c6794adca9
* Changed flows and components to support different models * Changed onboarding to redirect automatically * Added new components and ids to settings * Changed flows service to change llm text components as well * changed models service to not remove : on ollama * fix edge not connecting on nudges flow
2288 lines
No EOL
124 KiB
JSON
2288 lines
No EOL
124 KiB
JSON
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"value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs.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 documentation: str = \"https://docs.langflow.org/components-io#chat-input\"\n icon = \"MessagesSquare\"\n name = \"ChatInput\"\n minimized = True\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Input 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 temp_file=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=\"Chat 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"
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},
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"type": "str",
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},
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"sender": {
|
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"_input_type": "DropdownInput",
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|
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"combobox": false,
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"dialog_inputs": {},
|
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"display_name": "Sender Type",
|
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"dynamic": false,
|
||
"info": "Type of sender.",
|
||
"name": "sender",
|
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"options": [
|
||
"Machine",
|
||
"User"
|
||
],
|
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"options_metadata": [],
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"sender_name": {
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"_input_type": "MessageTextInput",
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"_input_type": "MessageTextInput",
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"advanced": true,
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"display_name": "Session ID",
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"dynamic": false,
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"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
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"input_types": [
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"list": false,
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"required": false,
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"trace_as_input": true,
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"trace_as_metadata": true,
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"type": "str",
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"value": ""
|
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},
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"should_store_message": {
|
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"_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
|
||
},
|
||
"selected_output": "message",
|
||
"showNode": false,
|
||
"type": "ChatInput"
|
||
},
|
||
"dragging": false,
|
||
"id": "ChatInput-bqH7H",
|
||
"measured": {
|
||
"height": 48,
|
||
"width": 192
|
||
},
|
||
"position": {
|
||
"x": 1241.018940999428,
|
||
"y": 1088.6203941617916
|
||
},
|
||
"selected": false,
|
||
"type": "genericNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"id": "ChatOutput-BMVN5",
|
||
"node": {
|
||
"base_classes": [
|
||
"Message"
|
||
],
|
||
"beta": false,
|
||
"category": "outputs",
|
||
"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",
|
||
"clean_data"
|
||
],
|
||
"frozen": false,
|
||
"icon": "MessagesSquare",
|
||
"key": "ChatOutput",
|
||
"legacy": false,
|
||
"lf_version": "1.5.0.post1",
|
||
"metadata": {},
|
||
"minimized": true,
|
||
"output_types": [],
|
||
"outputs": [
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Output Message",
|
||
"group_outputs": false,
|
||
"method": "message_response",
|
||
"name": "message",
|
||
"selected": "Message",
|
||
"tool_mode": true,
|
||
"types": [
|
||
"Message"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
}
|
||
],
|
||
"pinned": false,
|
||
"score": 0.003169567463043492,
|
||
"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\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\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.template.field.base import Output\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 documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\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=\"Output 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\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 _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\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 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([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return 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": "HandleInput",
|
||
"advanced": false,
|
||
"display_name": "Inputs",
|
||
"dynamic": false,
|
||
"info": "Message to be passed as output.",
|
||
"input_types": [
|
||
"Data",
|
||
"DataFrame",
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"name": "input_value",
|
||
"placeholder": "",
|
||
"required": true,
|
||
"show": true,
|
||
"title_case": false,
|
||
"trace_as_metadata": true,
|
||
"type": "other",
|
||
"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-BMVN5",
|
||
"measured": {
|
||
"height": 48,
|
||
"width": 192
|
||
},
|
||
"position": {
|
||
"x": 2642.8863560882705,
|
||
"y": 740.3042509819792
|
||
},
|
||
"selected": false,
|
||
"type": "genericNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"id": "OpenSearch-iYfjf",
|
||
"node": {
|
||
"base_classes": [
|
||
"Data",
|
||
"DataFrame",
|
||
"VectorStore"
|
||
],
|
||
"beta": false,
|
||
"conditional_paths": [],
|
||
"custom_fields": {},
|
||
"description": "Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.",
|
||
"display_name": "OpenSearch (Hybrid)",
|
||
"documentation": "",
|
||
"edited": true,
|
||
"field_order": [
|
||
"opensearch_url",
|
||
"index_name",
|
||
"ingest_data",
|
||
"search_query",
|
||
"should_cache_vector_store",
|
||
"embedding",
|
||
"vector_field",
|
||
"number_of_results",
|
||
"filter_expression",
|
||
"auth_mode",
|
||
"username",
|
||
"password",
|
||
"jwt_token",
|
||
"jwt_header",
|
||
"bearer_prefix",
|
||
"use_ssl",
|
||
"verify_certs"
|
||
],
|
||
"frozen": false,
