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ytkimirti/langflow-workflow.json

Created September 5, 2024 12:47
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OpenAI & Upstash Vector RAG.json
Raw
langflow-workflow.json
{
"icon_bg_color": null,
"updated_at": "2024-09-05T12:40:30+00:00",
"webhook": false,
"id": "0e95ed7c-5e49-436f-a10b-c648010a6531",
"folder_id": "cafb0f8f-75f4-439d-8f7a-0391b5e23932",
"name": "OpenAI & Upstash Vector RAG",
"description": "Driving Innovation in Business Communication.",
"icon": null,
"is_component": false,
"endpoint_name": null,
"data": {
"nodes": [
{
"id": "ChatInput-gANZa",
"type": "genericNode",
"position": {
"x": -376.2036735306894,
"y": 401.83923396270535
},
"data": {
"type": "ChatInput",
"node": {
"template": {
"_type": "Component",
"files": {
"trace_as_metadata": true,
"file_path": "",
"fileTypes": [
"txt",
"md",
"mdx",
"csv",
"json",
"yaml",
"yml",
"xml",
"html",
"htm",
"pdf",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx",
"jpg",
"jpeg",
"png",
"bmp",
"image"
],
"list": true,
"required": false,
"placeholder": "",
"show": true,
"name": "files",
"value": "",
"display_name": "Files",
"advanced": true,
"dynamic": false,
"info": "Files to be sent with the message.",
"title_case": false,
"type": "file",
"_input_type": "FileInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, MESSAGE_SENDER_NAME_USER\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Playground.\"\n icon = \"ChatInput\"\n name = \"ChatInput\"\n\n inputs = [\n MultilineInput(\n name=\"input_value\",\n display_name=\"Text\",\n value=\"\",\n info=\"Message to be passed as input.\",\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_USER,\n info=\"Type of sender.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_USER,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n FileInput(\n name=\"files\",\n display_name=\"Files\",\n file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n info=\"Files to be sent with the message.\",\n advanced=True,\n is_list=True,\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n files=self.files,\n )\n\n if (\n self.session_id\n and isinstance(message, Message)\n and isinstance(message.text, str)\n and self.should_store_message\n ):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "input_value",
"value": "who is his friend?",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as input.",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"combobox": false,
"required": false,
"placeholder": "",
"show": true,
"name": "sender",
"value": "User",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str",
"_input_type": "DropdownInput"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "sender_name",
"value": "User",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "session_id",
"value": "",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"should_store_message": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "should_store_message",
"value": true,
"display_name": "Store Messages",
"advanced": true,
"dynamic": false,
"info": "Store the message in the history.",
"title_case": false,
"type": "bool",
"_input_type": "BoolInput"
}
},
"description": "Get chat inputs from the Playground.",
"icon": "ChatInput",
"base_classes": [
"Message"
],
"display_name": "Chat Input",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value",
"should_store_message",
"sender",
"sender_name",
"session_id",
"files"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "ChatInput-gANZa"
},
"selected": false,
"width": 384,
"height": 298,
"positionAbsolute": {
"x": -376.2036735306894,
"y": 401.83923396270535
},
"dragging": false
},
{
"id": "OpenAIModel-Z5rm4",
"type": "genericNode",
"position": {
"x": 1606.2498562665157,
"y": 380.8237660377299
},
"data": {
"type": "OpenAIModel",
"node": {
"template": {
"_type": "Component",
"api_key": {
"load_from_db": true,
"required": false,
"placeholder": "",
"show": true,
"name": "api_key",
"value": null,
"display_name": "OpenAI API Key",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The OpenAI API Key to use for the OpenAI model.",
"title_case": false,
"password": true,
"type": "str",
"_input_type": "SecretStrInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "import operator\nfrom functools import reduce\n\nfrom langflow.field_typing.range_spec import RangeSpec\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import OPENAI_MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n BoolInput,\n DictInput,\n DropdownInput,\n FloatInput,\n IntInput,\n SecretStrInput,\n StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n name = \"OpenAIModel\"\n\n inputs = LCModelComponent._base_inputs + [\n IntInput(\n name=\"max_tokens\",\n display_name=\"Max Tokens\",\n advanced=True,\n info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n range_spec=RangeSpec(min=0, max=128000),\n ),\n DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n BoolInput(\n name=\"json_mode\",\n display_name=\"JSON Mode\",\n advanced=True,\n info=\"If True, it will output JSON regardless of passing a schema.