add langflow

Memory Chatbot.json

@@ -0,0 +1 @@
+{"id":"8f09acf2-a0c9-4cc2-aeb5-b9f4e3e6a270","data":{"nodes":[{"data":{"description":"Create a prompt template with dynamic variables.","display_name":"Prompt","id":"Prompt-84CoT","node":{"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from langflow.base.prompts.api_utils import process_prompt_template\nfrom langflow.custom import Component\nfrom langflow.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 post_code_processing(self, new_build_config: dict, current_build_config: dict):\n        \"\"\"\n        This function is called after the code validation is done.\n        \"\"\"\n        frontend_node = super().post_code_processing(new_build_config, current_build_config)\n        template = frontend_node[\"template\"][\"template\"][\"value\"]\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_build_config\n        # and update the frontend_node with those values\n        update_template_values(new_template=frontend_node, previous_template=current_build_config[\"template\"])\n        return frontend_node\n"},"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"},"template":{"advanced":false,"display_name":"Template","dynamic":false,"info":"","list":false,"load_from_db":false,"name":"template","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"type":"prompt","value":"Answer the users question based on the following context:\n\nThe context is this: {context}\n\nthe users question is this: {question}"},"question":{"field_type":"str","required":false,"placeholder":"","list":false,"show":true,"multiline":true,"value":"","fileTypes":[],"file_path":"","password":false,"name":"question","display_name":"question","advanced":false,"input_types":["Message","Text"],"dynamic":false,"info":"","load_from_db":false,"title_case":false,"type":"str"}},"description":"Create a prompt template with dynamic variables.","icon":"prompts","is_input":null,"is_output":null,"is_composition":null,"base_classes":["Message"],"name":"","display_name":"Prompt","documentation":"","custom_fields":{"template":["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},"type":"Prompt"},"dragging":false,"height":517,"id":"Prompt-84CoT","position":{"x":1880.8227904110583,"y":625.8049209882275},"positionAbsolute":{"x":1880.8227904110583,"y":625.8049209882275},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Generates text using OpenAI LLMs.","display_name":"OpenAI","id":"OpenAIModel-4FuG8","node":{"base_classes":["LanguageModel","Message"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Generates text using OpenAI LLMs.","display_name":"OpenAI","documentation":"","edited":false,"field_order":["input_value","max_tokens","model_kwargs","json_mode","output_schema","model_name","openai_api_base","openai_api_key","temperature","stream","system_message","seed"],"frozen":false,"icon":"OpenAI","output_types":[],"outputs":[{"cache":true,"display_name":"Text","method":"text_response","name":"text_output","selected":"Message","types":["Message"],"value":"__UNDEFINED__","hidden":false},{"cache":true,"display_name":"Language Model","method":"build_model","name":"model_output","selected":"LanguageModel","types":["LanguageModel"],"value":"__UNDEFINED__","hidden":false}],"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":"import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\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 = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\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        ),\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\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=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=\"openai_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        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        # self.output_schea is a list of dictionarie s\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.openai_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        model_kwargs[\"seed\"] = 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 or 0.1,\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"},"input_value":{"advanced":false,"display_name":"Input","dynamic":false,"info":"","input_types":["Message"],"list":false,"load_from_db":false,"name":"input_value","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""},"json_mode":{"advanced":true,"display_name":"JSON Mode","dynamic":false,"info":"If True, it will output JSON regardless of passing a schema.","list":false,"name":"json_mode","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"bool","value":false},"max_tokens":{"advanced":true,"display_name":"Max Tokens","dynamic":false,"info":"The maximum number of tokens to generate. Set to 0 for unlimited tokens.","list":false,"name":"max_tokens","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"int","value":""},"model_kwargs":{"advanced":true,"display_name":"Model Kwargs","dynamic":false,"info":"","list":false,"name":"model_kwargs","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"type":"dict","value":{}},"model_name":{"advanced":false,"display_name":"Model Name","dynamic":false,"info":"","name":"model_name","options":["gpt-4o","gpt-4-turbo","gpt-4-turbo-preview","gpt-4","gpt-3.5-turbo","gpt-3.5-turbo-0125"],"placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":"gpt-4o"},"openai_api_base":{"advanced":true,"display_name":"OpenAI API Base","dynamic":false,"info":"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.","list":false,"load_from_db":false,"name":"openai_api_base","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":""},"openai_api_key":{"advanced":false,"display_name":"OpenAI API Key","dynamic":false,"info":"The OpenAI API Key to use for the OpenAI