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https://js.langchain.com/v0.1/docs/integrations/vectorstores/rockset/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Memory](/v0.1/docs/integrations/vectorstores/memory/) * [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/) * [Astra DB](/v0.1/docs/integrations/vectorstores/astradb/) * [Azure AI Search](/v0.1/docs/integrations/vectorstores/azure_aisearch/) * [Azure Cosmos DB](/v0.1/docs/integrations/vectorstores/azure_cosmosdb/) * [Cassandra](/v0.1/docs/integrations/vectorstores/cassandra/) * [Chroma](/v0.1/docs/integrations/vectorstores/chroma/) * [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/) * [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/) * [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) * [Convex](/v0.1/docs/integrations/vectorstores/convex/) * [Couchbase](/v0.1/docs/integrations/vectorstores/couchbase/) * [Elasticsearch](/v0.1/docs/integrations/vectorstores/elasticsearch/) * [Faiss](/v0.1/docs/integrations/vectorstores/faiss/) * [Google Vertex AI Matching Engine](/v0.1/docs/integrations/vectorstores/googlevertexai/) * [SAP HANA Cloud Vector Engine](/v0.1/docs/integrations/vectorstores/hanavector/) * [HNSWLib](/v0.1/docs/integrations/vectorstores/hnswlib/) * [LanceDB](/v0.1/docs/integrations/vectorstores/lancedb/) * [Milvus](/v0.1/docs/integrations/vectorstores/milvus/) * [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/) * [MongoDB Atlas](/v0.1/docs/integrations/vectorstores/mongodb_atlas/) * [MyScale](/v0.1/docs/integrations/vectorstores/myscale/) * [Neo4j Vector Index](/v0.1/docs/integrations/vectorstores/neo4jvector/) * [Neon Postgres](/v0.1/docs/integrations/vectorstores/neon/) * [OpenSearch](/v0.1/docs/integrations/vectorstores/opensearch/) * [PGVector](/v0.1/docs/integrations/vectorstores/pgvector/) * [Pinecone](/v0.1/docs/integrations/vectorstores/pinecone/) * [Prisma](/v0.1/docs/integrations/vectorstores/prisma/) * [Qdrant](/v0.1/docs/integrations/vectorstores/qdrant/) * [Redis](/v0.1/docs/integrations/vectorstores/redis/) * [Rockset](/v0.1/docs/integrations/vectorstores/rockset/) * [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/) * [Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Rockset On this page Rockset ======= [Rockset](https://rockset.com) is a real-time analyitics SQL database that runs in the cloud. Rockset provides vector search capabilities, in the form of [SQL functions](https://rockset.com/docs/vector-functions/#vector-distance-functions), to support AI applications that rely on text similarity. Setup[](#setup "Direct link to Setup") --------------------------------------- Install the rockset client. yarn add @rockset/client ### Usage[](#usage "Direct link to Usage") tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Below is an example showcasing how to use OpenAI and Rockset to answer questions about a text file: import * as rockset from "@rockset/client";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { RocksetStore } from "@langchain/community/vectorstores/rockset";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { readFileSync } from "fs";import { ChatPromptTemplate } from "@langchain/core/prompts";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";const store = await RocksetStore.withNewCollection(new OpenAIEmbeddings(), { client: rockset.default.default( process.env.ROCKSET_API_KEY ?? "", `https://api.${process.env.ROCKSET_API_REGION ?? "usw2a1"}.rockset.com` ), collectionName: "langchain_demo",});const model = new ChatOpenAI({ model: "gpt-3.5-turbo-1106" });const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([ [ "system", "Answer the user's questions based on the below context:\n\n{context}", ], ["human", "{input}"],]);const combineDocsChain = await createStuffDocumentsChain({ llm: model, prompt: questionAnsweringPrompt,});const chain = await createRetrievalChain({ retriever: store.asRetriever(), combineDocsChain,});const text = readFileSync("state_of_the_union.txt", "utf8");const docs = await new RecursiveCharacterTextSplitter().createDocuments([text]);await store.addDocuments(docs);const response = await chain.invoke({ input: "When was America founded?",});console.log(response.answer);await store.destroy(); #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RocksetStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_rockset.RocksetStore.html) from `@langchain/community/vectorstores/rockset` * [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [createStuffDocumentsChain](https://api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` * * * #### Help us out by providing feedback on this documentation page: [ Previous Redis ](/v0.1/docs/integrations/vectorstores/redis/)[ Next SingleStore ](/v0.1/docs/integrations/vectorstores/singlestore/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/redis/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Memory](/v0.1/docs/integrations/vectorstores/memory/) * [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/) * [Astra DB](/v0.1/docs/integrations/vectorstores/astradb/) * [Azure AI Search](/v0.1/docs/integrations/vectorstores/azure_aisearch/) * [Azure Cosmos DB](/v0.1/docs/integrations/vectorstores/azure_cosmosdb/) * [Cassandra](/v0.1/docs/integrations/vectorstores/cassandra/) * [Chroma](/v0.1/docs/integrations/vectorstores/chroma/) * [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/) * [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/) * [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) * [Convex](/v0.1/docs/integrations/vectorstores/convex/) * [Couchbase](/v0.1/docs/integrations/vectorstores/couchbase/) * [Elasticsearch](/v0.1/docs/integrations/vectorstores/elasticsearch/) * [Faiss](/v0.1/docs/integrations/vectorstores/faiss/) * [Google Vertex AI Matching Engine](/v0.1/docs/integrations/vectorstores/googlevertexai/) * [SAP HANA Cloud Vector Engine](/v0.1/docs/integrations/vectorstores/hanavector/) * [HNSWLib](/v0.1/docs/integrations/vectorstores/hnswlib/) * [LanceDB](/v0.1/docs/integrations/vectorstores/lancedb/) * [Milvus](/v0.1/docs/integrations/vectorstores/milvus/) * [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/) * [MongoDB Atlas](/v0.1/docs/integrations/vectorstores/mongodb_atlas/) * [MyScale](/v0.1/docs/integrations/vectorstores/myscale/) * [Neo4j Vector Index](/v0.1/docs/integrations/vectorstores/neo4jvector/) * [Neon Postgres](/v0.1/docs/integrations/vectorstores/neon/) * [OpenSearch](/v0.1/docs/integrations/vectorstores/opensearch/) * [PGVector](/v0.1/docs/integrations/vectorstores/pgvector/) * [Pinecone](/v0.1/docs/integrations/vectorstores/pinecone/) * [Prisma](/v0.1/docs/integrations/vectorstores/prisma/) * [Qdrant](/v0.1/docs/integrations/vectorstores/qdrant/) * [Redis](/v0.1/docs/integrations/vectorstores/redis/) * [Rockset](/v0.1/docs/integrations/vectorstores/rockset/) * [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/) * [Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Redis On this page Redis ===== [Redis](https://redis.io/) is a fast open source, in-memory data store. As part of the [Redis Stack](https://redis.io/docs/stack/get-started/), [RediSearch](https://redis.io/docs/stack/search/) is the module that enables vector similarity semantic search, as well as many other types of searching. Compatibility Only available on Node.js. LangChain.js accepts [node-redis](https://github.com/redis/node-redis) as the client for Redis vectorstore. Setup[](#setup "Direct link to Setup") --------------------------------------- 1. Run Redis with Docker on your computer following [the docs](https://redis.io/docs/stack/get-started/install/docker/#redisredis-stack) 2. Install the node-redis JS client * npm * Yarn * pnpm npm install -S redis yarn add redis pnpm add redis tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Index docs[](#index-docs "Direct link to Index docs") ------------------------------------------------------ import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({ url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "consectetur adipiscing elit", }),];const vectorStore = await RedisVectorStore.fromDocuments( docs, new OpenAIEmbeddings(), { redisClient: client, indexName: "docs", });await client.disconnect(); #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` Query docs[](#query-docs "Direct link to Query docs") ------------------------------------------------------ import { createClient } from "redis";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { ChatPromptTemplate } from "@langchain/core/prompts";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";const client = createClient({ url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const vectorStore = new RedisVectorStore(new OpenAIEmbeddings(), { redisClient: client, indexName: "docs",});/* Simple standalone search in the vector DB /const simpleRes = await vectorStore.similaritySearch("redis", 1);console.log(simpleRes);/[ Document { pageContent: "redis is fast", metadata: { foo: "bar" } }]// Search in the vector DB using filters /const filterRes = await vectorStore.similaritySearch("redis", 3, ["qux"]);console.log(filterRes);/[ Document { pageContent: "consectetur adipiscing elit", metadata: { baz: "qux" }, }, Document { pageContent: "lorem ipsum dolor sit amet", metadata: { baz: "qux" }, }]// Usage as part of a chain /const model = new ChatOpenAI({ model: "gpt-3.5-turbo-1106" });const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([ [ "system", "Answer the user's questions based on the below context:\n\n{context}", ], ["human", "{input}"],]);const combineDocsChain = await createStuffDocumentsChain({ llm: model, prompt: questionAnsweringPrompt,});const chain = await createRetrievalChain({ retriever: vectorStore.asRetriever(), combineDocsChain,});const chainRes = await chain.invoke({ input: "What did the fox do?" });console.log(chainRes);/ { input: 'What did the fox do?', chat_history: [], context: [ Document { pageContent: 'the quick brown fox jumped over the lazy dog', metadata: [Object] }, Document { pageContent: 'lorem ipsum dolor sit amet', metadata: [Object] }, Document { pageContent: 'consectetur adipiscing elit', metadata: [Object] }, Document { pageContent: 'redis is fast', metadata: [Object] } ], answer: 'The fox jumped over the lazy dog.' }/await client.disconnect(); #### API Reference: [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [createStuffDocumentsChain](https://api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` Create index with options[](#create-index-with-options "Direct link to Create index with options") --------------------------------------------------------------------------------------------------- To pass arguments for [index creation](https://redis.io/commands/ft.create/), you can utilize the [available options](https://github.com/redis/node-redis/blob/294cbf8367295ac81cbe51ce2932493ab80493f1/packages/search/lib/commands/CREATE.ts#L4) offered by [node-redis](https://github.com/redis/node-redis) through `createIndexOptions` parameter. import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({ url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "consectetur adipiscing elit", }),];const vectorStore = await RedisVectorStore.fromDocuments( docs, new OpenAIEmbeddings(), { redisClient: client, indexName: "docs", createIndexOptions: { TEMPORARY: 1000, }, });await client.disconnect(); #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` Delete an index[](#delete-an-index "Direct link to Delete an index") --------------------------------------------------------------------- import { createClient } from "redis";import { OpenAIEmbeddings } from "@langchain/openai";import { RedisVectorStore } from "@langchain/redis";import { Document } from "@langchain/core/documents";const client = createClient({ url: process.env.REDIS_URL ?? "redis://localhost:6379",});await client.connect();const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "redis is fast", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "consectetur adipiscing elit", }),];const vectorStore = await RedisVectorStore.fromDocuments( docs, new OpenAIEmbeddings(), { redisClient: client, indexName: "docs", });await vectorStore.delete({ deleteAll: true });await client.disconnect(); #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * * * #### Help us out by providing feedback on this documentation page: [ Previous Qdrant ](/v0.1/docs/integrations/vectorstores/qdrant/)[ Next Rockset ](/v0.1/docs/integrations/vectorstores/rockset/) * [Setup](#setup) * [Index docs](#index-docs) * [Query docs](#query-docs) * [Create index with options](#create-index-with-options) * [Delete an index](#delete-an-index) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/qdrant/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Memory](/v0.1/docs/integrations/vectorstores/memory/) * [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/) * [Astra DB](/v0.1/docs/integrations/vectorstores/astradb/) * [Azure AI Search](/v0.1/docs/integrations/vectorstores/azure_aisearch/) * [Azure Cosmos DB](/v0.1/docs/integrations/vectorstores/azure_cosmosdb/) * [Cassandra](/v0.1/docs/integrations/vectorstores/cassandra/) * [Chroma](/v0.1/docs/integrations/vectorstores/chroma/) * [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/) * [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/) * [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) * [Convex](/v0.1/docs/integrations/vectorstores/convex/) * [Couchbase](/v0.1/docs/integrations/vectorstores/couchbase/) * [Elasticsearch](/v0.1/docs/integrations/vectorstores/elasticsearch/) * [Faiss](/v0.1/docs/integrations/vectorstores/faiss/) * [Google Vertex AI Matching Engine](/v0.1/docs/integrations/vectorstores/googlevertexai/) * [SAP HANA Cloud Vector Engine](/v0.1/docs/integrations/vectorstores/hanavector/) * [HNSWLib](/v0.1/docs/integrations/vectorstores/hnswlib/) * [LanceDB](/v0.1/docs/integrations/vectorstores/lancedb/) * [Milvus](/v0.1/docs/integrations/vectorstores/milvus/) * [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/) * [MongoDB Atlas](/v0.1/docs/integrations/vectorstores/mongodb_atlas/) * [MyScale](/v0.1/docs/integrations/vectorstores/myscale/) * [Neo4j Vector Index](/v0.1/docs/integrations/vectorstores/neo4jvector/) * [Neon Postgres](/v0.1/docs/integrations/vectorstores/neon/) * [OpenSearch](/v0.1/docs/integrations/vectorstores/opensearch/) * [PGVector](/v0.1/docs/integrations/vectorstores/pgvector/) * [Pinecone](/v0.1/docs/integrations/vectorstores/pinecone/) * [Prisma](/v0.1/docs/integrations/vectorstores/prisma/) * [Qdrant](/v0.1/docs/integrations/vectorstores/qdrant/) * [Redis](/v0.1/docs/integrations/vectorstores/redis/) * [Rockset](/v0.1/docs/integrations/vectorstores/rockset/) * [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/) * [Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Qdrant On this page Qdrant ====== [Qdrant](https://qdrant.tech/) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Setup[](#setup "Direct link to Setup") --------------------------------------- 1. Run a Qdrant instance with Docker on your computer by following the [Qdrant setup instructions](https://qdrant.tech/documentation/quick-start/). 2. Install the Qdrant Node.js SDK. * npm * Yarn * pnpm npm install -S @langchain/qdrant yarn add @langchain/qdrant pnpm add @langchain/qdrant 3. Setup Env variables for Qdrant before running the code export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HEREexport QDRANT_URL=YOUR_QDRANT_URL_HERE # for example http://localhost:6333 Usage[](#usage "Direct link to Usage") --------------------------------------- ### Create a new index from texts[](#create-a-new-index-from-texts "Direct link to Create a new index from texts") tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";// text sample from Godel, Escher, Bachconst vectorStore = await QdrantVectorStore.fromTexts( [ `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious LittleHarmonic Labyrinth of the dreaded Majotaur?`, `Achilles: Yiikes! What is that?`, `Tortoise: They say-although I person never believed it myself-that an I Majotaur has created a tiny labyrinth sits in a pit in the middle of it, waiting innocent victims to get lost in its fears complexity. Then, when they wander and dazed into the center, he laughs and laughs at them-so hard, that he laughs them to death!`, `Achilles: Oh, no!`, `Tortoise: But it's only a myth. Courage, Achilles.`, ], [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }], new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/[ Document { pageContent: 'Achilles: Oh, no!', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What is that?', metadata: { id: 1 } }]/ #### API Reference: * [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` ### Create a new index from docs[](#create-a-new-index-from-docs "Direct link to Create a new index from docs") import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();const vectorStore = await QdrantVectorStore.fromDocuments( docs, new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "a_test_collection", });// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/[ Document { pageContent: 'Foo\nBar\nBaz\n\n', metadata: { source: 'src/document_loaders/example_data/example.txt' } }]/ #### API Reference: * [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TextLoader](https://api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` ### Query docs from existing collection[](#query-docs-from-existing-collection "Direct link to Query docs from existing collection") import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await QdrantVectorStore.fromExistingCollection( new OpenAIEmbeddings(), { url: process.env.QDRANT_URL, collectionName: "goldel_escher_bach", });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/[ Document { pageContent: 'Achilles: Oh, no!', metadata: {} }, Document { pageContent: 'Achilles: Yiikes! What is that?', metadata: { id: 1 } }]/ #### API Reference: * [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * * * #### Help us out by providing feedback on this documentation page: [ Previous Prisma ](/v0.1/docs/integrations/vectorstores/prisma/)[ Next Redis ](/v0.1/docs/integrations/vectorstores/redis/) * [Setup](#setup) * [Usage](#usage) * [Create a new index from texts](#create-a-new-index-from-texts) * [Create a new index from docs](#create-a-new-index-from-docs) * [Query docs from existing collection](#query-docs-from-existing-collection) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/prisma/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Memory](/v0.1/docs/integrations/vectorstores/memory/) * [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/) * [Astra DB](/v0.1/docs/integrations/vectorstores/astradb/) * [Azure AI Search](/v0.1/docs/integrations/vectorstores/azure_aisearch/) * [Azure Cosmos DB](/v0.1/docs/integrations/vectorstores/azure_cosmosdb/) * [Cassandra](/v0.1/docs/integrations/vectorstores/cassandra/) * [Chroma](/v0.1/docs/integrations/vectorstores/chroma/) * [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/) * [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/) * [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) * [Convex](/v0.1/docs/integrations/vectorstores/convex/) * [Couchbase](/v0.1/docs/integrations/vectorstores/couchbase/) * [Elasticsearch](/v0.1/docs/integrations/vectorstores/elasticsearch/) * [Faiss](/v0.1/docs/integrations/vectorstores/faiss/) * [Google Vertex AI Matching Engine](/v0.1/docs/integrations/vectorstores/googlevertexai/) * [SAP HANA Cloud Vector Engine](/v0.1/docs/integrations/vectorstores/hanavector/) * [HNSWLib](/v0.1/docs/integrations/vectorstores/hnswlib/) * [LanceDB](/v0.1/docs/integrations/vectorstores/lancedb/) * [Milvus](/v0.1/docs/integrations/vectorstores/milvus/) * [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/) * [MongoDB Atlas](/v0.1/docs/integrations/vectorstores/mongodb_atlas/) * [MyScale](/v0.1/docs/integrations/vectorstores/myscale/) * [Neo4j Vector Index](/v0.1/docs/integrations/vectorstores/neo4jvector/) * [Neon Postgres](/v0.1/docs/integrations/vectorstores/neon/) * [OpenSearch](/v0.1/docs/integrations/vectorstores/opensearch/) * [PGVector](/v0.1/docs/integrations/vectorstores/pgvector/) * [Pinecone](/v0.1/docs/integrations/vectorstores/pinecone/) * [Prisma](/v0.1/docs/integrations/vectorstores/prisma/) * [Qdrant](/v0.1/docs/integrations/vectorstores/qdrant/) * [Redis](/v0.1/docs/integrations/vectorstores/redis/) * [Rockset](/v0.1/docs/integrations/vectorstores/rockset/) * [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/) * [Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Prisma On this page Prisma ====== For augmenting existing models in PostgreSQL database with vector search, Langchain supports using [Prisma](https://www.prisma.io/) together with PostgreSQL and [`pgvector`](https://github.com/pgvector/pgvector) Postgres extension. Setup[](#setup "Direct link to Setup") --------------------------------------- ### Setup database instance with Supabase[](#setup-database-instance-with-supabase "Direct link to Setup database instance with Supabase") Refer to the [Prisma and Supabase integration guide](https://supabase.com/docs/guides/integrations/prisma) to setup a new database instance with Supabase and Prisma. ### Install Prisma[](#install-prisma "Direct link to Install Prisma") * npm * Yarn * pnpm npm install prisma yarn add prisma pnpm add prisma ### Setup `pgvector` self hosted instance with `docker-compose`[](#setup-pgvector-self-hosted-instance-with-docker-compose "Direct link to setup-pgvector-self-hosted-instance-with-docker-compose") `pgvector` provides a prebuilt Docker image that can be used to quickly setup a self-hosted Postgres instance. services: db: image: ankane/pgvector ports: - 5432:5432 volumes: - db:/var/lib/postgresql/data environment: - POSTGRES_PASSWORD= - POSTGRES_USER= - POSTGRES_DB=volumes: db: ### Create a new schema[](#create-a-new-schema "Direct link to Create a new schema") Assuming you haven't created a schema yet, create a new model with a `vector` field of type `Unsupported("vector")`: model Document { id String @id @default(cuid()) content String vector Unsupported("vector")?