\",\n ),\n DictInput(\n name=\"output_schema\",\n is_list=True,\n display_name=\"Schema\",\n advanced=True,\n info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n ),\n DropdownInput(\n name=\"model_name\",\n display_name=\"Model Name\",\n advanced=False,\n options=OPENAI_MODEL_NAMES,\n value=OPENAI_MODEL_NAMES[0],\n ),\n StrInput(\n name=\"openai_api_base\",\n display_name=\"OpenAI API Base\",\n advanced=True,\n info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n ),\n SecretStrInput(\n name=\"api_key\",\n display_name=\"OpenAI API Key\",\n info=\"The OpenAI API Key to use for the OpenAI model.\",\n advanced=False,\n value=\"OPENAI_API_KEY\",\n ),\n FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n IntInput(\n name=\"seed\",\n display_name=\"Seed\",\n info=\"The seed controls the reproducibility of the job.\",\n advanced=True,\n value=1,\n ),\n ]\n\n def build_model(self) -> LanguageModel: # type: ignore[type-var]\n # self.output_schema is a list of dictionaries\n # let's convert it to a dictionary\n output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n openai_api_key = self.api_key\n temperature = self.temperature\n model_name: str = self.model_name\n max_tokens = self.max_tokens\n model_kwargs = self.model_kwargs or {}\n openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n json_mode = bool(output_schema_dict) or self.json_mode\n seed = self.seed\n\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature if temperature is not None else 0.1,\n seed=seed,\n )\n if json_mode:\n if output_schema_dict:\n output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\") # type: ignore\n else:\n output = output.bind(response_format={\"type\": \"json_object\"}) # type: ignore\n\n return output # type: ignore\n\n def _get_exception_message(self, e: Exception):\n \"\"\"\n Get a message from an OpenAI exception.\n\n Args:\n exception (Exception): The exception to get the message from.\n\n Returns:\n str: The message from the exception.\n \"\"\"\n\n try:\n from openai import BadRequestError\n except ImportError:\n return\n if isinstance(e, BadRequestError):\n message = e.body.get(\"message\") # type: ignore\n if message:\n return message\n return\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"input_value": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "input_value",
"value": "",
"display_name": "Input",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "str",
"_input_type": "MessageInput"
},
"json_mode": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "json_mode",
"value": false,
"display_name": "JSON Mode",
"advanced": true,
"dynamic": false,
"info": "If True, it will output JSON regardless of passing a schema.",
"title_case": false,
"type": "bool",
"_input_type": "BoolInput"
},
"max_tokens": {
"trace_as_metadata": true,
"range_spec": {
"step_type": "float",
"min": 0,
"max": 128000,
"step": 0.1
},
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "max_tokens",
"value": "",
"display_name": "Max Tokens",
"advanced": true,
"dynamic": false,
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"model_kwargs": {
"trace_as_input": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "model_kwargs",
"value": {},
"display_name": "Model Kwargs",
"advanced": true,
"dynamic": false,
"info": "",
"title_case": false,
"type": "dict",
"_input_type": "DictInput"
},
"model_name": {
"trace_as_metadata": true,
"options": [
"gpt-4o-mini",
"gpt-4o",
"gpt-4-turbo",
"gpt-4-turbo-preview",
"gpt-4",
"gpt-3.5-turbo",
"gpt-3.5-turbo-0125"
],
"combobox": false,
"required": false,
"placeholder": "",
"show": true,
"name": "model_name",
"value": "gpt-4o-mini",
"display_name": "Model Name",
"advanced": false,
"dynamic": false,
"info": "",
"title_case": false,
"type": "str",
"_input_type": "DropdownInput"
},
"openai_api_base": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "openai_api_base",
"value": "",
"display_name": "OpenAI API Base",
"advanced": true,
"dynamic": false,
"info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"output_schema": {
"trace_as_input": true,
"list": true,
"required": false,
"placeholder": "",
"show": true,
"name": "output_schema",
"value": {},
"display_name": "Schema",
"advanced": true,
"dynamic": false,
"info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
"title_case": false,
"type": "dict",
"_input_type": "DictInput"
},
"seed": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "seed",
"value": 1,
"display_name": "Seed",
"advanced": true,
"dynamic": false,
"info": "The seed controls the reproducibility of the job.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"stream": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "stream",
"value": false,
"display_name": "Stream",
"advanced": true,
"dynamic": false,
"info": "Stream the response from the model. Streaming works only in Chat.",
"title_case": false,
"type": "bool",
"_input_type": "BoolInput"
},
"system_message": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "system_message",
"value": "",
"display_name": "System Message",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "System message to pass to the model.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"temperature": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "temperature",
"value": 0.1,
"display_name": "Temperature",
"advanced": false,
"dynamic": false,
"info": "",
"title_case": false,
"type": "float",
"_input_type": "FloatInput"
