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https://js.langchain.com/v0.2/docs/integrations/vectorstores/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Astra DB On this page Astra DB ======== Compatibility Only available on Node.js. DataStax [Astra DB](https://astra.datastax.com/register) is a serverless vector-capable database built on [Apache Cassandra](https://cassandra.apache.org/_/index.html) and made conveniently available through an easy-to-use JSON API. Setup[](#setup "Direct link to Setup") --------------------------------------- 1. Create an [Astra DB account](https://astra.datastax.com/register). 2. Create a [vector enabled database](https://astra.datastax.com/createDatabase). 3. Grab your `API Endpoint` and `Token` from the Database Details. 4. Set up the following env vars: export ASTRA_DB_APPLICATION_TOKEN=YOUR_ASTRA_DB_APPLICATION_TOKEN_HEREexport ASTRA_DB_ENDPOINT=YOUR_ASTRA_DB_ENDPOINT_HEREexport ASTRA_DB_COLLECTION=YOUR_ASTRA_DB_COLLECTION_HEREexport OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE Where `ASTRA_DB_COLLECTION` is the desired name of your collection 6. Install the Astra TS Client & the LangChain community package tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai @datastax/astra-db-ts @langchain/community yarn add @langchain/openai @datastax/astra-db-ts @langchain/community pnpm add @langchain/openai @datastax/astra-db-ts @langchain/community Indexing docs[](#indexing-docs "Direct link to Indexing docs") --------------------------------------------------------------- import { OpenAIEmbeddings } from "@langchain/openai";import { AstraDBVectorStore, AstraLibArgs,} from "@langchain/community/vectorstores/astradb";const astraConfig: AstraLibArgs = { token: process.env.ASTRA_DB_APPLICATION_TOKEN as string, endpoint: process.env.ASTRA_DB_ENDPOINT as string, collection: process.env.ASTRA_DB_COLLECTION ?? "langchain_test", collectionOptions: { vector: { dimension: 1536, metric: "cosine", }, },};const vectorStore = await AstraDBVectorStore.fromTexts( [ "AstraDB is built on Apache Cassandra", "AstraDB is a NoSQL DB", "AstraDB supports vector search", ], [{ foo: "foo" }, { foo: "bar" }, { foo: "baz" }], new OpenAIEmbeddings(), astraConfig);// Querying docs:const results = await vectorStore.similaritySearch("Cassandra", 1);// or filtered query:const filteredQueryResults = await vectorStore.similaritySearch("A", 1, { foo: "bar",}); #### API Reference: * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [AstraDBVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_astradb.AstraDBVectorStore.html) from `@langchain/community/vectorstores/astradb` * [AstraLibArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_astradb.AstraLibArgs.html) from `@langchain/community/vectorstores/astradb` Vector Types[](#vector-types "Direct link to Vector Types") ------------------------------------------------------------ Astra DB supports `cosine` (the default), `dot_product`, and `euclidean` similarity search; this is defined when the vector store is first created as part of the `CreateCollectionOptions`: vector: { dimension: number; metric?: "cosine" \| "euclidean" \| "dot_product"; }; * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous AnalyticDB ](/v0.2/docs/integrations/vectorstores/analyticdb)[ Next Azure AI Search ](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Setup](#setup) * [Indexing docs](#indexing-docs) * [Vector Types](#vector-types) 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/v0.2/docs/integrations/vectorstores/azure_aisearch	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Azure AI Search On this page Azure AI Search =============== [Azure AI Search](https://azure.microsoft.com/products/ai-services/ai-search) (formerly known as Azure Search and Azure Cognitive Search) is a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads on Azure. It supports also vector search using the [k-nearest neighbor](https://en.wikipedia.org/wiki/Nearest_neighbor_search) (kNN) algorithm and also [semantic search](https://learn.microsoft.com/azure/search/semantic-search-overview). This vector store integration supports full text search, vector search and [hybrid search for best ranking performance](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167). Learn how to leverage the vector search capabilities of Azure AI Search from [this page](https://learn.microsoft.com/azure/search/vector-search-overview). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll first need to install the `@azure/search-documents` SDK and the [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) package: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/community @azure/search-documents yarn add @langchain/community @azure/search-documents pnpm add @langchain/community @azure/search-documents You'll also need to have an Azure AI Search instance running. You can deploy a free version on Azure Portal without any cost, following [this guide](https://learn.microsoft.com/azure/search/search-create-service-portal). Once you have your instance running, make sure you have the endpoint and the admin key (query keys can be used only to search document, not to index, update or delete). The endpoint is the URL of your instance which you can find in the Azure Portal, under the "Overview" section of your instance. The admin key can be found under the "Keys" section of your instance. Then you need to set the following environment variables: # Azure AI Search connection settingsAZURE_AISEARCH_ENDPOINT=AZURE_AISEARCH_KEY=# If you're using Azure OpenAI API, you'll need to set these variablesAZURE_OPENAI_API_KEY=AZURE_OPENAI_API_INSTANCE_NAME=AZURE_OPENAI_API_DEPLOYMENT_NAME=AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=AZURE_OPENAI_API_VERSION=# Or you can use the OpenAI API directlyOPENAI_API_KEY= #### API Reference: About hybrid search[](#about-hybrid-search "Direct link to About hybrid search") --------------------------------------------------------------------------------- Hybrid search is a feature that combines the strengths of full text search and vector search to provide the best ranking performance. It's enabled by default in Azure AI Search vector stores, but you can select a different search query type by setting the `search.type` property when creating the vector store. You can read more about hybrid search and how it may improve your search results in the [official documentation](https://learn.microsoft.com/azure/search/hybrid-search-overview). In some scenarios like retrieval-augmented generation (RAG), you may want to enable semantic ranking in addition to hybrid search to improve the relevance of the search results. You can enable semantic ranking by setting the `search.type` property to `AzureAISearchQueryType.SemanticHybrid` when creating the vector store. Note that semantic ranking capabilities are only available in the Basic and higher pricing tiers, and subject to [regional availability](https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/?products=search). You can read more about the performance of using semantic ranking with hybrid search in [this blog post](https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167). Example: index docs, vector search and LLM integration[](#example-index-docs-vector-search-and-llm-integration "Direct link to Example: index docs, vector search and LLM integration") ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Below is an example that indexes documents from a file in Azure AI Search, runs a hybrid search query, and finally uses a chain to answer a question in natural language based on the retrieved documents. import { AzureAISearchVectorStore, AzureAISearchQueryType,} from "@langchain/community/vectorstores/azure_aisearch";import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";import { TextLoader } from "langchain/document_loaders/fs/text";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";// Load documents from fileconst loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);// Create Azure AI Search vector storeconst store = await AzureAISearchVectorStore.fromDocuments( documents, new OpenAIEmbeddings(), { search: { type: AzureAISearchQueryType.SimilarityHybrid, }, });// The first time you run this, the index will be created.// You may need to wait a bit for the index to be created before you can perform// a search, or you can create the index manually beforehand.// Performs a similarity searchconst resultDocuments = await store.similaritySearch( "What did the president say about Ketanji Brown Jackson?");console.log("Similarity search results:");console.log(resultDocuments[0].pageContent);/* Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence./// Use the store as part of a chainconst 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 response = await chain.invoke({ input: "What is the president's top priority regarding prices?",});console.log("Chain response:");console.log(response.answer);/ The president's top priority is getting prices under control./ #### API Reference: [AzureAISearchVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_azure_aisearch.AzureAISearchVectorStore.html) from `@langchain/community/vectorstores/azure_aisearch` * [AzureAISearchQueryType](https://v02.api.js.langchain.com/types/langchain_community_vectorstores_azure_aisearch.AzureAISearchQueryType.html) from `@langchain/community/vectorstores/azure_aisearch` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [createStuffDocumentsChain](https://v02.api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://v02.api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` * [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Astra DB ](/v0.2/docs/integrations/vectorstores/astradb)[ Next Azure Cosmos DB ](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Setup](#setup) * [About hybrid search](#about-hybrid-search) * [Example: index docs, vector search and LLM integration](#example-index-docs-vector-search-and-llm-integration) 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/v0.2/docs/integrations/vectorstores/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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[Apache Cassandra®](https://cassandra.apache.org/_/index.html) is a NoSQL, row-oriented, highly scalable and highly available database. The [latest version](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes) of Apache Cassandra natively supports Vector Similarity Search. Setup[](#setup "Direct link to Setup") --------------------------------------- First, install the Cassandra Node.js driver: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install cassandra-driver @langchain/community @langchain/openai yarn add cassandra-driver @langchain/community @langchain/openai pnpm add cassandra-driver @langchain/community @langchain/openai 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®") Vector search is supported in [Apache Cassandra® 5.0](https://cassandra.apache.org/_/Apache-Cassandra-5.0-Moving-Toward-an-AI-Driven-Future.html) and above. You can use a standard connection document, for example: 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:`. Indexing docs[](#indexing-docs "Direct link to Indexing docs") --------------------------------------------------------------- import { CassandraStore } from "langchain/vectorstores/cassandra";import { OpenAIEmbeddings } from "@langchain/openai";// The configConnection document is defined aboveconst config = { ...configConnection, keyspace: "test", dimensions: 1536, table: "test", indices: [{ name: "name", value: "(name)" }], primaryKey: { name: "id", type: "int", }, metadataColumns: [ { name: "name", type: "text", }, ],};const vectorStore = await CassandraStore.fromTexts( ["I am blue", "Green yellow purple", "Hello there hello"], [ { id: 2, name: "2" }, { id: 1, name: "1" }, { id: 3, name: "3" }, ], new OpenAIEmbeddings(), cassandraConfig); Querying docs[](#querying-docs "Direct link to Querying docs") --------------------------------------------------------------- const results = await vectorStore.similaritySearch("Green yellow purple", 1); or filtered query: const results = await vectorStore.similaritySearch("B", 1, { name: "Bubba" }); Vector Types[](#vector-types "Direct link to Vector Types") ------------------------------------------------------------ Cassandra supports `cosine` (the default), `dot_product`, and `euclidean` similarity search; this is defined when the vector store is first created, and specifed in the constructor parameter `vectorType`, for example: ..., vectorType: "dot_product", ... Indices[](#indices "Direct link to Indices") --------------------------------------------- With Version 5, Cassandra introduced Storage Attached Indexes, or SAIs. These allow `WHERE` filtering without specifying the partition key, and allow for additional operator types such as non-equalities. You can define these with the `indices` parameter, which accepts zero or more dictionaries each containing `name` and `value` entries. Indices are optional, though required if using filtered queries on non-partition columns. * The `name` entry is part of the object name; on a table named `test_table` an index with `name: "some_column"` would be `idx_test_table_some_column`. * The `value` entry is the column on which the index is created, surrounded by `(` and `)`. With the above column `some_column` it would be specified as `value: "(some_column)"`. * An optional `options` entry is a map passed to the `WITH OPTIONS =` clause of the `CREATE CUSTOM INDEX` statement. The specific entries on this map are index type specific. indices: [{ name: "some_column", value: "(some_column)" }], Advanced Filtering[](#advanced-filtering "Direct link to Advanced Filtering") ------------------------------------------------------------------------------ By default, filters are applied with an equality `=`. For those fields that have an `indices` entry, you may provide an `operator` with a string of a value supported by the index; in this case, you specify one or more filters, as either a singleton or in a list (which will be `AND`\-ed together). For example: { name: "create_datetime", operator: ">", value: some_datetime_variable } or [ { userid: userid_variable }, { name: "create_datetime", operator: ">", value: some_date_variable },]; `value` can be a single value or an array. If it is not an array, or there is only one element in `value`, the resulting query will be along the lines of `${name} ${operator} ?