model.","input_types":[],"load_from_db":false,"name":"openai_api_key","password":true,"placeholder":"","required":false,"show":true,"title_case":false,"type":"str","value":null},"output_schema":{"advanced":true,"display_name":"Schema","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.","list":true,"name":"output_schema","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"type":"dict","value":{}},"seed":{"advanced":true,"display_name":"Seed","dynamic":false,"info":"The seed controls the reproducibility of the job.","list":false,"name":"seed","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"int","value":1},"stream":{"advanced":true,"display_name":"Stream","dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","list":false,"name":"stream","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"bool","value":false},"system_message":{"advanced":true,"display_name":"System Message","dynamic":false,"info":"System message to pass to the model.","list":false,"load_from_db":false,"name":"system_message","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":""},"temperature":{"advanced":false,"display_name":"Temperature","dynamic":false,"info":"","list":false,"name":"temperature","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"float","value":0.1}}},"type":"OpenAIModel"},"dragging":false,"height":623,"id":"OpenAIModel-4FuG8","position":{"x":2468.968379487559,"y":560.0689522326683},"positionAbsolute":{"x":2468.968379487559,"y":560.0689522326683},"selected":false,"type":"genericNode","width":384},{"data":{"description":"Display a chat message in the Playground.","display_name":"Chat Output","id":"ChatOutput-YQDTD","node":{"base_classes":["Message"],"beta":false,"conditional_paths":[],"custom_fields":{},"description":"Display a chat message in the Playground.","display_name":"Chat Output","documentation":"","edited":false,"field_order":["input_value","sender","sender_name","session_id","data_template"],"frozen":false,"icon":"ChatOutput","output_types":[],"outputs":[{"cache":true,"display_name":"Message","method":"message_response","name":"message","selected":"Message","types":["Message"],"value":"__UNDEFINED__"}],"pinned":false,"template":{"_type":"Component","code":{"advanced":true,"dynamic":true,"fileTypes":[],"file_path":"","info":"","list":false,"load_from_db":false,"multiline":true,"name":"code","password":false,"placeholder":"","required":true,"show":true,"title_case":false,"type":"code","value":"from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\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        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        MessageTextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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 self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"},"data_template":{"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,"load_from_db":false,"name":"data_template","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"{text}"},"input_value":{"advanced":false,"display_name":"Text","dynamic":false,"info":"Message to be passed as output.","input_types":["Message"],"list":false,"load_from_db":false,"name":"input_value","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""},"sender":{"advanced":true,"display_name":"Sender Type","dynamic":false,"info":"Type of sender.","name":"sender","options":["Machine","User"],"placeholder":"","required":false,"show":true,"title_case":false,"trace_as_metadata":true,"type":"str","value":"Machine"},"sender_name":{"advanced":true,"display_name":"Sender Name","dynamic":false,"info":"Name of the sender.","input_types":["Message"],"list":false,"load_from_db":false,"name":"sender_name","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":"AI"},"session_id":{"advanced":true,"display_name":"Session ID","dynamic":false,"info":"Session ID for the message.","input_types":["Message"],"list":false,"load_from_db":false,"name":"session_id","placeholder":"","required":false,"show":true,"title_case":false,"trace_as_input":true,"trace_as_metadata":true,"type":"str","value":""}}},"type":"ChatOutput"},"height":309,"id":"ChatOutput-YQDTD","position":{"x":3083.1710516244116,"y":701.521688846004},"selected":false,"type":"genericNode","width":384},{"id":"ChatInput-uDmE1","type":"genericNode","position":{"x":1175.246421932437,"y":1074.0122865414603},"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,"value":"","name":"files","display_name":"Files","advanced":true,"dynamic":false,"info":"Files to be sent with the message.","title_case":false,"type":"file"},"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.io import DropdownInput, FileInput, MessageTextInput, MultilineInput, Output\nfrom langflow.schema.message import Message\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        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"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=\"User\",\n            advanced=True,\n        ),\n        MessageTextInput(\n            name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", 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        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\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,"value":"hello","name":"input_value","display_name":"Text","advanced":false,"input_types":["Message"],"dynamic":false,"info":"Message to be passed as input.","title_case":false,"type":"str"},"sender":{"trace_as_metadata":true,"options":["Machine","User"],"required":false,"placeholder":"","show":true,"value":"User","name":"sender","display_name":"Sender Type","advanced":true,"dynamic":false,"info":"Type of sender.","title_case":false,"type":"str"},"sender_name":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"User","name":"sender_name","display_name":"Sender