|
||
"icon": "OpenSearch",
|
||
"last_updated": "2025-09-04T19:50:05.460Z",
|
||
"legacy": false,
|
||
"lf_version": "1.5.0.post2",
|
||
"metadata": {},
|
||
"minimized": false,
|
||
"output_types": [],
|
||
"outputs": [
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Search Results",
|
||
"group_outputs": false,
|
||
"hidden": null,
|
||
"method": "search_documents",
|
||
"name": "search_results",
|
||
"options": null,
|
||
"required_inputs": null,
|
||
"tool_mode": true,
|
||
"types": [
|
||
"Data"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
},
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "DataFrame",
|
||
"group_outputs": false,
|
||
"hidden": null,
|
||
"method": "as_dataframe",
|
||
"name": "dataframe",
|
||
"options": null,
|
||
"required_inputs": null,
|
||
"selected": "DataFrame",
|
||
"tool_mode": true,
|
||
"types": [
|
||
"DataFrame"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
},
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Vector Store Connection",
|
||
"group_outputs": false,
|
||
"hidden": true,
|
||
"method": "as_vector_store",
|
||
"name": "vectorstoreconnection",
|
||
"options": null,
|
||
"required_inputs": null,
|
||
"tool_mode": true,
|
||
"types": [
|
||
"VectorStore"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
}
|
||
],
|
||
"pinned": false,
|
||
"template": {
|
||
"_type": "Component",
|
||
"auth_mode": {
|
||
"_input_type": "DropdownInput",
|
||
"advanced": false,
|
||
"combobox": false,
|
||
"dialog_inputs": {},
|
||
"display_name": "Auth Mode",
|
||
"dynamic": false,
|
||
"info": "Choose Basic (username/password) or JWT (Bearer token).",
|
||
"load_from_db": false,
|
||
"name": "auth_mode",
|
||
"options": [
|
||
"basic",
|
||
"jwt"
|
||
],
|
||
"options_metadata": [],
|
||
"placeholder": "",
|
||
"real_time_refresh": true,
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"toggle": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "jwt"
|
||
},
|
||
"bearer_prefix": {
|
||
"_input_type": "BoolInput",
|
||
"advanced": true,
|
||
"display_name": "Prefix 'Bearer '",
|
||
"dynamic": false,
|
||
"info": "",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"name": "bearer_prefix",
|
||
"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 __future__ import annotations\n\nimport json\nfrom typing import Any, Dict, List\n\nfrom opensearchpy import OpenSearch, helpers\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.base.vectorstores.vector_store_connection_decorator import vector_store_connection\nfrom langflow.io import (\n BoolInput,\n HandleInput,\n IntInput,\n MultilineInput,\n SecretStrInput,\n StrInput,\n DropdownInput,\n)\nfrom langflow.schema.data import Data\n\n\n@vector_store_connection\nclass OpenSearchHybridComponent(LCVectorStoreComponent):\n \"\"\"OpenSearch hybrid search: KNN (k=10, boost=0.7) + multi_match (boost=0.3) with optional filters & min_score.\"\"\"\n display_name: str = \"OpenSearch (Hybrid)\"\n name: str = \"OpenSearchHybrid\"\n icon: str = \"OpenSearch\"\n description: str = \"Hybrid search: KNN + keyword, with optional filters, min_score, and aggregations.\"\n\n # Keys we consider baseline\n default_keys: list[str] = [\n \"opensearch_url\",\n \"index_name\",\n *[i.name for i in LCVectorStoreComponent.inputs], # search_query, add_documents, etc.\n \"embedding\",\n \"vector_field\",\n \"number_of_results\",\n \"auth_mode\",\n \"username\",\n \"password\",\n \"jwt_token\",\n \"jwt_header\",\n \"bearer_prefix\",\n \"use_ssl\",\n \"verify_certs\",\n \"filter_expression\",\n ]\n\n inputs = [\n StrInput(\n name=\"opensearch_url\",\n display_name=\"OpenSearch URL\",\n value=\"http://localhost:9200\",\n info=\"URL for your OpenSearch cluster.\"\n ),\n StrInput(\n name=\"index_name\",\n display_name=\"Index Name\",\n value=\"langflow\",\n info=\"The index to search.\"\n ),\n *LCVectorStoreComponent.inputs, # includes search_query, add_documents, etc.\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"]\n ),\n StrInput(\n name=\"vector_field\",\n display_name=\"Vector Field\",\n value=\"chunk_embedding\",\n advanced=True,\n info=\"Vector field used for KNN.\"\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Default Size (limit)\",\n value=10,\n advanced=True,\n info=\"Default number of hits when no limit provided in filter_expression.\"\n ),\n MultilineInput(\n name=\"filter_expression\",\n display_name=\"Filter Expression (JSON)\",\n value=\"\",\n info=(\n \"Optional JSON to control filters/limit/score threshold.\\n\"\n \"Accepted shapes:\\n\"\n '1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\\n'\n '2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\\n'\n \"Placeholders with __IMPOSSIBLE_VALUE__ are ignored.\"\n )\n ),\n\n # ----- Auth controls (dynamic) -----\n DropdownInput(\n name=\"auth_mode\",\n display_name=\"Auth Mode\",\n value=\"basic\",\n options=[\"basic\", \"jwt\"],\n info=\"Choose Basic (username/password) or JWT (Bearer token).\",\n real_time_refresh=True,\n advanced=False,\n ),\n StrInput(\n name=\"username\",\n display_name=\"Username\",\n value=\"admin\",\n show=True,\n ),\n SecretStrInput(\n name=\"password\",\n display_name=\"Password\",\n value=\"admin\",\n show=True,\n ),\n SecretStrInput(\n name=\"jwt_token\",\n display_name=\"JWT Token\",\n value=\"\",\n show=False,\n info=\"Paste a valid JWT (sent as a header).\",\n ),\n StrInput(\n name=\"jwt_header\",\n display_name=\"JWT Header Name\",\n value=\"Authorization\",\n show=False,\n advanced=True,\n ),\n BoolInput(\n name=\"bearer_prefix\",\n display_name=\"Prefix 'Bearer '\",\n value=True,\n show=False,\n advanced=True,\n ),\n\n # ----- TLS -----\n BoolInput(\n name=\"use_ssl\",\n display_name=\"Use SSL\",\n value=True,\n advanced=True\n ),\n BoolInput(\n name=\"verify_certs\",\n display_name=\"Verify Certificates\",\n value=False,\n advanced=True\n ),\n ]\n\n # ---------- auth / client ----------\n def _build_auth_kwargs(self) -> Dict[str, Any]:\n mode = (self.auth_mode or \"basic\").strip().lower()\n if mode == \"jwt\":\n token = (self.jwt_token or \"\").strip()\n if not token:\n raise ValueError(\"Auth Mode is 'jwt' but no jwt_token was provided.\")\n header_name = (self.jwt_header or \"Authorization\").strip()\n header_value = f\"Bearer {token}\" if self.bearer_prefix else token\n return {\"headers\": {header_name: header_value}}\n user = (self.username or \"\").strip()\n pwd = (self.password or \"\").strip()\n if not user or not pwd:\n raise ValueError(\"Auth Mode is 'basic' but username/password are missing.