` with `value` bound to the `?`. If there is more than one element in the `value` array, the number of unquoted `?` in `name` are counted and subtracted from the length of `value`, and this number of `?` is put on the right side of the operator; if there are more than one `?` then they will be encapsulated in `(` and `)`, e.g. `(?, ?, ?)`. This faciliates bind values on the left of the operator, which is useful for some functions; for example a geo-distance filter: { name: "GEO_DISTANCE(coord, ?)", operator: "<", value: [new Float32Array([53.3730617,-6.3000515]), 10000],}, Data Partitioning and Composite Keys[](#data-partitioning-and-composite-keys "Direct link to Data Partitioning and Composite Keys") ------------------------------------------------------------------------------------------------------------------------------------ In some systems, you may wish to partition the data for various reasons, perhaps by user or by session. Data in Cassandra is always partitioned; by default this library will partition by the first primary key field. You may specify multiple columns which comprise the primary (unique) key of a record, and optionally indicate those fields which should be part of the partition key. For example, the vector store could be partitioned by both `userid` and `collectionid`, with additional fields `docid` and `docpart` making an individual entry unique: ..., primaryKey: [ {name: "userid", type: "text", partition: true}, {name: "collectionid", type: "text", partition: true}, {name: "docid", type: "text"}, {name: "docpart", type: "int"}, ], ... When searching, you may include partition keys on the filter without defining `indices` for these columns; you do not need to specify all partition keys, but must specify those in the key first. In the above example, you could specify a filter of `{userid: userid_variable}` and `{userid: userid_variable, collectionid: collectionid_variable}`, but if you wanted to specify a filter of only `{collectionid: collectionid_variable}` you would have to include `collectionid` on the `indices` list. Additional Configuration Options[](#additional-configuration-options "Direct link to Additional Configuration Options") ------------------------------------------------------------------------------------------------------------------------ In the configuration document, further optional parameters are provided; their defaults are: ..., maxConcurrency: 25, batchSize: 1, withClause: "", ... Parameter Usage `maxConcurrency` How many concurrent requests will be sent to Cassandra at a given time. `batchSize` How many documents will be sent on a single request to Cassandra. When using a value > 1, you should ensure your batch size will not exceed the Cassandra parameter `batch_size_fail_threshold_in_kb`. Batches are unlogged. `withClause` Cassandra tables may be created with an optional `WITH` clause; this is generally not needed but provided for completeness. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Azure Cosmos DB ](/v0.2/docs/integrations/vectorstores/azure_cosmosdb)[ Next Chroma ](/v0.2/docs/integrations/vectorstores/chroma) * [Setup](#setup) * [Apache Cassandra®](#apache-cassandra) * [Astra DB](#astra-db) * [Indexing docs](#indexing-docs) * [Querying docs](#querying-docs) * [Vector Types](#vector-types) * [Indices](#indices) * [Advanced Filtering](#advanced-filtering) * [Data Partitioning and Composite Keys](#data-partitioning-and-composite-keys) * [Additional Configuration Options](#additional-configuration-options) 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/v0.2/docs/integrations/vectorstores/azure_cosmosdb	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Azure Cosmos DB On this page Azure Cosmos DB =============== > [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) makes it easy to create a database with full native MongoDB support. You can apply your MongoDB experience and continue to use your favorite MongoDB drivers, SDKs, and tools by pointing your application to the API for MongoDB vCore account’s connection string. Use vector search in Azure Cosmos DB for MongoDB vCore to seamlessly integrate your AI-based applications with your data that’s stored in Azure Cosmos DB. Azure Cosmos DB for MongoDB vCore provides developers with a fully managed MongoDB-compatible database service for building modern applications with a familiar architecture. Learn how to leverage the vector search capabilities of Azure Cosmos DB for MongoDB vCore from [this page](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/vector-search). If you don't have an Azure account, you can [create a free account](https://azure.microsoft.com/free/) to get started. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll first need to install the `mongodb` SDK and the [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) package: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai mongodb @langchain/community yarn add @langchain/openai mongodb @langchain/community pnpm add @langchain/openai mongodb @langchain/community You'll also need to have an Azure Cosmos DB for MongoDB vCore instance running. You can deploy a free version on Azure Portal without any cost, following [this guide](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/quickstart-portal). Once you have your instance running, make sure you have the connection string and the admin key. You can find them in the Azure Portal, under the "Connection strings" section of your instance. Then you need to set the following environment variables: # Azure CosmosDB for MongoDB vCore connection stringAZURE_COSMOSDB_CONNECTION_STRING=# If you're using Azure OpenAI API, you'll need to set these variablesAZURE_OPENAI_API_KEY=AZURE_OPENAI_API_INSTANCE_NAME=AZURE_OPENAI_API_DEPLOYMENT_NAME=AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME=AZURE_OPENAI_API_VERSION=# Or you can use the OpenAI API directlyOPENAI_API_KEY= #### API Reference: Example[](#example "Direct link to Example") --------------------------------------------- Below is an example that indexes documents from a file in Azure Cosmos DB for MongoDB vCore, runs a vector search query, and finally uses a chain to answer a question in natural language based on the retrieved documents. import { AzureCosmosDBVectorStore, AzureCosmosDBSimilarityType,} from "@langchain/community/vectorstores/azure_cosmosdb";import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";import { TextLoader } from "langchain/document_loaders/fs/text";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";// Load documents from fileconst loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);// Create Azure Cosmos DB vector storeconst store = await AzureCosmosDBVectorStore.fromDocuments( documents, new OpenAIEmbeddings(), { databaseName: "langchain", collectionName: "documents", indexOptions: { numLists: 100, dimensions: 1536, similarity: AzureCosmosDBSimilarityType.COS, }, });// Performs a similarity searchconst resultDocuments = await store.similaritySearch( "What did the president say about Ketanji Brown Jackson?");console.log("Similarity search results:");console.log(resultDocuments[0].pageContent);/* Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence./// Use the store as part of a chainconst 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 res = await chain.invoke({ input: "What is the president's top priority regarding prices?",});console.log("Chain response:");console.log(res.answer);/ The president's top priority is getting prices under control./// Clean upawait store.delete();await store.close(); #### API Reference: [AzureCosmosDBVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_azure_cosmosdb.AzureCosmosDBVectorStore.html) from `@langchain/community/vectorstores/azure_cosmosdb` * [AzureCosmosDBSimilarityType](https://v02.api.js.langchain.com/types/langchain_community_vectorstores_azure_cosmosdb.AzureCosmosDBSimilarityType.html) from `@langchain/community/vectorstores/azure_cosmosdb` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [createStuffDocumentsChain](https://v02.api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://v02.api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` * [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Azure AI Search ](/v0.2/docs/integrations/vectorstores/azure_aisearch)[ Next Cassandra ](/v0.2/docs/integrations/vectorstores/cassandra) * [Setup](#setup) * [Example](#example) 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/v0.2/docs/integrations/vectorstores/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Convex On this page Convex ====== LangChain.js supports [Convex](https://convex.dev/) as a [vector store](https://docs.convex.dev/vector-search), and supports the standard similarity search. 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 vector indexing): convex/schema.ts import { defineSchema, defineTable } from "convex/server";import { v } from "convex/values";export default defineSchema({ documents: defineTable({ embedding: v.array(v.number()), text: v.string(), metadata: v.any(), }).vectorIndex("byEmbedding", { vectorField: "embedding", dimensions: 1536, }),}); Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/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 ### Ingestion[](#ingestion "Direct link to Ingestion") convex/myActions.ts "use node";import { ConvexVectorStore } from "@langchain/community/vectorstores/convex";import { OpenAIEmbeddings } from "@langchain/openai";import { action } from "./_generated/server.js";export const ingest = action({ args: {}, handler: async (ctx) => { await ConvexVectorStore.fromTexts( ["Hello world", "Bye bye", "What's this?"], [{ prop: 2 }, { prop: 1 }, { prop: 3 }], new OpenAIEmbeddings(), { ctx } ); },}); #### API Reference: * [ConvexVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_convex.ConvexVectorStore.html) from `@langchain/community/vectorstores/convex` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` ### Search[](#search "Direct link to Search") convex/myActions.ts "use node";import { ConvexVectorStore } from "@langchain/community/vectorstores/convex";import { OpenAIEmbeddings } from "@langchain/openai";import { v } from "convex/values";import { action } from "./_generated/server.js";export const search = action({ args: { query: v.string(), }, handler: async (ctx, args) => { const vectorStore = new ConvexVectorStore(new OpenAIEmbeddings(), { ctx }); const resultOne = await vectorStore.similaritySearch(args.query, 1); console.log(resultOne); },}); #### API Reference: * [ConvexVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_convex.ConvexVectorStore.html) from `@langchain/community/vectorstores/convex` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Cloudflare Vectorize ](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize)[ Next Couchbase ](/v0.2/docs/integrations/vectorstores/couchbase) * [Setup](#setup) * [Create project](#create-project) * [Add database accessors](#add-database-accessors) * [Configure your schema](#configure-your-schema) * [Usage](#usage) * [Ingestion](#ingestion) * [Search](#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/v0.2/docs/integrations/vectorstores/couchbase	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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[TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Couchbase