (min: 20).\",\n required=True,\n tool_mode=True,\n ),\n IntInput(\n name=\"top_k\",\n display_name=\"Top K Results\",\n info=\"Number of search results to return (min: 20).\",\n value=20,\n required=True,\n ),\n ]\n\n outputs = [Output(display_name=\"Result\", name=\"result\", type_=\"Message\", method=\"run\")]\n\n def run(self) -> Message:\n # Extract query and top_k\n query_input = self.query\n actual_query = query_input.get(\"query\", \"\") if isinstance(query_input, dict) else query_input\n\n # Parse top_k from tool input or use default, always enforcing minimum of 20\n try:\n if isinstance(query_input, dict) and \"top_k\" in query_input:\n agent_top_k = query_input.get(\"top_k\")\n # Check if agent_top_k is not None before converting to int\n top_k = max(20, int(agent_top_k)) if agent_top_k is not None else max(20, self.top_k)\n else:\n top_k = max(20, self.top_k)\n except (ValueError, TypeError):\n top_k = max(20, self.top_k)\n\n # Validate required inputs\n if not self.needle_api_key or not self.needle_api_key.strip():\n error_msg = \"The Needle API key cannot be empty.\"\n raise ValueError(error_msg)\n if not self.collection_id or not self.collection_id.strip():\n error_msg = \"The Collection ID cannot be empty.\"\n raise ValueError(error_msg)\n if not actual_query or not actual_query.strip():\n error_msg = \"The query cannot be empty.\"\n raise ValueError(error_msg)\n\n try:\n # Initialize the retriever and get documents\n retriever = NeedleRetriever(\n needle_api_key=self.needle_api_key,\n collection_id=self.collection_id,\n top_k=top_k,\n )\n\n docs = retriever.get_relevant_documents(actual_query)\n\n # Format the response\n if not docs:\n text_content = \"No relevant documents found for the query.\"\n else:\n context = \"\\n\\n\".join([f\"Document {i + 1}:\\n{doc.page_content}\" for i, doc in enumerate(docs)])\n text_content = f\"Question: {actual_query}\\n\\nContext:\\n{context}\"\n\n # Return formatted message\n return Message(\n text=text_content,\n type=\"assistant\",\n sender=MESSAGE_SENDER_AI,\n additional_kwargs={\n \"source_documents\": [{\"page_content\": doc.page_content, \"metadata\": doc.metadata} for doc in docs],\n \"top_k_used\": top_k,\n },\n )\n\n except Exception as e:\n error_msg = f\"Error processing query: {e!s}\"\n raise ValueError(error_msg) from e\n"
+ },
+ "collection_id": {
+ "_input_type": "MessageTextInput",
+ "advanced": false,
+ "display_name": "Collection ID",
+ "dynamic": false,
+ "info": "The ID of the Needle collection.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "collection_id",
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "needle_api_key": {
+ "_input_type": "SecretStrInput",
+ "advanced": false,
+ "display_name": "Needle API Key",
+ "dynamic": false,
+ "info": "Your Needle API key.",
+ "input_types": [
+ "Message"
+ ],
+ "load_from_db": false,
+ "name": "needle_api_key",
+ "password": true,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "str",
+ "value": ""
+ },
+ "query": {
+ "_input_type": "MessageTextInput",
+ "advanced": false,
+ "display_name": "User Query",
+ "dynamic": false,
+ "info": "Enter your question here. In tool mode, you can also specify top_k parameter (min: 20).",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "query",
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "tool_mode": true,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "tools_metadata": {
+ "_input_type": "TableInput",
+ "advanced": false,
+ "display_name": "Edit tools",
+ "dynamic": false,
+ "info": "",
+ "is_list": true,
+ "list_add_label": "Add More",
+ "name": "tools_metadata",
+ "placeholder": "",
+ "real_time_refresh": true,
+ "required": false,
+ "show": true,
+ "table_icon": "Hammer",
+ "table_options": {
+ "block_add": true,
+ "block_delete": true,
+ "block_edit": true,
+ "block_filter": true,
+ "block_hide": true,
+ "block_select": true,
+ "block_sort": true,
+ "description": "Modify tool names and descriptions to help agents understand when to use each tool.",
+ "field_parsers": {
+ "commands": "commands",
+ "name": [
+ "snake_case",
+ "no_blank"
+ ]
+ },
+ "hide_options": true
+ },
+ "table_schema": {
+ "columns": [
+ {
+ "default": "None",
+ "description": "Specify the name of the tool.",
+ "disable_edit": false,
+ "display_name": "Tool Name",
+ "edit_mode": "inline",
+ "filterable": false,
+ "formatter": "text",
+ "hidden": false,
+ "name": "name",
+ "sortable": false,
+ "type": "str"
+ },
+ {
+ "default": "None",
+ "description": "Describe the purpose of the tool.",
+ "disable_edit": false,
+ "display_name": "Tool Description",
+ "edit_mode": "popover",
+ "filterable": false,
+ "formatter": "text",
+ "hidden": false,
+ "name": "description",
+ "sortable": false,
+ "type": "str"
+ },
+ {
+ "default": "None",
+ "description": "The default identifiers for the tools and cannot be changed.",
+ "disable_edit": true,