}
},
"description": "Generates text using OpenAI LLMs.",
"icon": "OpenAI",
"base_classes": [
"LanguageModel",
"Message"
],
"display_name": "OpenAI",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "text_output",
"display_name": "Text",
"method": "text_response",
"value": "__UNDEFINED__",
"cache": true
},
{
"types": [
"LanguageModel"
],
"selected": "LanguageModel",
"name": "model_output",
"display_name": "Language Model",
"method": "build_model",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"input_value",
"system_message",
"stream",
"max_tokens",
"model_kwargs",
"json_mode",
"output_schema",
"model_name",
"openai_api_base",
"api_key",
"temperature",
"seed"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "OpenAIModel-Z5rm4"
},
"selected": false,
"width": 384,
"height": 601,
"positionAbsolute": {
"x": 1606.2498562665157,
"y": 380.8237660377299
},
"dragging": false
},
{
"id": "ChatOutput-MxAb9",
"type": "genericNode",
"position": {
"x": 2081.0409327491257,
"y": 393.19993878756844
},
"data": {
"type": "ChatOutput",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.inputs import BoolInput\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.memory import store_message\nfrom langflow.schema.message import Message\nfrom langflow.utils.constants import MESSAGE_SENDER_NAME_AI, MESSAGE_SENDER_USER, MESSAGE_SENDER_AI\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n icon = \"ChatOutput\"\n name = \"ChatOutput\"\n\n inputs = [\n MessageTextInput(\n name=\"input_value\",\n display_name=\"Text\",\n info=\"Message to be passed as output.\",\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n ]\n\n def message_response(self) -> Message:\n message = Message(\n text=self.input_value,\n sender=self.sender,\n sender_name=self.sender_name,\n session_id=self.session_id,\n )\n if (\n self.session_id\n and isinstance(message, Message)\n and isinstance(message.text, str)\n and self.should_store_message\n ):\n store_message(\n message,\n flow_id=self.graph.flow_id,\n )\n self.message.value = message\n\n self.status = message\n return message\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"data_template": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "data_template",
"value": "{text}",
"display_name": "Data Template",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"input_value": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "input_value",
"value": "",
"display_name": "Text",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Message to be passed as output.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"sender": {
"trace_as_metadata": true,
"options": [
"Machine",
"User"
],
"combobox": false,
"required": false,
"placeholder": "",
"show": true,
"name": "sender",
"value": "Machine",
"display_name": "Sender Type",
"advanced": true,
"dynamic": false,
"info": "Type of sender.",
"title_case": false,
"type": "str",
"_input_type": "DropdownInput"
},
"sender_name": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "sender_name",
"value": "AI",
"display_name": "Sender Name",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Name of the sender.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"session_id": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "session_id",
"value": "",
"display_name": "Session ID",
"advanced": true,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The session ID of the chat. If empty, the current session ID parameter will be used.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
},
"should_store_message": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "should_store_message",
"value": true,
"display_name": "Store Messages",
"advanced": true,
"dynamic": false,
"info": "Store the message in the history.",
"title_case": false,
"type": "bool",
"_input_type": "BoolInput"
}
},
"description": "Display a chat message in the Playground.",
"icon": "ChatOutput",
"base_classes": [
"Message"
],
"display_name": "Chat Output",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "message",
"display_name": "Message",
"method": "message_response",
"value": "__UNDEFINED__",
"cache": true,
"hidden": true
}
],
"field_order": [
"input_value",
"should_store_message",
"sender",
"sender_name",
"session_id",
"data_template"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "ChatOutput-MxAb9"
},
"selected": false,
"width": 384,
"height": 253,
"positionAbsolute": {
"x": 2081.0409327491257,
"y": 393.19993878756844
},
"dragging": false
},
{
"id": "File-TGpoK",
"type": "genericNode",
"position": {
"x": 174.83158388605352,
"y": 1344.77628949614
},
"data": {
"type": "File",
"node": {
"template": {
"_type": "Component",
"path": {
"trace_as_metadata": true,
"file_path": "0e95ed7c-5e49-436f-a10b-c648010a6531/little_prince.txt",
"fileTypes": [
"txt",
"md",
"mdx",
"csv",
"json",
"yaml",
"yml",
"xml",
"html",
"htm",
"pdf",
"docx",
"py",
"sh",
"sql",
"js",
"ts",
"tsx"
],
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "path",
"value": "little_prince.txt",
"display_name": "Path",
"advanced": false,
"dynamic": false,
"info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
"title_case": false,
"type": "file",