} Afterwards, create a new migration with `--create-only` to avoid running the migration directly. * npm * Yarn * pnpm npx prisma migrate dev --create-only npx prisma migrate dev --create-only npx prisma migrate dev --create-only Add the following line to the newly created migration to enable `pgvector` extension if it hasn't been enabled yet: CREATE EXTENSION IF NOT EXISTS vector; Run the migration afterwards: * npm * Yarn * pnpm npx prisma migrate dev npx prisma migrate dev npx prisma migrate dev Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community danger Table names and column names (in fields such as `tableName`, `vectorColumnName`, `columns` and `filter`) are passed into SQL queries directly without parametrisation. These fields must be sanitized beforehand to avoid SQL injection. import { PrismaVectorStore } from "@langchain/community/vectorstores/prisma";import { OpenAIEmbeddings } from "@langchain/openai";import { PrismaClient, Prisma, Document } from "@prisma/client";export const run = async () => { const db = new PrismaClient(); // Use the `withModel` method to get proper type hints for `metadata` field: const vectorStore = PrismaVectorStore.withModel<Document>(db).create( new OpenAIEmbeddings(), { prisma: Prisma, tableName: "Document", vectorColumnName: "vector", columns: { id: PrismaVectorStore.IdColumn, content: PrismaVectorStore.ContentColumn, }, } ); const texts = ["Hello world", "Bye bye", "What's this?"]; await vectorStore.addModels( await db.$transaction( texts.map((content) => db.document.create({ data: { content } })) ) ); const resultOne = await vectorStore.similaritySearch("Hello world", 1); console.log(resultOne); // create an instance with default filter const vectorStore2 = PrismaVectorStore.withModel<Document>(db).create( new OpenAIEmbeddings(), { prisma: Prisma, tableName: "Document", vectorColumnName: "vector", columns: { id: PrismaVectorStore.IdColumn, content: PrismaVectorStore.ContentColumn, }, filter: { content: { equals: "default", }, }, } ); await vectorStore2.addModels( await db.$transaction( texts.map((content) => db.document.create({ data: { content } })) ) ); // Use the default filter a.k.a {"content": "default"} const resultTwo = await vectorStore.similaritySearch("Hello world", 1); console.log(resultTwo);}; #### API Reference: * [PrismaVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_prisma.PrismaVectorStore.html) from `@langchain/community/vectorstores/prisma` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` The following SQL operators are available as filters: `equals`, `in`, `isNull`, `isNotNull`, `like`, `lt`, `lte`, `gt`, `gte`, `not`. The samples above uses the following schema: // This is your Prisma schema file,// learn more about it in the docs: https://pris.ly/d/prisma-schemagenerator client { provider = "prisma-client-js"}datasource db { provider = "postgresql" url = env("DATABASE_URL")}model Document { id String @id @default(cuid()) content String namespace String? @default("default") vector Unsupported("vector")?} #### API Reference: You can remove `namespace` if you don't need it. * * * #### Help us out by providing feedback on this documentation page: [ Previous Pinecone ](/v0.1/docs/integrations/vectorstores/pinecone/)[ Next Qdrant ](/v0.1/docs/integrations/vectorstores/qdrant/) * [Setup](#setup) * [Setup database instance with Supabase](#setup-database-instance-with-supabase) * [Install Prisma](#install-prisma) * [Setup `pgvector` self hosted instance with `docker-compose`](#setup-pgvector-self-hosted-instance-with-docker-compose) * [Create a new schema](#create-a-new-schema) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/turbopuffer/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). 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[Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Turbopuffer On this page Turbopuffer =========== Setup[](#setup "Direct link to Setup") --------------------------------------- First you must sign up for a Turbopuffer account [here](https://turbopuffer.com/join). Then, once you have an account you can create an API key. Set your API key as an environment variable: export TURBOPUFFER_API_KEY=<YOUR_API_KEY> Usage[](#usage "Direct link to Usage") --------------------------------------- Here are some examples of how to use the class. You can filter your queries by previous specified metadata, but keep in mind that currently only string values are supported. See [here for more information](https://turbopuffer.com/docs/reference/query#filter-parameters) on acceptable filter formats. import { OpenAIEmbeddings } from "@langchain/openai";import { TurbopufferVectorStore } from "@langchain/community/vectorstores/turbopuffer";const embeddings = new OpenAIEmbeddings();const store = new TurbopufferVectorStore(embeddings, { apiKey: process.env.TURBOPUFFER_API_KEY, namespace: "my-namespace",});const createdAt = new Date().getTime();// Add some documents to your store.// Currently, only string metadata values are supported.const ids = await store.addDocuments([ { pageContent: "some content", metadata: { created_at: createdAt.toString() }, }, { pageContent: "hi", metadata: { created_at: (createdAt + 1).toString() } }, { pageContent: "bye", metadata: { created_at: (createdAt + 2).toString() } }, { pageContent: "what's this", metadata: { created_at: (createdAt + 3).toString() }, },]);// Retrieve documents from the storeconst results = await store.similaritySearch("hello", 1);console.log(results);/* [ Document { pageContent: 'hi', metadata: { created_at: '1705519164987' } } ]/// Filter by metadata// See https://turbopuffer.com/docs/reference/query#filter-parameters for more on// allowed filtersconst results2 = await store.similaritySearch("hello", 1, { created_at: [["Eq", (createdAt + 3).toString()]],});console.log(results2);/ [ Document { pageContent: "what's this", metadata: { created_at: '1705519164989' } } ]/// Upsert by passing idsawait store.addDocuments( [ { pageContent: "changed", metadata: { created_at: createdAt.toString() } }, { pageContent: "hi changed", metadata: { created_at: (createdAt + 1).toString() }, }, { pageContent: "bye changed", metadata: { created_at: (createdAt + 2).toString() }, }, { pageContent: "what's this changed", metadata: { created_at: (createdAt + 3).toString() }, }, ], { ids });// Filter by metadataconst results3 = await store.similaritySearch("hello", 10, { created_at: [["Eq", (createdAt + 3).toString()]],});console.log(results3);/ [ Document { pageContent: "what's this changed", metadata: { created_at: '1705519164989' } } ]/// Remove all vectors from the namespace.await store.delete({ deleteIndex: true,}); #### API Reference: [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TurbopufferVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_turbopuffer.TurbopufferVectorStore.html) from `@langchain/community/vectorstores/turbopuffer` * * * #### Help us out by providing feedback on this documentation page: [ Previous Tigris ](/v0.1/docs/integrations/vectorstores/tigris/)[ Next TypeORM ](/v0.1/docs/integrations/vectorstores/typeorm/) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/tigris/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). 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[Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Tigris On this page Tigris ====== Tigris makes it easy to build AI applications with vector embeddings. It is a fully managed cloud-native database that allows you store and index documents and vector embeddings for fast and scalable vector search. Compatibility Only available on Node.js. Setup[](#setup "Direct link to Setup") --------------------------------------- ### 1\. Install the Tigris SDK[](#1-install-the-tigris-sdk "Direct link to 1. Install the Tigris SDK") Install the SDK as follows * npm * Yarn * pnpm npm install -S @tigrisdata/vector yarn add @tigrisdata/vector pnpm add @tigrisdata/vector ### 2\. Fetch Tigris API credentials[](#2-fetch-tigris-api-credentials "Direct link to 2. Fetch Tigris API credentials") You can sign up for a free Tigris account [here](https://www.tigrisdata.com/). Once you have signed up for the Tigris account, create a new project called `vectordemo`. Next, make a note of the `clientId` and `clientSecret`, which you can get from the Application Keys section of the project. Index docs[](#index-docs "Direct link to Index docs") ------------------------------------------------------ tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { VectorDocumentStore } from "@tigrisdata/vector";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "@langchain/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({ connection: { serverUrl: "api.preview.tigrisdata.cloud", projectName: process.env.TIGRIS_PROJECT, clientId: process.env.TIGRIS_CLIENT_ID, clientSecret: process.env.TIGRIS_CLIENT_SECRET, }, indexName: "examples_index", numDimensions: 1536, // match the OpenAI embedding size});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "tigris is a cloud-native vector db", }), new Document({ metadata: { foo: "bar" }, pageContent: "the quick brown fox jumped over the lazy dog", }), new Document({ metadata: { baz: "qux" }, pageContent: "lorem ipsum dolor sit amet", }), new Document({ metadata: { baz: "qux" }, pageContent: "tigris is a river", }),];await TigrisVectorStore.fromDocuments(docs, new OpenAIEmbeddings(), { index }); Query docs[](#query-docs "Direct link to Query docs") ------------------------------------------------------ import { VectorDocumentStore } from "@tigrisdata/vector";import { OpenAIEmbeddings } from "@langchain/openai";import { TigrisVectorStore } from "langchain/vectorstores/tigris";const index = new VectorDocumentStore({ connection: { serverUrl: "api.preview.tigrisdata.cloud", projectName: process.env.TIGRIS_PROJECT, clientId: process.env.TIGRIS_CLIENT_ID, clientSecret: process.env.TIGRIS_CLIENT_SECRET, }, indexName: "examples_index", numDimensions: 1536, // match the OpenAI embedding size});const vectorStore = await TigrisVectorStore.fromExistingIndex( new OpenAIEmbeddings(), { index });/* Search the vector DB independently with metadata filters /const results = await vectorStore.similaritySearch("tigris", 1, { "metadata.foo": "bar",});console.log(JSON.stringify(results, null, 2));/[ Document { pageContent: 'tigris is a cloud-native vector db', metadata: { foo: 'bar' } }]/ * * #### Help us out by providing feedback on this documentation page: [ Previous Supabase ](/v0.1/docs/integrations/vectorstores/supabase/)[ Next Turbopuffer ](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [Setup](#setup) * [1\. Install the Tigris SDK](#1-install-the-tigris-sdk) * [2\. Fetch Tigris API credentials](#2-fetch-tigris-api-credentials) * [Index docs](#index-docs) * [Query docs](#query-docs) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/typesense/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). 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[Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Typesense On this page Typesense ========= Vector store that utilizes the Typesense search engine. ### Basic Usage[](#basic-usage "Direct link to Basic Usage") tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { Typesense, TypesenseConfig } from "langchain/vectorstores/typesense";import { OpenAIEmbeddings } from "@langchain/openai";import { Client } from "typesense";import { Document } from "langchain/document";const vectorTypesenseClient = new Client({ nodes: [ { // Ideally should come from your .env file host: "...", port: 123, protocol: "https", }, ], // Ideally should come from your .env file apiKey: "...", numRetries: 3, connectionTimeoutSeconds: 60,});const typesenseVectorStoreConfig = { // Typesense client typesenseClient: vectorTypesenseClient, // Name of the collection to store the vectors in schemaName: "your_schema_name", // Optional column names to be used in Typesense columnNames: { // "vec" is the default name for the vector column in Typesense but you can change it to whatever you want vector: "vec", // "text" is the default name for the text column in Typesense but you can change it to whatever you want pageContent: "text", // Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching metadataColumnNames: ["foo", "bar", "baz"], }, // Optional search parameters to be passed to Typesense when searching searchParams: { q: "", filter_by: "foo:[fooo]", query_by: "", }, // You can override the default Typesense import function if you want to do something more complex // Default import function: // async importToTypesense< // T extends Record<string, unknown> = Record<string, unknown> // >(data: T[], collectionName: string) { // const chunkSize = 2000; // for (let i = 0; i < data.length; i += chunkSize) { // const chunk = data.slice(i, i + chunkSize); // await this.caller.call(async () => { // await this.client // .collections<T>(collectionName) // .documents() // .import(chunk, { action: "emplace", dirty_values: "drop" }); // }); // } // } import: async (data, collectionName) => { await vectorTypesenseClient .collections(collectionName) .documents() .import(data, { action: "emplace", dirty_values: "drop" }); },} satisfies TypesenseConfig;/* * Creates a Typesense vector store from a list of documents. * Will update documents if there is a document with the same id, at least with the default import function. * @param documents list of documents to create the vector store from * @returns Typesense vector store /const createVectorStoreWithTypesense = async (documents: Document[] = []) => Typesense.fromDocuments( documents, new OpenAIEmbeddings(), typesenseVectorStoreConfig );/* * Returns a Typesense vector store from an existing index. * @returns Typesense vector store /const getVectorStoreWithTypesense = async () => new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);// Do a similarity searchconst vectorStore = await getVectorStoreWithTypesense();const documents = await vectorStore.similaritySearch("hello world");// Add filters based on metadata with the search parameters of Typesense// will exclude documents with author:JK Rowling, so if Joe Rowling & JK Rowling exists, only Joe Rowling will be returnedvectorStore.similaritySearch("Rowling", undefined, { filter_by: "author:!=JK Rowling",});// Delete a documentvectorStore.deleteDocuments(["document_id_1", "document_id_2"]); ### Constructor[](#constructor "Direct link to Constructor") Before starting, create a schema in Typesense with an id, a field for the vector and a field for the text. Add as many other fields as needed for the metadata. `constructor(embeddings: Embeddings, config: TypesenseConfig)`: Constructs a new instance of the `Typesense` class. * `embeddings`: An instance of the `Embeddings` class used for embedding documents. * `config`: Configuration object for the Typesense vector store. * `typesenseClient`: Typesense client instance. * `schemaName`: Name of the Typesense schema in which documents will be stored and searched. * `searchParams` (optional): Typesense search parameters. Default is `{ q: '', per_page: 5, query_by: '' }`. `columnNames` (optional): Column names configuration. * `vector` (optional): Vector column name. Default is `'vec'`. * `pageContent` (optional): Page content column name. Default is `'text'`. * `metadataColumnNames` (optional): Metadata column names. Default is an empty array `[]`. * `import` (optional): Replace the default import function for importing data to Typesense. This can affect the functionality of updating documents. ### Methods[](#methods "Direct link to Methods") * `async addDocuments(documents: Document[]): Promise<void>`: Adds documents to the vector store. The documents will be updated if there is a document with the same ID. * `static async fromDocuments(docs: Document[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of documents. Documents are added to the vector store during construction. * `static async fromTexts(texts: string[], metadatas: object[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>`: Creates a Typesense vector store from a list of texts and associated metadata. Texts are converted to documents and added to the vector store during construction. * `async similaritySearch(query: string, k?: number, filter?: Record<string, unknown>): Promise<Document[]>`: Searches for similar documents based on a query. Returns an array of similar documents. * `async deleteDocuments(documentIds: string[]): Promise<void>`: Deletes documents from the vector store based on their IDs. * * * #### Help us out by providing feedback on this documentation page: [ Previous TypeORM ](/v0.1/docs/integrations/vectorstores/typeorm/)[ Next Upstash Vector ](/v0.1/docs/integrations/vectorstores/upstash/) * [Basic Usage](#basic-usage) * [Constructor](#constructor) * [Methods](#methods) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/typeorm/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). 