Name","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Name of the sender.","title_case":false,"type":"str"},"session_id":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"session_id","display_name":"Session ID","advanced":true,"input_types":["Message"],"dynamic":false,"info":"Session ID for the message.","title_case":false,"type":"str"}},"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","sender","sender_name","session_id","files"],"beta":false,"edited":false},"id":"ChatInput-uDmE1"},"selected":false,"width":384,"height":309,"dragging":false,"positionAbsolute":{"x":1175.246421932437,"y":1074.0122865414603}},{"id":"AstraDB-AknHp","type":"genericNode","position":{"x":2211.772874934615,"y":1195.5806313894216},"data":{"type":"AstraDB","node":{"template":{"_type":"Component","embedding":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"embedding","display_name":"Embedding or Astra Vectorize","advanced":false,"input_types":["Embeddings","dict"],"dynamic":false,"info":"Allows either an embedding model or an Astra Vectorize configuration.","title_case":false,"type":"other"},"ingest_data":{"trace_as_input":true,"trace_as_metadata":true,"list":true,"required":false,"placeholder":"","show":true,"value":"","name":"ingest_data","display_name":"Ingest Data","advanced":false,"input_types":["Data"],"dynamic":false,"info":"","title_case":false,"type":"other"},"api_endpoint":{"load_from_db":false,"required":true,"placeholder":"","show":true,"value":"","name":"api_endpoint","display_name":"API Endpoint","advanced":false,"input_types":[],"dynamic":false,"info":"API endpoint URL for the Astra DB service.","title_case":false,"password":true,"type":"str"},"batch_size":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"batch_size","display_name":"Batch Size","advanced":true,"dynamic":false,"info":"Optional number of data to process in a single batch.","title_case":false,"type":"int"},"bulk_delete_concurrency":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"bulk_delete_concurrency","display_name":"Bulk Delete Concurrency","advanced":true,"dynamic":false,"info":"Optional concurrency level for bulk delete operations.","title_case":false,"type":"int"},"bulk_insert_batch_concurrency":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"bulk_insert_batch_concurrency","display_name":"Bulk Insert Batch Concurrency","advanced":true,"dynamic":false,"info":"Optional concurrency level for bulk insert operations.","title_case":false,"type":"int"},"bulk_insert_overwrite_concurrency":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"bulk_insert_overwrite_concurrency","display_name":"Bulk Insert Overwrite Concurrency","advanced":true,"dynamic":false,"info":"Optional concurrency level for bulk insert operations that overwrite existing data.","title_case":false,"type":"int"},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langchain_core.vectorstores import VectorStore\nfrom loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.helpers import docs_to_data\nfrom langflow.inputs import DictInput, FloatInput\nfrom langflow.io import (\n    BoolInput,\n    DataInput,\n    DropdownInput,\n    HandleInput,\n    IntInput,\n    MultilineInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n    name = \"AstraDB\"\n    icon: str = \"AstraDB\"\n\n    _cached_vectorstore: VectorStore | None = None\n\n    inputs = [\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n            required=True,\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n            required=True,\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DataInput(\n            name=\"ingest_data\",\n            display_name=\"Ingest Data\",\n            is_list=True,\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n            options=[\"Sync\", \"Async\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            advanced=True,\n        ),\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding or Astra Vectorize\",\n            input_types=[\"Embeddings\", \"dict\"],\n            info=\"Allows either an embedding model or an Astra Vectorize configuration.\",  # TODO: This should be optional, but need to refactor langchain-astradb first.\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info=\"Optional dictionary defining the indexing policy for the collection.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            info=\"Search type to use\",\n            options=[\"Similarity\", \"Similarity with score threshold\", \"MMR (Max Marginal Relevance)\"],\n            value=\"Similarity\",\n            advanced=True,\n        ),\n        FloatInput(\n            name=\"search_score_threshold\",\n            display_name=\"Search Score Threshold\",\n            info=\"Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')\",\n            value=0,\n            advanced=True,\n        ),\n        DictInput(\n            name=\"search_filter\",\n            display_name=\"Search Metadata Filter\",\n            info=\"Optional dictionary of filters to apply to the search query.