"_input_type": "FileInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n name = \"File\"\n\n inputs = [\n FileInput(\n name=\"path\",\n display_name=\"Path\",\n file_types=TEXT_FILE_TYPES,\n info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n ),\n BoolInput(\n name=\"silent_errors\",\n display_name=\"Silent Errors\",\n advanced=True,\n info=\"If true, errors will not raise an exception.\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n ]\n\n def load_file(self) -> Data:\n if not self.path:\n raise ValueError(\"Please, upload a file to use this component.\")\n resolved_path = self.resolve_path(self.path)\n silent_errors = self.silent_errors\n\n extension = Path(resolved_path).suffix[1:].lower()\n\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n\n data = parse_text_file_to_data(resolved_path, silent_errors)\n self.status = data if data else \"No data\"\n return data or Data()\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"silent_errors": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "silent_errors",
"value": false,
"display_name": "Silent Errors",
"advanced": true,
"dynamic": false,
"info": "If true, errors will not raise an exception.",
"title_case": false,
"type": "bool",
"_input_type": "BoolInput"
}
},
"description": "A generic file loader.",
"icon": "file-text",
"base_classes": [
"Data"
],
"display_name": "File",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Data"
],
"selected": "Data",
"name": "data",
"display_name": "Data",
"method": "load_file",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"path",
"silent_errors"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "File-TGpoK"
},
"selected": false,
"width": 384,
"height": 298,
"positionAbsolute": {
"x": 174.83158388605352,
"y": 1344.77628949614
},
"dragging": false
},
{
"id": "SplitText-H7iXy",
"type": "genericNode",
"position": {
"x": 699.72292665829,
"y": 1340.902785403657
},
"data": {
"type": "SplitText",
"node": {
"template": {
"_type": "Component",
"data_inputs": {
"trace_as_metadata": true,
"list": true,
"required": false,
"placeholder": "",
"show": true,
"name": "data_inputs",
"value": "",
"display_name": "Data Inputs",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "The data to split.",
"title_case": false,
"type": "other",
"_input_type": "HandleInput"
},
"chunk_overlap": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "chunk_overlap",
"value": 200,
"display_name": "Chunk Overlap",
"advanced": false,
"dynamic": false,
"info": "Number of characters to overlap between chunks.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"chunk_size": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "chunk_size",
"value": 1000,
"display_name": "Chunk Size",
"advanced": false,
"dynamic": false,
"info": "The maximum number of characters in each chunk.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from typing import List\n\nfrom langchain_text_splitters import CharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.io import HandleInput, IntInput, MessageTextInput, Output\nfrom langflow.schema import Data\nfrom langflow.utils.util import unescape_string\n\n\nclass SplitTextComponent(Component):\n display_name: str = \"Split Text\"\n description: str = \"Split text into chunks based on specified criteria.\"\n icon = \"scissors-line-dashed\"\n name = \"SplitText\"\n\n inputs = [\n HandleInput(\n name=\"data_inputs\",\n display_name=\"Data Inputs\",\n info=\"The data to split.\",\n input_types=[\"Data\"],\n is_list=True,\n ),\n IntInput(\n name=\"chunk_overlap\",\n display_name=\"Chunk Overlap\",\n info=\"Number of characters to overlap between chunks.\",\n value=200,\n ),\n IntInput(\n name=\"chunk_size\",\n display_name=\"Chunk Size\",\n info=\"The maximum number of characters in each chunk.\",\n value=1000,\n ),\n MessageTextInput(\n name=\"separator\",\n display_name=\"Separator\",\n info=\"The character to split on. Defaults to newline.\",\n value=\"\\n\",\n ),\n ]\n\n outputs = [\n Output(display_name=\"Chunks\", name=\"chunks\", method=\"split_text\"),\n ]\n\n def _docs_to_data(self, docs):\n data = []\n for doc in docs:\n data.append(Data(text=doc.page_content, data=doc.metadata))\n return data\n\n def split_text(self) -> List[Data]:\n separator = unescape_string(self.separator)\n\n documents = []\n for _input in self.data_inputs:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n\n splitter = CharacterTextSplitter(\n chunk_overlap=self.chunk_overlap,\n chunk_size=self.chunk_size,\n separator=separator,\n )\n docs = splitter.split_documents(documents)\n data = self._docs_to_data(docs)\n self.status = data\n return data\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"separator": {
"trace_as_input": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "separator",
"value": "\n",
"display_name": "Separator",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The character to split on. Defaults to newline.",
"title_case": false,
"type": "str",
"_input_type": "MessageTextInput"
}
},
"description": "Split text into chunks based on specified criteria.",
"icon": "scissors-line-dashed",
"base_classes": [
"Data"
],
"display_name": "Split Text",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Data"
],
"selected": "Data",
"name": "chunks",
"display_name": "Chunks",