\")\n return {\"http_auth\": (user, pwd)}\n\n def build_client(self) -> OpenSearch:\n auth_kwargs = self._build_auth_kwargs()\n return OpenSearch(\n hosts=[self.opensearch_url],\n use_ssl=self.use_ssl,\n verify_certs=self.verify_certs,\n ssl_assert_hostname=False,\n ssl_show_warn=False,\n **auth_kwargs,\n )\n\n @check_cached_vector_store\n def build_vector_store(self) -> OpenSearch:\n # Return raw OpenSearch client as our “vector store.”\n return self.build_client()\n\n # ---------- ingest ----------\n def _add_documents_to_vector_store(self, client: OpenSearch) -> None:\n docs = self._prepare_ingest_data() or []\n if not docs:\n self.log(\"No documents to ingest.\")\n return\n\n texts = [d.to_lc_document().page_content for d in docs]\n if not self.embedding:\n raise ValueError(\"Embedding handle is required to embed documents.\")\n vectors = self.embedding.embed_documents(texts)\n\n actions = []\n for doc_obj, vec in zip(docs, vectors):\n lc_doc = doc_obj.to_lc_document()\n body = {\n **lc_doc.metadata,\n \"text\": lc_doc.page_content,\n self.vector_field: vec,\n }\n actions.append({\n \"_op_type\": \"index\",\n \"_index\": self.index_name,\n \"_source\": body,\n })\n\n self.log(f\"Indexing {len(actions)} docs into '{self.index_name}'…\")\n helpers.bulk(client, actions)\n\n # ---------- helpers for filters ----------\n def _is_placeholder_term(self, term_obj: dict) -> bool:\n # term_obj like {\"filename\": \"__IMPOSSIBLE_VALUE__\"}\n return any(v == \"__IMPOSSIBLE_VALUE__\" for v in term_obj.values())\n\n def _coerce_filter_clauses(self, filter_obj: dict | None) -> List[dict]:\n \"\"\"\n Accepts either:\n A) {\"filter\":[ ...term/terms objects... ], \"limit\":..., \"score_threshold\":...}\n B) Context-style: {\"data_sources\":[...], \"document_types\":[...], \"owners\":[...]}\n Returns a list of OS filter clauses (term/terms), skipping placeholders and empty terms.\n \"\"\"\n \n if not filter_obj:\n return []\n\n # If it’s a string, try to parse it once\n if isinstance(filter_obj, str):\n try:\n filter_obj = json.loads(filter_obj)\n except Exception:\n # Not valid JSON → treat as no filters\n return []\n \n # Case A: already an explicit list/dict under \"filter\"\n if \"filter\" in filter_obj:\n raw = filter_obj[\"filter\"]\n if isinstance(raw, dict):\n raw = [raw]\n clauses: List[dict] = []\n for f in (raw or []):\n if \"term\" in f and isinstance(f[\"term\"], dict) and not self._is_placeholder_term(f[\"term\"]):\n clauses.append(f)\n elif \"terms\" in f and isinstance(f[\"terms\"], dict):\n field, vals = next(iter(f[\"terms\"].items()))\n if isinstance(vals, list) and len(vals) > 0:\n clauses.append(f)\n return clauses\n\n # Case B: convert context-style maps into clauses\n field_mapping = {\"data_sources\": \"filename\", \"document_types\": \"mimetype\", \"owners\": \"owner\"}\n print(f\"filter_obj {filter_obj}\")\n clauses: List[dict] = []\n for k, values in filter_obj.items():\n if not isinstance(values, list):\n continue\n field = field_mapping.get(k, k)\n if len(values) == 0:\n # Match-nothing placeholder (kept to mirror your tool semantics)\n clauses.append({\"term\": {field: \"__IMPOSSIBLE_VALUE__\"}})\n elif len(values) == 1:\n if values[0] != \"__IMPOSSIBLE_VALUE__\":\n clauses.append({\"term\": {field: values[0]}})\n else:\n clauses.append({\"terms\": {field: values}})\n return clauses\n\n # ---------- search (single hybrid path matching your tool) ----------\n def search(self, query: str | None = None) -> list[dict[str, Any]]:\n print(\"search method\")\n client = self.build_client()\n q = (query or \"\").strip()\n\n # Parse optional filter expression (can be either A or B shape; see _coerce_filter_clauses)\n filter_obj = None\n print(f\"DEBUG q {q}\")\n if getattr(self, \"filter_expression\", \"\") and self.filter_expression.strip():\n try:\n self.log(f\"DEBUG FILTER EXPRESSION {self.filter_expression}\")\n except json.JSONDecodeError as e:\n raise ValueError(f\"Invalid filter_expression JSON: {e}\") from e\n\n if not self.embedding:\n raise ValueError(\"Embedding is required to run hybrid search (KNN + keyword).\")\n\n # Embed the query\n vec = self.embedding.embed_query(q)\n\n # Build filter clauses (accept both shapes)\n clauses = self._coerce_filter_clauses(filter_obj)\n\n # Respect the tool's limit/threshold defaults\n limit = (filter_obj or {}).get(\"limit\", self.number_of_results)\n score_threshold = (filter_obj or {}).get(\"score_threshold\", 0)\n\n print(f\"DEBUG clauses {clauses}\")\n # Build the same hybrid body as your SearchService\n body = {\n \"query\": {\n \"bool\": {\n \"should\": [\n {\n \"knn\": {\n self.vector_field: {\n \"vector\": vec,\n \"k\": 10, # fixed to match the tool\n \"boost\": 0.7\n }\n }\n },\n {\n \"multi_match\": {\n \"query\": q,\n \"fields\": [\"text^2\", \"filename^1.5\"],\n \"type\": \"best_fields\",\n \"fuzziness\": \"AUTO\",\n \"boost\": 0.3\n }\n }\n ],\n \"minimum_should_match\": 1\n }\n },\n \"aggs\": {\n \"data_sources\": {\"terms\": {\"field\": \"filename\", \"size\": 20}},\n \"document_types\": {\"terms\": {\"field\": \"mimetype\", \"size\": 10}},\n \"owners\": {\"terms\": {\"field\": \"owner\", \"size\": 10}}\n },\n \"_source\": [\n \"filename\", \"mimetype\", \"page\", \"text\", \"source_url\",\n \"owner\", \"allowed_users\", \"allowed_groups\"\n ],\n \"size\": limit\n }\n print(f\"DEBUG BODY {body}\")\n if clauses:\n body[\"query\"][\"bool\"][\"filter\"] = clauses\n\n if isinstance(score_threshold, (int, float)) and score_threshold > 0:\n # top-level min_score (matches your tool)\n body[\"min_score\"] = score_threshold\n\n print(f\"DEBUG HYBRID BODY {body}\")\n resp = client.search(index=self.index_name, body=body)\n hits = resp.get(\"hits\", {}).get(\"hits\", [])\n return [\n {\n \"page_content\": hit[\"_source\"].get(\"text\", \"\"),\n \"metadata\": {k: v for k, v in hit[\"_source\"].items() if k != \"text\"},\n \"score\": hit.get(\"_score\"),\n }\n for hit in hits\n ]\n\n def search_documents(self) -> list[Data]:\n try:\n raw = self.search(self.search_query or \"\")\n return [\n Data(file_path=hit[\"metadata\"].get(\"file_path\", \"\"), text=hit[\"page_content\"])\n for hit in raw\n ]\n except Exception as e:\n print(f\"ERROR search_documents: {e}\")\n self.log(f\"search_documents error: {e}\")\n raise\n\n # -------- dynamic UI handling (auth switch) --------\n async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:\n try:\n if field_name == \"auth_mode\":\n mode = (field_value or \"basic\").strip().lower()\n is_basic = mode == \"basic\"\n is_jwt = mode == \"jwt\"\n\n build_config[\"username\"][\"show\"] = is_basic\n build_config[\"password\"][\"show\"] = is_basic\n\n build_config[\"jwt_token\"][\"show\"] = is_jwt\n build_config[\"jwt_header\"][\"show\"] = is_jwt\n build_config[\"bearer_prefix\"][\"show\"] = is_jwt\n\n build_config[\"username\"][\"required\"] = is_basic\n build_config[\"password\"][\"required\"] = is_basic\n\n build_config[\"jwt_token\"][\"required\"] = is_jwt\n build_config[\"jwt_header\"][\"required\"] = is_jwt\n build_config[\"bearer_prefix\"][\"required\"] = False\n\n if is_basic:\n build_config[\"jwt_token\"][\"value\"] = \"\"\n\n return build_config\n\n return build_config\n\n except Exception as e:\n self.log(f\"update_build_config error: {e}\")\n return build_config\n"