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n\n    def _build_vector_store(self):\n        # cache the vector store to avoid re-initializing and ingest data again\n        if self._cached_vectorstore:\n            return self._cached_vectorstore\n\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n        if not isinstance(self.embedding, dict):\n            embedding_dict = {\"embedding\": self.embedding}\n        else:\n            from astrapy.info import CollectionVectorServiceOptions\n\n            dict_options = self.embedding.get(\"collection_vector_service_options\", {})\n            dict_options[\"authentication\"] = {\n                k: v for k, v in dict_options.get(\"authentication\", {}).items() if k and v\n            }\n            dict_options[\"parameters\"] = {k: v for k, v in dict_options.get(\"parameters\", {}).items() if k and v}\n            embedding_dict = {\n                \"collection_vector_service_options\": CollectionVectorServiceOptions.from_dict(dict_options)\n            }\n            collection_embedding_api_key = self.embedding.get(\"collection_embedding_api_key\")\n            if collection_embedding_api_key:\n                embedding_dict[\"collection_embedding_api_key\"] = collection_embedding_api_key\n\n        vector_store_kwargs = {\n            **embedding_dict,\n            \"collection_name\": self.collection_name,\n            \"token\": self.token,\n            \"api_endpoint\": self.api_endpoint,\n            \"namespace\": self.namespace or None,\n            \"metric\": self.metric or None,\n            \"batch_size\": self.batch_size or None,\n            \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n            \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n            \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n            \"setup_mode\": setup_mode_value,\n            \"pre_delete_collection\": self.pre_delete_collection or False,\n        }\n\n        if self.metadata_indexing_include:\n            vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n        elif self.metadata_indexing_exclude:\n            vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n        elif self.collection_indexing_policy:\n            vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n        try:\n            vector_store = AstraDBVectorStore(**vector_store_kwargs)\n        except Exception as e:\n            raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n        self._add_documents_to_vector_store(vector_store)\n\n        self._cached_vectorstore = vector_store\n\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store):\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                raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n        if documents:\n            logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n        else:\n            logger.debug(\"No documents to add to the Vector Store.\")\n\n    def _map_search_type(self):\n        if self.search_type == \"Similarity with score threshold\":\n            return \"similarity_score_threshold\"\n        elif self.search_type == \"MMR (Max Marginal Relevance)\":\n            return \"mmr\"\n        else:\n            return \"similarity\"\n\n    def _build_search_args(self):\n        args = {\n            \"k\": self.number_of_results,\n            \"score_threshold\": self.search_score_threshold,\n        }\n\n        if self.search_filter:\n            clean_filter = {k: v for k, v in self.search_filter.items() if k and v}\n            if len(clean_filter) > 0:\n                args[\"filter\"] = clean_filter\n        return args\n\n    def search_documents(self) -> list[Data]:\n        vector_store = self._build_vector_store()\n\n        logger.debug(f\"Search input: {self.search_input}\")\n        logger.debug(f\"Search type: {self.search_type}\")\n        logger.debug(f\"Number of results: {self.number_of_results}\")\n\n        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                search_type = self._map_search_type()\n                search_args = self._build_search_args()\n\n                docs = vector_store.search(query=self.search_input, search_type=search_type, **search_args)\n            except Exception as e:\n                raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n            logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n            data = docs_to_data(docs)\n            logger.debug(f\"Converted documents to data: {len(data)}\")\n            self.status = data\n            return data\n        else:\n            logger.debug(\"No search input provided. Skipping search.\")\n            return []\n\n    def get_retriever_kwargs(self):\n        search_args = self._build_search_args()\n        return {\n            \"search_type\": self._map_search_type(),\n            \"search_kwargs\": search_args,\n        }\n\n    def build_vector_store(self):\n        vector_store = self._build_vector_store()\n        return vector_store\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"collection_indexing_policy":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"collection_indexing_policy","display_name":"Collection Indexing Policy","advanced":true,"dynamic":false,"info":"Optional dictionary defining the indexing policy for the collection.","title_case":false,"type":"str"},"collection_name":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":true,"placeholder":"","show":true,"value":"","name":"collection_name","display_name":"Collection Name","advanced":false,"dynamic":false,"info":"The name of the collection within Astra DB where the vectors will be stored.","title_case":false,"type":"str"},"metadata_indexing_exclude":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"metadata_indexing_exclude","display_name":"Metadata Indexing Exclude","advanced":true,"dynamic":false,"info":"Optional list of metadata fields to exclude from the indexing.","title_case":false,"type":"str"},"metadata_indexing_include":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"metadata_indexing_include","display_name":"Metadata Indexing Include","advanced":true,"dynamic":false,"info":"Optional list of metadata fields to include in the indexing.","title_case":false,"type":"str"},"metric":{"trace_as_metadata":true,"options":["cosine","dot_product","euclidean"],"required":false,"placeholder":"","show":true,"value":"","name":"metric","display_name":"Metric","advanced":true,"dynamic":false,"info":"Optional distance