+ "display_name": "Tool Identifiers",
+ "edit_mode": "inline",
+ "filterable": false,
+ "formatter": "text",
+ "hidden": true,
+ "name": "tags",
+ "sortable": false,
+ "type": "str"
+ },
+ {
+ "default": true,
+ "description": "Indicates whether the tool is currently active. Set to True to activate this tool.",
+ "disable_edit": false,
+ "display_name": "Enable",
+ "edit_mode": "popover",
+ "filterable": true,
+ "formatter": "boolean",
+ "hidden": false,
+ "name": "status",
+ "sortable": true,
+ "type": "boolean"
+ }
+ ]
+ },
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "trigger_icon": "Hammer",
+ "trigger_text": "",
+ "type": "table",
+ "value": [
+ {
+ "description": "run(collection_id: Message, needle_api_key: Message, query: Message, top_k: FieldTypes.INTEGER) - A retriever that uses the Needle API to search collections.",
+ "name": "needle-run",
+ "tags": [
+ "needle-run"
+ ]
+ }
+ ]
+ },
+ "top_k": {
+ "_input_type": "IntInput",
+ "advanced": false,
+ "display_name": "Top K Results",
+ "dynamic": false,
+ "info": "Number of search results to return (min: 20).",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "top_k",
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 20
+ }
+ },
+ "tool_mode": true
+ },
+ "showNode": true,
+ "type": "needle"
+ },
+ "id": "needle-nk3Lq",
+ "measured": {
+ "height": 582,
+ "width": 320
+ },
+ "position": {
+ "x": 540,
+ "y": -150
+ },
+ "selected": false,
+ "type": "genericNode"
+ },
+ {
+ "data": {
+ "id": "ChatInput-V0cjZ",
+ "node": {
+ "base_classes": [
+ "Message"
+ ],
+ "beta": false,
+ "category": "inputs",
+ "conditional_paths": [],
+ "custom_fields": {},
+ "description": "Get chat inputs from the Playground.",
+ "display_name": "Chat Input",
+ "documentation": "",
+ "edited": false,
+ "field_order": [
+ "input_value",
+ "should_store_message",
+ "sender",
+ "sender_name",
+ "session_id",
+ "files",
+ "background_color",
+ "chat_icon",
+ "text_color"
+ ],
+ "frozen": false,
+ "icon": "MessagesSquare",
+ "key": "ChatInput",
+ "legacy": false,
+ "metadata": {},
+ "minimized": true,
+ "output_types": [],
+ "outputs": [
+ {
+ "allows_loop": false,
+ "cache": true,
+ "display_name": "Message",
+ "method": "message_response",
+ "name": "message",
+ "selected": "Message",
+ "tool_mode": true,
+ "types": [
+ "Message"
+ ],
+ "value": "__UNDEFINED__"
+ }
+ ],
+ "pinned": false,
+ "score": 0.0020353564437605998,
+ "template": {
+ "_type": "Component",
+ "background_color": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Background Color",
+ "dynamic": false,
+ "info": "The background color of the icon.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "background_color",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "chat_icon": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Icon",
+ "dynamic": false,
+ "info": "The icon of the message.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "chat_icon",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "code": {
+ "advanced": true,
+ "dynamic": true,
+ "fileTypes": [],
+ "file_path": "",
+ "info": "",
+ "list": false,
+ "load_from_db": false,
+ "multiline": true,
+ "name": "code",
+ "password": false,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "code",
+ "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import (\n DropdownInput,\n FileInput,\n MessageTextInput,\n MultilineInput,\n Output,\n)\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_USER,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n input_types=[],\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n async def message_response(self) -> Message:\n background_color = self.background_color\n text_color = self.text_color\n icon = self.chat_icon\n\n message = await Message.create(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n properties={\n \"background_color\": background_color,\n \"text_color\": text_color,\n \"icon\": icon,\n },\n )\n if self.session_id and isinstance(message, Message) and self.should_store_message:\n stored_message = await self.send_message(\n message,\n )\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n"
+ },
+ "files": {
+ "_input_type": "FileInput",
+ "advanced": true,
+ "display_name": "Files",
+ "dynamic": false,
+ "fileTypes": [
+ "txt",
+ "md",
+ "mdx",
+ "csv",
+ "json",
+ "yaml",
+ "yml",
+ "xml",
+ "html",
+ "htm",
+ "pdf",
+ "docx",
+ "py",
+ "sh",
+ "sql",
+ "js",
+ "ts",
+ "tsx",
+ "jpg",
+ "jpeg",
+ "png",
+ "bmp",
+ "image"
+ ],
+ "file_path": "",
+ "info": "Files to be sent with the message.",
+ "list": true,
+ "list_add_label": "Add More",
+ "name": "files",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "trace_as_metadata": true,
+ "type": "file",
+ "value": ""
+ },
+ "input_value": {
+ "_input_type": "MultilineInput",
+ "advanced": false,
+ "display_name": "Text",
+ "dynamic": false,
+ "info": "Message to be passed as input.",