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[Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * TypeORM On this page TypeORM ======= To enable vector search in a generic PostgreSQL database, LangChain.js supports using [TypeORM](https://typeorm.io/) with the [`pgvector`](https://github.com/pgvector/pgvector) Postgres extension. Setup[](#setup "Direct link to Setup") --------------------------------------- To work with TypeORM, you need to install the `typeorm` and `pg` packages: * npm * Yarn * pnpm npm install typeorm yarn add typeorm pnpm add typeorm * npm * Yarn * pnpm npm install pg yarn add pg pnpm add pg tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community ### Setup a `pgvector` self hosted instance with `docker-compose`[](#setup-a-pgvector-self-hosted-instance-with-docker-compose "Direct link to setup-a-pgvector-self-hosted-instance-with-docker-compose") `pgvector` provides a prebuilt Docker image that can be used to quickly setup a self-hosted Postgres instance. Create a file below named `docker-compose.yml`: export default {services:{db:{image:'ankane/pgvector',ports:['5432:5432'],volumes:['./data:/var/lib/postgresql/data'],environment:['POSTGRES_PASSWORD=ChangeMe','POSTGRES_USER=myuser','POSTGRES_DB=api']}}}; #### API Reference: And then in the same directory, run `docker compose up` to start the container. You can find more information on how to setup `pgvector` in the [official repository](https://github.com/pgvector/pgvector). Usage[](#usage "Direct link to Usage") --------------------------------------- One complete example of using `TypeORMVectorStore` is the following: import { DataSourceOptions } from "typeorm";import { OpenAIEmbeddings } from "@langchain/openai";import { TypeORMVectorStore } from "@langchain/community/vectorstores/typeorm";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/typeormexport const run = async () => { const args = { postgresConnectionOptions: { type: "postgres", host: "localhost", port: 5432, username: "myuser", password: "ChangeMe", database: "api", } as DataSourceOptions, }; const typeormVectorStore = await TypeORMVectorStore.fromDataSource( new OpenAIEmbeddings(), args ); await typeormVectorStore.ensureTableInDatabase(); await typeormVectorStore.addDocuments([ { pageContent: "what's this", metadata: { a: 2 } }, { pageContent: "Cat drinks milk", metadata: { a: 1 } }, ]); const results = await typeormVectorStore.similaritySearch("hello", 2); console.log(results);}; #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TypeORMVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_typeorm.TypeORMVectorStore.html) from `@langchain/community/vectorstores/typeorm` * * * #### Help us out by providing feedback on this documentation page: [ Previous Turbopuffer ](/v0.1/docs/integrations/vectorstores/turbopuffer/)[ Next Typesense ](/v0.1/docs/integrations/vectorstores/typesense/) * [Setup](#setup) * [Setup a `pgvector` self hosted instance with `docker-compose`](#setup-a-pgvector-self-hosted-instance-with-docker-compose) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/vectorstores/upstash/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Memory](/v0.1/docs/integrations/vectorstores/memory/) * [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/) * [Astra DB](/v0.1/docs/integrations/vectorstores/astradb/) * [Azure AI Search](/v0.1/docs/integrations/vectorstores/azure_aisearch/) * [Azure Cosmos DB](/v0.1/docs/integrations/vectorstores/azure_cosmosdb/) * [Cassandra](/v0.1/docs/integrations/vectorstores/cassandra/) * [Chroma](/v0.1/docs/integrations/vectorstores/chroma/) * [ClickHouse](/v0.1/docs/integrations/vectorstores/clickhouse/) * [CloseVector](/v0.1/docs/integrations/vectorstores/closevector/) * [Cloudflare Vectorize](/v0.1/docs/integrations/vectorstores/cloudflare_vectorize/) * [Convex](/v0.1/docs/integrations/vectorstores/convex/) * [Couchbase](/v0.1/docs/integrations/vectorstores/couchbase/) * [Elasticsearch](/v0.1/docs/integrations/vectorstores/elasticsearch/) * [Faiss](/v0.1/docs/integrations/vectorstores/faiss/) * [Google Vertex AI Matching Engine](/v0.1/docs/integrations/vectorstores/googlevertexai/) * [SAP HANA Cloud Vector Engine](/v0.1/docs/integrations/vectorstores/hanavector/) * [HNSWLib](/v0.1/docs/integrations/vectorstores/hnswlib/) * [LanceDB](/v0.1/docs/integrations/vectorstores/lancedb/) * [Milvus](/v0.1/docs/integrations/vectorstores/milvus/) * [Momento Vector Index (MVI)](/v0.1/docs/integrations/vectorstores/momento_vector_index/) * [MongoDB Atlas](/v0.1/docs/integrations/vectorstores/mongodb_atlas/) * [MyScale](/v0.1/docs/integrations/vectorstores/myscale/) * [Neo4j Vector Index](/v0.1/docs/integrations/vectorstores/neo4jvector/) * [Neon Postgres](/v0.1/docs/integrations/vectorstores/neon/) * [OpenSearch](/v0.1/docs/integrations/vectorstores/opensearch/) * [PGVector](/v0.1/docs/integrations/vectorstores/pgvector/) * [Pinecone](/v0.1/docs/integrations/vectorstores/pinecone/) * [Prisma](/v0.1/docs/integrations/vectorstores/prisma/) * [Qdrant](/v0.1/docs/integrations/vectorstores/qdrant/) * [Redis](/v0.1/docs/integrations/vectorstores/redis/) * [Rockset](/v0.1/docs/integrations/vectorstores/rockset/) * [SingleStore](/v0.1/docs/integrations/vectorstores/singlestore/) * [Supabase](/v0.1/docs/integrations/vectorstores/supabase/) * [Tigris](/v0.1/docs/integrations/vectorstores/tigris/) * [Turbopuffer](/v0.1/docs/integrations/vectorstores/turbopuffer/) * [TypeORM](/v0.1/docs/integrations/vectorstores/typeorm/) * [Typesense](/v0.1/docs/integrations/vectorstores/typesense/) * [Upstash Vector](/v0.1/docs/integrations/vectorstores/upstash/) * [USearch](/v0.1/docs/integrations/vectorstores/usearch/) * [Vectara](/v0.1/docs/integrations/vectorstores/vectara/) * [Vercel Postgres](/v0.1/docs/integrations/vectorstores/vercel_postgres/) * [Voy](/v0.1/docs/integrations/vectorstores/voy/) * [Weaviate](/v0.1/docs/integrations/vectorstores/weaviate/) * [Xata](/v0.1/docs/integrations/vectorstores/xata/) * [Zep](/v0.1/docs/integrations/vectorstores/zep/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * Upstash Vector On this page Upstash Vector ============== Upstash Vector is a REST based serverless vector database, designed for working with vector embeddings. Setup[](#setup "Direct link to Setup") --------------------------------------- 1. Create Upstash Vector Index You can create an index from [Upstash Console](https://console.upstash.com/vector). For further reference, see [docs](https://upstash.com/docs/vector/overall/getstarted). 2. Install Upstash Vector SDK. * npm * Yarn * pnpm npm install -S @upstash/vector yarn add @upstash/vector pnpm add @upstash/vector We use OpenAI for the embeddings of the below examples. However, you can also create the embeddings using the model of your choice, that is available in the LangChain. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Create Upstash Vector Client[](#create-upstash-vector-client "Direct link to Create Upstash Vector Client") ------------------------------------------------------------------------------------------------------------ There are two ways to create the client. You can either pass the credentials as string manually from the `.env` file (or as string variables), or you can retrieve the credentials from the environment automatically. import { Index } from "@upstash/vector";import { OpenAIEmbeddings } from "@langchain/openai";import { UpstashVectorStore } from "@langchain/community/vectorstores/upstash";const embeddings = new OpenAIEmbeddings({});// Creating the index with the provided credentials.const indexWithCredentials = new Index({ url: process.env.UPSTASH_VECTOR_REST_URL as string, token: process.env.UPSTASH_VECTOR_REST_TOKEN as string,});const storeWithCredentials = new UpstashVectorStore(embeddings, { index: indexWithCredentials,});// Creating the index from the environment variables automatically.const indexFromEnv = new Index();const storeFromEnv = new UpstashVectorStore(embeddings, { index: indexFromEnv,}); #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [UpstashVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_upstash.UpstashVectorStore.html) from `@langchain/community/vectorstores/upstash` Index and Query Documents[](#index-and-query-documents "Direct link to Index and Query Documents") --------------------------------------------------------------------------------------------------- You can index the LangChain documents with any model of your choice, and perform a search over these documents. It's possible to apply metadata filtering to the search results. See [the related docs here](https://upstash.com/docs/vector/features/filtering). import { Index } from "@upstash/vector";import { OpenAIEmbeddings } from "@langchain/openai";import { Document } from "@langchain/core/documents";import { UpstashVectorStore } from "@langchain/community/vectorstores/upstash";const index = new Index({ url: process.env.UPSTASH_VECTOR_REST_URL as string, token: process.env.UPSTASH_VECTOR_REST_TOKEN as string,});const embeddings = new OpenAIEmbeddings({});const UpstashVector = new UpstashVectorStore(embeddings, { index });// Creating the docs to be indexed.const id = new Date().getTime();const documents = [ new Document({ metadata: { name: id }, pageContent: "Hello there!", }), new Document({ metadata: { name: id }, pageContent: "What are you building?", }), new Document({ metadata: { time: id }, pageContent: "Upstash Vector is great for building AI applications.", }), new Document({ metadata: { time: id }, pageContent: "To be, or not to be, that is the question.", }),];// Creating embeddings from the provided documents, and adding them to Upstash database.await UpstashVector.addDocuments(documents);// Waiting vectors to be indexed in the vector store.// eslint-disable-next-line no-promise-executor-returnawait new Promise((resolve) => setTimeout(resolve, 1000));const queryResult = await UpstashVector.similaritySearchWithScore( "Vector database", 2);console.log(queryResult);/*[ [ Document { pageContent: 'Upstash Vector is great for building AI applications.', metadata: [Object] }, 0.9016147 ], [ Document { pageContent: 'What are you building?', metadata: [Object] }, 0.8613077 ]] / #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * [UpstashVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_upstash.UpstashVectorStore.html) from `@langchain/community/vectorstores/upstash` Delete Documents[](#delete-documents "Direct link to Delete Documents") ------------------------------------------------------------------------ You can also delete the documents you've indexed previously. import { Index } from "@upstash/vector";import { OpenAIEmbeddings } from "@langchain/openai";import { UpstashVectorStore } from "@langchain/community/vectorstores/upstash";const index = new Index({ url: process.env.UPSTASH_VECTOR_REST_URL as string, token: process.env.UPSTASH_VECTOR_REST_TOKEN as string,});const embeddings = new OpenAIEmbeddings({});const UpstashVector = new UpstashVectorStore(embeddings, { index });// Creating the docs to be indexed.const createdAt = new Date().getTime();const IDs = await UpstashVector.addDocuments([ { pageContent: "hello", metadata: { a: createdAt + 1 } }, { pageContent: "car", metadata: { a: createdAt } }, { pageContent: "adjective", metadata: { a: createdAt } }, { pageContent: "hi", metadata: { a: createdAt } },]);// Waiting vectors to be indexed in the vector store.// eslint-disable-next-line no-promise-executor-returnawait new Promise((resolve) => setTimeout(resolve, 1000));await UpstashVector.delete({ ids: [IDs[0], IDs[2], IDs[3]] }); #### API Reference: * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [UpstashVectorStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_upstash.UpstashVectorStore.html) from `@langchain/community/vectorstores/upstash` * * * #### Help us out by providing feedback on this documentation page: [ Previous Typesense ](/v0.1/docs/integrations/vectorstores/typesense/)[ Next USearch ](/v0.1/docs/integrations/vectorstores/usearch/) * [Setup](#setup) * [Create Upstash Vector Client](#create-upstash-vector-client) * [Index and Query Documents](#index-and-query-documents) * [Delete Documents](#delete-documents) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/aiplugin-tool/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * ChatGPT Plugins ChatGPT Plugins =============== This example shows how to use ChatGPT Plugins within LangChain abstractions. Note 1: This currently only works for plugins with no auth. Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR! tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { ChatOpenAI } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";import { RequestsGetTool, RequestsPostTool } from "langchain/tools";import { AIPluginTool } from "@langchain/community/tools/aiplugin";export const run = async () => { const tools = [ new RequestsGetTool(), new RequestsPostTool(), await AIPluginTool.fromPluginUrl( "https://www.klarna.com/.well-known/ai-plugin.json" ), ]; const executor = await initializeAgentExecutorWithOptions( tools, new ChatOpenAI({ temperature: 0 }), { agentType: "chat-zero-shot-react-description", verbose: true } ); const result = await executor.invoke({ input: "what t shirts are available in klarna?", }); console.log({ result });}; #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [RequestsGetTool](https://api.js.langchain.com/classes/langchain_tools.RequestsGetTool.html) from `langchain/tools` * [RequestsPostTool](https://api.js.langchain.com/classes/langchain_tools.RequestsPostTool.html) from `langchain/tools` * [AIPluginTool](https://api.js.langchain.com/classes/langchain_community_tools_aiplugin.AIPluginTool.html) from `@langchain/community/tools/aiplugin` Entering new agent_executor chain...Thought: Klarna is a payment provider, not a store. I need to check if there is a Klarna Shopping API that I can use to search for t-shirts.Action:```{"action": "KlarnaProducts","action_input": ""}```Usage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user.OpenAPI Spec: {"openapi":"3.0.1","info":{"version":"v0","title":"Open AI Klarna product Api"},"servers":[{"url":"https://www.klarna.com/us/shopping"}],"tags":[{"name":"open-ai-product-endpoint","description":"Open AI Product Endpoint. Query for products."}],"paths":{"/public/openai/v0/products":{"get":{"tags":["open-ai-product-endpoint"],"summary":"API for fetching Klarna product information","operationId":"productsUsingGET","parameters":[{"name":"q","in":"query","description":"query, must be between 2 and 100 characters","required":true,"schema":{"type":"string"}},{"name":"size","in":"query","description":"number of products returned","required":false,"schema":{"type":"integer"}},{"name":"budget","in":"query","description":"maximum price of the matching product in local currency, filters results","required":false,"schema":{"type":"integer"}}],"responses":{"200":{"description":"Products found","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ProductResponse"}}}},"503":{"description":"one or more services are unavailable"}},"deprecated":false}}},"components":{"schemas":{"Product":{"type":"object","properties":{"attributes":{"type":"array","items":{"type":"string"}},"name":{"type":"string"},"price":{"type":"string"},"url":{"type":"string"}},"title":"Product"},"ProductResponse":{"type":"object","properties":{"products":{"type":"array","items":{"$ref":"#/components/schemas/Product"}}},"title":"ProductResponse"}}}}Now that I know there is a Klarna Shopping API, I can use it to search for t-shirts. I will make a GET request to the API with the query parameter "t-shirt".Action:```{"action": "requests_get","action_input": "https://www.klarna.com/us/shopping/public/openai/v0/products?q=t-shirt"}```{"products":[{"name":"Psycho Bunny Mens Copa Gradient Logo Graphic Tee","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203663222/Clothing/Psycho-Bunny-Mens-Copa-Gradient-Logo-Graphic-Tee/?source=openai","price":"$35.00","attributes":["Material:Cotton","Target Group:Man","Color:White,Blue,Black,Orange"]},{"name":"T-shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203506327/Clothing/T-shirt/?source=openai","price":"$20.45","attributes":["Material:Cotton","Target Group:Man","Color:Gray,White,Blue,Black,Orange"]},{"name":"Palm Angels Bear T-shirt - Black","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201090513/Clothing/Palm-Angels-Bear-T-shirt-Black/?source=openai","price":"$168.36","attributes":["Material:Cotton","Target Group:Man","Color:Black"]},{"name":"Tommy Hilfiger Essential Flag Logo T-shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3201840629/Clothing/Tommy-Hilfiger-Essential-Flag-Logo-T-shirt/?source=openai","price":"$22.52","attributes":["Material:Cotton","Target Group:Man","Color:Red,Gray,White,Blue,Black","Pattern:Solid Color","Environmental Attributes :Organic"]},{"name":"Coach Outlet Signature T Shirt","url":"https://www.klarna.com/us/shopping/pl/cl10001/3203005573/Clothing/Coach-Outlet-Signature-T-Shirt/?source=openai","price":"$75.00","attributes":["Material:Cotton","Target Group:Man","Color:Gray"]}]}Finished chain.{ result: { output: 'The available t-shirts in Klarna are Psycho Bunny Mens Copa Gradient Logo Graphic Tee, T-shirt, Palm Angels Bear T-shirt - Black, Tommy Hilfiger Essential Flag Logo T-shirt, and Coach Outlet Signature T Shirt.', intermediateSteps: [ [Object], [Object] ] }} * * * #### Help us out by providing feedback on this documentation page: [ Previous Tools ](/v0.1/docs/integrations/tools/)[ Next Connery Action Tool ](/v0.1/docs/integrations/tools/connery/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/connery/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Connery Action Tool On this page Connery Action Tool =================== Using this tool, you can integrate individual Connery Action into your LangChain agent. note If you want to use more than one Connery Action in your agent, check out the [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) documentation. What is Connery?[](#what-is-connery "Direct link to What is Connery?") ----------------------------------------------------------------------- Connery is an open-source plugin infrastructure for AI. With Connery, you can easily create a custom plugin with a set of actions and seamlessly integrate them into your LangChain agent. Connery will take care of critical aspects such as runtime, authorization, secret management, access management, audit logs, and other vital features. Furthermore, Connery, supported by our community, provides a diverse collection of ready-to-use open-source plugins for added convenience. Learn more about Connery: * GitHub: [https://github.com/connery-io/connery](https://github.com/connery-io/connery) * Documentation: [https://docs.connery.io](https://docs.connery.io) Prerequisites[](#prerequisites "Direct link to Prerequisites") --------------------------------------------------------------- To use Connery Actions in your LangChain agent, you need to do some preparation: 1. Set up the Connery runner using the [Quickstart](https://docs.connery.io/docs/runner/quick-start/) guide. 2. Install all the plugins with the actions you want to use in your agent. 3. Set environment variables `CONNERY_RUNNER_URL` and `CONNERY_RUNNER_API_KEY` so the toolkit can communicate with the Connery Runner. Example of using Connery Action Tool[](#example-of-using-connery-action-tool "Direct link to Example of using Connery Action Tool") ------------------------------------------------------------------------------------------------------------------------------------ ### Setup[](#setup "Direct link to Setup") To use the Connery Action Tool you need to install the following official peer dependency: * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). ### Usage[](#usage "Direct link to Usage") In the example below, we fetch action by its ID from the Connery Runner and then call it with the specified parameters. Here, we use the ID of the Send email action from the [Gmail](https://github.com/connery-io/gmail) plugin. info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/c4b6723d-f91c-440c-8682-16ec8297a602/r). import { ConneryService } from "@langchain/community/tools/connery";import { ChatOpenAI } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";// Specify your Connery Runner credentials.process.env.CONNERY_RUNNER_URL = "";process.env.CONNERY_RUNNER_API_KEY = "";// Specify OpenAI API key.process.env.OPENAI_API_KEY = "";// Specify your email address to receive the emails from examples below.const recepientEmail = "test@example.com";// Get the SendEmail action from the Connery Runner by ID.const conneryService = new ConneryService();const sendEmailAction = await conneryService.getAction( "CABC80BB79C15067CA983495324AE709");// Run the action manually.const manualRunResult = await sendEmailAction.invoke({ recipient: recepientEmail, subject: "Test email", body: "This is a test email sent by Connery.",});console.log(manualRunResult);// Run the action using the OpenAI Functions agent.const llm = new ChatOpenAI({ temperature: 0 });const agent = await initializeAgentExecutorWithOptions([sendEmailAction], llm, { agentType: "openai-functions", verbose: true,});const agentRunResult = await agent.invoke({ input: `Send an email to the ${recepientEmail} and say that I will be late for the meeting.