metric for vector comparisons in the vector store.","title_case":false,"type":"str"},"namespace":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"namespace","display_name":"Namespace","advanced":true,"dynamic":false,"info":"Optional namespace within Astra DB to use for the collection.","title_case":false,"type":"str"},"number_of_results":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":4,"name":"number_of_results","display_name":"Number of Results","advanced":true,"dynamic":false,"info":"Number of results to return.","title_case":false,"type":"int"},"pre_delete_collection":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"pre_delete_collection","display_name":"Pre Delete Collection","advanced":true,"dynamic":false,"info":"Boolean flag to determine whether to delete the collection before creating a new one.","title_case":false,"type":"bool"},"search_filter":{"trace_as_input":true,"list":true,"required":false,"placeholder":"","show":true,"value":{},"name":"search_filter","display_name":"Search Metadata Filter","advanced":true,"dynamic":false,"info":"Optional dictionary of filters to apply to the search query.","title_case":false,"type":"dict"},"search_input":{"trace_as_input":true,"multiline":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"search_input","display_name":"Search Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str"},"search_score_threshold":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0,"name":"search_score_threshold","display_name":"Search Score Threshold","advanced":true,"dynamic":false,"info":"Minimum similarity score threshold for search results. (when using 'Similarity with score threshold')","title_case":false,"type":"float"},"search_type":{"trace_as_metadata":true,"options":["Similarity","Similarity with score threshold","MMR (Max Marginal Relevance)"],"required":false,"placeholder":"","show":true,"value":"Similarity","name":"search_type","display_name":"Search Type","advanced":true,"dynamic":false,"info":"Search type to use","title_case":false,"type":"str"},"setup_mode":{"trace_as_metadata":true,"options":["Sync","Async","Off"],"required":false,"placeholder":"","show":true,"value":"Sync","name":"setup_mode","display_name":"Setup Mode","advanced":true,"dynamic":false,"info":"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.","title_case":false,"type":"str"},"token":{"load_from_db":false,"required":true,"placeholder":"","show":true,"value":"","name":"token","display_name":"Astra DB Application Token","advanced":false,"input_types":[],"dynamic":false,"info":"Authentication token for accessing Astra DB.","title_case":false,"password":true,"type":"str"}},"description":"Implementation of Vector Store using Astra DB with search capabilities","icon":"AstraDB","base_classes":["Data","Retriever","VectorStore"],"display_name":"Astra DB","documentation":"https://python.langchain.com/docs/integrations/vectorstores/astradb","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":["collection_name","token","api_endpoint","search_input","ingest_data","namespace","metric","batch_size","bulk_insert_batch_concurrency","bulk_insert_overwrite_concurrency","bulk_delete_concurrency","setup_mode","pre_delete_collection","metadata_indexing_include","embedding","metadata_indexing_exclude","collection_indexing_policy","number_of_results","search_type","search_score_threshold","search_filter"],"beta":false,"edited":false},"id":"AstraDB-AknHp"},"selected":false,"width":384,"height":803,"positionAbsolute":{"x":2211.772874934615,"y":1195.5806313894216},"dragging":false},{"id":"HuggingFaceModel-U9a9h","type":"genericNode","position":{"x":3196.6624655835294,"y":1112.0589805125867},"data":{"type":"HuggingFaceModel","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from langchain_community.chat_models.huggingface import ChatHuggingFace\nfrom langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, SecretStrInput, StrInput\n\n\nclass HuggingFaceEndpointsComponent(LCModelComponent):\n    display_name: str = \"Hugging Face API\"\n    description: str = \"Generate text using Hugging Face Inference APIs.\"\n    icon = \"HuggingFace\"\n    name = \"HuggingFaceModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        SecretStrInput(name=\"endpoint_url\", display_name=\"Endpoint URL\", password=True),\n        StrInput(\n            name=\"model_id\",\n            display_name=\"Model Id\",\n            info=\"Id field of endpoint_url response.\",\n        ),\n        DropdownInput(\n            name=\"task\",\n            display_name=\"Task\",\n            options=[\"text2text-generation\", \"text-generation\", \"summarization\"],\n        ),\n        SecretStrInput(name=\"huggingfacehub_api_token\", display_name=\"API token\", password=True),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Keyword Arguments\", advanced=True),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        endpoint_url = self.endpoint_url\n        task = self.task\n        huggingfacehub_api_token = self.huggingfacehub_api_token\n        model_kwargs = self.model_kwargs or {}\n\n        try:\n            llm = HuggingFaceEndpoint(  # type: ignore\n                endpoint_url=endpoint_url,\n                task=task,\n                huggingfacehub_api_token=huggingfacehub_api_token,\n                model_kwargs=model_kwargs,\n            )\n        except Exception as e:\n            raise ValueError(\"Could not connect to HuggingFace Endpoints API.