"method": "split_text",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"data_inputs",
"chunk_overlap",
"chunk_size",
"separator"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "SplitText-H7iXy"
},
"selected": false,
"width": 384,
"height": 546,
"positionAbsolute": {
"x": 699.72292665829,
"y": 1340.902785403657
},
"dragging": false
},
{
"id": "Upstash-dFmNR",
"type": "genericNode",
"position": {
"x": 1229.7499140204827,
"y": 1340.9027854036572
},
"data": {
"type": "Upstash",
"node": {
"template": {
"_type": "Component",
"embedding": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "embedding",
"value": "",
"display_name": "Embedding",
"advanced": false,
"input_types": [
"Embeddings"
],
"dynamic": false,
"info": "To use Upstash's embeddings, don't provide an embedding.",
"title_case": false,
"type": "other",
"_input_type": "HandleInput"
},
"ingest_data": {
"trace_as_metadata": true,
"list": true,
"trace_as_input": true,
"required": false,
"placeholder": "",
"show": true,
"name": "ingest_data",
"value": "",
"display_name": "Ingest Data",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "other",
"_input_type": "DataInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from typing import List\n\nfrom langchain_community.vectorstores import UpstashVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.io import (\n HandleInput,\n IntInput,\n StrInput,\n SecretStrInput,\n DataInput,\n MultilineInput,\n)\nfrom langflow.schema import Data\n\n\nclass UpstashVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Upstash\"\n description = \"Upstash Vector Store with search capabilities\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/vectorstores/upstash/\"\n name = \"Upstash\"\n icon = \"Upstash\"\n\n inputs = [\n StrInput(\n name=\"index_url\",\n display_name=\"Index URL\",\n info=\"The URL of the Upstash index.\",\n required=True,\n ),\n SecretStrInput(\n name=\"index_token\",\n display_name=\"Index Token\",\n info=\"The token for the Upstash index.\",\n required=True,\n ),\n StrInput(\n name=\"text_key\",\n display_name=\"Text Key\",\n info=\"The key in the record to use as text.\",\n value=\"text\",\n advanced=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Leave empty for default namespace.\",\n ),\n MultilineInput(name=\"search_query\", display_name=\"Search Query\"),\n MultilineInput(\n name=\"metadata_filter\",\n display_name=\"Metadata Filter\",\n info=\"Filters documents by metadata. Look at the documentation for more information.\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n info=\"To use Upstash's embeddings, don't provide an embedding.\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> UpstashVectorStore:\n use_upstash_embedding = self.embedding is None\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n if use_upstash_embedding:\n upstash_vs = UpstashVectorStore(\n embedding=use_upstash_embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n upstash_vs.add_documents(documents)\n else:\n upstash_vs = UpstashVectorStore.from_documents(\n documents=documents,\n embedding=self.embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n else:\n upstash_vs = UpstashVectorStore(\n embedding=self.embedding or use_upstash_embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n\n return upstash_vs\n\n def search_documents(self) -> List[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n filter=self.metadata_filter,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n else:\n return []\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"index_token": {
"load_from_db": true,
"required": true,
"placeholder": "",
"show": true,
"name": "index_token",
"value": "UPSTASH_VECTOR_TOKEN",
"display_name": "Index Token",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The token for the Upstash index.",
"title_case": false,
"password": true,
"type": "str",
"_input_type": "SecretStrInput"
},
"index_url": {
"trace_as_metadata": true,
"load_from_db": true,
"list": false,
"required": true,
"placeholder": "",
"show": true,
"name": "index_url",
"value": "UPSTASH_VECTOR_URL",
"display_name": "Index URL",
"advanced": false,
"dynamic": false,
"info": "The URL of the Upstash index.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"metadata_filter": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "metadata_filter",
"value": "",
"display_name": "Metadata Filter",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Filters documents by metadata. Look at the documentation for more information.",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
},
"namespace": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "namespace",
"value": "",
"display_name": "Namespace",
"advanced": false,
"dynamic": false,
"info": "Leave empty for default namespace.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"number_of_results": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "number_of_results",
"value": 4,
"display_name": "Number of Results",
"advanced": true,
"dynamic": false,
"info": "Number of results to return.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"search_query": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "search_query",
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"display_name": "Search Query",
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],