+ "input_types": [],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "multiline": true,
+ "name": "input_value",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "Analyze recent invoices and generate a detailed expense report. Steps: 1. Group expenses and calculate total spend by vendor. 2. Calculate the gross total spend. 3. Identify potential cost-saving opportunities. Provide the report in markdown format, including: - Executive Summary - Vendor-wise Breakdown - Recommendations for Cost Optimization. "
+ },
+ "sender": {
+ "_input_type": "DropdownInput",
+ "advanced": true,
+ "combobox": false,
+ "dialog_inputs": {},
+ "display_name": "Sender Type",
+ "dynamic": false,
+ "info": "Type of sender.",
+ "name": "sender",
+ "options": [
+ "Machine",
+ "User"
+ ],
+ "options_metadata": [],
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "User"
+ },
+ "sender_name": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Sender Name",
+ "dynamic": false,
+ "info": "Name of the sender.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "sender_name",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "User"
+ },
+ "session_id": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Session ID",
+ "dynamic": false,
+ "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "session_id",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "should_store_message": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Store Messages",
+ "dynamic": false,
+ "info": "Store the message in the history.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "should_store_message",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ },
+ "text_color": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Text Color",
+ "dynamic": false,
+ "info": "The text color of the name",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "text_color",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ }
+ },
+ "tool_mode": false
+ },
+ "showNode": true,
+ "type": "ChatInput"
+ },
+ "dragging": false,
+ "id": "ChatInput-V0cjZ",
+ "measured": {
+ "height": 230,
+ "width": 320
+ },
+ "position": {
+ "x": 533.1085055774705,
+ "y": 456.54948541243164
+ },
+ "selected": false,
+ "type": "genericNode"
+ },
+ {
+ "data": {
+ "id": "Agent-IDDA1",
+ "node": {
+ "base_classes": [
+ "Message"
+ ],
+ "beta": false,
+ "category": "agents",
+ "conditional_paths": [],
+ "custom_fields": {},
+ "description": "Define the agent's instructions, then enter a task to complete using tools.",
+ "display_name": "Agent",
+ "documentation": "",
+ "edited": false,
+ "field_order": [
+ "agent_llm",
+ "max_tokens",
+ "model_kwargs",
+ "json_mode",
+ "model_name",
+ "openai_api_base",
+ "api_key",
+ "temperature",
+ "seed",
+ "max_retries",
+ "timeout",
+ "system_prompt",
+ "tools",
+ "input_value",
+ "handle_parsing_errors",
+ "verbose",
+ "max_iterations",
+ "agent_description",
+ "memory",
+ "sender",
+ "sender_name",
+ "n_messages",
+ "session_id",
+ "order",
+ "template",
+ "add_current_date_tool"
+ ],
+ "frozen": false,
+ "icon": "bot",
+ "key": "Agent",
+ "legacy": false,
+ "metadata": {},
+ "minimized": false,
+ "output_types": [],
+ "outputs": [
+ {
+ "allows_loop": false,
+ "cache": true,
+ "display_name": "Response",
+ "method": "message_response",
+ "name": "response",
+ "selected": "Message",
+ "tool_mode": true,
+ "types": [
+ "Message"
+ ],
+ "value": "__UNDEFINED__"
+ }
+ ],
+ "pinned": false,
+ "score": 1.1732828199964098e-19,
+ "template": {
+ "_type": "Component",
+ "add_current_date_tool": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Current Date",
+ "dynamic": false,
+ "info": "If true, will add a tool to the agent that returns the current date.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "add_current_date_tool",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ },
+ "agent_description": {
+ "_input_type": "MultilineInput",
+ "advanced": true,
+ "copy_field": false,
+ "display_name": "Agent Description [Deprecated]",
+ "dynamic": false,
+ "info": "The description of the agent. This is only used when in Tool Mode. Defaults to 'A helpful assistant with access to the following tools:' and tools are added dynamically. This feature is deprecated and will be removed in future versions.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "multiline": true,
+ "name": "agent_description",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "A helpful assistant with access to the following tools:"
+ },
+ "agent_llm": {
+ "_input_type": "DropdownInput",
+ "advanced": false,
+ "combobox": false,
+ "dialog_inputs": {},
+ "display_name": "Model Provider",
+ "dynamic": false,
+ "info": "The provider of the language model that the agent will use to generate responses.",
+ "input_types": [],
+ "name": "agent_llm",
+ "options": [
+ "Amazon Bedrock",
+ "Anthropic",
+ "Azure OpenAI",
+ "Google Generative AI",
+ "Groq",
+ "NVIDIA",
+ "OpenAI",
+ "SambaNova",
+ "Custom"
+ ],