\") from e\n\n        output = ChatHuggingFace(llm=llm, model_id=self.model_id)\n        return output  # type: ignore\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"endpoint_url":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":"","name":"endpoint_url","display_name":"Endpoint URL","advanced":false,"input_types":[],"dynamic":false,"info":"","title_case":false,"password":true,"type":"str"},"huggingfacehub_api_token":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":null,"name":"huggingfacehub_api_token","display_name":"API token","advanced":false,"input_types":[],"dynamic":false,"info":"","title_case":false,"password":true,"type":"str"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str"},"model_id":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"model_id","display_name":"Model Id","advanced":false,"dynamic":false,"info":"Id field of endpoint_url response.","title_case":false,"type":"str"},"model_kwargs":{"trace_as_input":true,"list":false,"required":false,"placeholder":"","show":true,"value":{},"name":"model_kwargs","display_name":"Model Keyword Arguments","advanced":true,"dynamic":false,"info":"","title_case":false,"type":"dict"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool"},"system_message":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str"},"task":{"trace_as_metadata":true,"options":["text2text-generation","text-generation","summarization"],"required":false,"placeholder":"","show":true,"value":"","name":"task","display_name":"Task","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"str"}},"description":"Generate text using Hugging Face Inference APIs.","icon":"HuggingFace","base_classes":["LanguageModel","Message"],"display_name":"Hugging Face API","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","endpoint_url","model_id","task","huggingfacehub_api_token","model_kwargs","stream","system_message"],"beta":false,"edited":false},"id":"HuggingFaceModel-U9a9h"},"selected":false,"width":384,"height":745,"positionAbsolute":{"x":3196.6624655835294,"y":1112.0589805125867},"dragging":false},{"id":"GoogleGenerativeAIModel-ifyw5","type":"genericNode","position":{"x":1034.163177455093,"y":181.3989390337319},"data":{"type":"GoogleGenerativeAIModel","node":{"template":{"_type":"Component","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, SecretStrInput, StrInput\n\n\nclass GoogleGenerativeAIComponent(LCModelComponent):\n    display_name = \"Google Generative AI\"\n    description = \"Generate text using Google Generative AI.\"\n    icon = \"GoogleGenerativeAI\"\n    name = \"GoogleGenerativeAIModel\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_output_tokens\",\n            display_name=\"Max Output Tokens\",\n            info=\"The maximum number of tokens to generate.\",\n        ),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            info=\"The name of the model to use.\",\n            options=[\"gemini-1.5-pro\", \"gemini-1.5-flash\", \"gemini-1.0-pro\", \"gemini-1.0-pro-vision\"],\n            value=\"gemini-1.5-pro\",\n        ),\n        SecretStrInput(\n            name=\"google_api_key\",\n            display_name=\"Google API Key\",\n            info=\"The Google API Key to use for the Google Generative AI.\",\n        ),\n        FloatInput(\n            name=\"top_p\",\n            display_name=\"Top P\",\n            info=\"The maximum cumulative probability of tokens to consider when sampling.\",\n            advanced=True,\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        IntInput(\n            name=\"n\",\n            display_name=\"N\",\n            info=\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"top_k\",\n            display_name=\"Top K\",\n            info=\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\n            advanced=True,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:  # type: ignore[type-var]\n        try:\n            from langchain_google_genai import ChatGoogleGenerativeAI\n        except ImportError:\n            raise ImportError(\"The 'langchain_google_genai' package is required to use the Google Generative AI model.\")\n\n        google_api_key = self.google_api_key\n        model = self.model\n        max_output_tokens = self.max_output_tokens\n        temperature = self.temperature\n        top_k = self.top_k\n        top_p = self.top_p\n        n = self.n\n\n        output = ChatGoogleGenerativeAI(  # type: ignore\n            model=model,\n            max_output_tokens=max_output_tokens or None,\n            temperature=temperature,\n            top_k=top_k or None,\n            top_p=top_p or None,\n            n=n or 1,\n            google_api_key=SecretStr(google_api_key),\n        )\n\n        return output  # type: ignore\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"google_api_key":{"load_from_db":false,"required":false,"placeholder":"","show":true,"value":null,"name":"google_api_key","display_name":"Google API Key","advanced":false,"input_types":[],"dynamic":false,"info":"The Google API Key to use for the Google Generative AI.","title_case":false,"password":true,"type":"str"},"input_value":{"trace_as_input":true,"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"input_value","display_name":"Input","advanced":false,"input_types":["Message"],"dynamic":false,"info":"","title_case":false,"type":"str"},"max_output_tokens":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"max_output_tokens","display_name":"Max Output Tokens","advanced":false,"dynamic":false,"info":"The maximum number of tokens to generate.","title_case":false,"type":"int"},"model":{"trace_as_metadata":true,"options":["gemini-1.5-pro","gemini-1.5-flash","gemini-1.0-pro","gemini-1.0-pro-vision"],"required":false,"placeholder":"","show":true,"value":"gemini-1.5-pro","name":"model","display_name":"Model","advanced":false,"dynamic":false,"info":"The name of the model to use.","title_case":false,"type":"str"},"n":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"n","display_name":"N","advanced":true,"dynamic":false,"info":"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.","title_case":false,"type":"int"},"stream":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":false,"name":"stream","display_name":"Stream","advanced":true,"dynamic":false,"info":"Stream