|
||
},
|
||
"embedding": {
|
||
"_input_type": "HandleInput",
|
||
"advanced": false,
|
||
"display_name": "Embedding",
|
||
"dynamic": false,
|
||
"info": "",
|
||
"input_types": [
|
||
"Embeddings"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"name": "embedding",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"trace_as_metadata": true,
|
||
"type": "other",
|
||
"value": ""
|
||
},
|
||
"filter_expression": {
|
||
"_input_type": "MultilineInput",
|
||
"advanced": false,
|
||
"copy_field": false,
|
||
"display_name": "Filter Expression (JSON)",
|
||
"dynamic": false,
|
||
"info": "Optional JSON to control filters/limit/score threshold.\nAccepted shapes:\n1) {\"filter\": [ {\"term\": {\"filename\":\"foo\"}}, {\"terms\":{\"owner\":[\"u1\",\"u2\"]}} ], \"limit\": 10, \"score_threshold\": 1.6 }\n2) Context-style maps: {\"data_sources\":[\"fileA\"], \"document_types\":[\"application/pdf\"], \"owners\":[\"123\"]}\nPlaceholders with __IMPOSSIBLE_VALUE__ are ignored.",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"multiline": true,
|
||
"name": "filter_expression",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": ""
|
||
},
|
||
"index_name": {
|
||
"_input_type": "StrInput",
|
||
"advanced": false,
|
||
"display_name": "Index Name",
|
||
"dynamic": false,
|
||
"info": "The index to search.",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "index_name",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "documents"
|
||
},
|
||
"ingest_data": {
|
||
"_input_type": "HandleInput",
|
||
"advanced": false,
|
||
"display_name": "Ingest Data",
|
||
"dynamic": false,
|
||
"info": "",
|
||
"input_types": [
|
||
"Data",
|
||
"DataFrame"
|
||
],
|
||
"list": true,
|
||
"list_add_label": "Add More",
|
||
"name": "ingest_data",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"trace_as_metadata": true,
|
||
"type": "other",
|
||
"value": ""
|
||
},
|
||
"jwt_header": {
|
||
"_input_type": "StrInput",
|
||
"advanced": true,
|
||
"display_name": "JWT Header Name",
|
||
"dynamic": false,
|
||
"info": "",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "jwt_header",
|
||
"placeholder": "",
|
||
"required": true,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "Authorization"
|
||
},
|
||
"jwt_token": {
|
||
"_input_type": "SecretStrInput",
|
||
"advanced": false,
|
||
"display_name": "JWT Token",
|
||
"dynamic": false,
|
||
"info": "Paste a valid JWT (sent as a header).",
|
||
"input_types": [],
|
||
"load_from_db": true,
|
||
"name": "jwt_token",
|
||
"password": true,
|
||
"placeholder": "",
|
||
"required": true,
|
||
"show": true,
|
||
"title_case": false,
|
||
"type": "str",
|
||
"value": "JWT"
|
||
},
|
||
"number_of_results": {
|
||
"_input_type": "IntInput",
|
||
"advanced": true,
|
||
"display_name": "Default Size (limit)",
|
||
"dynamic": false,
|
||
"info": "Default number of hits when no limit provided in filter_expression.",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "number_of_results",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "int",
|
||
"value": 4
|
||
},
|
||
"opensearch_url": {
|
||
"_input_type": "StrInput",
|
||
"advanced": false,
|
||
"display_name": "OpenSearch URL",
|
||
"dynamic": false,
|
||
"info": "URL for your OpenSearch cluster.",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "opensearch_url",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "https://opensearch:9200"
|
||
},
|
||
"password": {
|
||
"_input_type": "SecretStrInput",
|
||
"advanced": false,
|
||
"display_name": "Password",
|
||
"dynamic": false,
|
||
"info": "",
|
||
"input_types": [],
|
||
"load_from_db": false,
|
||
"name": "password",
|
||
"password": true,
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": false,
|
||
"title_case": false,
|
||
"type": "str",
|
||
"value": "o8h@xwLus123o"
|
||
},
|
||
"search_query": {
|
||
"_input_type": "QueryInput",
|
||
"advanced": false,
|
||
"display_name": "Search Query",
|
||
"dynamic": false,
|
||
"info": "Enter a query to run a similarity search.",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "search_query",
|
||
"placeholder": "Enter a query...",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": true,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "query",
|
||
"value": ""
|
||
},
|
||
"should_cache_vector_store": {
|
||
"_input_type": "BoolInput",
|
||
"advanced": true,
|
||
"display_name": "Cache Vector Store",
|
||
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||
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||
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"value": "from typing import Any\n\nfrom langchain_openai import OpenAIEmbeddings\n\nfrom langflow.base.embeddings.model import LCEmbeddingsModel\nfrom langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n MessageTextInput,\n SecretStrInput,\n)\nfrom langflow.schema.dotdict import dotdict\n\n\nclass EmbeddingModelComponent(LCEmbeddingsModel):\n display_name = \"Embedding Model\"\n description = \"Generate embeddings using a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-embedding-models\"\n icon = \"binary\"\n name = \"EmbeddingModel\"\n category = \"models\"\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\"],\n value=\"OpenAI\",\n info=\"Select the embedding model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}],\n ),\n DropdownInput(\n name=\"model\",\n display_name=\"Model Name\",\n options=OPENAI_EMBEDDING_MODEL_NAMES,\n value=OPENAI_EMBEDDING_MODEL_NAMES[0],\n info=\"Select the embedding model to use\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=True,\n show=True,\n real_time_refresh=True,\n ),\n MessageTextInput(\n name=\"api_base\",\n display_name=\"API Base URL\",\n info=\"Base URL for the API. Leave empty for default.\",\n advanced=True,\n ),\n IntInput(\n name=\"dimensions\",\n display_name=\"Dimensions\",\n info=\"The number of dimensions the resulting output embeddings should have. \"\n \"Only supported by certain models.\",\n advanced=True,\n ),\n IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n IntInput(name=\"max_retries\", display_name=\"Max Retries\", advanced=True, value=3),\n BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n DictInput(\n name=\"model_kwargs\",\n display_name=\"Model Kwargs\",\n advanced=True,\n info=\"Additional keyword arguments to pass to the model.