`,});console.log(agentRunResult); #### API Reference: * [ConneryService](https://api.js.langchain.com/classes/langchain_community_tools_connery.ConneryService.html) from `@langchain/community/tools/connery` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` note Connery Action is a structured tool, so you can only use it in the agents supporting structured tools. * * * #### Help us out by providing feedback on this documentation page: [ Previous ChatGPT Plugins ](/v0.1/docs/integrations/tools/aiplugin-tool/)[ Next Dall-E Tool ](/v0.1/docs/integrations/tools/dalle/) * [What is Connery?](#what-is-connery) * [Prerequisites](#prerequisites) * [Example of using Connery Action Tool](#example-of-using-connery-action-tool) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/dalle/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Dall-E Tool Dall-E Tool =========== The Dall-E tool allows your agent to create images using OpenAI's Dall-E image generation tool. Setup[](#setup "Direct link to Setup") --------------------------------------- You will need an OpenAI API Key which you can get from the [OpenAI web site](https://openai.com) and then set the OPENAI\_API\_KEY environment variable to the key you just created. To use the Dall-E Tool you need to install the LangChain OpenAI integration package: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai /* eslint-disable no-process-env /import { DallEAPIWrapper } from "@langchain/openai";const tool = new DallEAPIWrapper({ n: 1, // Default model: "dall-e-3", // Default apiKey: process.env.OPENAI_API_KEY, // Default});const imageURL = await tool.invoke("a painting of a cat");console.log(imageURL); #### API Reference: [DallEAPIWrapper](https://api.js.langchain.com/classes/langchain_openai.DallEAPIWrapper.html) from `@langchain/openai` * * * #### Help us out by providing feedback on this documentation page: [ Previous Connery Action Tool ](/v0.1/docs/integrations/tools/connery/)[ Next Discord Tool ](/v0.1/docs/integrations/tools/discord/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/discord/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Discord Tool Discord Tool ============ The Discord Tool gives your agent the ability to search, read, and write messages to discord channels. It is useful for when you need to interact with a discord channel. Setup[](#setup "Direct link to Setup") --------------------------------------- To use the Discord Tool you need to install the following official peer depencency: * npm * Yarn * pnpm npm install discord.js yarn add discord.js pnpm add discord.js Usage, standalone[](#usage-standalone "Direct link to Usage, standalone") -------------------------------------------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { DiscordGetMessagesTool, DiscordChannelSearchTool, DiscordSendMessagesTool, DiscordGetGuildsTool, DiscordGetTextChannelsTool,} from "@langchain/community/tools/discord";// Get messages from a channel given channel IDconst getMessageTool = new DiscordGetMessagesTool();const messageResults = await getMessageTool.invoke("1153400523718938780");console.log(messageResults);// Get guilds/serversconst getGuildsTool = new DiscordGetGuildsTool();const guildResults = await getGuildsTool.invoke("");console.log(guildResults);// Search results in a given channel (case-insensitive)const searchTool = new DiscordChannelSearchTool();const searchResults = await searchTool.invoke("Test");console.log(searchResults);// Get all text channels of a serverconst getChannelsTool = new DiscordGetTextChannelsTool();const channelResults = await getChannelsTool.invoke("1153400523718938775");console.log(channelResults);// Send a messageconst sendMessageTool = new DiscordSendMessagesTool();const sendMessageResults = await sendMessageTool.invoke("test message");console.log(sendMessageResults); #### API Reference: * [DiscordGetMessagesTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordGetMessagesTool.html) from `@langchain/community/tools/discord` * [DiscordChannelSearchTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordChannelSearchTool.html) from `@langchain/community/tools/discord` * [DiscordSendMessagesTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordSendMessagesTool.html) from `@langchain/community/tools/discord` * [DiscordGetGuildsTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordGetGuildsTool.html) from `@langchain/community/tools/discord` * [DiscordGetTextChannelsTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordGetTextChannelsTool.html) from `@langchain/community/tools/discord` Usage, in an Agent[](#usage-in-an-agent "Direct link to Usage, in an Agent") ----------------------------------------------------------------------------- import { ChatOpenAI } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";import { DiscordSendMessagesTool } from "@langchain/community/tools/discord";import { DadJokeAPI } from "@langchain/community/tools/dadjokeapi";const model = new ChatOpenAI({ temperature: 0,});const tools = [new DiscordSendMessagesTool(), new DadJokeAPI()];const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: "zero-shot-react-description", verbose: true,});const res = await executor.invoke({ input: `Tell a joke in the discord channel`,});console.log(res.output);// "What's the best thing about elevator jokes? They work on so many levels." #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [DiscordSendMessagesTool](https://api.js.langchain.com/classes/langchain_community_tools_discord.DiscordSendMessagesTool.html) from `@langchain/community/tools/discord` * [DadJokeAPI](https://api.js.langchain.com/classes/langchain_community_tools_dadjokeapi.DadJokeAPI.html) from `@langchain/community/tools/dadjokeapi` * * * #### Help us out by providing feedback on this documentation page: [ Previous Dall-E Tool ](/v0.1/docs/integrations/tools/dalle/)[ Next DuckDuckGoSearch ](/v0.1/docs/integrations/tools/duckduckgo_search/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/duckduckgo_search/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * DuckDuckGoSearch DuckDuckGoSearch ================ DuckDuckGoSearch offers a privacy-focused search API designed for LLM Agents. It provides seamless integration with a wide range of data sources, prioritizing user privacy and relevant search results. Setup[](#setup "Direct link to Setup") --------------------------------------- Install the `@langchain/community` package, along with the `duck-duck-scrape` dependency: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community * npm * Yarn * pnpm npm install duck-duck-scrape yarn add duck-duck-scrape pnpm add duck-duck-scrape Usage[](#usage "Direct link to Usage") --------------------------------------- You can call `.invoke` on `DuckDuckGoSearch` to search for a query: import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";// Instantiate the DuckDuckGoSearch tool.const tool = new DuckDuckGoSearch({ maxResults: 1 });// Get the results of a query by calling .invoke on the tool.const result = await tool.invoke( "What is Anthropic's estimated revenue for 2024?");console.log(result);/[{ "title": "Anthropic forecasts more than $850 mln in annualized revenue rate by ...", "link": "https://www.reuters.com/technology/anthropic-forecasts-more-than-850-mln-annualized-revenue-rate-by-2024-end-report-2023-12-26/", "snippet": "Dec 26 (Reuters) - Artificial intelligence startup <b>Anthropic</b> has projected it will generate more than $850 million in annualized <b>revenue</b> by the end of <b>2024</b>, the Information reported on Tuesday ..."}]/ #### API Reference: * [DuckDuckGoSearch](https://api.js.langchain.com/classes/langchain_community_tools_duckduckgo_search.DuckDuckGoSearch.html) from `@langchain/community/tools/duckduckgo_search` tip See the LangSmith trace [here](https://smith.langchain.com/public/c352faaf-e617-4779-a943-96f963dc19a5/r) ### With an agent[](#with-an-agent "Direct link to With an agent") import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";import { ChatOpenAI } from "@langchain/openai";import type { ChatPromptTemplate } from "@langchain/core/prompts";import { pull } from "langchain/hub";import { AgentExecutor, createOpenAIFunctionsAgent } from "langchain/agents";// Define the tools the agent will have access to.const tools = [new DuckDuckGoSearch({ maxResults: 1 })];// Get the prompt to use - you can modify this!// If you want to see the prompt in full, you can at:// https://smith.langchain.com/hub/hwchase17/openai-functions-agentconst prompt = await pull<ChatPromptTemplate>( "hwchase17/openai-functions-agent");const llm = new ChatOpenAI({ model: "gpt-4-turbo-preview", temperature: 0,});const agent = await createOpenAIFunctionsAgent({ llm, tools, prompt,});const agentExecutor = new AgentExecutor({ agent, tools,});const result = await agentExecutor.invoke({ input: "What is Anthropic's estimated revenue for 2024?",});console.log(result);/{ input: "What is Anthropic's estimated revenue for 2024?", output: 'Anthropic has projected that it will generate more than $850 million in annualized revenue by the end of 2024. For more details, you can refer to the [Reuters article](https://www.reuters.com/technology/anthropic-forecasts-more-than-850-mln-annualized-revenue-rate-by-2024-end-report-2023-12-26/).'}/ #### API Reference: * [DuckDuckGoSearch](https://api.js.langchain.com/classes/langchain_community_tools_duckduckgo_search.DuckDuckGoSearch.html) from `@langchain/community/tools/duckduckgo_search` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [pull](https://api.js.langchain.com/functions/langchain_hub.pull.html) from `langchain/hub` * [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://api.js.langchain.com/functions/langchain_agents.createOpenAIFunctionsAgent.html) from `langchain/agents` tip See the LangSmith trace for the Agent example [here](https://smith.langchain.com/public/48f84a32-4fb5-4863-a8cd-324abebfce91/r) * * * #### Help us out by providing feedback on this documentation page: [ Previous Discord Tool ](/v0.1/docs/integrations/tools/discord/)[ Next Exa Search ](/v0.1/docs/integrations/tools/exa_search/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/exa_search/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Exa Search Exa Search ========== Exa (formerly Metaphor Search) is a search engine fully designed for use by LLMs. Search for documents on the internet using natural language queries, then retrieve cleaned HTML content from desired documents. Unlike keyword-based search (Google), Exa's neural search capabilities allow it to semantically understand queries and return relevant documents. For example, we could search `"fascinating article about cats"` and compare the search results from Google and Exa. Google gives us SEO-optimized listicles based on the keyword “fascinating”. Exa just works. This notebook goes over how to use Exa Search with LangChain. First, get an Exa API key and add it as an environment variable. Get 1000 free searches/month by [signing up here.](https://dashboard.exa.ai/login) Usage[](#usage "Direct link to Usage") --------------------------------------- First, install the LangChain integration package for Exa: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/exa @langchain/openai langchain yarn add @langchain/exa @langchain/openai langchain pnpm add @langchain/exa @langchain/openai langchain You'll need to set your API key as an environment variable. The `Exa` class defaults to `EXASEARCH_API_KEY` when searching for your API key. Usage[](#usage-1 "Direct link to Usage") ----------------------------------------- import { ExaSearchResults } from "@langchain/exa";import { ChatOpenAI } from "@langchain/openai";import type { ChatPromptTemplate } from "@langchain/core/prompts";import Exa from "exa-js";import { pull } from "langchain/hub";import { AgentExecutor, createOpenAIFunctionsAgent } from "langchain/agents";// Define the tools the agent will have access to.const tools = [ new ExaSearchResults({ // @ts-expect-error Some TS Config's will cause this to give a TypeScript error, even though it works. client: new Exa(process.env.EXASEARCH_API_KEY), }),];// Get the prompt to use - you can modify this!// If you want to see the prompt in full, you can at:// https://smith.langchain.com/hub/hwchase17/openai-functions-agentconst prompt = await pull<ChatPromptTemplate>( "hwchase17/openai-functions-agent");const llm = new ChatOpenAI({ model: "gpt-3.5-turbo-1106", temperature: 0,});const agent = await createOpenAIFunctionsAgent({ llm, tools, prompt,});const agentExecutor = new AgentExecutor({ agent, tools,});const result = await agentExecutor.invoke({ input: "what is the weather in wailea?",});console.log(result);/{ input: 'what is the weather in wailea?', output: 'I found a weather forecast for Wailea-Makena on Windfinder.com. You can check the forecast [here](https://www.windfinder.com/forecast/wailea-makena).'}/ #### API Reference: * [ExaSearchResults](https://api.js.langchain.com/classes/langchain_exa.ExaSearchResults.html) from `@langchain/exa` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [pull](https://api.js.langchain.com/functions/langchain_hub.pull.html) from `langchain/hub` * [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://api.js.langchain.com/functions/langchain_agents.createOpenAIFunctionsAgent.html) from `langchain/agents` tip You can see a LangSmith trace for this example [here](https://smith.langchain.com/public/775ea9a8-d54c-405c-9126-a012405d9099/r). Using the Exa SDK as LangChain Agent Tools[](#using-the-exa-sdk-as-langchain-agent-tools "Direct link to Using the Exa SDK as LangChain Agent Tools") ------------------------------------------------------------------------------------------------------------------------------------------------------ We can create LangChain tools which use the [`ExaRetriever`](/v0.1/docs/integrations/retrievers/exa/) and the [`createRetrieverTool`](https://api.js.langchain.com/functions/langchain_tools_retriever.createRetrieverTool.html) Using these tools we can construct a simple search agent that can answer questions about any topic. import { ChatPromptTemplate, MessagesPlaceholder,} from "@langchain/core/prompts";import { ChatOpenAI } from "@langchain/openai";import Exa from "exa-js";import { AgentExecutor, createOpenAIFunctionsAgent } from "langchain/agents";import { createRetrieverTool } from "langchain/tools/retriever";import { ExaRetriever } from "@langchain/exa";// @ts-expect-error Some TS Config's will cause this to give a TypeScript error, even though it works.const client: Exa.default = new Exa(process.env.EXASEARCH_API_KEY);const exaRetriever = new ExaRetriever({ client, searchArgs: { numResults: 2, },});// Convert the ExaRetriever into a toolconst searchTool = createRetrieverTool(exaRetriever, { name: "search", description: "Get the contents of a webpage given a string search query.",});const tools = [searchTool];const llm = new ChatOpenAI({ model: "gpt-4", temperature: 0 });const prompt = ChatPromptTemplate.fromMessages([ [ "system", `You are a web researcher who answers user questions by looking up information on the internet and retrieving contents of helpful documents. Cite your sources.`, ], ["human", "{input}"], new MessagesPlaceholder("agent_scratchpad"),]);const agentExecutor = new AgentExecutor({ agent: await createOpenAIFunctionsAgent({ llm, tools, prompt, }), tools,});console.log( await agentExecutor.invoke({ input: "Summarize for me a fascinating article about cats.", })); #### API Reference: * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [MessagesPlaceholder](https://api.js.langchain.com/classes/langchain_core_prompts.MessagesPlaceholder.html) from `@langchain/core/prompts` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://api.js.langchain.com/functions/langchain_agents.createOpenAIFunctionsAgent.html) from `langchain/agents` * [createRetrieverTool](https://api.js.langchain.com/functions/langchain_tools_retriever.createRetrieverTool.html) from `langchain/tools/retriever` * [ExaRetriever](https://api.js.langchain.com/classes/langchain_exa.ExaRetriever.html) from `@langchain/exa` { input: 'Summarize for me a fascinating article about cats.', output: 'The article discusses the research of biologist Jaroslav Flegr, who has been investigating the effects of a single-celled parasite called Toxoplasma gondii (T. gondii or Toxo), which is excreted by cats in their feces. Flegr began to suspect in the early 1990s that this parasite was subtly manipulating his personality, causing him to behave in strange, often self-destructive ways. He reasoned that if it was affecting him, it was probably doing the same to others.\n' + '\n' + "T. gondii is the microbe that causes toxoplasmosis, a disease that can be transmitted from a pregnant woman to her fetus, potentially resulting in severe brain damage or death. It's also a major threat to people with weakened immunity. However, healthy children and adults usually experience nothing worse than brief flu-like symptoms before quickly fighting off the protozoan, which then lies dormant inside brain cells.\n" + '\n' + "Flegr's research is unconventional and suggests that these tiny organisms carried by house cats could be creeping into our brains, causing everything from car wrecks to schizophrenia.\n" + '\n' + '(Source: [The Atlantic](https://www.theatlantic.com/magazine/archive/2012/03/how-your-cat-is-making-you-crazy/308873/))'} tip You can see a LangSmith trace for this example [here](https://smith.langchain.com/public/d123ba5f-8535-4669-9e43-ac7ab3c6735e/r). * * * #### Help us out by providing feedback on this documentation page: [ Previous DuckDuckGoSearch ](/v0.1/docs/integrations/tools/duckduckgo_search/)[ Next Gmail Tool ](/v0.1/docs/integrations/tools/gmail/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/gmail/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Gmail Tool Gmail Tool ========== The Gmail Tool allows your agent to create and view messages from a linked email account. Setup[](#setup "Direct link to Setup") --------------------------------------- You will need to get an API key from [Google here](https://developers.google.com/gmail/api/guides) and [enable the new Gmail API](https://console.cloud.google.com/apis/library/gmail.googleapis.com). Then, set the environment variables for `GMAIL_CLIENT_EMAIL`, and either `GMAIL_PRIVATE_KEY`, or `GMAIL_KEYFILE`. To use the Gmail Tool you need to install the following official peer dependency: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai googleapis @langchain/community yarn add @langchain/openai googleapis @langchain/community pnpm add @langchain/openai googleapis @langchain/community Usage[](#usage "Direct link to Usage") --------------------------------------- import { initializeAgentExecutorWithOptions } from "langchain/agents";import { OpenAI } from "@langchain/openai";import { GmailCreateDraft, GmailGetMessage, GmailGetThread, GmailSearch, GmailSendMessage,} from "@langchain/community/tools/gmail";import { StructuredTool } from "@langchain/core/tools";export async function run() { const model = new OpenAI({ temperature: 0, apiKey: process.env.OPENAI_API_KEY, }); // These are the default parameters for the Gmail tools // const gmailParams = { // credentials: { // clientEmail: process.env.GMAIL_CLIENT_EMAIL, // privateKey: process.env.GMAIL_PRIVATE_KEY, // }, // scopes: ["https://mail.google.com/"], // }; // For custom parameters, uncomment the code above, replace the values with your own, and pass it to the tools below const tools: StructuredTool[] = [ new GmailCreateDraft(), new GmailGetMessage(), new GmailGetThread(), new GmailSearch(), new GmailSendMessage(), ]; const gmailAgent = await initializeAgentExecutorWithOptions(tools, model, { agentType: "structured-chat-zero-shot-react-description", verbose: true, }); const createInput = `Create a gmail draft for me to edit of a letter from the perspective of a sentient parrot who is looking to collaborate on some research with her estranged friend, a cat. Under no circumstances may you send the message, however.`; const createResult = await gmailAgent.invoke({ input: createInput }); // Create Result { // output: 'I have created a draft email for you to edit. The draft Id is r5681294731961864018.' // } console.log("Create Result", createResult); const viewInput = `Could you search in my drafts for the latest email?