the response from the model. Streaming works only in Chat.","title_case":false,"type":"bool"},"system_message":{"trace_as_metadata":true,"load_from_db":false,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"system_message","display_name":"System Message","advanced":true,"dynamic":false,"info":"System message to pass to the model.","title_case":false,"type":"str"},"temperature":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":0.1,"name":"temperature","display_name":"Temperature","advanced":false,"dynamic":false,"info":"","title_case":false,"type":"float"},"top_k":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"top_k","display_name":"Top K","advanced":true,"dynamic":false,"info":"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.","title_case":false,"type":"int"},"top_p":{"trace_as_metadata":true,"list":false,"required":false,"placeholder":"","show":true,"value":"","name":"top_p","display_name":"Top P","advanced":true,"dynamic":false,"info":"The maximum cumulative probability of tokens to consider when sampling.","title_case":false,"type":"float"}},"description":"Generate text using Google Generative AI.","icon":"GoogleGenerativeAI","base_classes":["LanguageModel","Message"],"display_name":"Google Generative AI","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","max_output_tokens","model","google_api_key","top_p","temperature","stream","n","system_message","top_k"],"beta":false,"edited":false},"id":"GoogleGenerativeAIModel-ifyw5"},"selected":false,"width":384,"height":709,"dragging":false,"positionAbsolute":{"x":1034.163177455093,"y":181.3989390337319}},{"id":"GoogleSerperAPIWrapper-wqM7n","type":"genericNode","position":{"x":1921.5639133983402,"y":-42.34532507498576},"data":{"type":"GoogleSerperAPIWrapper","node":{"template":{"_type":"CustomComponent","code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import Dict\n\n# Assuming the existence of GoogleSerperAPIWrapper class in the serper module\n# If this class does not exist, you would need to create it or import the appropriate class from another module\nfrom langchain_community.utilities.google_serper import GoogleSerperAPIWrapper\n\nfrom langflow.custom import CustomComponent\n\n\nclass GoogleSerperAPIWrapperComponent(CustomComponent):\n    display_name = \"GoogleSerperAPIWrapper\"\n    description = \"Wrapper around the Serper.dev Google Search API.\"\n    name = \"GoogleSerperAPIWrapper\"\n\n    def build_config(self) -> Dict[str, Dict]:\n        return {\n            \"result_key_for_type\": {\n                \"display_name\": \"Result Key for Type\",\n                \"show\": True,\n                \"multiline\": False,\n                \"password\": False,\n                \"advanced\": False,\n                \"dynamic\": False,\n                \"info\": \"\",\n                \"field_type\": \"dict\",\n                \"list\": False,\n                \"value\": {\n                    \"news\": \"news\",\n                    \"places\": \"places\",\n                    \"images\": \"images\",\n                    \"search\": \"organic\",\n                },\n            },\n            \"serper_api_key\": {\n                \"display_name\": \"Serper API Key\",\n                \"show\": True,\n                \"multiline\": False,\n                \"password\": True,\n                \"advanced\": False,\n                \"dynamic\": False,\n                \"info\": \"\",\n                \"type\": \"str\",\n                \"list\": False,\n            },\n        }\n\n    def build(\n        self,\n        serper_api_key: str,\n    ) -> GoogleSerperAPIWrapper:\n        return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"serper_api_key":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"serper_api_key","display_name":"Serper API Key","advanced":false,"dynamic":false,"info":"","load_from_db":false,"title_case":false,"input_types":["Text"],"value":null}},"description":"Wrapper around the Serper.dev Google Search API.","base_classes":["GoogleSerperAPIWrapper"],"display_name":"GoogleSerperAPIWrapper","documentation":"","custom_fields":{"serper_api_key":null},"output_types":["GoogleSerperAPIWrapper"],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["GoogleSerperAPIWrapper"],"selected":"GoogleSerperAPIWrapper","name":"googleserperapiwrapper","hidden":null,"display_name":"GoogleSerperAPIWrapper","method":null,"value":"__UNDEFINED__","cache":true}],"field_order":[],"beta":false,"edited":false},"id":"GoogleSerperAPIWrapper-wqM7n"},"selected":false,"width":384,"height":337},{"id":"SearchApi-oOzpd","type":"genericNode","position":{"x":2480.3537869305633,"y":-18.660080144991632},"data":{"type":"SearchApi","node":{"template":{"_type":"CustomComponent","api_key":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":true,"name":"api_key","display_name":"API Key","advanced":false,"dynamic":false,"info":"The API key to use SearchApi.","load_from_db":false,"title_case":false,"input_types":["Text"],"value":null},"code":{"type":"code","required":true,"placeholder":"","list":false,"show":true,"multiline":true,"value":"from typing import Optional\n\nfrom langchain_community.utilities.searchapi import SearchApiAPIWrapper\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Data\nfrom langflow.services.database.models.base import orjson_dumps\n\n\nclass SearchApi(CustomComponent):\n    display_name: str = \"SearchApi\"\n    description: str = \"Real-time search engine results API.