\",\n ),\n ]\n\n def build_embeddings(self) -> Embeddings:\n provider = self.provider\n model = self.model\n api_key = self.api_key\n api_base = self.api_base\n dimensions = self.dimensions\n chunk_size = self.chunk_size\n request_timeout = self.request_timeout\n max_retries = self.max_retries\n show_progress_bar = self.show_progress_bar\n model_kwargs = self.model_kwargs or {}\n\n if provider == \"OpenAI\":\n if not api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n return OpenAIEmbeddings(\n model=model,\n dimensions=dimensions or None,\n base_url=api_base or None,\n api_key=api_key,\n chunk_size=chunk_size,\n max_retries=max_retries,\n timeout=request_timeout or None,\n show_progress_bar=show_progress_bar,\n model_kwargs=model_kwargs,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\" and field_value == \"OpenAI\":\n build_config[\"model\"][\"options\"] = OPENAI_EMBEDDING_MODEL_NAMES\n build_config[\"model\"][\"value\"] = OPENAI_EMBEDDING_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n build_config[\"api_base\"][\"display_name\"] = \"OpenAI API Base URL\"\n return build_config\n"
|
||
},
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||
"dimensions": {
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
"info": "Select the embedding model to use",
|
||
"name": "model",
|
||
"options": [
|
||
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|
||
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|
||
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|
||
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|
||
"options_metadata": [],
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||
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||
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||
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||
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||
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||
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||
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||
"type": "str",
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||
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|
||
},
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||
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|
||
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|
||
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|
||
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||
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||
"info": "Additional keyword arguments to pass to the model.",
|
||
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||
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||
"name": "model_kwargs",
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
},
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"info": "Select the embedding model provider",
|
||
"name": "provider",
|
||
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|
||
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||
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||
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|
||
{
|
||
"icon": "OpenAI"
|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
},
|
||
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|
||
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|
||
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||
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||
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||
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|
||
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||
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||
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||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
},
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"info": "",
|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
}
|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
},
|
||
{
|
||
"data": {
|
||
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||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
"value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom.custom_component.component 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 Template\"\n description: str = \"Create a prompt template with dynamic variables.\"\n documentation: str = \"https://docs.langflow.org/components-prompts\"\n icon = \"braces\"\n trace_type = \"prompt\"\n name = \"Prompt Template\"\n priority = 0 # Set priority to 0 to make it appear first\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\", 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"
|
||
},
|
||
"docs": {
|
||
"advanced": false,
|
||
"display_name": "docs",
|
||
"dynamic": false,
|
||
"field_type": "str",
|
||
"fileTypes": [],
|
||
"file_path": "",
|
||
"info": "",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"load_from_db": false,
|
||
"multiline": true,
|
||
"name": "docs",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"type": "str",
|
||
"value": ""
|
||
},
|
||
"prompt": {
|
||
"advanced": false,
|
||
"display_name": "prompt",
|
||
"dynamic": false,
|
||
"field_type": "str",
|
||
"fileTypes": [],
|
||
"file_path": "",
|
||
"info": "",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"load_from_db": false,
|
||
"multiline": true,
|
||
"name": "prompt",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"type": "str",
|
||
"value": ""
|
||
},
|
||
"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 generating prompt nudges to help a user explore a corpus.\n\nTask:\n1) Skim the documents to infer common themes, entities, or tasks.\n2) Propose exactly three concise, distinct prompt nudges that encourage useful next queries.\n3) If the chat history is provided, use it to generate new questions that the user might have, based on the llm's response to his previous query. DO NOT repeat user questions.\n4) Make the nudges concise, close to 40 characters.\n5) The nudges are questions or commands that the user can make to the chatbot, which will respond looking at the corpus.\n4) Return strings only, separating the nudges by a newline. Don't include quotation marks.\n5) If any error occured, return blank. This will be used in production, so don't ask for more info or confirm a info like you're talking to me still. If, for some reason, you can't provide the nudges, your job failed and you just return blank.\nRules: Be brief. No duplicates. No explanations outside the strings of the nudges. English only.\n\nExamples:\n Show me this quarter's top 10 deals\n Summarize recent client interactions\n Search OpenSearch for mentions of our competitors\n\nChat history:\n{prompt}\n\nDocuments:\n{docs}\n\n"