Couchbase ========= [Couchbase](http://couchbase.com/) is an award-winning distributed NoSQL cloud database that delivers unmatched versatility, performance, scalability, and financial value for all of your cloud, mobile, AI, and edge computing applications. Couchbase embraces AI with coding assistance for developers and vector search for their applications. Vector Search is a part of the [Full Text Search Service](https://docs.couchbase.com/server/current/learn/services-and-indexes/services/search-service.html) (Search Service) in Couchbase. This tutorial explains how to use Vector Search in Couchbase. You can work with both [Couchbase Capella](https://www.couchbase.com/products/capella/) and your self-managed Couchbase Server. Installation[](#installation "Direct link to Installation") ------------------------------------------------------------ You will need couchbase and langchain community to use couchbase vector store. For this tutorial, we will use OpenAI embeddings * npm * Yarn * pnpm npm install couchbase @langchain/openai @langchain/community yarn add couchbase @langchain/openai @langchain/community pnpm add couchbase @langchain/openai @langchain/community Create Couchbase Connection Object[](#create-couchbase-connection-object "Direct link to Create Couchbase Connection Object") ------------------------------------------------------------------------------------------------------------------------------ We create a connection to the Couchbase cluster initially and then pass the cluster object to the Vector Store. Here, we are connecting using the username and password. You can also connect using any other supported way to your cluster. For more information on connecting to the Couchbase cluster, please check the [Node SDK documentation](https://docs.couchbase.com/nodejs-sdk/current/hello-world/start-using-sdk.html#connect). import { Cluster } from "couchbase";const connectionString = "couchbase://localhost"; // or couchbases://localhost if you are using TLSconst dbUsername = "Administrator"; // valid database user with read access to the bucket being queriedconst dbPassword = "Password"; // password for the database userconst couchbaseClient = await Cluster.connect(connectionString, { username: dbUsername, password: dbPassword, configProfile: "wanDevelopment",}); Create the Search Index[](#create-the-search-index "Direct link to Create the Search Index") --------------------------------------------------------------------------------------------- Currently, the Search index needs to be created from the Couchbase Capella or Server UI or using the REST interface. For this example, let us use the Import Index feature on the Search Service on the UI. Let us define a Search index with the name `vector-index` on the testing bucket. We are defining an index on the `testing` bucket's `_default` scope on the `_default` collection with the vector field set to `embedding` with 1536 dimensions and the text field set to `text`. We are also indexing and storing all the fields under `metadata` in the document as a dynamic mapping to account for varying document structures. The similarity metric is set to `dot_product`. ### How to Import an Index to the Full Text Search service?[](#how-to-import-an-index-to-the-full-text-search-service "Direct link to How to Import an Index to the Full Text Search service?") * [Couchbase Server](https://docs.couchbase.com/server/current/search/import-search-index.html) * Click on Search -> Add Index -> Import * Copy the following Index definition in the Import screen * Click on Create Index to create the index. * [Couchbase Capella](https://docs.couchbase.com/cloud/search/import-search-index.html) * Copy the following index definition to a new file `index.json` * Import the file in Capella using the instructions in the documentation. * Click on Create Index to create the index. ### Index Definition[](#index-definition "Direct link to Index Definition") { "name": "vector-index", "type": "fulltext-index", "params": { "doc_config": { "docid_prefix_delim": "", "docid_regexp": "", "mode": "type_field", "type_field": "type" }, "mapping": { "default_analyzer": "standard", "default_datetime_parser": "dateTimeOptional", "default_field": "_all", "default_mapping": { "dynamic": true, "enabled": true, "properties": { "metadata": { "dynamic": true, "enabled": true }, "embedding": { "enabled": true, "dynamic": false, "fields": [ { "dims": 1536, "index": true, "name": "embedding", "similarity": "dot_product", "type": "vector", "vector_index_optimized_for": "recall" } ] }, "text": { "enabled": true, "dynamic": false, "fields": [ { "index": true, "name": "text", "store": true, "type": "text" } ] } } }, "default_type": "_default", "docvalues_dynamic": false, "index_dynamic": true, "store_dynamic": true, "type_field": "_type" }, "store": { "indexType": "scorch", "segmentVersion": 16 } }, "sourceType": "gocbcore", "sourceName": "testing", "sourceParams": {}, "planParams": { "maxPartitionsPerPIndex": 103, "indexPartitions": 10, "numReplicas": 0 }} For more details on how to create a search index with support for Vector fields, please refer to the documentation: * [Couchbase Capella](https://docs.couchbase.com/cloud/search/create-search-indexes.html) * [Couchbase Server](https://docs.couchbase.com/server/current/search/create-search-indexes.html) For using this vector store, CouchbaseVectorStoreArgs needs to be configured. textKey and embeddingKey are optional fields, required if you want to use specific keys const couchbaseConfig: CouchbaseVectorStoreArgs = { cluster: couchbaseClient, bucketName: "testing", scopeName: "_default", collectionName: "_default", indexName: "vector-index", textKey: "text", embeddingKey: "embedding",}; Create Vector Store[](#create-vector-store "Direct link to Create Vector Store") --------------------------------------------------------------------------------- We create the vector store object with the cluster information and the search index name. const store = await CouchbaseVectorStore.initialize( embeddings, // embeddings object to create embeddings from text couchbaseConfig); Basic Vector Search Example[](#basic-vector-search-example "Direct link to Basic Vector Search Example") --------------------------------------------------------------------------------------------------------- The following example showcases how to use couchbase vector search and perform similarity search. For this example, we are going to load the "state\_of\_the\_union.txt" file via the TextLoader, chunk the text into 500 character chunks with no overlaps and index all these chunks into Couchbase. After the data is indexed, we perform a simple query to find the top 4 chunks that are similar to the query "What did president say about Ketanji Brown Jackson". At the emd, also shows how to get similarity score import { OpenAIEmbeddings } from "@langchain/openai";import { CouchbaseVectorStoreArgs, CouchbaseVectorStore,} from "@langchain/community/vectorstores/couchbase";import { Cluster } from "couchbase";import { TextLoader } from "langchain/document_loaders/fs/text";import { CharacterTextSplitter } from "@langchain/textsplitters";const connectionString = process.env.COUCHBASE_DB_CONN_STR ?? "couchbase://localhost";const databaseUsername = process.env.COUCHBASE_DB_USERNAME ?? "Administrator";const databasePassword = process.env.COUCHBASE_DB_PASSWORD ?? "Password";// Load documents from fileconst loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new CharacterTextSplitter({ chunkSize: 500, chunkOverlap: 0,});const docs = await splitter.splitDocuments(rawDocuments);const couchbaseClient = await Cluster.connect(connectionString, { username: databaseUsername, password: databasePassword, configProfile: "wanDevelopment",});// Open AI API Key is required to use OpenAIEmbeddings, some other embeddings may also be usedconst embeddings = new OpenAIEmbeddings({ apiKey: process.env.OPENAI_API_KEY,});const couchbaseConfig: CouchbaseVectorStoreArgs = { cluster: couchbaseClient, bucketName: "testing", scopeName: "_default", collectionName: "_default", indexName: "vector-index", textKey: "text", embeddingKey: "embedding",};const store = await CouchbaseVectorStore.fromDocuments( docs, embeddings, couchbaseConfig);const query = "What did president say about Ketanji Brown Jackson";const resultsSimilaritySearch = await store.similaritySearch(query);console.log("resulting documents: ", resultsSimilaritySearch[0]);// Similarity Search With Scoreconst resultsSimilaritySearchWithScore = await store.similaritySearchWithScore( query, 1);console.log("resulting documents: ", resultsSimilaritySearchWithScore[0][0]);console.log("resulting scores: ", resultsSimilaritySearchWithScore[0][1]);const result = await store.similaritySearch(query, 1, { fields: ["metadata.source"],});console.log(result[0]); #### API Reference: * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [CouchbaseVectorStoreArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_couchbase.CouchbaseVectorStoreArgs.html) from `@langchain/community/vectorstores/couchbase` * [CouchbaseVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_couchbase.CouchbaseVectorStore.html) from `@langchain/community/vectorstores/couchbase` * [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [CharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.CharacterTextSplitter.html) from `@langchain/textsplitters` Specifying Fields to Return[](#specifying-fields-to-return "Direct link to Specifying Fields to Return") --------------------------------------------------------------------------------------------------------- You can specify the fields to return from the document using `fields` parameter in the filter during searches. These fields are returned as part of the `metadata` object. You can fetch any field that is stored in the index. The `textKey` of the document is returned as part of the document's `pageContent`. If you do not specify any fields to be fetched, all the fields stored in the index are returned. If you want to fetch one of the fields in the metadata, you need to specify it using `.` For example, to fetch the `source` field in the metadata, you need to use `metadata.source`. const result = await store.similaritySearch(query, 1, { fields: ["metadata.source"],});console.log(result[0]); Hybrid Search[](#hybrid-search "Direct link to Hybrid Search") --------------------------------------------------------------- Couchbase allows you to do hybrid searches by combining vector search results with searches on non-vector fields of the document like the `metadata` object. The results will be based on the combination of the results from both vector search and the searches supported by full text search service. The scores of each of the component searches are added up to get the total score of the result. To perform hybrid searches, there is an optional key, `searchOptions` in `fields` parameter that can be passed to all the similarity searches. The different search/query possibilities for the `searchOptions` can be found [here](https://docs.couchbase.com/server/current/search/search-request-params.html#query-object). ### Create Diverse Metadata for Hybrid Search[](#create-diverse-metadata-for-hybrid-search "Direct link to Create Diverse Metadata for Hybrid Search") In order to simulate hybrid search, let us create some random metadata from the existing documents. We uniformly add three fields to the metadata, `date` between 2010 & 2020, `rating` between 1 & 5 and `author` set to either John Doe or Jane Doe. We will also declare few sample queries. for (let i = 0; i < docs.length; i += 1) { docs[i].metadata.date = `${2010 + (i % 10)}-01-01`; docs[i].metadata.rating = 1 + (i % 5); docs[i].metadata.author = ["John Doe", "Jane Doe"][i % 2];}const store = await CouchbaseVectorStore.fromDocuments( docs, embeddings, couchbaseConfig);const query = "What did the president say about Ketanji Brown Jackson";const independenceQuery = "Any mention about independence?"; ### Example: Search by Exact Value[](#example-search-by-exact-value "Direct link to Example: Search by Exact Value") We can search for exact matches on a textual field like the author in the `metadata` object. const exactValueResult = await store.similaritySearch(query, 4, { fields: ["metadata.author"], searchOptions: { query: { field: "metadata.author", match: "John Doe" }, },});console.log(exactValueResult[0]); ### Example: Search by Partial Match[](#example-search-by-partial-match "Direct link to Example: Search by Partial Match") We can search for partial matches by specifying a fuzziness for the search. This is useful when you want to search for slight variations or misspellings of a search query. Here, "Johny" is close (fuzziness of 1) to "John Doe". const partialMatchResult = await store.similaritySearch(query, 4, { fields: ["metadata.author"], searchOptions: { query: { field: "metadata.author", match: "Johny", fuzziness: 1 }, },});console.log(partialMatchResult[0]); ### Example: Search by Date Range Query[](#example-search-by-date-range-query "Direct link to Example: Search by Date Range Query") We can search for documents that are within a date range query on a date field like `metadata.date`. const dateRangeResult = await store.similaritySearch(independenceQuery, 4, { fields: ["metadata.date", "metadata.author"], searchOptions: { query: { start: "2016-12-31", end: "2017-01-02", inclusiveStart: true, inclusiveEnd: false, field: "metadata.date", }, },});console.log(dateRangeResult[0]); ### Example: Search by Numeric Range Query[](#example-search-by-numeric-range-query "Direct link to Example: Search by Numeric Range Query") We can search for documents that are within a range for a numeric field like `metadata.rating`. const ratingRangeResult = await store.similaritySearch(independenceQuery, 4, { fields: ["metadata.rating"], searchOptions: { query: { min: 3, max: 5, inclusiveMin: false, inclusiveMax: true, field: "metadata.rating", }, },});console.log(ratingRangeResult[0]); ### Example: Combining Multiple Search Conditions[](#example-combining-multiple-search-conditions "Direct link to Example: Combining Multiple Search Conditions") Different queries can by combined using AND (conjuncts) or OR (disjuncts) operators. In this example, we are checking for documents with a rating between 3 & 4 and dated between 2015 & 2018. const multipleConditionsResult = await store.similaritySearch(texts[0], 4, { fields: ["metadata.rating", "metadata.date"], searchOptions: { query: { conjuncts: [ { min: 3, max: 4, inclusive_max: true, field: "metadata.rating" }, { start: "2016-12-31", end: "2017-01-02", field: "metadata.date" }, ], }, },});console.log(multipleConditionsResult[0]); ### Other Queries[](#other-queries "Direct link to Other Queries") Similarly, you can use any of the supported Query methods like Geo Distance, Polygon Search, Wildcard, Regular Expressions, etc in the `searchOptions` Key of `filter` parameter. Please refer to the documentation for more details on the available query methods and their syntax. * [Couchbase Capella](https://docs.couchbase.com/cloud/search/search-request-params.html#query-object) * [Couchbase Server](https://docs.couchbase.com/server/current/search/search-request-params.html#query-object) Frequently Asked Questions ========================== Question: Should I create the Search index before creating the CouchbaseVectorStore object?[](#question-should-i-create-the-search-index-before-creating-the-couchbasevectorstore-object "Direct link to Question: Should I create the Search index before creating the CouchbaseVectorStore object?") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Yes, currently you need to create the Search index before creating the `CouchbaseVectorStore` object. Question: I am not seeing all the fields that I specified in my search results.[](#question-i-am-not-seeing-all-the-fields-that-i-specified-in-my-search-results "Direct link to Question: I am not seeing all the fields that I specified in my search results.") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- In Couchbase, we can only return the fields stored in the Search index. Please ensure that the field that you are trying to access in the search results is part of the Search index. One way to handle this is to index and store a document's fields dynamically in the index. * In Capella, you need to go to "Advanced Mode" then under the chevron "General Settings" you can check "\[X\] Store Dynamic Fields" or "\[X\] Index Dynamic Fields" * In Couchbase Server, in the Index Editor (not Quick Editor) under the chevron "Advanced" you can check "\[X\] Store Dynamic Fields" or "\[X\] Index Dynamic Fields" Note that these options will increase the size of the index. For more details on dynamic mappings, please refer to the [documentation](https://docs.couchbase.com/cloud/search/customize-index.html). Question: I am unable to see the metadata object in my search results.[](#question-i-am-unable-to-see-the-metadata-object-in-my-search-results "Direct link to Question: I am unable to see the metadata object in my search results.") ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This is most likely due to the `metadata` field in the document not being indexed and/or stored by the Couchbase Search index. In order to index the `metadata` field in the document, you need to add it to the index as a child mapping. If you select to map all the fields in the mapping, you will be able to search by all metadata fields. Alternatively, to optimize the index, you can select the specific fields inside `metadata` object to be indexed. You can refer to the [docs](https://docs.couchbase.com/cloud/search/customize-index.html) to learn more about indexing child mappings. To create Child Mappings, you can refer to the following docs - * [Couchbase Capella](https://docs.couchbase.com/cloud/search/create-child-mapping.html) * [Couchbase Server](https://docs.couchbase.com/server/current/fts/fts-creating-index-from-UI-classic-editor-dynamic.html) * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). 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https://js.langchain.com/v0.2/docs/integrations/vectorstores/hanavector	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is a vector store fully integrated into the `SAP HANA Cloud database`. Setup[](#setup "Direct link to Setup") --------------------------------------- You'll first need to install either the [`@sap/hana-client`](https://www.npmjs.com/package/@sap/hana-client) or the [`hdb`](https://www.npmjs.com/package/hdb) package, and the [`@langchain/community`](https://www.npmjs.com/package/@langchain/community) package: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/community @sap/hana-client# ornpm install -S @langchain/community hdb yarn add @langchain/community @sap/hana-client# oryarn add @langchain/community hdb pnpm add @langchain/community @sap/hana-client# orpnpm add @langchain/community hdb You'll also need to have database connection to a HANA Cloud instance. OPENAI_API_KEY = "Your OpenAI API key"HANA_HOST = "HANA_DB_ADDRESS"HANA_PORT = "HANA_DB_PORT"HANA_UID = "HANA_DB_USER"HANA_PWD = "HANA_DB_PASSWORD" #### API Reference: Create a new index from texts[](#create-a-new-index-from-texts "Direct link to Create a new index from texts") --------------------------------------------------------------------------------------------------------------- import { OpenAIEmbeddings } from "@langchain/openai";import hanaClient from "hdb";import { HanaDB, HanaDBArgs,} from "@langchain/community/vectorstores/hanavector";const connectionParams = { host: process.env.HANA_HOST, port: process.env.HANA_PORT, user: process.env.HANA_UID, password: process.env.HANA_PWD, // useCesu8 : false};const client = hanaClient.createClient(connectionParams);// connet to hanaDBawait new Promise<void>((resolve, reject) => { client.connect((err: Error) => { // Use arrow function here if (err) { reject(err); } else { console.log("Connected to SAP HANA successfully."); resolve(); } });});const embeddings = new OpenAIEmbeddings();const args: HanaDBArgs = { connection: client, tableName: "test_fromTexts",};// This function will create a table "test_fromTexts" if not exist, if exists,// then the value will be appended to the table.const vectorStore = await HanaDB.fromTexts( ["Bye bye", "Hello world", "hello nice world"], [ { id: 2, name: "2" }, { id: 1, name: "1" }, { id: 3, name: "3" }, ], embeddings, args);const response = await vectorStore.similaritySearch("hello world", 2);console.log(response);/* This result is based on no table "test_fromTexts" existing in the database. [ { pageContent: 'Hello world', metadata: { id: 1, name: '1' } }, { pageContent: 'hello nice world', metadata: { id: 3, name: '3' } } ]/client.disconnect(); #### API Reference: [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [HanaDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hanavector.HanaDB.html) from `@langchain/community/vectorstores/hanavector` * [HanaDBArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_hanavector.HanaDBArgs.html) from `@langchain/community/vectorstores/hanavector` Create a new index from a loader and perform similarity searches[](#create-a-new-index-from-a-loader-and-perform-similarity-searches "Direct link to Create a new index from a loader and perform similarity searches") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ import hanaClient from "hdb";import { HanaDB, HanaDBArgs,} from "@langchain/community/vectorstores/hanavector";import { OpenAIEmbeddings } from "@langchain/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import { CharacterTextSplitter } from "@langchain/textsplitters";const connectionParams = { host: process.env.HANA_HOST, port: process.env.HANA_PORT, user: process.env.HANA_UID, password: process.env.HANA_PWD, // useCesu8 : false};const client = hanaClient.createClient(connectionParams);// connet to hanaDBawait new Promise<void>((resolve, reject) => { client.connect((err: Error) => { // Use arrow function here if (err) { reject(err); } else { console.log("Connected to SAP HANA successfully."); resolve(); } });});const embeddings = new OpenAIEmbeddings();const args: HanaDBArgs = { connection: client, tableName: "test_fromDocs",};// Load documents from fileconst loader = new TextLoader("./state_of_the_union.txt");const rawDocuments = await loader.load();const splitter = new CharacterTextSplitter({ chunkSize: 500, chunkOverlap: 0,});const documents = await splitter.splitDocuments(rawDocuments);// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.const vectorStore = new HanaDB(embeddings, args);await vectorStore.initialize();// Delete already existing documents from the tableawait vectorStore.delete({ filter: {} });// add the loaded document chunksawait vectorStore.addDocuments(documents);// similarity search (default:“Cosine Similarity”, options:["euclidean", "cosine"])const query = "What did the president say about Ketanji Brown Jackson";const docs = await vectorStore.similaritySearch(query, 2);docs.forEach((doc) => { console.log("-".repeat(80)); console.log(doc.pageContent);});/* -------------------------------------------------------------------------------- One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. -------------------------------------------------------------------------------- As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice/// similiarity search using euclidean distance methodconst argsL2d: HanaDBArgs = { connection: client, tableName: "test_fromDocs", distanceStrategy: "euclidean",};const vectorStoreL2d = new HanaDB(embeddings, argsL2d);const docsL2d = await vectorStoreL2d.similaritySearch(query, 2);docsL2d.forEach((docsL2d) => { console.log("-".repeat(80)); console.log(docsL2d.pageContent);});// Output should be the same as the cosine similarity search method.