\"\n    name = \"SearchApi\"\n\n    output_types: list[str] = [\"Document\"]\n    documentation: str = \"https://www.searchapi.io/docs/google\"\n    field_config = {\n        \"engine\": {\n            \"display_name\": \"Engine\",\n            \"field_type\": \"str\",\n            \"info\": \"The search engine to use.\",\n        },\n        \"params\": {\n            \"display_name\": \"Parameters\",\n            \"info\": \"The parameters to send with the request.\",\n        },\n        \"code\": {\"show\": False},\n        \"api_key\": {\n            \"display_name\": \"API Key\",\n            \"field_type\": \"str\",\n            \"required\": True,\n            \"password\": True,\n            \"info\": \"The API key to use SearchApi.\",\n        },\n    }\n\n    def build(\n        self,\n        engine: str,\n        api_key: str,\n        params: Optional[dict] = None,\n    ) -> Data:\n        if params is None:\n            params = {}\n\n        search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)\n\n        q = params.pop(\"q\", \"SearchApi Langflow\")\n        results = search_api_wrapper.results(q, **params)\n\n        result = orjson_dumps(results, indent_2=False)\n\n        record = Data(data=result)\n        self.status = record\n        return record\n","fileTypes":[],"file_path":"","password":false,"name":"code","advanced":true,"dynamic":true,"info":"","load_from_db":false,"title_case":false},"engine":{"type":"str","required":true,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"engine","display_name":"Engine","advanced":false,"dynamic":false,"info":"The search engine to use.","load_from_db":false,"title_case":false,"input_types":["Text"]},"params":{"type":"dict","required":false,"placeholder":"","list":false,"show":true,"multiline":false,"fileTypes":[],"file_path":"","password":false,"name":"params","display_name":"Parameters","advanced":false,"dynamic":false,"info":"The parameters to send with the request.","load_from_db":false,"title_case":false}},"description":"Real-time search engine results API.","base_classes":["Data"],"display_name":"SearchApi","documentation":"https://www.searchapi.io/docs/google","custom_fields":{"engine":null,"api_key":null,"params":null},"output_types":["Data"],"pinned":false,"conditional_paths":[],"frozen":false,"outputs":[{"types":["Data"],"selected":"Data","name":"data","hidden":null,"display_name":"Data","method":null,"value":"__UNDEFINED__","cache":true}],"field_order":["engine","params","code","api_key"],"beta":false,"edited":false},"id":"SearchApi-oOzpd"},"selected":false,"width":384,"height":489,"positionAbsolute":{"x":2480.3537869305633,"y":-18.660080144991632},"dragging":false}],"edges":[{"className":"","data":{"sourceHandle":{"dataType":"Prompt","id":"Prompt-84CoT","name":"prompt","output_types":["Message"]},"targetHandle":{"fieldName":"input_value","id":"OpenAIModel-4FuG8","inputTypes":["Message"],"type":"str"}},"id":"reactflow__edge-Prompt-84CoT{œdataTypeœ:œPromptœ,œidœ:œPrompt-84CoTœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-4FuG8{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-4FuG8œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","source":"Prompt-84CoT","sourceHandle":"{œdataTypeœ:œPromptœ,œidœ:œPrompt-84CoTœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}","target":"OpenAIModel-4FuG8","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-4FuG8œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"},{"source":"ChatInput-uDmE1","sourceHandle":"{œdataTypeœ:œChatInputœ,œidœ:œChatInput-uDmE1œ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}","target":"Prompt-84CoT","targetHandle":"{œfieldNameœ:œquestionœ,œidœ:œPrompt-84CoTœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"question","id":"Prompt-84CoT","inputTypes":["Message","Text"],"type":"str"},"sourceHandle":{"dataType":"ChatInput","id":"ChatInput-uDmE1","name":"message","output_types":["Message"]}},"id":"reactflow__edge-ChatInput-uDmE1{œdataTypeœ:œChatInputœ,œidœ:œChatInput-uDmE1œ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-84CoT{œfieldNameœ:œquestionœ,œidœ:œPrompt-84CoTœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}","className":""},{"source":"OpenAIModel-4FuG8","sourceHandle":"{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-4FuG8œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}","target":"ChatOutput-YQDTD","targetHandle":"{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-YQDTDœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","data":{"targetHandle":{"fieldName":"input_value","id":"ChatOutput-YQDTD","inputTypes":["Message"],"type":"str"},"sourceHandle":{"dataType":"OpenAIModel","id":"OpenAIModel-4FuG8","name":"text_output","output_types":["Message"]}},"id":"reactflow__edge-OpenAIModel-4FuG8{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-4FuG8œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-YQDTD{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-YQDTDœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}","className":""}],"viewport":{"x":-874.7972507026786,"y":-221.17335075497084,"zoom":0.9958746067820836}},"description":"This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.","name":"Memory Chatbot","last_tested_version":"1.0.8","endpoint_name":null,"is_component":false}

app.py

@@ -9,6 +9,9 @@ from langserve import add_routes
 
 import os
 from dotenv import load_dotenv
+from langflow.load import load_flow_from_json
+
+chain_flow = load_flow_from_json("Memory Chatbot.json")
 
 load_dotenv()
 
@@ -32,7 +35,8 @@ app = FastAPI(
 # /invoke
 # /batch
 # /stream
-add_routes(app, retriever)
+add_routes(app, retriever, path="/retriever")
+add_routes(app, chain_flow, path="/chat")
 
 if __name__ == "__main__":
     import uvicorn

requirements.txt

@@ -2,4 +2,5 @@ fastapi
 uvicorn[standard]
 langserve[all]
 faiss-cpu
-langchain-community
+langchain-community
+langflow