|
||
},
|
||
"tool_placeholder": {
|
||
"_input_type": "MessageTextInput",
|
||
"advanced": true,
|
||
"display_name": "Tool Placeholder",
|
||
"dynamic": false,
|
||
"info": "A placeholder input for tool mode.",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"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 Template"
|
||
},
|
||
"dragging": false,
|
||
"id": "Prompt Template-Wo6kR",
|
||
"measured": {
|
||
"height": 449,
|
||
"width": 320
|
||
},
|
||
"position": {
|
||
"x": 1669.0365272581178,
|
||
"y": 712.1086273287026
|
||
},
|
||
"selected": false,
|
||
"type": "genericNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"id": "ParserComponent-tZs7s",
|
||
"node": {
|
||
"base_classes": [
|
||
"Message"
|
||
],
|
||
"beta": false,
|
||
"conditional_paths": [],
|
||
"custom_fields": {},
|
||
"description": "Extracts text using a template.",
|
||
"display_name": "Parser",
|
||
"documentation": "https://docs.langflow.org/components-processing#parser",
|
||
"edited": false,
|
||
"field_order": [
|
||
"input_data",
|
||
"mode",
|
||
"pattern",
|
||
"sep"
|
||
],
|
||
"frozen": false,
|
||
"icon": "braces",
|
||
"legacy": false,
|
||
"metadata": {
|
||
"code_hash": "556209520650",
|
||
"dependencies": {
|
||
"dependencies": [
|
||
{
|
||
"name": "langflow",
|
||
"version": "0.5.0.post2"
|
||
}
|
||
],
|
||
"total_dependencies": 1
|
||
},
|
||
"module": "langflow.components.processing.parser.ParserComponent"
|
||
},
|
||
"minimized": false,
|
||
"output_types": [],
|
||
"outputs": [
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Parsed Text",
|
||
"group_outputs": false,
|
||
"method": "parse_combined_text",
|
||
"name": "parsed_text",
|
||
"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.custom.custom_component.component import Component\nfrom langflow.helpers.data import safe_convert\nfrom langflow.inputs.inputs import BoolInput, HandleInput, MessageTextInput, MultilineInput, TabInput\nfrom langflow.schema.data import Data\nfrom langflow.schema.dataframe import DataFrame\nfrom langflow.schema.message import Message\nfrom langflow.template.field.base import Output\n\n\nclass ParserComponent(Component):\n display_name = \"Parser\"\n description = \"Extracts text using a template.\"\n documentation: str = \"https://docs.langflow.org/components-processing#parser\"\n icon = \"braces\"\n\n inputs = [\n HandleInput(\n name=\"input_data\",\n display_name=\"Data or DataFrame\",\n input_types=[\"DataFrame\", \"Data\"],\n info=\"Accepts either a DataFrame or a Data object.\",\n required=True,\n ),\n TabInput(\n name=\"mode\",\n display_name=\"Mode\",\n options=[\"Parser\", \"Stringify\"],\n value=\"Parser\",\n info=\"Convert into raw string instead of using a template.\",\n real_time_refresh=True,\n ),\n MultilineInput(\n name=\"pattern\",\n display_name=\"Template\",\n info=(\n \"Use variables within curly brackets to extract column values for DataFrames \"\n \"or key values for Data.\"\n \"For example: `Name: {Name}, Age: {Age}, Country: {Country}`\"\n ),\n value=\"Text: {text}\", # Example default\n dynamic=True,\n show=True,\n required=True,\n ),\n MessageTextInput(\n name=\"sep\",\n display_name=\"Separator\",\n advanced=True,\n value=\"\\n\",\n info=\"String used to separate rows/items.\",\n ),\n ]\n\n outputs = [\n Output(\n display_name=\"Parsed Text\",\n name=\"parsed_text\",\n info=\"Formatted text output.\",\n method=\"parse_combined_text\",\n ),\n ]\n\n def update_build_config(self, build_config, field_value, field_name=None):\n \"\"\"Dynamically hide/show `template` and enforce requirement based on `stringify`.\"\"\"\n if field_name == \"mode\":\n build_config[\"pattern\"][\"show\"] = self.mode == \"Parser\"\n build_config[\"pattern\"][\"required\"] = self.mode == \"Parser\"\n if field_value:\n clean_data = BoolInput(\n name=\"clean_data\",\n display_name=\"Clean Data\",\n info=(\n \"Enable to clean the data by removing empty rows and lines \"\n \"in each cell of the DataFrame/ Data object.\"\n ),\n value=True,\n advanced=True,\n required=False,\n )\n build_config[\"clean_data\"] = clean_data.to_dict()\n else:\n build_config.pop(\"clean_data\", None)\n\n return build_config\n\n def _clean_args(self):\n \"\"\"Prepare arguments based on input type.\"\"\"\n input_data = self.input_data\n\n match input_data:\n case list() if all(isinstance(item, Data) for item in input_data):\n msg = \"List of Data objects is not supported.\"\n raise ValueError(msg)\n case DataFrame():\n return input_data, None\n case Data():\n return None, input_data\n case dict() if \"data\" in input_data:\n try:\n if \"columns\" in input_data: # Likely a DataFrame\n return DataFrame.from_dict(input_data), None\n # Likely a Data object\n return None, Data(**input_data)\n except (TypeError, ValueError, KeyError) as e:\n msg = f\"Invalid structured input provided: {e!s}\"\n raise ValueError(msg) from e\n case _:\n msg = f\"Unsupported input type: {type(input_data)}. Expected DataFrame or Data.\"\n raise ValueError(msg)\n\n def parse_combined_text(self) -> Message:\n \"\"\"Parse all rows/items into a single text or convert input to string if `stringify` is enabled.\"\"\"\n # Early return for stringify option\n if self.mode == \"Stringify\":\n return self.convert_to_string()\n\n df, data = self._clean_args()\n\n lines = []\n if df is not None:\n for _, row in df.iterrows():\n formatted_text = self.pattern.format(**row.to_dict())\n lines.append(formatted_text)\n elif data is not None:\n formatted_text = self.pattern.format(**data.data)\n lines.append(formatted_text)\n\n combined_text = self.sep.join(lines)\n self.status = combined_text\n return Message(text=combined_text)\n\n def convert_to_string(self) -> Message:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n result = \"\"\n if isinstance(self.input_data, list):\n result = \"\\n\".join([safe_convert(item, clean_data=self.clean_data or False) for item in self.input_data])\n else:\n result = safe_convert(self.input_data or False)\n self.log(f\"Converted to string with length: {len(result)}\")\n\n message = Message(text=result)\n self.status = message\n return message\n"
|
||
},
|
||
"input_data": {
|
||
"_input_type": "HandleInput",
|
||
"advanced": false,
|
||
"display_name": "Data or DataFrame",
|
||
"dynamic": false,
|
||
"info": "Accepts either a DataFrame or a Data object.",
|
||
"input_types": [
|
||
"DataFrame",
|
||
"Data"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"name": "input_data",