// Maximal Marginal Relevance Search (MMR)const docsMMR = await vectorStore.maxMarginalRelevanceSearch(query, { k: 2, fetchK: 20,});docsMMR.forEach((docsMMR) => { console.log("-".repeat(80)); console.log(docsMMR.pageContent);});/ -------------------------------------------------------------------------------- One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. -------------------------------------------------------------------------------- Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. Let each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world./client.disconnect(); #### API Reference: [HanaDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hanavector.HanaDB.html) from `@langchain/community/vectorstores/hanavector` * [HanaDBArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_hanavector.HanaDBArgs.html) from `@langchain/community/vectorstores/hanavector` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * [CharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.CharacterTextSplitter.html) from `@langchain/textsplitters` Basic Vectorstore Operations[](#basic-vectorstore-operations "Direct link to Basic Vectorstore Operations") ------------------------------------------------------------------------------------------------------------ import { OpenAIEmbeddings } from "@langchain/openai";import hanaClient from "hdb";// or import another node.js driver// import hanaClient from "@sap/haha-client";import { Document } from "@langchain/core/documents";import { HanaDB, HanaDBArgs,} from "@langchain/community/vectorstores/hanavector";const connectionParams = { host: process.env.HANA_HOST, port: process.env.HANA_PORT, user: process.env.HANA_UID, password: process.env.HANA_PWD, // useCesu8 : false};const client = hanaClient.createClient(connectionParams);// connet to hanaDBawait new Promise<void>((resolve, reject) => { client.connect((err: Error) => { // Use arrow function here if (err) { reject(err); } else { console.log("Connected to SAP HANA successfully."); resolve(); } });});const embeddings = new OpenAIEmbeddings();// define instance argsconst args: HanaDBArgs = { connection: client, tableName: "testBasics",};// Add documents with metadata.const docs: Document[] = [ { pageContent: "foo", metadata: { start: 100, end: 150, docName: "foo.txt", quality: "bad" }, }, { pageContent: "bar", metadata: { start: 200, end: 250, docName: "bar.txt", quality: "good" }, },];// Create a LangChain VectorStore interface for the HANA database and specify the table (collection) to use in args.const vectorStore = new HanaDB(embeddings, args);// need to initialize once an instance is created.await vectorStore.initialize();// Delete already existing documents from the tableawait vectorStore.delete({ filter: {} });await vectorStore.addDocuments(docs);// Query documents with specific metadata.const filterMeta = { quality: "bad" };const query = "foobar";// With filtering on {"quality": "bad"}, only one document should be returnedconst results = await vectorStore.similaritySearch(query, 1, filterMeta);console.log(results);/* [ { pageContent: "foo", metadata: { start: 100, end: 150, docName: "foo.txt", quality: "bad" } } ]/// Delete documents with specific metadata.await vectorStore.delete({ filter: filterMeta });// Now the similarity search with the same filter will return no resultsconst resultsAfterFilter = await vectorStore.similaritySearch( query, 1, filterMeta);console.log(resultsAfterFilter);/ []/client.disconnect(); #### API Reference: [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * [HanaDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hanavector.HanaDB.html) from `@langchain/community/vectorstores/hanavector` * [HanaDBArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_hanavector.HanaDBArgs.html) from `@langchain/community/vectorstores/hanavector` Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)[](#using-a-vectorstore-as-a-retriever-in-chains-for-retrieval-augmented-generation-rag "Direct link to Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { createStuffDocumentsChain } from "langchain/chains/combine_documents";import { createRetrievalChain } from "langchain/chains/retrieval";import hanaClient from "hdb";import { HanaDB, HanaDBArgs,} from "@langchain/community/vectorstores/hanavector";// Connection parametersconst connectionParams = { host: process.env.HANA_HOST, port: process.env.HANA_PORT, user: process.env.HANA_UID, password: process.env.HANA_PWD, // useCesu8 : false};const client = hanaClient.createClient(connectionParams);// connet to hanaDBawait new Promise<void>((resolve, reject) => { client.connect((err: Error) => { // Use arrow function here if (err) { reject(err); } else { console.log("Connected to SAP HANA successfully."); resolve(); } });});const embeddings = new OpenAIEmbeddings();const args: HanaDBArgs = { connection: client, tableName: "test_fromDocs",};const vectorStore = new HanaDB(embeddings, args);await vectorStore.initialize();// Use the store as part of a chain, under the premise that "test_fromDocs" exists and contains the chunked docs.const model = new ChatOpenAI({ modelName: "gpt-3.5-turbo-1106" });const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([ [ "system", "You are an expert in state of the union topics. You are provided multiple context items that are related to the prompt you have to answer. Use the following pieces of context to answer the question at the end.\n\n{context}", ], ["human", "{input}"],]);const combineDocsChain = await createStuffDocumentsChain({ llm: model, prompt: questionAnsweringPrompt,});const chain = await createRetrievalChain({ retriever: vectorStore.asRetriever(), combineDocsChain,});// Ask the first question (and verify how many text chunks have been used).const response = await chain.invoke({ input: "What about Mexico and Guatemala?",});console.log("Chain response:");console.log(response.answer);console.log( `Number of used source document chunks: ${response.context.length}`);/* The United States has set up joint patrols with Mexico and Guatemala to catch more human traffickers. Number of used source document chunks: 4/const responseOther = await chain.invoke({ input: "What about other countries?",});console.log("Chain response:");console.log(responseOther.answer);/ Ask another question on the same conversational chain. The answer should relate to the previous answer given.....including members of NATO, the European Union, and other allies such as Canada..../client.disconnect(); #### API Reference: [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [createStuffDocumentsChain](https://v02.api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://v02.api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` * [HanaDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hanavector.HanaDB.html) from `@langchain/community/vectorstores/hanavector` * [HanaDBArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_hanavector.HanaDBArgs.html) from `@langchain/community/vectorstores/hanavector` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Google Vertex AI Matching Engine ](/v0.2/docs/integrations/vectorstores/googlevertexai)[ Next HNSWLib ](/v0.2/docs/integrations/vectorstores/hnswlib) * [Setup](#setup) * [Create a new index from texts](#create-a-new-index-from-texts) * [Create a new index from a loader and perform similarity searches](#create-a-new-index-from-a-loader-and-perform-similarity-searches) * [Basic Vectorstore Operations](#basic-vectorstore-operations) * [Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)](#using-a-vectorstore-as-a-retriever-in-chains-for-retrieval-augmented-generation-rag) 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/v0.2/docs/integrations/vectorstores/googlevertexai	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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The Google Vertex AI Matching Engine "provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service." Setup[](#setup "Direct link to Setup") --------------------------------------- caution This module expects an endpoint and deployed index already created as the creation time takes close to one hour. To learn more, see the LangChain python documentation [Create Index and deploy it to an Endpoint](https://python.langchain.com/docs/integrations/vectorstores/matchingengine#create-index-and-deploy-it-to-an-endpoint). Before running this code, you should make sure the Vertex AI API is enabled for the relevant project in your Google Cloud dashboard and that you've authenticated to Google Cloud using one of these methods: * You are logged into an account (using `gcloud auth application-default login`) permitted to that project. * You are running on a machine using a service account that is permitted to the project. * You have downloaded the credentials for a service account that is permitted to the project and set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to the path of this file. Install the authentication library with: * npm * Yarn * pnpm npm install google-auth-library yarn add google-auth-library pnpm add google-auth-library The Matching Engine does not store the actual document contents, only embeddings. Therefore, you'll need a docstore. The below example uses Google Cloud Storage, which requires the following: * npm * Yarn * pnpm npm install @google-cloud/storage yarn add @google-cloud/storage pnpm add @google-cloud/storage Usage[](#usage "Direct link to Usage") --------------------------------------- ### Initializing the engine[](#initializing-the-engine "Direct link to Initializing the engine") When creating the `MatchingEngine` object, you'll need some information about the matching engine configuration. You can get this information from the Cloud Console for Matching Engine: * The id for the Index * The id for the Index Endpoint You will also need a document store. While an `InMemoryDocstore` is ok for initial testing, you will want to use something like a [GoogleCloudStorageDocstore](https://v02.api.js.langchain.com/classes/langchain_stores_doc_gcs.GoogleCloudStorageDocstore.html) to store it more permanently. import { MatchingEngine } from "langchain/vectorstores/googlevertexai";import { Document } from "langchain/document";import { SyntheticEmbeddings } from "langchain/embeddings/fake";import { GoogleCloudStorageDocstore } from "langchain/stores/doc/gcs";const embeddings = new SyntheticEmbeddings({ vectorSize: Number.parseInt( process.env.SYNTHETIC_EMBEDDINGS_VECTOR_SIZE ?? "768", 10 ),});const store = new GoogleCloudStorageDocstore({ bucket: process.env.GOOGLE_CLOUD_STORAGE_BUCKET!,});const config = { index: process.env.GOOGLE_VERTEXAI_MATCHINGENGINE_INDEX!, indexEndpoint: process.env.GOOGLE_VERTEXAI_MATCHINGENGINE_INDEXENDPOINT!, apiVersion: "v1beta1", docstore: store,};const engine = new MatchingEngine(embeddings, config); ### Adding documents[](#adding-documents "Direct link to Adding documents") const doc = new Document({ pageContent: "this" });await engine.addDocuments([doc]); Any metadata in a document is converted into Matching Engine "allow list" values that can be used to filter during a query. const documents = [ new Document({ pageContent: "this apple", metadata: { color: "red", category: "edible", }, }), new Document({ pageContent: "this blueberry", metadata: { color: "blue", category: "edible", }, }), new Document({ pageContent: "this firetruck", metadata: { color: "red", category: "machine", }, }),];// Add all our documentsawait engine.addDocuments(documents); The documents are assumed to have an "id" parameter available as well. If this is not set, then an ID will be assigned and returned as part of the Document. ### Querying documents[](#querying-documents "Direct link to Querying documents") Doing a straightforward k-nearest-neighbor search which returns all results is done using any of the standard methods: const results = await engine.similaritySearch("this"); ### Querying documents with a filter / restriction[](#querying-documents-with-a-filter--restriction "Direct link to Querying documents with a filter / restriction") We can limit what documents are returned based on the metadata that was set for the document. So if we just wanted to limit the results to those with a red color, we can do: import { Restriction } from `langchain/vectorstores/googlevertexai`;const redFilter: Restriction[] = [ { namespace: "color", allowList: ["red"], },];const redResults = await engine.similaritySearch("this", 4, redFilter); If we wanted to do something more complicated, like things that are red, but not edible: const filter: Restriction[] = [ { namespace: "color", allowList: ["red"], }, { namespace: "category", denyList: ["edible"], },];const results = await engine.similaritySearch("this", 4, filter); ### Deleting documents[](#deleting-documents "Direct link to Deleting documents") Deleting documents are done using ID. import { IdDocument } from `langchain/vectorstores/googlevertexai`;const oldResults: IdDocument[] = await engine.similaritySearch("this", 10);const oldIds = oldResults.map( doc => doc.id! );await engine.delete({ids: oldIds}); * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Faiss ](/v0.2/docs/integrations/vectorstores/faiss)[ Next SAP HANA Cloud Vector Engine ](/v0.2/docs/integrations/vectorstores/hanavector) * [Setup](#setup) * [Usage](#usage) * [Initializing the engine](#initializing-the-engine) * [Adding documents](#adding-documents) * [Querying documents](#querying-documents) * [Querying documents with a filter / restriction](#querying-documents-with-a-filter--restriction) * [Deleting documents](#deleting-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/v0.2/docs/integrations/vectorstores/mongodb_atlas	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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). LangChain.js supports MongoDB Atlas as a vector store, and supports both standard similarity search and maximal marginal relevance search, which takes a combination of documents are most similar to the inputs, then reranks and optimizes for diversity. Setup[](#setup "Direct link to Setup") --------------------------------------- ### Installation[](#installation "Direct link to Installation") First, add the Node MongoDB SDK to your project: * npm * Yarn * pnpm npm install -S mongodb yarn add mongodb pnpm add mongodb ### Initial Cluster Configuration[](#initial-cluster-configuration "Direct link to Initial Cluster Configuration") Next, you'll need create a MongoDB Atlas cluster. Navigate to the [MongoDB Atlas website](https://www.mongodb.com/atlas/database) and create an account if you don't already have one. Create and name a cluster when prompted, then find it under `Database`. Select `Collections` and create either a blank collection or one from the provided sample data. Note The cluster created must be MongoDB 7.0 or higher. If