+ "options_metadata": [
+ {
+ "icon": "Amazon"
+ },
+ {
+ "icon": "Anthropic"
+ },
+ {
+ "icon": "Azure"
+ },
+ {
+ "icon": "GoogleGenerativeAI"
+ },
+ {
+ "icon": "Groq"
+ },
+ {
+ "icon": "NVIDIA"
+ },
+ {
+ "icon": "OpenAI"
+ },
+ {
+ "icon": "SambaNova"
+ },
+ {
+ "icon": "brain"
+ }
+ ],
+ "placeholder": "",
+ "real_time_refresh": true,
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "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": false,
+ "name": "api_key",
+ "password": true,
+ "placeholder": "",
+ "required": true,
+ "show": true,
+ "title_case": false,
+ "type": "str",
+ "value": ""
+ },
+ "code": {
+ "advanced": true,
+ "dynamic": true,
+ "fileTypes": [],
+ "file_path": "",
+ "info": "",
+ "list": false,
+ "load_from_db": false,
+ "multiline": true,
+ "name": "code",
+ "password": false,
+ "placeholder": "",
+ "required": true,
+ "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.agents.events import ExceptionWithMessageError\nfrom langflow.base.models.model_input_constants import (\n ALL_PROVIDER_FIELDS,\n MODEL_DYNAMIC_UPDATE_FIELDS,\n MODEL_PROVIDERS_DICT,\n MODELS_METADATA,\n)\nfrom langflow.base.models.model_utils import get_model_name\nfrom langflow.components.helpers import CurrentDateComponent\nfrom langflow.components.helpers.memory import MemoryComponent\nfrom langflow.components.langchain_utilities.tool_calling import ToolCallingAgentComponent\nfrom langflow.custom.custom_component.component import _get_component_toolkit\nfrom langflow.custom.utils import update_component_build_config\nfrom langflow.field_typing import Tool\nfrom langflow.io import BoolInput, DropdownInput, MultilineInput, Output\nfrom langflow.logging import logger\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 options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())] + [{\"icon\": \"brain\"}],\n ),\n *MODEL_PROVIDERS_DICT[\"OpenAI\"][\"inputs\"],\n MultilineInput(\n name=\"system_prompt\",\n display_name=\"Agent Instructions\",\n info=\"System Prompt: Initial instructions and context provided to guide the agent's behavior.\",\n value=\"You are a helpful assistant that can use tools to answer questions and perform tasks.\",\n advanced=False,\n ),\n *LCToolsAgentComponent._base_inputs,\n *memory_inputs,\n BoolInput(\n name=\"add_current_date_tool\",\n display_name=\"Current Date\",\n advanced=True,\n info=\"If true, will add a tool to the agent that returns the current date.\",\n value=True,\n ),\n ]\n outputs = [Output(name=\"response\", display_name=\"Response\", method=\"message_response\")]\n\n async def message_response(self) -> Message:\n try:\n # Get LLM model and validate\n llm_model, display_name = self.get_llm()\n if llm_model is None:\n msg = \"No language model selected. Please choose a model to proceed.\"\n raise ValueError(msg)\n self.model_name = get_model_name(llm_model, display_name=display_name)\n\n # Get memory data\n self.chat_history = await self.get_memory_data()\n\n # Add current date tool if enabled\n if self.add_current_date_tool:\n if not isinstance(self.tools, list): # type: ignore[has-type]\n self.tools = []\n current_date_tool = (await CurrentDateComponent(**self.get_base_args()).to_toolkit()).pop(0)\n if not isinstance(current_date_tool, StructuredTool):\n msg = \"CurrentDateComponent must be converted to a StructuredTool\"\n raise TypeError(msg)\n self.tools.append(current_date_tool)\n\n # Validate tools\n if not self.tools:\n msg = \"Tools are required to run the agent. Please add at least one tool.\"\n raise ValueError(msg)\n\n # Set up and run agent\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 except (ValueError, TypeError, KeyError) as e:\n logger.error(f\"{type(e).__name__}: {e!s}\")\n raise\n except ExceptionWithMessageError as e:\n logger.error(f\"ExceptionWithMessageError occurred: {e}\")\n raise\n except Exception as e:\n logger.error(f\"Unexpected error: {e!s}\")\n raise\n\n async def get_memory_data(self):\n memory_kwargs = {\n component_input.name: getattr(self, f\"{component_input.name}\") for component_input in self.memory_inputs\n }\n # filter out empty values\n memory_kwargs = {k: v for k, v in memory_kwargs.items() if v}\n\n return await MemoryComponent(**self.get_base_args()).set(**memory_kwargs).retrieve_messages()\n\n def get_llm(self):\n if not isinstance(self.agent_llm, str):\n return self.agent_llm, None\n\n try:\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if not provider_info:\n msg = f\"Invalid model provider: {self.agent_llm}\"\n raise ValueError(msg)\n\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\n return self._build_llm_model(component_class, inputs, prefix), display_name\n\n except Exception as e:\n logger.error(f\"Error building {self.agent_llm} language model: {e!s}\")\n msg = f\"Failed to initialize language model: {e!s}\"\n raise ValueError(msg) from e\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 