"dynamic": false,
"info": "",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
},
"text_key": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "text_key",
"value": "text",
"display_name": "Text Key",
"advanced": true,
"dynamic": false,
"info": "The key in the record to use as text.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
}
},
"description": "Upstash Vector Store with search capabilities",
"icon": "Upstash",
"base_classes": [
"Data",
"Retriever",
"VectorStore"
],
"display_name": "Upstash",
"documentation": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/upstash/",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Retriever"
],
"selected": "Retriever",
"name": "base_retriever",
"display_name": "Retriever",
"method": "build_base_retriever",
"value": "__UNDEFINED__",
"cache": true
},
{
"types": [
"Data"
],
"selected": "Data",
"name": "search_results",
"display_name": "Search Results",
"method": "search_documents",
"value": "__UNDEFINED__",
"cache": true
},
{
"types": [
"VectorStore"
],
"selected": "VectorStore",
"name": "vector_store",
"display_name": "Vector Store",
"method": "cast_vector_store",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"index_url",
"index_token",
"text_key",
"namespace",
"search_query",
"metadata_filter",
"ingest_data",
"embedding",
"number_of_results"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "Upstash-dFmNR"
},
"selected": false,
"width": 384,
"height": 828,
"positionAbsolute": {
"x": 1229.7499140204827,
"y": 1340.9027854036572
},
"dragging": false
},
{
"id": "Upstash-FIMbg",
"type": "genericNode",
"position": {
"x": 192.88830896698607,
"y": 404.21485713968906
},
"data": {
"type": "Upstash",
"node": {
"template": {
"_type": "Component",
"embedding": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "embedding",
"value": "",
"display_name": "Embedding",
"advanced": false,
"input_types": [
"Embeddings"
],
"dynamic": false,
"info": "To use Upstash's embeddings, don't provide an embedding.",
"title_case": false,
"type": "other",
"_input_type": "HandleInput"
},
"ingest_data": {
"trace_as_metadata": true,
"list": true,
"trace_as_input": true,
"required": false,
"placeholder": "",
"show": true,
"name": "ingest_data",
"value": "",
"display_name": "Ingest Data",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "other",
"_input_type": "DataInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from typing import List\n\nfrom langchain_community.vectorstores import UpstashVectorStore\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store\nfrom langflow.helpers.data import docs_to_data\nfrom langflow.io import (\n HandleInput,\n IntInput,\n StrInput,\n SecretStrInput,\n DataInput,\n MultilineInput,\n)\nfrom langflow.schema import Data\n\n\nclass UpstashVectorStoreComponent(LCVectorStoreComponent):\n display_name = \"Upstash\"\n description = \"Upstash Vector Store with search capabilities\"\n documentation = \"https://python.langchain.com/v0.2/docs/integrations/vectorstores/upstash/\"\n name = \"Upstash\"\n icon = \"Upstash\"\n\n inputs = [\n StrInput(\n name=\"index_url\",\n display_name=\"Index URL\",\n info=\"The URL of the Upstash index.\",\n required=True,\n ),\n SecretStrInput(\n name=\"index_token\",\n display_name=\"Index Token\",\n info=\"The token for the Upstash index.\",\n required=True,\n ),\n StrInput(\n name=\"text_key\",\n display_name=\"Text Key\",\n info=\"The key in the record to use as text.\",\n value=\"text\",\n advanced=True,\n ),\n StrInput(\n name=\"namespace\",\n display_name=\"Namespace\",\n info=\"Leave empty for default namespace.\",\n ),\n MultilineInput(name=\"search_query\", display_name=\"Search Query\"),\n MultilineInput(\n name=\"metadata_filter\",\n display_name=\"Metadata Filter\",\n info=\"Filters documents by metadata. Look at the documentation for more information.\",\n ),\n DataInput(\n name=\"ingest_data\",\n display_name=\"Ingest Data\",\n is_list=True,\n ),\n HandleInput(\n name=\"embedding\",\n display_name=\"Embedding\",\n input_types=[\"Embeddings\"],\n info=\"To use Upstash's embeddings, don't provide an embedding.\",\n ),\n IntInput(\n name=\"number_of_results\",\n display_name=\"Number of Results\",\n info=\"Number of results to return.\",\n value=4,\n advanced=True,\n ),\n ]\n\n @check_cached_vector_store\n def build_vector_store(self) -> UpstashVectorStore:\n use_upstash_embedding = self.embedding is None\n\n documents = []\n for _input in self.ingest_data or []:\n if isinstance(_input, Data):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n\n if documents:\n if use_upstash_embedding:\n upstash_vs = UpstashVectorStore(\n embedding=use_upstash_embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n upstash_vs.add_documents(documents)\n else:\n upstash_vs = UpstashVectorStore.from_documents(\n documents=documents,\n embedding=self.embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n else:\n upstash_vs = UpstashVectorStore(\n embedding=self.embedding or use_upstash_embedding,\n text_key=self.text_key,\n index_url=self.index_url,\n index_token=self.index_token,\n namespace=self.namespace,\n )\n\n return upstash_vs\n\n def search_documents(self) -> List[Data]:\n vector_store = self.build_vector_store()\n\n if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():\n docs = vector_store.similarity_search(\n query=self.search_query,\n k=self.number_of_results,\n filter=self.metadata_filter,\n )\n\n data = docs_to_data(docs)\n self.status = data\n return data\n else:\n return []\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"index_token": {