you are using a pre-7.0 version of MongoDB, you must use a version of langchainjs<=0.0.163. ### Creating an Index[](#creating-an-index "Direct link to Creating an Index") After configuring your cluster, you'll need to create an index on the collection field you want to search over. Switch to the `Atlas Search` tab and click `Create Search Index`. From there, make sure you select `Atlas Vector Search - JSON Editor`, then select the appropriate database and collection and paste the following into the textbox: { "fields": [ { "numDimensions": 1024, "path": "embedding", "similarity": "euclidean", "type": "vector" } ]} Note that the `dimensions` property should match the dimensionality of the embeddings you are using. For example, Cohere embeddings have 1024 dimensions, and by default OpenAI embeddings have 1536: Note: By default the vector store expects an index name of `default`, an indexed collection field name of `embedding`, and a raw text field name of `text`. You should initialize the vector store with field names matching your index name collection schema as shown below. Finally, proceed to build the index. Usage[](#usage "Direct link to Usage") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community ### Ingestion[](#ingestion "Direct link to Ingestion") import { MongoDBAtlasVectorSearch } from "@langchain/mongodb";import { CohereEmbeddings } from "@langchain/cohere";import { MongoClient } from "mongodb";const client = new MongoClient(process.env.MONGODB_ATLAS_URI \|\| "");const namespace = "langchain.test";const [dbName, collectionName] = namespace.split(".");const collection = client.db(dbName).collection(collectionName);const vectorstore = await MongoDBAtlasVectorSearch.fromTexts( ["Hello world", "Bye bye", "What's this?"], [{ id: 2 }, { id: 1 }, { id: 3 }], new CohereEmbeddings(), { collection, indexName: "default", // The name of the Atlas search index. Defaults to "default" textKey: "text", // The name of the collection field containing the raw content. Defaults to "text" embeddingKey: "embedding", // The name of the collection field containing the embedded text. Defaults to "embedding" });const assignedIds = await vectorstore.addDocuments([ { pageContent: "upsertable", metadata: {} },]);const upsertedDocs = [{ pageContent: "overwritten", metadata: {} }];await vectorstore.addDocuments(upsertedDocs, { ids: assignedIds });await client.close(); #### API Reference: * [MongoDBAtlasVectorSearch](https://v02.api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html) from `@langchain/mongodb` * [CohereEmbeddings](https://v02.api.js.langchain.com/classes/langchain_cohere.CohereEmbeddings.html) from `@langchain/cohere` ### Search[](#search "Direct link to Search") import { MongoDBAtlasVectorSearch } from "@langchain/mongodb";import { CohereEmbeddings } from "@langchain/cohere";import { MongoClient } from "mongodb";const client = new MongoClient(process.env.MONGODB_ATLAS_URI \|\| "");const namespace = "langchain.test";const [dbName, collectionName] = namespace.split(".");const collection = client.db(dbName).collection(collectionName);const vectorStore = new MongoDBAtlasVectorSearch(new CohereEmbeddings(), { collection, indexName: "default", // The name of the Atlas search index. Defaults to "default" textKey: "text", // The name of the collection field containing the raw content. Defaults to "text" embeddingKey: "embedding", // The name of the collection field containing the embedded text. Defaults to "embedding"});const resultOne = await vectorStore.similaritySearch("Hello world", 1);console.log(resultOne);await client.close(); #### API Reference: * [MongoDBAtlasVectorSearch](https://v02.api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html) from `@langchain/mongodb` * [CohereEmbeddings](https://v02.api.js.langchain.com/classes/langchain_cohere.CohereEmbeddings.html) from `@langchain/cohere` ### Maximal marginal relevance[](#maximal-marginal-relevance "Direct link to Maximal marginal relevance") import { MongoDBAtlasVectorSearch } from "@langchain/mongodb";import { CohereEmbeddings } from "@langchain/cohere";import { MongoClient } from "mongodb";const client = new MongoClient(process.env.MONGODB_ATLAS_URI \|\| "");const namespace = "langchain.test";const [dbName, collectionName] = namespace.split(".");const collection = client.db(dbName).collection(collectionName);const vectorStore = new MongoDBAtlasVectorSearch(new CohereEmbeddings(), { collection, indexName: "default", // The name of the Atlas search index. Defaults to "default" textKey: "text", // The name of the collection field containing the raw content. Defaults to "text" embeddingKey: "embedding", // The name of the collection field containing the embedded text. Defaults to "embedding"});const resultOne = await vectorStore.maxMarginalRelevanceSearch("Hello world", { k: 4, fetchK: 20, // The number of documents to return on initial fetch});console.log(resultOne);// Using MMR in a vector store retrieverconst retriever = await vectorStore.asRetriever({ searchType: "mmr", searchKwargs: { fetchK: 20, lambda: 0.1, },});const retrieverOutput = await retriever.invoke("Hello world");console.log(retrieverOutput);await client.close(); #### API Reference: * [MongoDBAtlasVectorSearch](https://v02.api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html) from `@langchain/mongodb` * [CohereEmbeddings](https://v02.api.js.langchain.com/classes/langchain_cohere.CohereEmbeddings.html) from `@langchain/cohere` ### Metadata filtering[](#metadata-filtering "Direct link to Metadata filtering") MongoDB Atlas supports pre-filtering of results on other fields. They require you to define which metadata fields you plan to filter on by updating the index. Here's an example: { "fields": [ { "numDimensions": 1024, "path": "embedding", "similarity": "euclidean", "type": "vector" }, { "path": "docstore_document_id", "type": "filter" } ]} Above, the first item in `fields` is the vector index, and the second item is the metadata property you want to filter on. The name of the property is `path`, so the above index would allow us to search on a metadata field named `docstore_document_id`. Then, in your code you can use [MQL Query Operators](https://www.mongodb.com/docs/manual/reference/operator/query/) for filtering. Here's an example: import { MongoDBAtlasVectorSearch } from "@langchain/mongodb";import { CohereEmbeddings } from "@langchain/cohere";import { MongoClient } from "mongodb";import { sleep } from "langchain/util/time";const client = new MongoClient(process.env.MONGODB_ATLAS_URI \|\| "");const namespace = "langchain.test";const [dbName, collectionName] = namespace.split(".");const collection = client.db(dbName).collection(collectionName);const vectorStore = new MongoDBAtlasVectorSearch(new CohereEmbeddings(), { collection, indexName: "default", // The name of the Atlas search index. Defaults to "default" textKey: "text", // The name of the collection field containing the raw content. Defaults to "text" embeddingKey: "embedding", // The name of the collection field containing the embedded text. Defaults to "embedding"});await vectorStore.addDocuments([ { pageContent: "Hey hey hey", metadata: { docstore_document_id: "somevalue" }, },]);const retriever = vectorStore.asRetriever({ filter: { preFilter: { docstore_document_id: { $eq: "somevalue", }, }, },});// Mongo has a slight processing delay between ingest and availabilityawait sleep(2000);const results = await retriever.invoke("goodbye");console.log(results);await client.close(); #### API Reference: * [MongoDBAtlasVectorSearch](https://v02.api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html) from `@langchain/mongodb` * [CohereEmbeddings](https://v02.api.js.langchain.com/classes/langchain_cohere.CohereEmbeddings.html) from `@langchain/cohere` * [sleep](https://v02.api.js.langchain.com/functions/langchain_util_time.sleep.html) from `langchain/util/time` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Momento Vector Index (MVI) ](/v0.2/docs/integrations/vectorstores/momento_vector_index)[ Next MyScale ](/v0.2/docs/integrations/vectorstores/myscale) * [Setup](#setup) * [Installation](#installation) * [Initial Cluster Configuration](#initial-cluster-configuration) * [Creating an Index](#creating-an-index) * [Usage](#usage) * [Ingestion](#ingestion) * [Search](#search) * [Maximal marginal relevance](#maximal-marginal-relevance) * [Metadata filtering](#metadata-filtering) 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/v0.2/docs/integrations/vectorstores/neo4jvector	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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It supports: * approximate nearest neighbor search * Euclidean similarity and cosine similarity * Hybrid search combining vector and keyword searches Setup[](#setup "Direct link to Setup") --------------------------------------- To work with Neo4j Vector Index, you need to install the `neo4j-driver` package: * npm * Yarn * pnpm npm install neo4j-driver yarn add neo4j-driver pnpm add neo4j-driver tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/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 `Neo4j` self hosted instance with `docker-compose`[](#setup-a-neo4j-self-hosted-instance-with-docker-compose "Direct link to setup-a-neo4j-self-hosted-instance-with-docker-compose") `Neo4j` provides a prebuilt Docker image that can be used to quickly setup a self-hosted Neo4j database instance. Create a file below named `docker-compose.yml`: export default {services:{database:{image:'neo4j',ports:['7687:7687','7474:7474'],environment:['NEO4J_AUTH=neo4j/pleaseletmein']}}}; #### 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 `Neo4j` on their [website](https://neo4j.com/docs/operations-manual/current/installation/). Usage[](#usage "Direct link to Usage") --------------------------------------- One complete example of using `Neo4jVectorStore` is the following: import { OpenAIEmbeddings } from "@langchain/openai";import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";// Configuration object for Neo4j connection and other related settingsconst config = { url: "bolt://localhost:7687", // URL for the Neo4j instance username: "neo4j", // Username for Neo4j authentication password: "pleaseletmein", // Password for Neo4j authentication indexName: "vector", // Name of the vector index keywordIndexName: "keyword", // Name of the keyword index if using hybrid search searchType: "vector" as const, // Type of search (e.g., vector, hybrid) nodeLabel: "Chunk", // Label for the nodes in the graph textNodeProperty: "text", // Property of the node containing text embeddingNodeProperty: "embedding", // Property of the node containing embedding};const documents = [ { pageContent: "what's this", metadata: { a: 2 } }, { pageContent: "Cat drinks milk", metadata: { a: 1 } },];const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments( documents, new OpenAIEmbeddings(), config);const results = await neo4jVectorIndex.similaritySearch("water", 1);console.log(results);/* [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]/await neo4jVectorIndex.close(); #### API Reference: [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Neo4jVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_neo4j_vector.Neo4jVectorStore.html) from `@langchain/community/vectorstores/neo4j_vector` ### Use retrievalQuery parameter to customize responses[](#use-retrievalquery-parameter-to-customize-responses "Direct link to Use retrievalQuery parameter to customize responses") import { OpenAIEmbeddings } from "@langchain/openai";import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";/* * The retrievalQuery is a customizable Cypher query fragment used in the Neo4jVectorStore class to define how * search results should be retrieved and presented from the Neo4j database. It allows developers to specify * the format and structure of the data returned after a similarity search. * Mandatory columns for `retrievalQuery`: * * 1. text: * - Description: Represents the textual content of the node. * - Type: String * * 2. score: * - Description: Represents the similarity score of the node in relation to the search query. A * higher score indicates a closer match. * - Type: Float (ranging between 0 and 1, where 1 is a perfect match) * * 3. metadata: * - Description: Contains additional properties and information about the node. This can include * any other attributes of the node that might be relevant to the application. * - Type: Object (key-value pairs) * - Example: { "id": "12345", "category": "Books", "author": "John Doe" } * * Note: While you can customize the `retrievalQuery` to fetch additional columns or perform * transformations, never omit the mandatory columns. The names of these columns (`text`, `score`, * and `metadata`) should remain consistent. Renaming them might lead to errors or unexpected behavior. /// Configuration object for Neo4j connection