set_component_params(self, component):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n inputs = provider_info.get(\"inputs\")\n prefix = provider_info.get(\"prefix\")\n model_kwargs = {input_.name: getattr(self, f\"{prefix}{input_.name}\") for input_ in inputs}\n\n return component.set(**model_kwargs)\n return component\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 async def update_build_config(\n self, build_config: dotdict, field_value: str, field_name: str | None = None\n ) -> dotdict:\n # Iterate over all providers in the MODEL_PROVIDERS_DICT\n # Existing logic for updating build_config\n if field_name in (\"agent_llm\",):\n build_config[\"agent_llm\"][\"value\"] = field_value\n provider_info = MODEL_PROVIDERS_DICT.get(field_value)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call the component class's update_build_config method\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n\n provider_configs: dict[str, tuple[dict, list[dict]]] = {\n provider: (\n MODEL_PROVIDERS_DICT[provider][\"fields\"],\n [\n MODEL_PROVIDERS_DICT[other_provider][\"fields\"]\n for other_provider in MODEL_PROVIDERS_DICT\n if other_provider != provider\n ],\n )\n for provider in MODEL_PROVIDERS_DICT\n }\n if field_value in provider_configs:\n fields_to_add, fields_to_delete = provider_configs[field_value]\n\n # Delete fields from other providers\n for fields in fields_to_delete:\n self.delete_fields(build_config, fields)\n\n # Add provider-specific fields\n if field_value == \"OpenAI\" and not any(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 options_metadata=[MODELS_METADATA[key] for key in sorted(MODELS_METADATA.keys())]\n + [{\"icon\": \"brain\"}],\n )\n build_config.update({\"agent_llm\": custom_component.to_dict()})\n # Update input types for all fields\n build_config = self.update_input_types(build_config)\n\n # Validate required keys\n default_keys = [\n \"code\",\n \"_type\",\n \"agent_llm\",\n \"tools\",\n \"input_value\",\n \"add_current_date_tool\",\n \"system_prompt\",\n \"agent_description\",\n \"max_iterations\",\n \"handle_parsing_errors\",\n \"verbose\",\n ]\n missing_keys = [key for key in default_keys if key not in build_config]\n if missing_keys:\n msg = f\"Missing required keys in build_config: {missing_keys}\"\n raise ValueError(msg)\n if (\n isinstance(self.agent_llm, str)\n and self.agent_llm in MODEL_PROVIDERS_DICT\n and field_name in MODEL_DYNAMIC_UPDATE_FIELDS\n ):\n provider_info = MODEL_PROVIDERS_DICT.get(self.agent_llm)\n if provider_info:\n component_class = provider_info.get(\"component_class\")\n component_class = self.set_component_params(component_class)\n prefix = provider_info.get(\"prefix\")\n if component_class and hasattr(component_class, \"update_build_config\"):\n # Call each component class's update_build_config method\n # remove the prefix from the field_name\n if isinstance(field_name, str) and isinstance(prefix, str):\n field_name = field_name.replace(prefix, \"\")\n build_config = await update_component_build_config(\n component_class, build_config, field_value, \"model_name\"\n )\n return dotdict({k: v.to_dict() if hasattr(v, \"to_dict\") else v for k, v in build_config.items()})\n\n async def to_toolkit(self) -> list[Tool]:\n component_toolkit = _get_component_toolkit()\n tools_names = self._build_tools_names()\n agent_description = self.get_tool_description()\n # TODO: Agent Description Depreciated Feature to be removed\n description = f\"{agent_description}{tools_names}\"\n tools = component_toolkit(component=self).get_tools(\n tool_name=self.get_tool_name(), tool_description=description, callbacks=self.get_langchain_callbacks()\n )\n if hasattr(self, \"tools_metadata\"):\n tools = component_toolkit(component=self, metadata=self.tools_metadata).update_tools_metadata(tools=tools)\n return tools\n"
+ },
+ "handle_parsing_errors": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Handle Parse Errors",
+ "dynamic": false,
+ "info": "Should the Agent fix errors when reading user input for better processing?",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "handle_parsing_errors",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ },
+ "input_value": {
+ "_input_type": "MessageTextInput",
+ "advanced": false,
+ "display_name": "Input",
+ "dynamic": false,
+ "info": "The input provided by the user for the agent to process.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "input_value",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": true,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "json_mode": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "JSON Mode",
+ "dynamic": false,
+ "info": "If True, it will output JSON regardless of passing a schema.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "json_mode",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": false
+ },
+ "max_iterations": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Max Iterations",