|
||
"placeholder": "",
|
||
"required": true,
|
||
"show": true,
|
||
"title_case": false,
|
||
"trace_as_metadata": true,
|
||
"type": "other",
|
||
"value": ""
|
||
},
|
||
"mode": {
|
||
"_input_type": "TabInput",
|
||
"advanced": false,
|
||
"display_name": "Mode",
|
||
"dynamic": false,
|
||
"info": "Convert into raw string instead of using a template.",
|
||
"name": "mode",
|
||
"options": [
|
||
"Parser",
|
||
"Stringify"
|
||
],
|
||
"placeholder": "",
|
||
"real_time_refresh": true,
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "tab",
|
||
"value": "Parser"
|
||
},
|
||
"pattern": {
|
||
"_input_type": "MultilineInput",
|
||
"advanced": false,
|
||
"copy_field": false,
|
||
"display_name": "Template",
|
||
"dynamic": true,
|
||
"info": "Use variables within curly brackets to extract column values for DataFrames or key values for Data.For example: `Name: {Name}, Age: {Age}, Country: {Country}`",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"multiline": true,
|
||
"name": "pattern",
|
||
"placeholder": "",
|
||
"required": true,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "{text}"
|
||
},
|
||
"sep": {
|
||
"_input_type": "MessageTextInput",
|
||
"advanced": true,
|
||
"display_name": "Separator",
|
||
"dynamic": false,
|
||
"info": "String used to separate rows/items.",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"name": "sep",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "\n"
|
||
}
|
||
},
|
||
"tool_mode": false
|
||
},
|
||
"showNode": true,
|
||
"type": "ParserComponent"
|
||
},
|
||
"dragging": false,
|
||
"id": "ParserComponent-tZs7s",
|
||
"measured": {
|
||
"height": 329,
|
||
"width": 320
|
||
},
|
||
"position": {
|
||
"x": 1282.0613788430787,
|
||
"y": 564.2200355777322
|
||
},
|
||
"selected": false,
|
||
"type": "genericNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"id": "LanguageModelComponent-NSTA6",
|
||
"node": {
|
||
"base_classes": [
|
||
"LanguageModel",
|
||
"Message"
|
||
],
|
||
"beta": false,
|
||
"conditional_paths": [],
|
||
"custom_fields": {},
|
||
"description": "Runs a language model given a specified provider.",
|
||
"display_name": "Language Model",
|
||
"documentation": "https://docs.langflow.org/components-models",
|
||
"edited": false,
|
||
"field_order": [
|
||
"provider",
|
||
"model_name",
|
||
"api_key",
|
||
"input_value",
|
||
"system_message",
|
||
"stream",
|
||
"temperature"
|
||
],
|
||
"frozen": false,
|
||
"icon": "brain-circuit",
|
||
"last_updated": "2025-09-22T20:02:40.428Z",
|
||
"legacy": false,
|
||
"metadata": {
|
||
"code_hash": "6ac42a7167a4",
|
||
"dependencies": {
|
||
"dependencies": [
|
||
{
|
||
"name": "langchain_anthropic",
|
||
"version": "0.3.14"
|
||
},
|
||
{
|
||
"name": "langchain_google_genai",
|
||
"version": "2.0.6"
|
||
},
|
||
{
|
||
"name": "langchain_openai",
|
||
"version": "0.3.23"
|
||
},
|
||
{
|
||
"name": "langflow",
|
||
"version": null
|
||
}
|
||
],
|
||
"total_dependencies": 4
|
||
},
|
||
"keywords": [
|
||
"model",
|
||
"llm",
|
||
"language model",
|
||
"large language model"
|
||
],
|
||
"module": "langflow.components.models.language_model.LanguageModelComponent"
|
||
},
|
||
"minimized": false,
|
||
"output_types": [],
|
||
"outputs": [
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Model Response",
|
||
"group_outputs": false,
|
||
"method": "text_response",
|
||
"name": "text_output",
|
||
"options": null,
|
||
"required_inputs": null,
|
||
"selected": "Message",
|
||
"tool_mode": true,
|
||
"types": [
|
||
"Message"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
},
|
||
{
|
||
"allows_loop": false,
|
||
"cache": true,
|
||
"display_name": "Language Model",
|
||
"group_outputs": false,
|
||
"method": "build_model",
|
||
"name": "model_output",
|
||
"options": null,
|
||
"required_inputs": null,
|
||
"selected": "LanguageModel",
|
||
"tool_mode": true,
|
||
"types": [
|
||
"LanguageModel"
|
||
],
|
||
"value": "__UNDEFINED__"
|
||
}
|
||
],
|
||
"pinned": false,
|
||
"priority": 0,
|
||
"template": {
|
||
"_type": "Component",
|
||
"api_key": {
|
||
"_input_type": "SecretStrInput",
|
||
"advanced": false,
|
||
"display_name": "OpenAI API Key",
|
||
"dynamic": false,
|
||
"info": "Model Provider API key",
|
||
"input_types": [],
|
||
"load_from_db": true,
|
||
"name": "api_key",
|
||
"password": true,
|
||
"placeholder": "",
|
||
"real_time_refresh": true,
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"type": "str",
|
||
"value": "OPENAI_API_KEY"
|
||
},
|
||
"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 typing import Any\n\nfrom langchain_anthropic import ChatAnthropic\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.models.anthropic_constants import ANTHROPIC_MODELS\nfrom langflow.base.models.google_generative_ai_constants import GOOGLE_GENERATIVE_AI_MODELS\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_CHAT_MODEL_NAMES, OPENAI_REASONING_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langflow.inputs.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageInput, MultilineInput, SecretStrInput, SliderInput\nfrom langflow.schema.dotdict import dotdict\n\n\nclass LanguageModelComponent(LCModelComponent):\n display_name = \"Language Model\"\n description = \"Runs a language model given a specified provider.\"\n documentation: str = \"https://docs.langflow.org/components-models\"\n icon = \"brain-circuit\"\n category = \"models\"\n priority = 0 # Set priority to 0 to make it appear first\n\n inputs = [\n DropdownInput(\n name=\"provider\",\n display_name=\"Model Provider\",\n options=[\"OpenAI\", \"Anthropic\", \"Google\"],\n value=\"OpenAI\",\n info=\"Select the model provider\",\n real_time_refresh=True,\n options_metadata=[{\"icon\": \"OpenAI\"}, {\"icon\": \"Anthropic\"}, {\"icon\": \"GoogleGenerativeAI\"}],\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n options=OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES,\n value=OPENAI_CHAT_MODEL_NAMES[0],\n info=\"Select the model to use\",\n real_time_refresh=True,\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"Model Provider API key\",\n required=False,\n show=True,\n real_time_refresh=True,\n ),\n MessageInput(\n name=\"input_value\",\n display_name=\"Input\",\n info=\"The input text to send to the model\",\n ),\n MultilineInput(\n name=\"system_message\",\n display_name=\"System Message\",\n info=\"A system message that helps set the behavior of the assistant\",\n advanced=False,\n ),\n BoolInput(\n name=\"stream\",\n display_name=\"Stream\",\n info=\"Whether to stream the response\",\n value=False,\n advanced=True,\n ),\n SliderInput(\n name=\"temperature\",\n