`; const viewResult = await gmailAgent.invoke({ input: viewInput }); // View Result { // output: "The latest email in your drafts is from hopefulparrot@gmail.com with the subject 'Collaboration Opportunity'. The body of the email reads: 'Dear [Friend], I hope this letter finds you well. I am writing to you in the hopes of rekindling our friendship and to discuss the possibility of collaborating on some research together. I know that we have had our differences in the past, but I believe that we can put them aside and work together for the greater good. I look forward to hearing from you. Sincerely, [Parrot]'" // } console.log("View Result", viewResult);} #### API Reference: * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [GmailCreateDraft](https://api.js.langchain.com/classes/langchain_community_tools_gmail.GmailCreateDraft.html) from `@langchain/community/tools/gmail` * [GmailGetMessage](https://api.js.langchain.com/classes/langchain_community_tools_gmail.GmailGetMessage.html) from `@langchain/community/tools/gmail` * [GmailGetThread](https://api.js.langchain.com/classes/langchain_community_tools_gmail.GmailGetThread.html) from `@langchain/community/tools/gmail` * [GmailSearch](https://api.js.langchain.com/classes/langchain_community_tools_gmail.GmailSearch.html) from `@langchain/community/tools/gmail` * [GmailSendMessage](https://api.js.langchain.com/classes/langchain_community_tools_gmail.GmailSendMessage.html) from `@langchain/community/tools/gmail` * [StructuredTool](https://api.js.langchain.com/classes/langchain_core_tools.StructuredTool.html) from `@langchain/core/tools` * * * #### Help us out by providing feedback on this documentation page: [ Previous Exa Search ](/v0.1/docs/integrations/tools/exa_search/)[ Next Google Calendar Tool ](/v0.1/docs/integrations/tools/google_calendar/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/lambda_agent/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Agent with AWS Lambda Agent with AWS Lambda Integration ================================= Full docs here: [https://docs.aws.amazon.com/lambda/index.html](https://docs.aws.amazon.com/lambda/index.html) AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS), designed to allow developers to build and run applications and services without the need for provisioning or managing servers. This serverless architecture enables you to focus on writing and deploying code, while AWS automatically takes care of scaling, patching, and managing the infrastructure required to run your applications. By including a AWSLambda in the list of tools provided to an Agent, you can grant your Agent the ability to invoke code running in your AWS Cloud for whatever purposes you need. When an Agent uses the AWSLambda tool, it will provide an argument of type `string` which will in turn be passed into the Lambda function via the `event` parameter. This quick start will demonstrate how an Agent could use a Lambda function to send an email via [Amazon Simple Email Service](https://aws.amazon.com/ses/). The lambda code which sends the email is not provided, but if you'd like to learn how this could be done, see [here](https://repost.aws/knowledge-center/lambda-send-email-ses). Keep in mind this is an intentionally simple example; Lambda can used to execute code for a near infinite number of other purposes (including executing more Langchains)! ### Note about credentials:[](#note-about-credentials "Direct link to Note about credentials:") * If you have not run [`aws configure`](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html) via the AWS CLI, the `region`, `accessKeyId`, and `secretAccessKey` must be provided to the AWSLambda constructor. * The IAM role corresponding to those credentials must have permission to invoke the lambda function. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAI } from "@langchain/openai";import { SerpAPI } from "langchain/tools";import { AWSLambda } from "langchain/tools/aws_lambda";import { initializeAgentExecutorWithOptions } from "langchain/agents";const model = new OpenAI({ temperature: 0 });const emailSenderTool = new AWSLambda({ name: "email-sender", // tell the Agent precisely what the tool does description: "Sends an email with the specified content to testing123@gmail.com", region: "us-east-1", // optional: AWS region in which the function is deployed accessKeyId: "abc123", // optional: access key id for a IAM user with invoke permissions secretAccessKey: "xyz456", // optional: secret access key for that IAM user functionName: "SendEmailViaSES", // the function name as seen in AWS Console});const tools = [emailSenderTool, new SerpAPI("api_key_goes_here")];const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: "zero-shot-react-description",});const input = `Find out the capital of Croatia. Once you have it, email the answer to testing123@gmail.com.`;const result = await executor.invoke({ input });console.log(result); * * * #### Help us out by providing feedback on this documentation page: [ Previous Google Places Tool ](/v0.1/docs/integrations/tools/google_places/)[ Next Python interpreter tool ](/v0.1/docs/integrations/tools/pyinterpreter/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/google_places/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Google Places Tool Google Places Tool ================== The Google Places Tool allows your agent to utilize the Google Places API in order to find addresses, phone numbers, website, etc. from text about a location listed on Google Places. Setup[](#setup "Direct link to Setup") --------------------------------------- You will need to get an API key from [Google here](https://developers.google.com/maps/documentation/places/web-service/overview) and [enable the new Places API](https://console.cloud.google.com/apis/library/places.googleapis.com). Then, set your API key as `process.env.GOOGLE_PLACES_API_KEY` or pass it in as an `apiKey` constructor argument. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community import { GooglePlacesAPI } from "@langchain/community/tools/google_places";import { OpenAI } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";export async function run() { const model = new OpenAI({ temperature: 0, }); const tools = [new GooglePlacesAPI()]; const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: "zero-shot-react-description", verbose: true, }); const res = await executor.invoke({ input: "Where is the University of Toronto - Scarborough? ", }); console.log(res.output);} #### API Reference: * [GooglePlacesAPI](https://api.js.langchain.com/classes/langchain_community_tools_google_places.GooglePlacesAPI.html) from `@langchain/community/tools/google_places` * [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * * * #### Help us out by providing feedback on this documentation page: [ Previous Google Calendar Tool ](/v0.1/docs/integrations/tools/google_calendar/)[ Next Agent with AWS Lambda ](/v0.1/docs/integrations/tools/lambda_agent/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/google_calendar/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Google Calendar Tool Google Calendar Tool ==================== The Google Calendar Tools allow your agent to create and view Google Calendar events from a linked calendar. Setup[](#setup "Direct link to Setup") --------------------------------------- To use the Google Calendar Tools you need to install the following official peer dependency: * npm * Yarn * pnpm npm install googleapis yarn add googleapis pnpm add googleapis Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { initializeAgentExecutorWithOptions } from "langchain/agents";import { OpenAI } from "@langchain/openai";import { Calculator } from "@langchain/community/tools/calculator";import { GoogleCalendarCreateTool, GoogleCalendarViewTool,} from "@langchain/community/tools/google_calendar";export async function run() { const model = new OpenAI({ temperature: 0, apiKey: process.env.OPENAI_API_KEY, }); const googleCalendarParams = { credentials: { clientEmail: process.env.GOOGLE_CALENDAR_CLIENT_EMAIL, privateKey: process.env.GOOGLE_CALENDAR_PRIVATE_KEY, calendarId: process.env.GOOGLE_CALENDAR_CALENDAR_ID, }, scopes: [ "https://www.googleapis.com/auth/calendar", "https://www.googleapis.com/auth/calendar.events", ], model, }; const tools = [ new Calculator(), new GoogleCalendarCreateTool(googleCalendarParams), new GoogleCalendarViewTool(googleCalendarParams), ]; const calendarAgent = await initializeAgentExecutorWithOptions(tools, model, { agentType: "zero-shot-react-description", verbose: true, }); const createInput = `Create a meeting with John Doe next Friday at 4pm - adding to the agenda of it the result of 99 + 99`; const createResult = await calendarAgent.invoke({ input: createInput }); // Create Result { // output: 'A meeting with John Doe on 29th September at 4pm has been created and the result of 99 + 99 has been added to the agenda.' // } console.log("Create Result", createResult); const viewInput = `What meetings do I have this week?`; const viewResult = await calendarAgent.invoke({ input: viewInput }); // View Result { // output: "You have no meetings this week between 8am and 8pm." // } console.log("View Result", viewResult);} #### API Reference: * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [Calculator](https://api.js.langchain.com/classes/langchain_community_tools_calculator.Calculator.html) from `@langchain/community/tools/calculator` * [GoogleCalendarCreateTool](https://api.js.langchain.com/classes/langchain_community_tools_google_calendar.GoogleCalendarCreateTool.html) from `@langchain/community/tools/google_calendar` * [GoogleCalendarViewTool](https://api.js.langchain.com/classes/langchain_community_tools_google_calendar.GoogleCalendarViewTool.html) from `@langchain/community/tools/google_calendar` * * * #### Help us out by providing feedback on this documentation page: [ Previous Gmail Tool ](/v0.1/docs/integrations/tools/gmail/)[ Next Google Places Tool ](/v0.1/docs/integrations/tools/google_places/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/pyinterpreter/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Python interpreter tool Python interpreter tool ======================= danger This tool executes code and can potentially perform destructive actions. Be careful that you trust any code passed to it! LangChain offers an experimental tool for executing arbitrary Python code. This can be useful in combination with an LLM that can generate code to perform more powerful computations. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAI } from "@langchain/openai";import { PythonInterpreterTool } from "langchain/experimental/tools/pyinterpreter";import { ChatPromptTemplate } from "@langchain/core/prompts";import { StringOutputParser } from "@langchain/core/output_parsers";const prompt = ChatPromptTemplate.fromTemplate( `Generate python code that does {input}. Do not generate anything else.`);const model = new OpenAI({});const interpreter = await PythonInterpreterTool.initialize({ indexURL: "../node_modules/pyodide",});// Note: In Deno, it may be easier to initialize the interpreter yourself:// import pyodideModule from "npm:pyodide/pyodide.js";// import { PythonInterpreterTool } from "npm:langchain/experimental/tools/pyinterpreter";// const pyodide = await pyodideModule.loadPyodide();// const pythonTool = new PythonInterpreterTool({instance: pyodide})const chain = prompt .pipe(model) .pipe(new StringOutputParser()) .pipe(interpreter);const result = await chain.invoke({ input: `prints "Hello LangChain"`,});console.log(JSON.parse(result).stdout);// To install python packages:// This uses the loadPackages command.// This works for packages built with pyodide.await interpreter.addPackage("numpy");// But for other packages, you will want to use micropip.// See: https://pyodide.org/en/stable/usage/loading-packages.html// for more informationawait interpreter.addPackage("micropip");// The following is roughly equivalent to:// pyodide.runPython(`import ${pkgname}; ${pkgname}`);const micropip = interpreter.pyodideInstance.pyimport("micropip");await micropip.install("numpy"); #### API Reference: * [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [PythonInterpreterTool](https://api.js.langchain.com/classes/langchain_experimental_tools_pyinterpreter.PythonInterpreterTool.html) from `langchain/experimental/tools/pyinterpreter` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers` * * * #### Help us out by providing feedback on this documentation page: [ Previous Agent with AWS Lambda ](/v0.1/docs/integrations/tools/lambda_agent/)[ Next SearchApi tool ](/v0.1/docs/integrations/tools/searchapi/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/searchapi/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * SearchApi tool SearchApi tool ============== The `SearchApi` tool connects your agents and chains to the internet. A wrapper around the Search API. This tool is handy when you need to answer questions about current events. Usage[](#usage "Direct link to Usage") --------------------------------------- Input should be a search query. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { ChatOpenAI } from "@langchain/openai";import { AgentExecutor } from "langchain/agents";import { ChatPromptTemplate } from "@langchain/core/prompts";import { RunnableSequence } from "@langchain/core/runnables";import { AgentFinish, AgentAction } from "@langchain/core/agents";import { BaseMessageChunk } from "@langchain/core/messages";import { SearchApi } from "@langchain/community/tools/searchapi";const model = new ChatOpenAI({ temperature: 0,});const tools = [ new SearchApi(process.env.SEARCHAPI_API_KEY, { engine: "google_news", }),];const prefix = ChatPromptTemplate.fromMessages([ [ "ai", "Answer the following questions as best you can. In your final answer, use a bulleted list markdown format.", ], ["human", "{input}"],]);// Replace this with your actual output parser.const customOutputParser = ( input: BaseMessageChunk): AgentAction \| AgentFinish => ({ log: "test", returnValues: { output: input, },});// Replace this placeholder agent with your actual implementation.const agent = RunnableSequence.from([prefix, model, customOutputParser]);const executor = AgentExecutor.fromAgentAndTools({ agent, tools,});const res = await executor.invoke({ input: "What's happening in Ukraine today?",});console.log(res); #### API Reference: * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables` * [AgentFinish](https://api.js.langchain.com/types/langchain_core_agents.AgentFinish.html) from `@langchain/core/agents` * [AgentAction](https://api.js.langchain.com/types/langchain_core_agents.AgentAction.html) from `@langchain/core/agents` * [BaseMessageChunk](https://api.js.langchain.com/classes/langchain_core_messages.BaseMessageChunk.html) from `@langchain/core/messages` * [SearchApi](https://api.js.langchain.com/classes/langchain_community_tools_searchapi.SearchApi.html) from `@langchain/community/tools/searchapi` * * * #### Help us out by providing feedback on this documentation page: [ Previous Python interpreter tool ](/v0.1/docs/integrations/tools/pyinterpreter/)[ Next Searxng Search tool ](/v0.1/docs/integrations/tools/searxng/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/searxng/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Searxng Search tool Searxng Search tool =================== The `SearxngSearch` tool connects your agents and chains to the internet. A wrapper around the SearxNG API, this tool is useful for performing meta-search engine queries using the SearxNG API. It is particularly helpful in answering questions about current events. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { ChatOpenAI } from "@langchain/openai";import { AgentExecutor } from "langchain/agents";import { BaseMessageChunk } from "@langchain/core/messages";import { AgentAction, AgentFinish } from "@langchain/core/agents";import { RunnableSequence } from "@langchain/core/runnables";import { ChatPromptTemplate } from "@langchain/core/prompts";import { SearxngSearch } from "@langchain/community/tools/searxng_search";const model = new ChatOpenAI({ maxTokens: 1000, model: "gpt-4",});// `apiBase` will be automatically parsed from .env file, set "SEARXNG_API_BASE" in .env,const tools = [ new SearxngSearch({ params: { format: "json", // Do not change this, format other than "json" is will throw error engines: "google", }, // Custom Headers to support rapidAPI authentication Or any instance that requires custom headers headers: {}, }),];const prefix = ChatPromptTemplate.fromMessages([ [ "ai", "Answer the following questions as best you can. In your final answer, use a bulleted list markdown format.", ], ["human", "{input}"],]);// Replace this with your actual output parser.const customOutputParser = ( input: BaseMessageChunk): AgentAction \| AgentFinish => ({ log: "test", returnValues: { output: input, },});// Replace this placeholder agent with your actual implementation.const agent = RunnableSequence.from([prefix, model, customOutputParser]);const executor = AgentExecutor.fromAgentAndTools({ agent, tools,});console.log("Loaded agent.");const input = `What is Langchain? Describe in 50 words`;console.log(`Executing with input "${input}"...`);const result = await executor.invoke({ input });console.log(result);/** * Langchain is a framework for developing applications powered by language models, such as chatbots, Generative Question-Answering, summarization, and more. It provides a standard interface, integrations with other tools, and end-to-end chains for common applications. Langchain enables data-aware and powerful applications. / #### API Reference: [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [BaseMessageChunk](https://api.js.langchain.com/classes/langchain_core_messages.BaseMessageChunk.html) from `@langchain/core/messages` * [AgentAction](https://api.js.langchain.com/types/langchain_core_agents.AgentAction.html) from `@langchain/core/agents` * [AgentFinish](https://api.js.langchain.com/types/langchain_core_agents.AgentFinish.html) from `@langchain/core/agents` * [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [SearxngSearch](https://api.js.langchain.com/classes/langchain_community_tools_searxng_search.SearxngSearch.html) from `@langchain/community/tools/searxng_search` * * * #### Help us out by providing feedback on this documentation page: [ Previous SearchApi tool ](/v0.1/docs/integrations/tools/searchapi/)[ Next StackExchange Tool ](/v0.1/docs/integrations/tools/stackexchange/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/stackexchange/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * StackExchange Tool StackExchange Tool ================== The StackExchange tool connects your agents and chains to StackExchange's API. Usage[](#usage "Direct link to Usage") --------------------------------------- import { StackExchangeAPI } from "@langchain/community/tools/stackexchange";// Get results from StackExchange APIconst stackExchangeTool = new StackExchangeAPI();const result = await stackExchangeTool.invoke("zsh: command not found: python");console.log(result);// Get results from StackExchange API with title queryconst stackExchangeTitleTool = new StackExchangeAPI({ queryType: "title",});const titleResult = await stackExchangeTitleTool.invoke( "zsh: command not found: python");console.log(titleResult);// Get results from StackExchange API with bad queryconst stackExchangeBadTool = new StackExchangeAPI();const badResult = await stackExchangeBadTool.invoke( "sjefbsmnazdkhbazkbdoaencopebfoubaef");console.log(badResult); #### API Reference: * [StackExchangeAPI](https://api.js.langchain.com/classes/langchain_community_tools_stackexchange.StackExchangeAPI.html) from `@langchain/community/tools/stackexchange` * * * #### Help us out by providing feedback on this documentation page: [ Previous Searxng Search tool ](/v0.1/docs/integrations/tools/searxng/)[ Next Tavily Search ](/v0.1/docs/integrations/tools/tavily_search/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/webbrowser/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Web Browser Tool Web Browser Tool ================ The Webbrowser Tool gives your agent the ability to visit a website and extract information. It is described to the agent as useful for when you need to find something on or summarize a webpage. input should be a comma separated list of "valid URL including protocol","what you want to find on the page or empty string for a summary". It exposes two modes of operation: * when called by the Agent with only a URL it produces a summary of the website contents * when called by the Agent with a URL and a description of what to find it will instead use an in-memory Vector Store to find the most relevant snippets and summarise those Setup[](#setup "Direct link to Setup") --------------------------------------- To use the Webbrowser Tool you need to install the dependencies: * npm * Yarn * pnpm npm install cheerio axios yarn add cheerio axios pnpm add cheerio axios Usage, standalone[](#usage-standalone "Direct link to Usage, standalone") -------------------------------------------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { WebBrowser } from "langchain/tools/webbrowser";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";export async function run() { // this will not work with Azure OpenAI API yet // Azure OpenAI API does not support embedding with multiple inputs yet // Too many inputs. The max number of inputs is 1. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions. // So we will fail fast, when Azure OpenAI API is used if (process.env.AZURE_OPENAI_API_KEY) { throw new Error( "Azure OpenAI API does not support embedding with multiple inputs yet" ); } const model = new ChatOpenAI({ temperature: 0 }); const embeddings = new OpenAIEmbeddings( process.env.AZURE_OPENAI_API_KEY ? { azureOpenAIApiDeploymentName: "Embeddings2" } : {} ); const browser = new WebBrowser({ model, embeddings }); const result = await browser.invoke( `"https://www.themarginalian.org/2015/04/09/find-your-bliss-joseph-campbell-power-of-myth","who is joseph campbell"` ); console.log(result); /* Joseph Campbell was a mythologist and writer who discussed spirituality, psychological archetypes, cultural myths, and the mythology of self. He sat down with Bill Moyers for a lengthy conversation at George Lucas’s Skywalker Ranch in California, which continued the following year at the American Museum of Natural History in New York. The resulting 24 hours of raw footage were edited down to six one-hour episodes and broadcast on PBS in 1988, shortly after Campbell’s death, in what became one of the most popular in the history of public television. Relevant Links: - [The Holstee Manifesto](http://holstee.com/manifesto-bp) - [The Silent Music of the Mind: Remembering Oliver Sacks](https://www.themarginalian.org/2015/08/31/remembering-oliver-sacks) - [Joseph Campbell series](http://billmoyers.com/spotlight/download-joseph-campbell-and-the-power-of-myth-audio/) - [Bill Moyers](https://www.themarginalian.org/tag/bill-moyers/) - [books](https://www.themarginalian.org/tag/books/) /} #### API Reference: [WebBrowser](https://api.js.langchain.com/classes/langchain_tools_webbrowser.WebBrowser.html) from `langchain/tools/webbrowser` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` Usage, in an Agent[](#usage-in-an-agent "Direct link to Usage, in an Agent") ----------------------------------------------------------------------------- import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";import { Calculator } from "@langchain/community/tools/calculator";import { WebBrowser } from "langchain/tools/webbrowser";import { SerpAPI } from "@langchain/community/tools/serpapi";export const run = async () => { const model = new OpenAI({ temperature: 0 }); const embeddings = new OpenAIEmbeddings(); const tools = [ new SerpAPI(process.env.SERPAPI_API_KEY, { location: "Austin,Texas,United States", hl: "en", gl: "us", }), new Calculator(), new WebBrowser({ model, embeddings }), ]; const executor = await initializeAgentExecutorWithOptions(tools, model, { agentType: "zero-shot-react-description", verbose: true, }); console.log("Loaded agent."); const input = `What is the word of the day on merriam webster. What is the top result on google for that word`; console.log(`Executing with input "${input}"...`); const result = await executor.invoke({ input }); /* Entering new agent_executor chain... I need to find the word of the day on Merriam Webster and then search for it on Google Action: web-browser Action Input: "https://www.merriam-webster.com/word-of-the-day", "" Summary: Merriam-Webster is a website that provides users with a variety of resources, including a dictionary, thesaurus, word finder, word of the day, games and quizzes, and more. The website also allows users to log in and save words, view recents, and access their account settings. The Word of the Day for April 14, 2023 is "lackadaisical", which means lacking in life, spirit, or zest. The website also provides quizzes and games to help users build their vocabulary. Relevant Links: - [Test Your Vocabulary](https://www.merriam-webster.com/games) - [Thesaurus](https://www.merriam-webster.com/thesaurus) - [Word Finder](https://www.merriam-webster.com/wordfinder) - [Word of the Day](https://www.merriam-webster.com/word-of-the-day) - [Shop](https://shop.merriam-webster.com/?utm_source=mwsite&utm_medium=nav&utm_content= I now need to search for the word of the day on Google Action: search Action Input: "lackadaisical" lackadaisical implies a carefree indifference marked by half-hearted efforts. lackadaisical college seniors pretending to study. listless suggests a lack of ... Finished chain. / console.log(`Got output ${JSON.stringify(result, null, 2)}`); / Got output { "output": "The word of the day on Merriam Webster is \"lackadaisical\", which implies a carefree indifference marked by half-hearted efforts.", "intermediateSteps": [ { "action": { "tool": "web-browser", "toolInput": "https://www.merriam-webster.com/word-of-the-day\", ", "log": " I need to find the word of the day on Merriam Webster and then search for it on Google\nAction: web-browser\nAction Input: \"https://www.merriam-webster.com/word-of-the-day\", \"\"" }, "observation": "\n\nSummary: Merriam-Webster is a website that provides users with a variety of resources, including a dictionary, thesaurus, word finder, word of the day, games and quizzes, and more. The website also allows users to log in and save words, view recents, and access their account settings. The Word of the Day for April 14, 2023 is \"lackadaisical\", which means lacking in life, spirit, or zest. The website also provides quizzes and games to help users build their vocabulary.\n\nRelevant Links: \n- [Test Your Vocabulary](https://www.merriam-webster.com/games)\n- [Thesaurus](https://www.merriam-webster.com/thesaurus)\n- [Word Finder](https://www.merriam-webster.com/wordfinder)\n- [Word of the Day](https://www.merriam-webster.com/word-of-the-day)\n- [Shop](https://shop.merriam-webster.com/?utm_source=mwsite&utm_medium=nav&utm_content=" }, { "action": { "tool": "search", "toolInput": "lackadaisical", "log": " I now need to search for the word of the day on Google\nAction: search\nAction Input: \"lackadaisical\"" }, "observation": "lackadaisical implies a carefree indifference marked by half-hearted efforts. lackadaisical college seniors pretending to study. listless suggests a lack of ..." } ] } /}; #### API Reference: [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [Calculator](https://api.js.langchain.com/classes/langchain_community_tools_calculator.Calculator.html) from `@langchain/community/tools/calculator` * [WebBrowser](https://api.js.langchain.com/classes/langchain_tools_webbrowser.WebBrowser.html) from `langchain/tools/webbrowser` * [SerpAPI](https://api.js.langchain.com/classes/langchain_community_tools_serpapi.SerpAPI.html) from `@langchain/community/tools/serpapi` * * * #### Help us out by providing feedback on this documentation page: [ Previous Tavily Search ](/v0.1/docs/integrations/tools/tavily_search/)[ Next Wikipedia tool ](/v0.1/docs/integrations/tools/wikipedia/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/wolframalpha/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * WolframAlpha Tool WolframAlpha Tool ================= The WolframAlpha tool connects your agents and chains to WolframAlpha's state-of-the-art computational intelligence engine. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to create an app from the [WolframAlpha portal](https://developer.wolframalpha.com/) and obtain an `appid`. Usage[](#usage "Direct link to Usage") --------------------------------------- import { WolframAlphaTool } from "@langchain/community/tools/wolframalpha";const tool = new WolframAlphaTool({ appid: "YOUR_APP_ID",});const res = await tool.invoke("What is 2 * 2?");console.log(res); #### API Reference: * [WolframAlphaTool](https://api.js.langchain.com/classes/langchain_community_tools_wolframalpha.WolframAlphaTool.html) from `@langchain/community/tools/wolframalpha` * * * #### Help us out by providing feedback on this documentation page: [ Previous Wikipedia tool ](/v0.1/docs/integrations/tools/wikipedia/)[ Next Agent with Zapier NLA Integration ](/v0.1/docs/integrations/tools/zapier_agent/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/wikipedia/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Wikipedia tool Wikipedia tool ============== The `WikipediaQueryRun` tool connects your agents and chains to Wikipedia. Usage[](#usage "Direct link to Usage") --------------------------------------- import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia_query_run";const tool = new WikipediaQueryRun({ topKResults: 3, maxDocContentLength: 4000,});const res = await tool.invoke("Langchain");console.log(res); #### API Reference: * [WikipediaQueryRun](https://api.js.langchain.com/classes/langchain_community_tools_wikipedia_query_run.WikipediaQueryRun.html) from `@langchain/community/tools/wikipedia_query_run` * * * #### Help us out by providing feedback on this documentation page: [ Previous Web Browser Tool ](/v0.1/docs/integrations/tools/webbrowser/)[ Next WolframAlpha Tool ](/v0.1/docs/integrations/tools/wolframalpha/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/tools/zapier_agent/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [ChatGPT Plugins](/v0.1/docs/integrations/tools/aiplugin-tool/) * [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) * [Dall-E Tool](/v0.1/docs/integrations/tools/dalle/) * [Discord Tool](/v0.1/docs/integrations/tools/discord/) * [DuckDuckGoSearch](/v0.1/docs/integrations/tools/duckduckgo_search/) * [Exa Search](/v0.1/docs/integrations/tools/exa_search/) * [Gmail Tool](/v0.1/docs/integrations/tools/gmail/) * [Google Calendar Tool](/v0.1/docs/integrations/tools/google_calendar/) * [Google Places Tool](/v0.1/docs/integrations/tools/google_places/) * [Agent with AWS Lambda](/v0.1/docs/integrations/tools/lambda_agent/) * [Python interpreter tool](/v0.1/docs/integrations/tools/pyinterpreter/) * [SearchApi tool](/v0.1/docs/integrations/tools/searchapi/) * [Searxng Search tool](/v0.1/docs/integrations/tools/searxng/) * [StackExchange Tool](/v0.1/docs/integrations/tools/stackexchange/) * [Tavily Search](/v0.1/docs/integrations/tools/tavily_search/) * [Web Browser Tool](/v0.1/docs/integrations/tools/webbrowser/) * [Wikipedia tool](/v0.1/docs/integrations/tools/wikipedia/) * [WolframAlpha Tool](/v0.1/docs/integrations/tools/wolframalpha/) * [Agent with Zapier NLA Integration](/v0.1/docs/integrations/tools/zapier_agent/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Tools](/v0.1/docs/integrations/tools/) * Agent with Zapier NLA Integration Agent with Zapier NLA Integration ================================= Full docs here: [https://nla.zapier.com/start/](https://nla.zapier.com/start/) Zapier Natural Language Actions gives you access to the 5k+ apps and 20k+ actions on Zapier's platform through a natural language API interface. NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: [https://zapier.com/apps](https://zapier.com/apps) Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API. NLA offers both API Key and OAuth for signing NLA API requests. Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com) User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com Attach NLA credentials via either an environment variable (`ZAPIER_NLA_OAUTH_ACCESS_TOKEN` or `ZAPIER_NLA_API_KEY`) or refer to the params argument in the API reference for `ZapierNLAWrapper`. Review [auth docs](https://nla.zapier.com/docs/authentication/) for more details. The example below demonstrates how to use the Zapier integration as an Agent: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { OpenAI } from "@langchain/openai";import { ZapierNLAWrapper } from "langchain/tools";import { initializeAgentExecutorWithOptions, ZapierToolKit,} from "langchain/agents";const model = new OpenAI({ temperature: 0 });const zapier = new ZapierNLAWrapper();const toolkit = await ZapierToolKit.fromZapierNLAWrapper(zapier);const executor = await initializeAgentExecutorWithOptions( toolkit.tools, model, { agentType: "zero-shot-react-description", verbose: true, });console.log("Loaded agent.");const input = `Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier Slack channel.`;console.log(`Executing with input "${input}"...`);const result = await executor.invoke({ input });console.log(`Got output ${result.output}`); #### API Reference: * [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [ZapierNLAWrapper](https://api.js.langchain.com/classes/langchain_tools.ZapierNLAWrapper.html) from `langchain/tools` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [ZapierToolKit](https://api.js.langchain.com/classes/langchain_agents.ZapierToolKit.html) from `langchain/agents` * * * #### Help us out by providing feedback on this documentation page: [ Previous WolframAlpha Tool ](/v0.1/docs/integrations/tools/wolframalpha/)[ Next Agents and toolkits ](/v0.1/docs/integrations/toolkits/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/toolkits/json/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) * [JSON Agent Toolkit](/v0.1/docs/integrations/toolkits/json/) * [OpenAPI Agent Toolkit](/v0.1/docs/integrations/toolkits/openapi/) * [AWS Step Functions Toolkit](/v0.1/docs/integrations/toolkits/sfn_agent/) * [SQL Agent Toolkit](/v0.1/docs/integrations/toolkits/sql/) * [VectorStore Agent Toolkit](/v0.1/docs/integrations/toolkits/vectorstore/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * JSON Agent Toolkit JSON Agent Toolkit ================== This example shows how to load and use an agent with a JSON toolkit. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import * as fs from "fs";import * as yaml from "js-yaml";import { OpenAI } from "@langchain/openai";import { JsonSpec, JsonObject } from "langchain/tools";import { JsonToolkit, createJsonAgent } from "langchain/agents";export const run = async () => { let data: JsonObject; try { const yamlFile = fs.readFileSync("openai_openapi.yaml", "utf8"); data = yaml.load(yamlFile) as JsonObject; if (!data) { throw new Error("Failed to load OpenAPI spec"); } } catch (e) { console.error(e); return; } const toolkit = new JsonToolkit(new JsonSpec(data)); const model = new OpenAI({ temperature: 0 }); const executor = createJsonAgent(model, toolkit); const input = `What are the required parameters in the request body to the /completions endpoint?`; console.log(`Executing with input "${input}"...`); const result = await executor.invoke({ input }); console.log(`Got output ${result.output}`); console.log( `Got intermediate steps ${JSON.stringify( result.intermediateSteps, null, 2 )}` );}; * * * #### Help us out by providing feedback on this documentation page: [ Previous Connery Toolkit ](/v0.1/docs/integrations/toolkits/connery/)[ Next OpenAPI Agent Toolkit ](/v0.1/docs/integrations/toolkits/openapi/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/toolkits/connery/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) * [JSON Agent Toolkit](/v0.1/docs/integrations/toolkits/json/) * [OpenAPI Agent Toolkit](/v0.1/docs/integrations/toolkits/openapi/) * [AWS Step Functions Toolkit](/v0.1/docs/integrations/toolkits/sfn_agent/) * [SQL Agent Toolkit](/v0.1/docs/integrations/toolkits/sql/) * [VectorStore Agent Toolkit](/v0.1/docs/integrations/toolkits/vectorstore/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * Connery Toolkit On this page Connery Toolkit =============== Using this toolkit, you can integrate Connery Actions into your LangChain agent. note If you want to use only one particular Connery Action in your agent, check out the [Connery Action Tool](/v0.1/docs/integrations/tools/connery/) documentation. What is Connery?[](#what-is-connery "Direct link to What is Connery?") ----------------------------------------------------------------------- Connery is an open-source plugin infrastructure for AI. With Connery, you can easily create a custom plugin with a set of actions and seamlessly integrate them into your LangChain agent. Connery will take care of critical aspects such as runtime, authorization, secret management, access management, audit logs, and other vital features. Furthermore, Connery, supported by our community, provides a diverse collection of ready-to-use open-source plugins for added convenience. Learn more about Connery: * GitHub: [https://github.com/connery-io/connery](https://github.com/connery-io/connery) * Documentation: [https://docs.connery.io](https://docs.connery.io) Prerequisites[](#prerequisites "Direct link to Prerequisites") --------------------------------------------------------------- To use Connery Actions in your LangChain agent, you need to do some preparation: 1. Set up the Connery runner using the [Quickstart](https://docs.connery.io/docs/runner/quick-start/) guide. 2. Install all the plugins with the actions you want to use in your agent. 3. Set environment variables `CONNERY_RUNNER_URL` and `CONNERY_RUNNER_API_KEY` so the toolkit can communicate with the Connery Runner. Example of using Connery Toolkit[](#example-of-using-connery-toolkit "Direct link to Example of using Connery Toolkit") ------------------------------------------------------------------------------------------------------------------------ ### Setup[](#setup "Direct link to Setup") To use the Connery Toolkit you need to install the following official peer dependency: * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). ### Usage[](#usage "Direct link to Usage") In the example below, we create an agent that uses two Connery Actions to summarize a public webpage and send the summary by email: 1. Summarize public webpage action from the [Summarization](https://github.com/connery-io/summarization-plugin) plugin. 2. Send email action from the [Gmail](https://github.com/connery-io/gmail) plugin. info You can see a LangSmith trace of this example [here](https://smith.langchain.com/public/5485cb37-b73d-458f-8162-43639f2b49e1/r). import { ConneryService } from "@langchain/community/tools/connery";import { ConneryToolkit } from "@langchain/community/agents/toolkits/connery";import { ChatOpenAI } from "@langchain/openai";import { initializeAgentExecutorWithOptions } from "langchain/agents";// Specify your Connery Runner credentials.process.env.CONNERY_RUNNER_URL = "";process.env.CONNERY_RUNNER_API_KEY = "";// Specify OpenAI API key.process.env.OPENAI_API_KEY = "";// Specify your email address to receive the emails from examples below.const recepientEmail = "test@example.com";// Create a Connery Toolkit with all the available actions from the Connery Runner.const conneryService = new ConneryService();const conneryToolkit = await ConneryToolkit.createInstance(conneryService);// Use OpenAI Functions agent to execute the prompt using actions from the Connery Toolkit.const llm = new ChatOpenAI({ temperature: 0 });const agent = await initializeAgentExecutorWithOptions( conneryToolkit.tools, llm, { agentType: "openai-functions", verbose: true, });const result = await agent.invoke({ input: `Make a short summary of the webpage http://www.paulgraham.com/vb.html in three sentences ` + `and send it to ${recepientEmail}. Include the link to the webpage into the body of the email.`,});console.log(result.output); #### API Reference: * [ConneryService](https://api.js.langchain.com/classes/langchain_community_tools_connery.ConneryService.html) from `@langchain/community/tools/connery` * [ConneryToolkit](https://api.js.langchain.com/classes/langchain_community_agents_toolkits_connery.ConneryToolkit.html) from `@langchain/community/agents/toolkits/connery` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` note Connery Action is a structured tool, so you can only use it in the agents supporting structured tools. * * * #### Help us out by providing feedback on this documentation page: [ Previous Agents and toolkits ](/v0.1/docs/integrations/toolkits/)[ Next JSON Agent Toolkit ](/v0.1/docs/integrations/toolkits/json/) * [What is Connery?](#what-is-connery) * [Prerequisites](#prerequisites) * [Example of using Connery Toolkit](#example-of-using-connery-toolkit) * [Setup](#setup) * [Usage](#usage) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/toolkits/openapi/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) * [JSON Agent Toolkit](/v0.1/docs/integrations/toolkits/json/) * [OpenAPI Agent Toolkit](/v0.1/docs/integrations/toolkits/openapi/) * [AWS Step Functions Toolkit](/v0.1/docs/integrations/toolkits/sfn_agent/) * [SQL Agent Toolkit](/v0.1/docs/integrations/toolkits/sql/) * [VectorStore Agent Toolkit](/v0.1/docs/integrations/toolkits/vectorstore/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * OpenAPI Agent Toolkit OpenAPI Agent Toolkit ===================== This example shows how to load and use an agent with a OpenAPI toolkit. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import * as fs from "fs";import * as yaml from "js-yaml";import { OpenAI } from "@langchain/openai";import { JsonSpec, JsonObject } from "langchain/tools";import { createOpenApiAgent, OpenApiToolkit } from "langchain/agents";export const run = async () => { let data: JsonObject; try { const yamlFile = fs.readFileSync("openai_openapi.yaml", "utf8"); data = yaml.load(yamlFile) as JsonObject; if (!data) { throw new Error("Failed to load OpenAPI spec"); } } catch (e) { console.error(e); return; } const headers = { "Content-Type": "application/json", Authorization: `Bearer ${process.env.OPENAI_API_KEY}`, }; const model = new OpenAI({ temperature: 0 }); const toolkit = new OpenApiToolkit(new JsonSpec(data), model, headers); const executor = createOpenApiAgent(model, toolkit); const input = `Make a POST request to openai /completions. The prompt should be 'tell me a joke.'