+ "dynamic": false,
+ "info": "The maximum number of attempts the agent can make to complete its task before it stops.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "max_iterations",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 15
+ },
+ "max_retries": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Max Retries",
+ "dynamic": false,
+ "info": "The maximum number of retries to make when generating.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "max_retries",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 5
+ },
+ "max_tokens": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Max Tokens",
+ "dynamic": false,
+ "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "max_tokens",
+ "placeholder": "",
+ "range_spec": {
+ "max": 128000,
+ "min": 0,
+ "step": 0.1,
+ "step_type": "float"
+ },
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": ""
+ },
+ "memory": {
+ "_input_type": "HandleInput",
+ "advanced": true,
+ "display_name": "External Memory",
+ "dynamic": false,
+ "info": "Retrieve messages from an external memory. If empty, it will use the Langflow tables.",
+ "input_types": [
+ "Memory"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "memory",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "trace_as_metadata": true,
+ "type": "other",
+ "value": ""
+ },
+ "model_kwargs": {
+ "_input_type": "DictInput",
+ "advanced": true,
+ "display_name": "Model Kwargs",
+ "dynamic": false,
+ "info": "Additional keyword arguments to pass to the model.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "model_kwargs",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "type": "dict",
+ "value": {}
+ },
+ "model_name": {
+ "_input_type": "DropdownInput",
+ "advanced": false,
+ "combobox": true,
+ "dialog_inputs": {},
+ "display_name": "Model Name",
+ "dynamic": false,
+ "info": "To see the model names, first choose a provider. Then, enter your API key and click the refresh button next to the model name.",
+ "name": "model_name",
+ "options": [
+ "gpt-4o-mini",
+ "gpt-4o",
+ "gpt-4.5-preview",
+ "gpt-4-turbo",
+ "gpt-4-turbo-preview",
+ "gpt-4",
+ "gpt-3.5-turbo"
+ ],
+ "options_metadata": [],
+ "placeholder": "",
+ "real_time_refresh": false,
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "gpt-4o"
+ },
+ "n_messages": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Number of Messages",
+ "dynamic": false,
+ "info": "Number of messages to retrieve.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "n_messages",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 100
+ },
+ "openai_api_base": {
+ "_input_type": "StrInput",
+ "advanced": true,
+ "display_name": "OpenAI API Base",
+ "dynamic": false,
+ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "openai_api_base",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "order": {
+ "_input_type": "DropdownInput",
+ "advanced": true,
+ "combobox": false,
+ "dialog_inputs": {},
+ "display_name": "Order",
+ "dynamic": false,
+ "info": "Order of the messages.",
+ "name": "order",
+ "options": [
+ "Ascending",
+ "Descending"
+ ],
+ "options_metadata": [],
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "Ascending"
+ },
+ "seed": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Seed",
+ "dynamic": false,
+ "info": "The seed controls the reproducibility of the job.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "seed",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 1
+ },
+ "sender": {
+ "_input_type": "DropdownInput",
+ "advanced": true,
+ "combobox": false,
+ "dialog_inputs": {},
+ "display_name": "Sender Type",
+ "dynamic": false,
+ "info": "Filter by sender type.",
+ "name": "sender",
+ "options": [
+ "Machine",
+ "User",
+ "Machine and User"
+ ],
+ "options_metadata": [],
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "Machine and User"
+ },
+ "sender_name": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Sender Name",
+ "dynamic": false,
+ "info": "Filter by sender name.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "sender_name",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "session_id": {
+ "_input_type": "MessageTextInput",
+ "advanced": true,
+ "display_name": "Session ID",
+ "dynamic": false,
+ "info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "name": "session_id",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": ""
+ },
+ "system_prompt": {
+ "_input_type": "MultilineInput",
+ "advanced": false,
+ "copy_field": false,
+ "display_name": "Agent Instructions",
+ "dynamic": false,
+ "info": "System Prompt: Initial instructions and context provided to guide the agent's behavior.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "multiline": true,
+ "name": "system_prompt",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "You are a helpful assistant that can use tools to answer questions and perform tasks."