and other related settingsconst config = { url: "bolt://localhost:7687", // URL for the Neo4j instance username: "neo4j", // Username for Neo4j authentication password: "pleaseletmein", // Password for Neo4j authentication retrievalQuery: ` RETURN node.text AS text, score, {a: node.a 2} AS metadata `,};const documents = [ { pageContent: "what's this", metadata: { a: 2 } }, { pageContent: "Cat drinks milk", metadata: { a: 1 } },];const neo4jVectorIndex = await Neo4jVectorStore.fromDocuments( documents, new OpenAIEmbeddings(), config);const results = await neo4jVectorIndex.similaritySearch("water", 1);console.log(results);/* [ Document { pageContent: 'Cat drinks milk', metadata: { a: 2 } } ]/await neo4jVectorIndex.close(); #### API Reference: [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Neo4jVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_neo4j_vector.Neo4jVectorStore.html) from `@langchain/community/vectorstores/neo4j_vector` ### Instantiate Neo4jVectorStore from existing graph[](#instantiate-neo4jvectorstore-from-existing-graph "Direct link to Instantiate Neo4jVectorStore from existing graph") import { OpenAIEmbeddings } from "@langchain/openai";import { Neo4jVectorStore } from "@langchain/community/vectorstores/neo4j_vector";/** * `fromExistingGraph` Method: * * Description: * This method initializes a `Neo4jVectorStore` instance using an existing graph in the Neo4j database. * It's designed to work with nodes that already have textual properties but might not have embeddings. * The method will compute and store embeddings for nodes that lack them. * * Note: * This method is particularly useful when you have a pre-existing graph with textual data and you want * to enhance it with vector embeddings for similarity searches without altering the original data structure. /// Configuration object for Neo4j connection and other related settingsconst config = { url: "bolt://localhost:7687", // URL for the Neo4j instance username: "neo4j", // Username for Neo4j authentication password: "pleaseletmein", // Password for Neo4j authentication indexName: "wikipedia", nodeLabel: "Wikipedia", textNodeProperties: ["title", "description"], embeddingNodeProperty: "embedding", searchType: "hybrid" as const,};// You should have a populated Neo4j database to use this methodconst neo4jVectorIndex = await Neo4jVectorStore.fromExistingGraph( new OpenAIEmbeddings(), config);await neo4jVectorIndex.close(); #### API Reference: [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Neo4jVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_neo4j_vector.Neo4jVectorStore.html) from `@langchain/community/vectorstores/neo4j_vector` Disclaimer ⚠️ ============= _Security note_: Make sure that the database connection uses credentials that are narrowly-scoped to only include necessary permissions. Failure to do so may result in data corruption or loss, since the calling code may attempt commands that would result in deletion, mutation of data if appropriately prompted or reading sensitive data if such data is present in the database. The best way to guard against such negative outcomes is to (as appropriate) limit the permissions granted to the credentials used with this tool. For example, creating read only users for the database is a good way to ensure that the calling code cannot mutate or delete data. See the [security page](/v0.2/docs/security) for more information. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous MyScale ](/v0.2/docs/integrations/vectorstores/myscale)[ Next Neon Postgres ](/v0.2/docs/integrations/vectorstores/neon) * [Setup](#setup) * [Setup a `Neo4j` self hosted instance with `docker-compose`](#setup-a-neo4j-self-hosted-instance-with-docker-compose) * [Usage](#usage) * [Use retrievalQuery parameter to customize responses](#use-retrievalquery-parameter-to-customize-responses) * [Instantiate Neo4jVectorStore from existing graph](#instantiate-neo4jvectorstore-from-existing-graph) 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/v0.2/docs/integrations/vectorstores/neon	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Neon Postgres On this page Neon Postgres ============= Neon is a fully managed serverless PostgreSQL database. It separates storage and compute to offer features such as instant branching and automatic scaling. With the `pgvector` extension, Neon provides a vector store that can be used with LangChain.js to store and query embeddings. Setup[](#setup "Direct link to Setup") --------------------------------------- ### Select a Neon project[](#select-a-neon-project "Direct link to Select a Neon project") If you do not have a Neon account, sign up for one at [Neon](https://neon.tech). After logging into the Neon Console, proceed to the [Projects](https://console.neon.tech/app/projects) section and select an existing project or create a new one. Your Neon project comes with a ready-to-use Postgres database named `neondb` that you can use to store embeddings. Navigate to the Connection Details section to find your database connection string. It should look similar to this: postgres://alex:AbC123dEf@ep-cool-darkness-123456.us-east-2.aws.neon.tech/dbname?sslmode=require Keep your connection string handy for later use. ### Application code[](#application-code "Direct link to Application code") To work with Neon Postgres, you need to install the `@neondatabase/serverless` package which provides a JavaScript/TypeScript driver to connect to the database. * npm * Yarn * pnpm npm install @neondatabase/serverless yarn add @neondatabase/serverless pnpm add @neondatabase/serverless tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/community yarn add @langchain/community pnpm add @langchain/community To initialize a `NeonPostgres` vectorstore, you need to provide your Neon database connection string. You can use the connection string we fetched above directly, or store it as an environment variable and use it in your code. const vectorStore = await NeonPostgres.initialize(embeddings, { connectionString: NEON_POSTGRES_CONNECTION_STRING,}); Usage[](#usage "Direct link to Usage") --------------------------------------- import { OpenAIEmbeddings } from "@langchain/openai";import { NeonPostgres } from "@langchain/community/vectorstores/neon";// Initialize an embeddings instanceconst embeddings = new OpenAIEmbeddings({ apiKey: process.env.OPENAI_API_KEY, dimensions: 256, model: "text-embedding-3-small",});// Initialize a NeonPostgres instance to store embedding vectorsconst vectorStore = await NeonPostgres.initialize(embeddings, { connectionString: process.env.DATABASE_URL as string,});// You can add documents to the store, strings in the `pageContent` field will be embedded// and stored in the databaseconst documents = [ { pageContent: "Hello world", metadata: { topic: "greeting" } }, { pageContent: "Bye bye", metadata: { topic: "greeting" } }, { pageContent: "Mitochondria is the powerhouse of the cell", metadata: { topic: "science" }, },];const idsInserted = await vectorStore.addDocuments(documents);// You can now query the store for similar documents to the input queryconst resultOne = await vectorStore.similaritySearch("hola", 1);console.log(resultOne);/[ Document { pageContent: 'Hello world', metadata: { topic: 'greeting' } }]/// You can also filter by metadataconst resultTwo = await vectorStore.similaritySearch("Irrelevant query", 2, { topic: "science",});console.log(resultTwo);/[ Document { pageContent: 'Mitochondria is the powerhouse of the cell', metadata: { topic: 'science' } }]/// Metadata filtering with IN-filters works as wellconst resultsThree = await vectorStore.similaritySearch("Irrelevant query", 2, { topic: { in: ["greeting"] },});console.log(resultsThree);/[ Document { pageContent: 'Bye bye', metadata: { topic: 'greeting' } }, Document { pageContent: 'Hello world', metadata: { topic: 'greeting' } }]/// Upserting is supported as wellawait vectorStore.addDocuments( [ { pageContent: "ATP is the powerhouse of the cell", metadata: { topic: "science" }, }, ], { ids: [idsInserted[2]] });const resultsFour = await vectorStore.similaritySearch( "powerhouse of the cell", 1);console.log(resultsFour);/[ Document { pageContent: 'ATP is the powerhouse of the cell', metadata: { topic: 'science' } }]/ #### API Reference: * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [NeonPostgres](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_neon.NeonPostgres.html) from `@langchain/community/vectorstores/neon` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Neo4j Vector Index ](/v0.2/docs/integrations/vectorstores/neo4jvector)[ Next OpenSearch ](/v0.2/docs/integrations/vectorstores/opensearch) * [Setup](#setup) * [Select a Neon project](#select-a-neon-project) * [Application code](#application-code) * [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/v0.2/docs/integrations/vectorstores/opensearch	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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[OpenSearch](https://opensearch.org/) is a fork of [Elasticsearch](https://www.elastic.co/elasticsearch/) that is fully compatible with the Elasticsearch API. Read more about their support for Approximate Nearest Neighbors [here](https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn/). Langchain.js accepts [@opensearch-project/opensearch](https://opensearch.org/docs/latest/clients/javascript/index/) as the client for OpenSearch vectorstore. Setup[](#setup "Direct link to Setup") --------------------------------------- tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install -S @langchain/openai @opensearch-project/opensearch yarn add @langchain/openai @opensearch-project/opensearch pnpm add @langchain/openai @opensearch-project/opensearch You'll also need to have an OpenSearch instance running. You can use the [official Docker image](https://opensearch.org/docs/latest/opensearch/install/docker/) to get started. You can also find an example docker-compose file [here](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/indexes/vector_stores/opensearch/docker-compose.yml). Index docs[](#index-docs "Direct link to Index docs") ------------------------------------------------------ import { Client } from "@opensearch-project/opensearch";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "@langchain/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = new Client({ nodes: [process.env.OPENSEARCH_URL ?? "http://127.0.0.1:9200"],});const docs = [ new Document({ metadata: { foo: "bar" }, pageContent: "opensearch is also a 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: "OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications", }),];await OpenSearchVectorStore.fromDocuments(docs, new OpenAIEmbeddings(), { client, indexName: process.env.OPENSEARCH_INDEX, // Will default to `documents`}); Query docs[](#query-docs "Direct link to Query docs") ------------------------------------------------------ import { Client } from "@opensearch-project/opensearch";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "@langchain/openai";import { OpenAI } from "@langchain/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = new Client({ nodes: [process.env.OPENSEARCH_URL ?? "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), { client,});/* Search the vector DB independently with meta filters /const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/ [ { "pageContent": "Hello world", "metadata": { "id": 2 } } ] // Use as part of a chain (currently no metadata filters) /const model = new OpenAI();const chain = VectorDBQAChain.fromLLM(model, vectorStore, { k: 1, returnSourceDocuments: true,});const response = await chain.call({ query: "What is opensearch?" });console.log(JSON.stringify(response, null, 2));/ { "text": " Opensearch is a collection of technologies that allow search engines to publish search results in a standard format, making it easier for users to search across multiple sites.", "sourceDocuments": [ { "pageContent": "What's this?", "metadata": { "id": 3 } } ] } / * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Neon Postgres ](/v0.2/docs/integrations/vectorstores/neon)[ Next PGVector ](/v0.2/docs/integrations/vectorstores/pgvector) * [Setup](#setup) * [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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://v02.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://v02.api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TextLoader](https://v02.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://v02.api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Prisma ](/v0.2/docs/integrations/vectorstores/prisma)[ Next Redis ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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[Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_community_vectorstores_prisma.PrismaVectorStore.html) from `@langchain/community/vectorstores/prisma` * [OpenAIEmbeddings](https://v02.