"load_from_db": true,
"required": true,
"placeholder": "",
"show": true,
"name": "index_token",
"value": "UPSTASH_VECTOR_TOKEN",
"display_name": "Index Token",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The token for the Upstash index.",
"title_case": false,
"password": true,
"type": "str",
"_input_type": "SecretStrInput"
},
"index_url": {
"trace_as_metadata": true,
"load_from_db": true,
"list": false,
"required": true,
"placeholder": "",
"show": true,
"name": "index_url",
"value": "UPSTASH_VECTOR_URL",
"display_name": "Index URL",
"advanced": false,
"dynamic": false,
"info": "The URL of the Upstash index.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"metadata_filter": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "metadata_filter",
"value": "",
"display_name": "Metadata Filter",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "Filters documents by metadata. Look at the documentation for more information.",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
},
"namespace": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "namespace",
"value": "",
"display_name": "Namespace",
"advanced": false,
"dynamic": false,
"info": "Leave empty for default namespace.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"number_of_results": {
"trace_as_metadata": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "number_of_results",
"value": 4,
"display_name": "Number of Results",
"advanced": true,
"dynamic": false,
"info": "Number of results to return.",
"title_case": false,
"type": "int",
"_input_type": "IntInput"
},
"search_query": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "search_query",
"value": "",
"display_name": "Search Query",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
},
"text_key": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "text_key",
"value": "text",
"display_name": "Text Key",
"advanced": true,
"dynamic": false,
"info": "The key in the record to use as text.",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
}
},
"description": "Upstash Vector Store with search capabilities",
"icon": "Upstash",
"base_classes": [
"Data",
"Retriever",
"VectorStore"
],
"display_name": "Upstash",
"documentation": "https://python.langchain.com/v0.2/docs/integrations/vectorstores/upstash/",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Retriever"
],
"selected": "Retriever",
"name": "base_retriever",
"display_name": "Retriever",
"method": "build_base_retriever",
"value": "__UNDEFINED__",
"cache": true
},
{
"types": [
"Data"
],
"selected": "Data",
"name": "search_results",
"display_name": "Search Results",
"method": "search_documents",
"value": "__UNDEFINED__",
"cache": true
},
{
"types": [
"VectorStore"
],
"selected": "VectorStore",
"name": "vector_store",
"display_name": "Vector Store",
"method": "cast_vector_store",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"index_url",
"index_token",
"text_key",
"namespace",
"search_query",
"metadata_filter",
"ingest_data",
"embedding",
"number_of_results"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "Upstash-FIMbg"
},
"selected": true,
"width": 384,
"height": 828,
"positionAbsolute": {
"x": 192.88830896698607,
"y": 404.21485713968906
},
"dragging": false
},
{
"id": "ParseData-p3WYI",
"type": "genericNode",
"position": {
"x": 657.0366507725095,
"y": 859.5390859831298
},
"data": {
"type": "ParseData",
"node": {
"template": {
"_type": "Component",
"data": {
"trace_as_metadata": true,
"list": false,
"trace_as_input": true,
"required": false,
"placeholder": "",
"show": true,
"name": "data",
"value": "",
"display_name": "Data",
"advanced": false,
"input_types": [
"Data"
],
"dynamic": false,
"info": "The data to convert to text.",
"title_case": false,
"type": "other",
"_input_type": "DataInput"
},
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Parse Data\"\n description = \"Convert Data into plain text following a specified template.\"\n icon = \"braces\"\n name = \"ParseData\"\n\n inputs = [\n DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n ]\n\n def parse_data(self) -> Message:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n\n result_string = data_to_text(template, data, sep=self.sep)\n self.status = result_string\n return Message(text=result_string)\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"sep": {
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "sep",
"value": "\n",
"display_name": "Separator",
"advanced": true,
"dynamic": false,
"info": "",
"title_case": false,
"type": "str",
"_input_type": "StrInput"
},
"template": {
"trace_as_input": true,
"multiline": true,
"trace_as_metadata": true,
"load_from_db": false,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "template",
"value": "{text}",
"display_name": "Template",
"advanced": false,
"input_types": [
"Message"
],
"dynamic": false,
"info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
"title_case": false,