+ },
+ "temperature": {
+ "_input_type": "SliderInput",
+ "advanced": true,
+ "display_name": "Temperature",
+ "dynamic": false,
+ "info": "",
+ "max_label": "",
+ "max_label_icon": "",
+ "min_label": "",
+ "min_label_icon": "",
+ "name": "temperature",
+ "placeholder": "",
+ "range_spec": {
+ "max": 1,
+ "min": 0,
+ "step": 0.01,
+ "step_type": "float"
+ },
+ "required": false,
+ "show": true,
+ "slider_buttons": false,
+ "slider_buttons_options": [],
+ "slider_input": false,
+ "title_case": false,
+ "tool_mode": false,
+ "type": "slider",
+ "value": 0.1
+ },
+ "template": {
+ "_input_type": "MultilineInput",
+ "advanced": true,
+ "copy_field": false,
+ "display_name": "Template",
+ "dynamic": false,
+ "info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
+ "input_types": [
+ "Message"
+ ],
+ "list": false,
+ "list_add_label": "Add More",
+ "load_from_db": false,
+ "multiline": true,
+ "name": "template",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_input": true,
+ "trace_as_metadata": true,
+ "type": "str",
+ "value": "{sender_name}: {text}"
+ },
+ "timeout": {
+ "_input_type": "IntInput",
+ "advanced": true,
+ "display_name": "Timeout",
+ "dynamic": false,
+ "info": "The timeout for requests to OpenAI completion API.",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "timeout",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "int",
+ "value": 700
+ },
+ "tools": {
+ "_input_type": "HandleInput",
+ "advanced": false,
+ "display_name": "Tools",
+ "dynamic": false,
+ "info": "These are the tools that the agent can use to help with tasks.",
+ "input_types": [
+ "Tool"
+ ],
+ "list": true,
+ "list_add_label": "Add More",
+ "name": "tools",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "trace_as_metadata": true,
+ "type": "other",
+ "value": ""
+ },
+ "verbose": {
+ "_input_type": "BoolInput",
+ "advanced": true,
+ "display_name": "Verbose",
+ "dynamic": false,
+ "info": "",
+ "list": false,
+ "list_add_label": "Add More",
+ "name": "verbose",
+ "placeholder": "",
+ "required": false,
+ "show": true,
+ "title_case": false,
+ "tool_mode": false,
+ "trace_as_metadata": true,
+ "type": "bool",
+ "value": true
+ }
+ },
+ "tool_mode": false
+ },
+ "showNode": true,
+ "type": "Agent"
+ },
+ "id": "Agent-IDDA1",
+ "measured": {
+ "height": 624,
+ "width": 320
+ },
+ "position": {
+ "x": 1095,
+ "y": -330
+ },
+ "selected": false,
+ "type": "genericNode"
+ }
+ ],
+ "viewport": {
+ "x": 220.76849981202122,
+ "y": 353.4961393018069,
+ "zoom": 0.6618875325223841
+ }
+ },
+ "description": "Your AI Agent Accountant - Leverage Needle with Langflows Agent.",
+ "endpoint_name": null,
+ "id": "94da35d5-a43e-4260-b205-9d0df477cb69",
+ "is_component": false,
+ "last_tested_version": "1.2.0",
+ "name": "Invoice Summarizer",
+ "tags": [
+ "chatbots",
+ "content-generation",
+ "agent",
+ "assistants",
+ "agents"
+ ]
+}
\ No newline at end of file
diff --git a/src/backend/tests/unit/api/v1/test_api_schemas.py b/src/backend/tests/unit/api/v1/test_api_schemas.py
index 2a73afe22..f58bcf45d 100644
--- a/src/backend/tests/unit/api/v1/test_api_schemas.py
+++ b/src/backend/tests/unit/api/v1/test_api_schemas.py
@@ -6,6 +6,7 @@ from langflow.api.v1.schemas import ResultDataResponse, VertexBuildResponse
from langflow.schema.schema import OutputValue
from langflow.serialization import serialize
from langflow.services.tracing.schema import Log
+
from pydantic import BaseModel
# Use a smaller test size for hypothesis
diff --git a/src/backend/tests/unit/base/tools/test_component_toolkit.py b/src/backend/tests/unit/base/tools/test_component_toolkit.py
index bf8df7a18..c9d627973 100644
--- a/src/backend/tests/unit/base/tools/test_component_toolkit.py
+++ b/src/backend/tests/unit/base/tools/test_component_toolkit.py
@@ -8,6 +8,7 @@ from langflow.components.outputs.chat import ChatOutput
from langflow.components.tools.calculator import CalculatorToolComponent
from langflow.graph import Graph
from langflow.schema.data import Data
+
from pydantic import BaseModel
diff --git a/src/backend/tests/unit/base/tools/test_toolmodemixin.py b/src/backend/tests/unit/base/tools/test_toolmodemixin.py
index 27518360c..059464113 100644
--- a/src/backend/tests/unit/base/tools/test_toolmodemixin.py
+++ b/src/backend/tests/unit/base/tools/test_toolmodemixin.py
@@ -21,6 +21,7 @@ from langflow.io import (
TableInput,
)
from langflow.schema import Data
+
from pydantic import BaseModel
diff --git a/src/backend/tests/unit/components/helpers/test_structured_output_component.py b/src/backend/tests/unit/components/helpers/test_structured_output_component.py
index 8e1d1c32d..afe7adf59 100644
--- a/src/backend/tests/unit/components/helpers/test_structured_output_component.py
+++ b/src/backend/tests/unit/components/helpers/test_structured_output_component.py
@@ -5,8 +5,8 @@ import pytest
from langflow.components.helpers.structured_output import StructuredOutputComponent
from langflow.helpers.base_model import build_model_from_schema