`; console.log(`Executing with input "${input}"...`); const result = await executor.invoke({ input }); console.log(`Got output ${result.output}`); console.log( `Got intermediate steps ${JSON.stringify( result.intermediateSteps, null, 2 )}` );}; Disclaimer ⚠️ ============= This agent can make requests to external APIs. Use with caution, especially when granting access to users. Be aware that this agent could theoretically send requests with provided credentials or other sensitive data to unverified or potentially malicious URLs --although it should never in theory. Consider adding limitations to what actions can be performed via the agent, what APIs it can access, what headers can be passed, and more. In addition, consider implementing measures to validate URLs before sending requests, and to securely handle and protect sensitive data such as credentials. * * * #### Help us out by providing feedback on this documentation page: [ Previous JSON Agent Toolkit ](/v0.1/docs/integrations/toolkits/json/)[ Next AWS Step Functions Toolkit ](/v0.1/docs/integrations/toolkits/sfn_agent/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/toolkits/vectorstore/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) * [JSON Agent Toolkit](/v0.1/docs/integrations/toolkits/json/) * [OpenAPI Agent Toolkit](/v0.1/docs/integrations/toolkits/openapi/) * [AWS Step Functions Toolkit](/v0.1/docs/integrations/toolkits/sfn_agent/) * [SQL Agent Toolkit](/v0.1/docs/integrations/toolkits/sql/) * [VectorStore Agent Toolkit](/v0.1/docs/integrations/toolkits/vectorstore/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * VectorStore Agent Toolkit VectorStore Agent Toolkit ========================= tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community This example shows how to load and use an agent with a vectorstore toolkit. import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import * as fs from "fs";import { VectorStoreToolkit, createVectorStoreAgent, VectorStoreInfo,} from "langchain/agents";const model = new OpenAI({ temperature: 0 });/* Load in the file we want to do question answering over /const text = fs.readFileSync("state_of_the_union.txt", "utf8");/ Split the text into chunks using character, not token, size /const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });const docs = await textSplitter.createDocuments([text]);/ Create the vectorstore /const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());/ Create the agent /const vectorStoreInfo: VectorStoreInfo = { name: "state_of_union_address", description: "the most recent state of the Union address", vectorStore,};const toolkit = new VectorStoreToolkit(vectorStoreInfo, model);const agent = createVectorStoreAgent(model, toolkit);const input = "What did biden say about Ketanji Brown Jackson is the state of the union address?";console.log(`Executing: ${input}`);const result = await agent.invoke({ input });console.log(`Got output ${result.output}`);console.log( `Got intermediate steps ${JSON.stringify(result.intermediateSteps, null, 2)}`); #### API Reference: [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [HNSWLib](https://api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib` * [RecursiveCharacterTextSplitter](https://api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `langchain/text_splitter` * [VectorStoreToolkit](https://api.js.langchain.com/classes/langchain_agents.VectorStoreToolkit.html) from `langchain/agents` * [createVectorStoreAgent](https://api.js.langchain.com/functions/langchain_agents.createVectorStoreAgent.html) from `langchain/agents` * [VectorStoreInfo](https://api.js.langchain.com/interfaces/langchain_agents.VectorStoreInfo.html) from `langchain/agents` * * * #### Help us out by providing feedback on this documentation page: [ Previous SQL Agent Toolkit ](/v0.1/docs/integrations/toolkits/sql/)[ Next Chat Memory ](/v0.1/docs/integrations/chat_memory/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/toolkits/sfn_agent/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Connery Toolkit](/v0.1/docs/integrations/toolkits/connery/) * [JSON Agent Toolkit](/v0.1/docs/integrations/toolkits/json/) * [OpenAPI Agent Toolkit](/v0.1/docs/integrations/toolkits/openapi/) * [AWS Step Functions Toolkit](/v0.1/docs/integrations/toolkits/sfn_agent/) * [SQL Agent Toolkit](/v0.1/docs/integrations/toolkits/sql/) * [VectorStore Agent Toolkit](/v0.1/docs/integrations/toolkits/vectorstore/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * AWS Step Functions Toolkit AWS Step Functions Toolkit ========================== AWS Step Functions are a visual workflow service that helps developers use AWS services to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning (ML) pipelines. By including a `AWSSfn` tool in the list of tools provided to an Agent, you can grant your Agent the ability to invoke async workflows running in your AWS Cloud. When an Agent uses the `AWSSfn` tool, it will provide an argument of type `string` which will in turn be passed into one of the supported actions this tool supports. The supported actions are: `StartExecution`, `DescribeExecution`, and `SendTaskSuccess`. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the Node AWS Step Functions SDK: * npm * Yarn * pnpm npm install @aws-sdk/client-sfn yarn add @aws-sdk/client-sfn pnpm add @aws-sdk/client-sfn Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community ### Note about credentials:[](#note-about-credentials "Direct link to Note about credentials:") * If you have not run [`aws configure`](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html) via the AWS CLI, the `region`, `accessKeyId`, and `secretAccessKey` must be provided to the AWSSfn constructor. * The IAM role corresponding to those credentials must have permission to invoke the Step Function. import { OpenAI } from "@langchain/openai";import { AWSSfnToolkit } from "@langchain/community/agents/toolkits/aws_sfn";import { createAWSSfnAgent } from "langchain/agents/toolkits/aws_sfn";const _EXAMPLE_STATE_MACHINE_ASL = `{ "Comment": "A simple example of the Amazon States Language to define a state machine for new client onboarding.", "StartAt": "OnboardNewClient", "States": { "OnboardNewClient": { "Type": "Pass", "Result": "Client onboarded!", "End": true } }}`;/** * This example uses a deployed AWS Step Function state machine with the above Amazon State Language (ASL) definition. * You can test by provisioning a state machine using the above ASL within your AWS environment, or you can use a tool like LocalStack * to mock AWS services locally. See https://localstack.cloud/ for more information. /export const run = async () => { const model = new OpenAI({ temperature: 0 }); const toolkit = new AWSSfnToolkit({ name: "onboard-new-client-workflow", description: "Onboard new client workflow. Can also be used to get status of any excuting workflow or state machine.", stateMachineArn: "arn:aws:states:us-east-1:1234567890:stateMachine:my-state-machine", // Update with your state machine ARN accordingly region: "<your Sfn's region>", accessKeyId: "<your access key id>", secretAccessKey: "<your secret access key>", }); const executor = createAWSSfnAgent(model, toolkit); const input = `Onboard john doe (john@example.com) as a new client.`; console.log(`Executing with input "${input}"...`); const result = await executor.invoke({ input }); console.log(`Got output ${result.output}`); console.log( `Got intermediate steps ${JSON.stringify( result.intermediateSteps, null, 2 )}` );}; #### API Reference: [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [AWSSfnToolkit](https://api.js.langchain.com/classes/langchain_community_agents_toolkits_aws_sfn.AWSSfnToolkit.html) from `@langchain/community/agents/toolkits/aws_sfn` * [createAWSSfnAgent](https://api.js.langchain.com/functions/langchain_agents_toolkits_aws_sfn.createAWSSfnAgent.html) from `langchain/agents/toolkits/aws_sfn` * * * #### Help us out by providing feedback on this documentation page: [ Previous OpenAPI Agent Toolkit ](/v0.1/docs/integrations/toolkits/openapi/)[ Next SQL Agent Toolkit ](/v0.1/docs/integrations/toolkits/sql/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/astradb/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Astra DB Chat Memory Astra DB Chat Memory ==================== For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for Astra DB. Setup[](#setup "Direct link to Setup") --------------------------------------- You need to install the Astra DB TS client: * npm * Yarn * pnpm npm install @datastax/astra-db-ts yarn add @datastax/astra-db-ts pnpm add @datastax/astra-db-ts tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Configuration and Initalization[](#configuration-and-initalization "Direct link to Configuration and Initalization") --------------------------------------------------------------------------------------------------------------------- There are two ways to inialize your `AstraDBChatMessageHistory` If you already have an instance of the `AstraDB` client defined you can connect to your collection and initialize an instance of the `ChatMessageHistory` using the constuctor. const client = (client = new AstraDB( process.env.ASTRA_DB_APPLICATION_TOKEN, process.env.ASTRA_DB_ENDPOINT, process.env.ASTRA_DB_NAMESPACE));const collection = await client.collection("YOUR_COLLECTION_NAME");const chatHistory = new AstraDBChatMessageHistory({ collection, sessionId: "YOUR_SESSION_ID",}); If you don't already have an instance of an `AstraDB` client you can use the `initialize` method. const chatHistory = await AstraDBChatMessageHistory.initialize({ token: process.env.ASTRA_DB_APPLICATION_TOKEN ?? "token", endpoint: process.env.ASTRA_DB_ENDPOINT ?? "endpoint", namespace: process.env.ASTRA_DB_NAMESPACE, collectionName: "YOUR_COLLECTION_NAME", sessionId: "YOUR_SESSION_ID",}); Usage[](#usage "Direct link to Usage") --------------------------------------- Tip Your collection must already exist import { RunnableWithMessageHistory } from "@langchain/core/runnables";import { ChatPromptTemplate, MessagesPlaceholder,} from "@langchain/core/prompts";import { StringOutputParser } from "@langchain/core/output_parsers";import { ChatOpenAI } from "@langchain/openai";import { AstraDBChatMessageHistory } from "@langchain/community/stores/message/astradb";const model = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0,});const prompt = ChatPromptTemplate.fromMessages([ [ "system", "You are a helpful assistant. Answer all questions to the best of your ability.", ], new MessagesPlaceholder("chat_history"), ["human", "{input}"],]);const chain = prompt.pipe(model).pipe(new StringOutputParser());const chainWithHistory = new RunnableWithMessageHistory({ runnable: chain, inputMessagesKey: "input", historyMessagesKey: "chat_history", getMessageHistory: async (sessionId) => { const chatHistory = await AstraDBChatMessageHistory.initialize({ token: process.env.ASTRA_DB_APPLICATION_TOKEN as string, endpoint: process.env.ASTRA_DB_ENDPOINT as string, namespace: process.env.ASTRA_DB_NAMESPACE, collectionName: "YOUR_COLLECTION_NAME", sessionId, }); return chatHistory; },});const res1 = await chainWithHistory.invoke( { input: "Hi! I'm Jim.", }, { configurable: { sessionId: "langchain-test-session" } });console.log({ res1 });/{ res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" }}/const res2 = await chainWithHistory.invoke( { input: "What did I just say my name was?" }, { configurable: { sessionId: "langchain-test-session" } });console.log({ res2 });/{ res2: { text: "You said your name was Jim." }}/ #### API Reference: * [RunnableWithMessageHistory](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableWithMessageHistory.html) from `@langchain/core/runnables` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [MessagesPlaceholder](https://api.js.langchain.com/classes/langchain_core_prompts.MessagesPlaceholder.html) from `@langchain/core/prompts` * [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AstraDBChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_astradb.AstraDBChatMessageHistory.html) from `@langchain/community/stores/message/astradb` * * * #### Help us out by providing feedback on this documentation page: [ Previous Chat Memory ](/v0.1/docs/integrations/chat_memory/)[ Next Cassandra Chat Memory ](/v0.1/docs/integrations/chat_memory/cassandra/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/cassandra/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Cassandra Chat Memory Cassandra Chat Memory ===================== For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for a Cassandra cluster. Setup[](#setup "Direct link to Setup") --------------------------------------- First, install the Cassandra Node.js driver: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install cassandra-driver @langchain/openai @langchain/community yarn add cassandra-driver @langchain/openai @langchain/community pnpm add cassandra-driver @langchain/openai @langchain/community Depending on your database providers, the specifics of how to connect to the database will vary. We will create a document `configConnection` which will be used as part of the vector store configuration. ### Apache Cassandra®[](#apache-cassandra "Direct link to Apache Cassandra®") const configConnection = { contactPoints: ['h1', 'h2'], localDataCenter: 'datacenter1', credentials: { username: <...> as string, password: <...> as string, },}; ### Astra DB[](#astra-db "Direct link to Astra DB") Astra DB is a cloud-native Cassandra-as-a-Service platform. 1. Create an [Astra DB account](https://astra.datastax.com/register). 2. Create a [vector enabled database](https://astra.datastax.com/createDatabase). 3. Create a [token](https://docs.datastax.com/en/astra/docs/manage-application-tokens.html) for your database. const configConnection = { serviceProviderArgs: { astra: { token: <...> as string, endpoint: <...> as string, }, },}; Instead of `endpoint:`, you many provide property `datacenterID:` and optionally `regionName:`. Usage[](#usage "Direct link to Usage") --------------------------------------- import { BufferMemory } from "langchain/memory";import { CassandraChatMessageHistory } from "@langchain/community/stores/message/cassandra";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";// The example below uses Astra DB, but you can use any Cassandra connectionconst configConnection = { serviceProviderArgs: { astra: { token: "<your Astra Token>" as string, endpoint: "<your Astra Endpoint>" as string, }, },};const memory = new BufferMemory({ chatHistory: new CassandraChatMessageHistory({ ...configConnection, keyspace: "langchain", table: "message_history", sessionId: "<some unique session identifier>", }),});const model = new ChatOpenAI();const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jonathan." });console.log({ res1 });/{ res1: { text: "Hello Jonathan! How can I assist you today?" }}/const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/{ res1: { text: "You said your name was Jonathan." }}/ #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [CassandraChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_cassandra.CassandraChatMessageHistory.html) from `@langchain/community/stores/message/cassandra` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` * * * #### Help us out by providing feedback on this documentation page: [ Previous Astra DB Chat Memory ](/v0.1/docs/integrations/chat_memory/astradb/)[ Next Cloudflare D1-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/cloudflare_d1/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Cloudflare D1-Backed Chat Memory Cloudflare D1-Backed Chat Memory ================================ info This integration is only supported in Cloudflare Workers. For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for a Cloudflare D1 instance. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to install the LangChain Cloudflare integration package. For the below example, we also use Anthropic, but you can use any model you'd like: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/cloudflare @langchain/anthropic yarn add @langchain/cloudflare @langchain/anthropic pnpm add @langchain/cloudflare @langchain/anthropic Set up a D1 instance for your worker by following [the official documentation](https://developers.cloudflare.com/d1/). Your project's `wrangler.toml` file should look something like this: name = "YOUR_PROJECT_NAME"main = "src/index.ts"compatibility_date = "2024-01-10"[vars]ANTHROPIC_API_KEY = "YOUR_ANTHROPIC_KEY"[[d1_databases]]binding = "DB" # available in your Worker as env.DBdatabase_name = "YOUR_D1_DB_NAME"database_id = "YOUR_D1_DB_ID" Usage[](#usage "Direct link to Usage") --------------------------------------- You can then use D1 to store your history as follows: import type { D1Database } from "@cloudflare/workers-types";import { BufferMemory } from "langchain/memory";import { CloudflareD1MessageHistory } from "@langchain/cloudflare";import { ChatPromptTemplate, MessagesPlaceholder,} from "@langchain/core/prompts";import { RunnableSequence } from "@langchain/core/runnables";import { StringOutputParser } from "@langchain/core/output_parsers";import { ChatAnthropic } from "@langchain/anthropic";export interface Env { DB: D1Database; ANTHROPIC_API_KEY: string;}export default { async fetch(request: Request, env: Env): Promise<Response> { try { const { searchParams } = new URL(request.url); const input = searchParams.get("input"); if (!input) { throw new Error(`Missing "input" parameter`); } const memory = new BufferMemory({ returnMessages: true, chatHistory: new CloudflareD1MessageHistory({ tableName: "stored_message", sessionId: "example", database: env.DB, }), }); const prompt = ChatPromptTemplate.fromMessages([ ["system", "You are a helpful chatbot"], new MessagesPlaceholder("history"), ["human", "{input}"], ]); const model = new ChatAnthropic({ apiKey: env.ANTHROPIC_API_KEY, }); const chain = RunnableSequence.from([ { input: (initialInput) => initialInput.input, memory: () => memory.loadMemoryVariables({}), }, { input: (previousOutput) => previousOutput.input, history: (previousOutput) => previousOutput.memory.history, }, prompt, model, new StringOutputParser(), ]); const chainInput = { input }; const res = await chain.invoke(chainInput); await memory.saveContext(chainInput, { output: res, }); return new Response(JSON.stringify(res), { headers: { "content-type": "application/json" }, }); } catch (err: any) { console.log(err.message); return new Response(err.message, { status: 500 }); } },}; #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [CloudflareD1MessageHistory](https://api.js.langchain.com/classes/langchain_cloudflare.CloudflareD1MessageHistory.html) from `@langchain/cloudflare` * [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [MessagesPlaceholder](https://api.js.langchain.com/classes/langchain_core_prompts.MessagesPlaceholder.html) from `@langchain/core/prompts` * [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables` * [StringOutputParser](https://api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers` * [ChatAnthropic](https://api.js.langchain.com/classes/langchain_anthropic.ChatAnthropic.html) from `@langchain/anthropic` * * * #### Help us out by providing feedback on this documentation page: [ Previous Cassandra Chat Memory ](/v0.1/docs/integrations/chat_memory/cassandra/)[ Next Convex Chat Memory ](/v0.1/docs/integrations/chat_memory/convex/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/convex/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Convex Chat Memory Convex Chat Memory ================== For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for [Convex](https://convex.dev/). Setup[](#setup "Direct link to Setup") --------------------------------------- ### Create project[](#create-project "Direct link to Create project") Get a working [Convex](https://docs.convex.dev/) project set up, for example by using: npm create convex@latest ### Add database accessors[](#add-database-accessors "Direct link to Add database accessors") Add query and mutation helpers to `convex/langchain/db.ts`: convex/langchain/db.ts export * from "langchain/util/convex"; ### Configure your schema[](#configure-your-schema "Direct link to Configure your schema") Set up your schema (for indexing): convex/schema.ts import { defineSchema, defineTable } from "convex/server";import { v } from "convex/values";export default defineSchema({ messages: defineTable({ sessionId: v.string(), message: v.object({ type: v.string(), data: v.object({ content: v.string(), role: v.optional(v.string()), name: v.optional(v.string()), additional_kwargs: v.optional(v.any()), }), }), }).index("bySessionId", ["sessionId"]),}); Usage[](#usage "Direct link to Usage") --------------------------------------- Each chat history session stored in Convex must have a unique session id. tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community convex/myActions.ts "use node";import { v } from "convex/values";import { BufferMemory } from "langchain/memory";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";import { ConvexChatMessageHistory } from "@langchain/community/stores/message/convex";import { action } from "./_generated/server.js";export const ask = action({ args: { sessionId: v.string() }, handler: async (ctx, args) => { // pass in a sessionId string const { sessionId } = args; const memory = new BufferMemory({ chatHistory: new ConvexChatMessageHistory({ sessionId, ctx }), }); const model = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0, }); const chain = new ConversationChain({ llm: model, memory }); const res1 = await chain.invoke({ input: "Hi! I'm Jim." }); console.log({ res1 }); /* { res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" } } / const res2 = await chain.invoke({ input: "What did I just say my name was?", }); console.log({ res2 }); / { res2: { text: "You said your name was Jim." } } / // See the chat history in the Convex database console.log(await memory.chatHistory.getMessages()); // clear chat history await memory.chatHistory.clear(); },}); #### API Reference: [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` * [ConvexChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_convex.ConvexChatMessageHistory.html) from `@langchain/community/stores/message/convex` * * * #### Help us out by providing feedback on this documentation page: [ Previous Cloudflare D1-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/cloudflare_d1/)[ Next DynamoDB-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/dynamodb/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/firestore/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Firestore Chat Memory Firestore Chat Memory ===================== For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for a firestore. Setup[](#setup "Direct link to Setup") --------------------------------------- First, install the Firebase admin package in your project: * npm * Yarn * pnpm npm install firebase-admin yarn add firebase-admin pnpm add firebase-admin tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community Visit the `Project Settings` page from your Firebase project and select the `Service accounts` tab. Inside the `Service accounts` tab, click the `Generate new private key` button inside the `Firebase Admin SDK` section to download a JSON file containing your service account's credentials. Using the downloaded JSON file, pass in the `projectId`, `privateKey`, and `clientEmail` to the `config` object of the `FirestoreChatMessageHistory` class, like shown below: import { FirestoreChatMessageHistory } from "@langchain/community/stores/message/firestore";import admin from "firebase-admin";const messageHistory = new FirestoreChatMessageHistory({ collections: ["chats"], docs: ["user-id"], sessionId: "user-id", userId: "a@example.com", config: { projectId: "YOUR-PROJECT-ID", credential: admin.credential.cert({ projectId: "YOUR-PROJECT-ID", privateKey: "-----BEGIN PRIVATE KEY-----\nCHANGE-ME\n-----END PRIVATE KEY-----\n", clientEmail: "CHANGE-ME@CHANGE-ME-TOO.iam.gserviceaccount.com", }), },}); Here, the `collections` field should match the names and ordering of the `collections` in your database. The same goes for `docs`, it should match the names and ordering of the `docs` in your database. Usage[](#usage "Direct link to Usage") --------------------------------------- import { BufferMemory } from "langchain/memory";import { FirestoreChatMessageHistory } from "@langchain/community/stores/message/firestore";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";import admin from "firebase-admin";const memory = new BufferMemory({ chatHistory: new FirestoreChatMessageHistory({ collections: ["langchain"], docs: ["lc-example"], sessionId: "lc-example-id", userId: "a@example.com", config: { projectId: "YOUR-PROJECT-ID", credential: admin.credential.cert({ projectId: "YOUR-PROJECT-ID", privateKey: "-----BEGIN PRIVATE KEY-----\nnCHANGE-ME\n-----END PRIVATE KEY-----\n", clientEmail: "CHANGE-ME@CHANGE-ME-TOO.iam.gserviceaccount.com", }), }, }),});const model = new ChatOpenAI();const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jim." });console.log({ res1 });/{ res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" } }/const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/{ res1: { text: "You said your name was Jim." } }/ #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [FirestoreChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_firestore.FirestoreChatMessageHistory.html) from `@langchain/community/stores/message/firestore` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` ### Nested Collections[](#nested-collections "Direct link to Nested Collections") The `FirestoreChatMessageHistory` class supports nested collections, and dynamic collection/doc names. The example below shows how to add and retrieve messages from a database with the following structure: /chats/{chat-id}/bots/{bot-id}/messages/{message-id} import { BufferMemory } from "langchain/memory";import { FirestoreChatMessageHistory } from "@langchain/community/stores/message/firestore";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";import admin from "firebase-admin";const memory = new BufferMemory({ chatHistory: new FirestoreChatMessageHistory({ collections: ["chats", "bots"], docs: ["chat-id", "bot-id"], sessionId: "user-id", userId: "a@example.com", config: { projectId: "YOUR-PROJECT-ID", credential: admin.credential.cert({ projectId: "YOUR-PROJECT-ID", privateKey: "-----BEGIN PRIVATE KEY-----\nnCHANGE-ME\n-----END PRIVATE KEY-----\n", clientEmail: "CHANGE-ME@CHANGE-ME-TOO.iam.gserviceaccount.com", }), }, }),});const model = new ChatOpenAI();const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jim." });console.log({ res1 });/{ res1: { response: 'Hello Jim! How can I assist you today?' } }/const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/{ res2: { response: 'You just said that your name is Jim.' } }/ #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [FirestoreChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_firestore.FirestoreChatMessageHistory.html) from `@langchain/community/stores/message/firestore` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` Firestore Rules[](#firestore-rules "Direct link to Firestore Rules") --------------------------------------------------------------------- If your collection name is "chathistory," you can configure Firestore rules as follows. match /chathistory/{sessionId} { allow read: if request.auth.uid == resource.data.createdBy; allow write: if request.auth.uid == request.resource.data.createdBy; } match /chathistory/{sessionId}/messages/{messageId} { allow read: if request.auth.uid == resource.data.createdBy; allow write: if request.auth.uid == request.resource.data.createdBy; } * * * #### Help us out by providing feedback on this documentation page: [ Previous DynamoDB-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/dynamodb/)[ Next IPFS Datastore Chat Memory ](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/ipfs_datastore/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * IPFS Datastore Chat Memory IPFS Datastore Chat Memory ========================== For a storage backend you can use the IPFS Datastore Chat Memory to wrap an IPFS Datastore allowing you to use any IPFS compatible datastore. Setup[](#setup "Direct link to Setup") --------------------------------------- First, install the integration dependencies: tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install cborg interface-datastore it-all @langchain/community yarn add cborg interface-datastore it-all @langchain/community pnpm add cborg interface-datastore it-all @langchain/community Now you can install and use an IPFS Datastore of your choice. Here are some options: * [datastore-core](https://github.com/ipfs/js-stores/blob/main/packages/datastore-core) Datastore in-memory implementation. * [datastore-fs](https://github.com/ipfs/js-stores/blob/main/packages/datastore-fs) Datastore implementation with file system backend. * [datastore-idb](https://github.com/ipfs/js-stores/blob/main/packages/datastore-idb) Datastore implementation with IndexedDB backend. * [datastore-level](https://github.com/ipfs/js-stores/blob/main/packages/datastore-level) Datastore implementation with level(up\|down) backend * [datastore-s3](https://github.com/ipfs/js-stores/blob/main/packages/datastore-s3) Datastore implementation backed by s3. Usage[](#usage "Direct link to Usage") --------------------------------------- // Replace FsDatastore with the IPFS Datastore of your choice.import { FsDatastore } from "datastore-fs";import { IPFSDatastoreChatMessageHistory } from "@langchain/community/stores/message/ipfs_datastore";const datastore = new FsDatastore("path/to/store");const sessionId = "my-session";const history = new IPFSDatastoreChatMessageHistory({ datastore, sessionId }); * * * #### Help us out by providing feedback on this documentation page: [ Previous Firestore Chat Memory ](/v0.1/docs/integrations/chat_memory/firestore/)[ Next Momento-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/momento/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/momento/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * Momento-Backed Chat Memory Momento-Backed Chat Memory ========================== For distributed, serverless persistence across chat sessions, you can swap in a [Momento](https://gomomento.com/)\-backed chat message history. Because a Momento cache is instantly available and requires zero infrastructure maintenance, it's a great way to get started with chat history whether building locally or in production. Setup[](#setup "Direct link to Setup") --------------------------------------- You will need to install the [Momento Client Library](https://github.com/momentohq/client-sdk-javascript) in your project. Given Momento's compatibility with Node.js, browser, and edge environments, ensure you install the relevant package. To install for Node.js: * npm * Yarn * pnpm npm install @gomomento/sdk yarn add @gomomento/sdk pnpm add @gomomento/sdk To install for browser/edge workers: * npm * Yarn * pnpm npm install @gomomento/sdk-web yarn add @gomomento/sdk-web pnpm add @gomomento/sdk-web tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community You will also need an API key from [Momento](https://gomomento.com/). You can sign up for a free account [here](https://console.gomomento.com/). Usage[](#usage "Direct link to Usage") --------------------------------------- To distinguish one chat history session from another, we need a unique `sessionId`. You may also provide an optional `sessionTtl` to make sessions expire after a given number of seconds. import { CacheClient, Configurations, CredentialProvider,} from "@gomomento/sdk"; // `from "gomomento/sdk-web";` for browser/edgeimport { BufferMemory } from "langchain/memory";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";import { MomentoChatMessageHistory } from "@langchain/community/stores/message/momento";// See https://github.com/momentohq/client-sdk-javascript for connection optionsconst client = new CacheClient({ configuration: Configurations.Laptop.v1(), credentialProvider: CredentialProvider.fromEnvironmentVariable({ environmentVariableName: "MOMENTO_API_KEY", }), defaultTtlSeconds: 60 * 60 * 24,});// Create a unique session IDconst sessionId = new Date().toISOString();const cacheName = "langchain";const memory = new BufferMemory({ chatHistory: await MomentoChatMessageHistory.fromProps({ client, cacheName, sessionId, sessionTtl: 300, }),});console.log( `cacheName=${cacheName} and sessionId=${sessionId} . This will be used to store the chat history. You can inspect the values at your Momento console at https://console.gomomento.com.`);const model = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0,});const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jim." });console.log({ res1 });/{ res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" }}/const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/{ res1: { text: "You said your name was Jim." }}/// See the chat history in the Momentoconsole.log(await memory.chatHistory.getMessages()); #### API Reference: * [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` * [MomentoChatMessageHistory](https://api.js.langchain.com/classes/langchain_community_stores_message_momento.MomentoChatMessageHistory.html) from `@langchain/community/stores/message/momento` * * * #### Help us out by providing feedback on this documentation page: [ Previous IPFS Datastore Chat Memory ](/v0.1/docs/integrations/chat_memory/ipfs_datastore/)[ Next MongoDB Chat Memory ](/v0.1/docs/integrations/chat_memory/mongodb/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.
https://js.langchain.com/v0.1/docs/integrations/chat_memory/mongodb/	!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()\|\|function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}()) [Skip to main content](#__docusaurus_skipToContent_fallback) LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/). [ ![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png) ](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com) [More](#) * [People](/v0.1/docs/people/) * [Community](/v0.1/docs/community/) * [Tutorials](/v0.1/docs/additional_resources/tutorials/) * [Contributing](/v0.1/docs/contributing/) [v0.1](#) * [v0.2](https://js.langchain.com/v0.2/docs/introduction) * [v0.1](/v0.1/docs/get_started/introduction/) [🦜🔗](#) * [LangSmith](https://smith.langchain.com) * [LangSmith Docs](https://docs.smith.langchain.com) * [LangChain Hub](https://smith.langchain.com/hub) * [LangServe](https://github.com/langchain-ai/langserve) * [Python Docs](https://python.langchain.com/) [Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs) Search * [Providers](/v0.1/docs/integrations/platforms/) * [Providers](/v0.1/docs/integrations/platforms/) * [Anthropic](/v0.1/docs/integrations/platforms/anthropic/) * [AWS](/v0.1/docs/integrations/platforms/aws/) * [Google](/v0.1/docs/integrations/platforms/google/) * [Microsoft](/v0.1/docs/integrations/platforms/microsoft/) * [OpenAI](/v0.1/docs/integrations/platforms/openai/) * [Components](/v0.1/docs/integrations/components/) * [LLMs](/v0.1/docs/integrations/llms/) * [Chat models](/v0.1/docs/integrations/chat/) * [Document loaders](/v0.1/docs/integrations/document_loaders/) * [Document transformers](/v0.1/docs/integrations/document_transformers/) * [Document compressors](/v0.1/docs/integrations/document_compressors/) * [Text embedding models](/v0.1/docs/integrations/text_embedding/) * [Vector stores](/v0.1/docs/integrations/vectorstores/) * [Retrievers](/v0.1/docs/integrations/retrievers/) * [Tools](/v0.1/docs/integrations/tools/) * [Agents and toolkits](/v0.1/docs/integrations/toolkits/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * [Astra DB Chat Memory](/v0.1/docs/integrations/chat_memory/astradb/) * [Cassandra Chat Memory](/v0.1/docs/integrations/chat_memory/cassandra/) * [Cloudflare D1-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/cloudflare_d1/) * [Convex Chat Memory](/v0.1/docs/integrations/chat_memory/convex/) * [DynamoDB-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/dynamodb/) * [Firestore Chat Memory](/v0.1/docs/integrations/chat_memory/firestore/) * [IPFS Datastore Chat Memory](/v0.1/docs/integrations/chat_memory/ipfs_datastore/) * [Momento-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/momento/) * [MongoDB Chat Memory](/v0.1/docs/integrations/chat_memory/mongodb/) * [Motörhead Memory](/v0.1/docs/integrations/chat_memory/motorhead_memory/) * [PlanetScale Chat Memory](/v0.1/docs/integrations/chat_memory/planetscale/) * [Postgres Chat Memory](/v0.1/docs/integrations/chat_memory/postgres/) * [Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/redis/) * [Upstash Redis-Backed Chat Memory](/v0.1/docs/integrations/chat_memory/upstash_redis/) * [Xata Chat Memory](/v0.1/docs/integrations/chat_memory/xata/) * [Zep Memory](/v0.1/docs/integrations/chat_memory/zep_memory/) * [Stores](/v0.1/docs/integrations/stores/) * [](/v0.1/) * [Components](/v0.1/docs/integrations/components/) * [Chat Memory](/v0.1/docs/integrations/chat_memory/) * MongoDB Chat Memory MongoDB Chat Memory =================== Compatibility Only available on Node.js. You can still create API routes that use MongoDB with Next.js by setting the `runtime` variable to `nodejs` like so: export const runtime = "nodejs"; You can read more about Edge runtimes in the Next.js documentation [here](https://nextjs.org/docs/app/building-your-application/rendering/edge-and-nodejs-runtimes). For longer-term persistence across chat sessions, you can swap out the default in-memory `chatHistory` that backs chat memory classes like `BufferMemory` for a MongoDB instance. Setup[](#setup "Direct link to Setup") --------------------------------------- You need to install Node MongoDB SDK in your project: * npm * Yarn * pnpm npm install -S mongodb yarn add mongodb pnpm add mongodb tip See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @langchain/community yarn add @langchain/openai @langchain/community pnpm add @langchain/openai @langchain/community You will also need a MongoDB instance to connect to. Usage[](#usage "Direct link to Usage") --------------------------------------- Each chat history session stored in MongoDB must have a unique session id. import { MongoClient, ObjectId } from "mongodb";import { BufferMemory } from "langchain/memory";import { ChatOpenAI } from "@langchain/openai";import { ConversationChain } from "langchain/chains";import { MongoDBChatMessageHistory } from "@langchain/mongodb";const client = new MongoClient(process.env.MONGODB_ATLAS_URI \|\| "", { driverInfo: { name: "langchainjs" },});await client.connect();const collection = client.db("langchain").collection("memory");// generate a new sessionId stringconst sessionId = new ObjectId().toString();const memory = new BufferMemory({ chatHistory: new MongoDBChatMessageHistory({ collection, sessionId, }),});const model = new ChatOpenAI({ model: "gpt-3.5-turbo", temperature: 0,});const chain = new ConversationChain({ llm: model, memory });const res1 = await chain.invoke({ input: "Hi! I'm Jim." });console.log({ res1 });/* { res1: { text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?" } } /const res2 = await chain.invoke({ input: "What did I just say my name was?" });console.log({ res2 });/ { res1: { text: "You said your name was Jim." } } /// See the chat history in the MongoDbconsole.log(await memory.chatHistory.getMessages());// clear chat historyawait memory.chatHistory.clear(); #### API Reference: [BufferMemory](https://api.js.langchain.com/classes/langchain_memory.BufferMemory.html) from `langchain/memory` * [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ConversationChain](https://api.js.langchain.com/classes/langchain_chains.ConversationChain.html) from `langchain/chains` * [MongoDBChatMessageHistory](https://api.js.langchain.com/classes/langchain_mongodb.MongoDBChatMessageHistory.html) from `@langchain/mongodb` * * * #### Help us out by providing feedback on this documentation page: [ Previous Momento-Backed Chat Memory ](/v0.1/docs/integrations/chat_memory/momento/)[ Next Motörhead Memory ](/v0.1/docs/integrations/chat_memory/motorhead_memory/) Community * [Discord](https://discord.gg/cU2adEyC7w) * [Twitter](https://twitter.com/LangChainAI) GitHub * [Python](https://github.com/langchain-ai/langchain) * [JS/TS](https://github.com/langchain-ai/langchainjs) More * [Homepage](https://langchain.com) * [Blog](https://blog.langchain.dev) Copyright © 2024 LangChain, Inc.