"type": "str",
"_input_type": "MultilineInput"
}
},
"description": "Convert Data into plain text following a specified template.",
"icon": "braces",
"base_classes": [
"Message"
],
"display_name": "Parse Data",
"documentation": "",
"custom_fields": {},
"output_types": [],
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "text",
"display_name": "Text",
"method": "parse_data",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"data",
"template",
"sep"
],
"beta": false,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "ParseData-p3WYI"
},
"selected": false,
"width": 384,
"height": 374,
"positionAbsolute": {
"x": 657.0366507725095,
"y": 859.5390859831298
},
"dragging": false
},
{
"id": "Prompt-EUmv1",
"type": "genericNode",
"position": {
"x": 1142.6912335461534,
"y": 392.4151818157433
},
"data": {
"type": "Prompt",
"node": {
"template": {
"_type": "Component",
"code": {
"type": "code",
"required": true,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DefaultPromptField\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\nfrom langflow.template.utils import update_template_values\n\n\nclass PromptComponent(Component):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n trace_type = \"prompt\"\n name = \"Prompt\"\n\n inputs = [\n PromptInput(name=\"template\", display_name=\"Template\"),\n ]\n\n outputs = [\n Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n ]\n\n async def build_prompt(\n self,\n ) -> Message:\n prompt = await Message.from_template_and_variables(**self._attributes)\n self.status = prompt.text\n return prompt\n\n def _update_template(self, frontend_node: dict):\n prompt_template = frontend_node[\"template\"][\"template\"][\"value\"]\n custom_fields = frontend_node[\"custom_fields\"]\n frontend_node_template = frontend_node[\"template\"]\n _ = process_prompt_template(\n template=prompt_template,\n name=\"template\",\n custom_fields=custom_fields,\n frontend_node_template=frontend_node_template,\n )\n return frontend_node\n\n def post_code_processing(self, new_frontend_node: dict, current_frontend_node: dict):\n \"\"\"\n This function is called after the code validation is done.\n \"\"\"\n frontend_node = super().post_code_processing(new_frontend_node, current_frontend_node)\n template = frontend_node[\"template\"][\"template\"][\"value\"]\n # Kept it duplicated for backwards compatibility\n _ = process_prompt_template(\n template=template,\n name=\"template\",\n custom_fields=frontend_node[\"custom_fields\"],\n frontend_node_template=frontend_node[\"template\"],\n )\n # Now that template is updated, we need to grab any values that were set in the current_frontend_node\n # and update the frontend_node with those values\n update_template_values(new_template=frontend_node, previous_template=current_frontend_node[\"template\"])\n return frontend_node\n\n def _get_fallback_input(self, **kwargs):\n return DefaultPromptField(**kwargs)\n",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "code",
"advanced": true,
"dynamic": true,
"info": "",
"load_from_db": false,
"title_case": false
},
"template": {
"trace_as_input": true,
"list": false,
"required": false,
"placeholder": "",
"show": true,
"name": "template",
"value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
"display_name": "Template",
"advanced": false,
"dynamic": false,
"info": "",
"title_case": false,
"type": "prompt",
"_input_type": "PromptInput"
},
"context": {
"field_type": "str",
"required": false,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "context",
"display_name": "context",
"advanced": false,
"input_types": [
"Message",
"Text"
],
"dynamic": false,
"info": "",
"load_from_db": false,
"title_case": false,
"type": "str"
},
"question": {
"field_type": "str",
"required": false,
"placeholder": "",
"list": false,
"show": true,
"multiline": true,
"value": "",
"fileTypes": [],
"file_path": "",
"password": false,
"name": "question",
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"input_types": [
"Message",
"Text"
],
"dynamic": false,
"info": "",
"load_from_db": false,
"title_case": false,
"type": "str"
}
},
"description": "Create a prompt template with dynamic variables.",
"icon": "prompts",
"is_input": null,
"is_output": null,
"is_composition": null,
"base_classes": [
"Message"
],
"name": "",
"display_name": "Prompt",
"documentation": "",
"custom_fields": {
"template": [
"context",
"question"
]
},
"output_types": [],
"full_path": null,
"pinned": false,
"conditional_paths": [],
"frozen": false,
"outputs": [
{
"types": [
"Message"
],
"selected": "Message",
"name": "prompt",
"hidden": null,
"display_name": "Prompt Message",
"method": "build_prompt",
"value": "__UNDEFINED__",
"cache": true
}
],
"field_order": [
"template"
],
"beta": false,
"error": null,
"edited": false,
"lf_version": "1.0.16.post4"
},
"id": "Prompt-EUmv1"
},
"selected": true,
"width": 384,
"height": 498,
"positionAbsolute": {
"x": 1142.6912335461534,
"y": 392.4151818157433
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}
],
"edges": [
{
"source": "OpenAIModel-Z5rm4",
"sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Z5rm4œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}",
"target": "ChatOutput-MxAb9",
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