display_name=\"Temperature\",\n value=0.1,\n info=\"Controls randomness in responses\",\n range_spec=RangeSpec(min=0, max=1, step=0.01),\n advanced=True,\n ),\n ]\n\n def build_model(self) -> LanguageModel:\n provider = self.provider\n model_name = self.model_name\n temperature = self.temperature\n stream = self.stream\n\n if provider == \"OpenAI\":\n if not self.api_key:\n msg = \"OpenAI API key is required when using OpenAI provider\"\n raise ValueError(msg)\n\n if model_name in OPENAI_REASONING_MODEL_NAMES:\n # reasoning models do not support temperature (yet)\n temperature = None\n\n return ChatOpenAI(\n model_name=model_name,\n temperature=temperature,\n streaming=stream,\n openai_api_key=self.api_key,\n )\n if provider == \"Anthropic\":\n if not self.api_key:\n msg = \"Anthropic API key is required when using Anthropic provider\"\n raise ValueError(msg)\n return ChatAnthropic(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n anthropic_api_key=self.api_key,\n )\n if provider == \"Google\":\n if not self.api_key:\n msg = \"Google API key is required when using Google provider\"\n raise ValueError(msg)\n return ChatGoogleGenerativeAI(\n model=model_name,\n temperature=temperature,\n streaming=stream,\n google_api_key=self.api_key,\n )\n msg = f\"Unknown provider: {provider}\"\n raise ValueError(msg)\n\n def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:\n if field_name == \"provider\":\n if field_value == \"OpenAI\":\n build_config[\"model_name\"][\"options\"] = OPENAI_CHAT_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES\n build_config[\"model_name\"][\"value\"] = OPENAI_CHAT_MODEL_NAMES[0]\n build_config[\"api_key\"][\"display_name\"] = \"OpenAI API Key\"\n elif field_value == \"Anthropic\":\n build_config[\"model_name\"][\"options\"] = ANTHROPIC_MODELS\n build_config[\"model_name\"][\"value\"] = ANTHROPIC_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Anthropic API Key\"\n elif field_value == \"Google\":\n build_config[\"model_name\"][\"options\"] = GOOGLE_GENERATIVE_AI_MODELS\n build_config[\"model_name\"][\"value\"] = GOOGLE_GENERATIVE_AI_MODELS[0]\n build_config[\"api_key\"][\"display_name\"] = \"Google API Key\"\n elif field_name == \"model_name\" and field_value.startswith(\"o1\") and self.provider == \"OpenAI\":\n # Hide system_message for o1 models - currently unsupported\n if \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = False\n elif field_name == \"model_name\" and not field_value.startswith(\"o1\") and \"system_message\" in build_config:\n build_config[\"system_message\"][\"show\"] = True\n return build_config\n"
|
||
},
|
||
"input_value": {
|
||
"_input_type": "MessageInput",
|
||
"advanced": false,
|
||
"display_name": "Input",
|
||
"dynamic": false,
|
||
"info": "The input text to send to the model",
|
||
"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": false,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": ""
|
||
},
|
||
"model_name": {
|
||
"_input_type": "DropdownInput",
|
||
"advanced": false,
|
||
"combobox": false,
|
||
"dialog_inputs": {},
|
||
"display_name": "Model Name",
|
||
"dynamic": false,
|
||
"info": "Select the model to use",
|
||
"name": "model_name",
|
||
"options": [
|
||
"gpt-4o-mini",
|
||
"gpt-4o",
|
||
"gpt-4.1",
|
||
"gpt-4.1-mini",
|
||
"gpt-4.1-nano",
|
||
"gpt-4-turbo",
|
||
"gpt-4-turbo-preview",
|
||
"gpt-4",
|
||
"gpt-3.5-turbo",
|
||
"gpt-5",
|
||
"gpt-5-mini",
|
||
"gpt-5-nano",
|
||
"gpt-5-chat-latest",
|
||
"o1",
|
||
"o3-mini",
|
||
"o3",
|
||
"o3-pro",
|
||
"o4-mini",
|
||
"o4-mini-high"
|
||
],
|
||
"options_metadata": [],
|
||
"placeholder": "",
|
||
"real_time_refresh": true,
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"toggle": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "gpt-4o-mini"
|
||
},
|
||
"provider": {
|
||
"_input_type": "DropdownInput",
|
||
"advanced": false,
|
||
"combobox": false,
|
||
"dialog_inputs": {},
|
||
"display_name": "Model Provider",
|
||
"dynamic": false,
|
||
"info": "Select the model provider",
|
||
"name": "provider",
|
||
"options": [
|
||
"OpenAI",
|
||
"Anthropic",
|
||
"Google"
|
||
],
|
||
"options_metadata": [
|
||
{
|
||
"icon": "OpenAI"
|
||
},
|
||
{
|
||
"icon": "Anthropic"
|
||
},
|
||
{
|
||
"icon": "GoogleGenerativeAI"
|
||
}
|
||
],
|
||
"placeholder": "",
|
||
"real_time_refresh": true,
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"toggle": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": "OpenAI"
|
||
},
|
||
"stream": {
|
||
"_input_type": "BoolInput",
|
||
"advanced": true,
|
||
"display_name": "Stream",
|
||
"dynamic": false,
|
||
"info": "Whether to stream the response",
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"name": "stream",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_metadata": true,
|
||
"type": "bool",
|
||
"value": false
|
||
},
|
||
"system_message": {
|
||
"_input_type": "MultilineInput",
|
||
"advanced": false,
|
||
"copy_field": false,
|
||
"display_name": "System Message",
|
||
"dynamic": false,
|
||
"info": "A system message that helps set the behavior of the assistant",
|
||
"input_types": [
|
||
"Message"
|
||
],
|
||
"list": false,
|
||
"list_add_label": "Add More",
|
||
"load_from_db": false,
|
||
"multiline": true,
|
||
"name": "system_message",
|
||
"placeholder": "",
|
||
"required": false,
|
||
"show": true,
|
||
"title_case": false,
|
||
"tool_mode": false,
|
||
"trace_as_input": true,
|
||
"trace_as_metadata": true,
|
||
"type": "str",
|
||
"value": ""
|
||
},
|
||
"temperature": {
|
||
"_input_type": "SliderInput",
|
||
"advanced": true,
|
||
"display_name": "Temperature",
|
||
"dynamic": false,
|
||
"info": "Controls randomness in responses",
|
||
"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
|
||
}
|
||
},
|
||
"tool_mode": false
|
||
},
|
||
"selected_output": "text_output",
|
||
"showNode": true,
|
||
"type": "LanguageModelComponent"
|
||
},
|
||
"dragging": false,
|
||
"id": "LanguageModelComponent-NSTA6",
|
||
"measured": {
|
||
"height": 534,
|
||
"width": 320
|
||
},
|
||
"position": {
|
||
"x": 2172.8054035815467,
|
||
"y": 413.44064987070726
|
||
},
|
||
"selected": false,
|
||
"type": "genericNode"
|
||
}
|
||
],
|
||
"viewport": {
|
||
"x": -194.9599233786812,
|
||
"y": 65.54639038496208,
|
||
"zoom": 0.5547428131761893
|
||
}
|
||
},
|
||
"description": "OpenRAG Open Search Nudges generator, based on the Open Search documents and the chat history.",
|
||
"endpoint_name": null,
|
||
"id": "ebc01d31-1976-46ce-a385-b0240327226c",
|
||
"is_component": false,
|
||
"last_tested_version": "1.5.0.post2",
|
||
"name": "OpenRAG Open Search Nudges",
|
||
"tags": [
|
||
"assistants",
|
||
"agents"
|
||
]
|
||
} |