from langflow.inputs.inputs import TableInput
-from pydantic import BaseModel
+from pydantic import BaseModel
from tests.base import ComponentTestBaseWithoutClient
from tests.unit.mock_language_model import MockLanguageModel
diff --git a/src/backend/tests/unit/graph/graph/state/test_state_model.py b/src/backend/tests/unit/graph/graph/state/test_state_model.py
index 869366b8e..4278637b9 100644
--- a/src/backend/tests/unit/graph/graph/state/test_state_model.py
+++ b/src/backend/tests/unit/graph/graph/state/test_state_model.py
@@ -5,6 +5,7 @@ from langflow.graph import Graph
from langflow.graph.graph.constants import Finish
from langflow.graph.state.model import create_state_model
from langflow.template.field.base import UNDEFINED
+
from pydantic import Field
diff --git a/src/backend/tests/unit/helpers/test_base_model_from_schema.py b/src/backend/tests/unit/helpers/test_base_model_from_schema.py
index d07a4908e..3bc2bcd2d 100644
--- a/src/backend/tests/unit/helpers/test_base_model_from_schema.py
+++ b/src/backend/tests/unit/helpers/test_base_model_from_schema.py
@@ -4,9 +4,10 @@ from typing import Any
import pytest
from langflow.helpers.base_model import build_model_from_schema
-from pydantic import BaseModel
from pydantic_core import PydanticUndefined
+from pydantic import BaseModel
+
class TestBuildModelFromSchema:
# Successfully creates a Pydantic model from a valid schema
diff --git a/src/backend/tests/unit/inputs/test_inputs.py b/src/backend/tests/unit/inputs/test_inputs.py
index 2a946a2a9..eacb2a40b 100644
--- a/src/backend/tests/unit/inputs/test_inputs.py
+++ b/src/backend/tests/unit/inputs/test_inputs.py
@@ -23,6 +23,7 @@ from langflow.inputs.inputs import (
)
from langflow.inputs.utils import instantiate_input
from langflow.schema.message import Message
+
from pydantic import ValidationError
diff --git a/src/backend/tests/unit/mock_language_model.py b/src/backend/tests/unit/mock_language_model.py
index 186d127cf..8c06e112b 100644
--- a/src/backend/tests/unit/mock_language_model.py
+++ b/src/backend/tests/unit/mock_language_model.py
@@ -1,9 +1,10 @@
from unittest.mock import MagicMock
from langchain_core.language_models import BaseLanguageModel
-from pydantic import BaseModel, Field
from typing_extensions import override
+from pydantic import BaseModel, Field
+
class MockLanguageModel(BaseLanguageModel, BaseModel):
"""A mock language model for testing purposes."""
diff --git a/src/backend/tests/unit/serialization/test_serialization.py b/src/backend/tests/unit/serialization/test_serialization.py
index e8110906c..dff548472 100644
--- a/src/backend/tests/unit/serialization/test_serialization.py
+++ b/src/backend/tests/unit/serialization/test_serialization.py
@@ -9,6 +9,7 @@ from hypothesis import strategies as st
from langchain_core.documents import Document
from langflow.serialization.constants import MAX_ITEMS_LENGTH, MAX_TEXT_LENGTH
from langflow.serialization.serialization import serialize, serialize_or_str
+
from pydantic import BaseModel as PydanticBaseModel
from pydantic.v1 import BaseModel as PydanticV1BaseModel
diff --git a/src/backend/tests/unit/test_schema.py b/src/backend/tests/unit/test_schema.py
index 97fd930e3..86aaf4500 100644
--- a/src/backend/tests/unit/test_schema.py
+++ b/src/backend/tests/unit/test_schema.py
@@ -7,6 +7,7 @@ from langflow.schema.data import Data
from langflow.template import Input, Output
from langflow.template.field.base import UNDEFINED
from langflow.type_extraction.type_extraction import post_process_type
+
from pydantic import ValidationError
diff --git a/src/backend/tests/unit/test_template.py b/src/backend/tests/unit/test_template.py
index 6b2127178..6a4ad938c 100644
--- a/src/backend/tests/unit/test_template.py
+++ b/src/backend/tests/unit/test_template.py
@@ -2,6 +2,7 @@ import importlib
import pytest
from langflow.utils.util import build_template_from_function, get_base_classes, get_default_factory
+
from pydantic import BaseModel
diff --git a/src/frontend/src/icons/Needle/NeedleIcon.jsx b/src/frontend/src/icons/Needle/NeedleIcon.jsx
deleted file mode 100644
index aa9b119f2..000000000
--- a/src/frontend/src/icons/Needle/NeedleIcon.jsx
+++ /dev/null
@@ -1,18 +0,0 @@
-const SvgNeedleIcon = (props) => (
-
-);
-
-export default SvgNeedleIcon;
diff --git a/src/frontend/src/icons/Needle/index.tsx b/src/frontend/src/icons/Needle/index.tsx
index 0ed5b6ad0..641b780da 100644
--- a/src/frontend/src/icons/Needle/index.tsx
+++ b/src/frontend/src/icons/Needle/index.tsx
@@ -1,9 +1,9 @@
import React, { forwardRef } from "react";
-import SvgNeedleIcon from "./NeedleIcon";
+import NeedleSvg from "./needle-icon.svg?react";
export const NeedleIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
- return ;
+ return ;
});
diff --git a/src/frontend/src/icons/Needle/needle-icon.svg b/src/frontend/src/icons/Needle/needle-icon.svg
index c9c79e7bf..2fff35b5b 100644
--- a/src/frontend/src/icons/Needle/needle-icon.svg
+++ b/src/frontend/src/icons/Needle/needle-icon.svg
@@ -1,5 +1,24 @@
-