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. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Pinecone ](/v0.2/docs/integrations/vectorstores/pinecone)[ Next Qdrant ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://v02.api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://v02.api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [createStuffDocumentsChain](https://v02.api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://v02.api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RedisVectorStore](https://v02.api.js.langchain.com/classes/langchain_redis.RedisVectorStore.html) from `@langchain/redis` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Qdrant ](/v0.2/docs/integrations/vectorstores/qdrant)[ Next Rockset ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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](/v0.2/docs/how_to/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/textsplitters";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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [RocksetStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_rockset.RocksetStore.html) from `@langchain/community/vectorstores/rockset` * [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [createStuffDocumentsChain](https://v02.api.js.langchain.com/functions/langchain_chains_combine_documents.createStuffDocumentsChain.html) from `langchain/chains/combine_documents` * [createRetrievalChain](https://v02.api.js.langchain.com/functions/langchain_chains_retrieval.createRetrievalChain.html) from `langchain/chains/retrieval` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Redis ](/v0.2/docs/integrations/vectorstores/redis)[ Next SingleStore ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/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](/v0.2/docs/how_to/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' } }]/ * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Supabase ](/v0.2/docs/integrations/vectorstores/supabase)[ Next Turbopuffer ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TurbopufferVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_turbopuffer.TurbopufferVectorStore.html) from `@langchain/community/vectorstores/turbopuffer` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Tigris ](/v0.2/docs/integrations/vectorstores/tigris)[ Next TypeORM ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TypeORMVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_typeorm.TypeORMVectorStore.html) from `@langchain/community/vectorstores/typeorm` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Turbopuffer ](/v0.2/docs/integrations/vectorstores/turbopuffer)[ Next Typesense ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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[TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/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](/v0.2/docs/how_to/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. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous TypeORM ](/v0.2/docs/integrations/vectorstores/typeorm)[ Next Upstash Vector ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [UpstashVectorStore](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * [UpstashVectorStore](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [UpstashVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_upstash.UpstashVectorStore.html) from `@langchain/community/vectorstores/upstash` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Typesense ](/v0.2/docs/integrations/vectorstores/typesense)[ Next USearch ](/v0.2/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/v0.2/docs/integrations/vectorstores/usearch	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * USearch On this page USearch ======= Compatibility Only available on Node.js. [USearch](https://github.com/unum-cloud/usearch) is a library for efficient similarity search and clustering of dense vectors. Setup[](#setup "Direct link to Setup") --------------------------------------- Install the [usearch](https://github.com/unum-cloud/usearch/tree/main/javascript) package, which is a Node.js binding for [USearch](https://github.com/unum-cloud/usearch). * npm * Yarn * pnpm npm install -S usearch yarn add usearch pnpm add usearch tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/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 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") import { USearch } from "@langchain/community/vectorstores/usearch";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await USearch.fromTexts( ["Hello world", "Bye bye", "hello nice world"], [{ id: 2 }, { id: 1 }, { id: 3 }], new OpenAIEmbeddings());const resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne); #### API Reference: * [USearch](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_usearch.USearch.html) from `@langchain/community/vectorstores/usearch` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` ### Create a new index from a loader[](#create-a-new-index-from-a-loader "Direct link to Create a new index from a loader") import { USearch } from "@langchain/community/vectorstores/usearch";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();// Load the docs into the vector storeconst vectorStore = await USearch.fromDocuments(docs, new OpenAIEmbeddings());// Search for the most similar documentconst resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne); #### API Reference: * [USearch](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_usearch.USearch.html) from `@langchain/community/vectorstores/usearch` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Upstash Vector ](/v0.2/docs/integrations/vectorstores/upstash)[ Next Vectara ](/v0.2/docs/integrations/vectorstores/vectara) * [Setup](#setup) * [Usage](#usage) * [Create a new index from texts](#create-a-new-index-from-texts) * [Create a new index from a loader](#create-a-new-index-from-a-loader) 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/v0.2/docs/integrations/vectorstores/vectara	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Vectara On this page Vectara ======= Vectara is a platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy. You can use Vectara as a vector store with LangChain.js. 👉 Embeddings Included[](#-embeddings-included "Direct link to 👉 Embeddings Included") ---------------------------------------------------------------------------------------- Vectara uses its own embeddings under the hood, so you don't have to provide any yourself or call another service to obtain embeddings. This also means that if you provide your own embeddings, they'll be a no-op. const store = await VectaraStore.fromTexts( ["hello world", "hi there"], [{ foo: "bar" }, { foo: "baz" }], // This won't have an effect. Provide a FakeEmbeddings instance instead for clarity. new OpenAIEmbeddings(), args); Setup[](#setup "Direct link to Setup") --------------------------------------- You'll need to: * Create a [free Vectara account](https://vectara.com/integrations/langchain). * Create a [corpus](https://docs.vectara.com/docs/console-ui/creating-a-corpus) to store your data * Create an [API key](https://docs.vectara.com/docs/common-use-cases/app-authn-authz/api-keys) with QueryService and IndexService access so you can access this corpus Configure your `.env` file or provide args to connect LangChain to your Vectara corpus: VECTARA_CUSTOMER_ID=your_customer_idVECTARA_CORPUS_ID=your_corpus_idVECTARA_API_KEY=your-vectara-api-key Note that you can provide multiple corpus IDs separated by commas for querying multiple corpora at once. For example: `VECTARA_CORPUS_ID=3,8,9,43`. For indexing multiple corpora, you'll need to create a separate VectaraStore instance for each corpus. Usage[](#usage "Direct link to Usage") --------------------------------------- import { VectaraStore } from "@langchain/community/vectorstores/vectara";import { VectaraSummaryRetriever } from "@langchain/community/retrievers/vectara_summary";import { Document } from "@langchain/core/documents";// Create the Vectara store.const store = new VectaraStore({ customerId: Number(process.env.VECTARA_CUSTOMER_ID), corpusId: Number(process.env.VECTARA_CORPUS_ID), apiKey: String(process.env.VECTARA_API_KEY), verbose: true,});// Add two documents with some metadata.const doc_ids = await store.addDocuments([ new Document({ pageContent: "Do I dare to eat a peach?", metadata: { foo: "baz", }, }), new Document({ pageContent: "In the room the women come and go talking of Michelangelo", metadata: { foo: "bar", }, }),]);// Perform a similarity search.const resultsWithScore = await store.similaritySearchWithScore( "What were the women talking about?", 1, { lambda: 0.025, });// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));/[ [ { "pageContent": "In the room the women come and go talking of Michelangelo", "metadata": { "lang": "eng", "offset": "0", "len": "57", "foo": "bar" } }, 0.4678752 ]]/const retriever = new VectaraSummaryRetriever({ vectara: store, topK: 3 });const documents = await retriever.invoke("What were the women talking about?");console.log(JSON.stringify(documents, null, 2));/[ { "pageContent": "<b>In the room the women come and go talking of Michelangelo</b>", "metadata": { "lang": "eng", "offset": "0", "len": "57", "foo": "bar" } }, { "pageContent": "<b>In the room the women come and go talking of Michelangelo</b>", "metadata": { "lang": "eng", "offset": "0", "len": "57", "foo": "bar" } }, { "pageContent": "<b>In the room the women come and go talking of Michelangelo</b>", "metadata": { "lang": "eng", "offset": "0", "len": "57", "foo": "bar" } }]/// Delete the documents.await store.deleteDocuments(doc_ids); #### API Reference: * [VectaraStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_vectara.VectaraStore.html) from `@langchain/community/vectorstores/vectara` * [VectaraSummaryRetriever](https://v02.api.js.langchain.com/classes/langchain_community_retrievers_vectara_summary.VectaraSummaryRetriever.html) from `@langchain/community/retrievers/vectara_summary` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` Note that `lambda` is a parameter related to Vectara's hybrid search capbility, providing a tradeoff between neural search and boolean/exact match as described [here](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching). We recommend the value of 0.025 as a default, while providing a way for advanced users to customize this value if needed. APIs[](#apis "Direct link to APIs") ------------------------------------ Vectara's LangChain vector store consumes Vectara's core APIs: * [Indexing API](https://docs.vectara.com/docs/indexing-apis/indexing) for storing documents in a Vectara corpus. * [Search API](https://docs.vectara.com/docs/search-apis/search) for querying this data. This API supports hybrid search. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous USearch ](/v0.2/docs/integrations/vectorstores/usearch)[ Next Vercel Postgres ](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [👉 Embeddings Included](#-embeddings-included) * [Setup](#setup) * [Usage](#usage) * [APIs](#apis) 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/v0.2/docs/integrations/document_transformers/openai_metadata_tagger	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [html-to-text](/v0.2/docs/integrations/document_transformers/html-to-text) * [@mozilla/readability](/v0.2/docs/integrations/document_transformers/mozilla_readability) * [OpenAI functions metadata tagger](/v0.2/docs/integrations/document_transformers/openai_metadata_tagger) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Document transformers](/v0.2/docs/integrations/document_transformers) * OpenAI functions metadata tagger On this page OpenAI functions metadata tagger ================================ It can often be useful to tag ingested documents with structured metadata, such as the title, tone, or length of a document, to allow for more targeted similarity search later. However, for large numbers of documents, performing this labelling process manually can be tedious. The `MetadataTagger` document transformer automates this process by extracting metadata from each provided document according to a provided schema. It uses a configurable OpenAI Functions-powered chain under the hood, so if you pass a custom LLM instance, it must be an OpenAI model with functions support. Note: This document transformer works best with complete documents, so it's best to run it first with whole documents before doing any other splitting or processing! ### Usage[](#usage "Direct link to Usage") For example, let's say you wanted to index a set of movie reviews. You could initialize the document transformer as follows: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai import { z } from "zod";import { createMetadataTaggerFromZod } from "langchain/document_transformers/openai_functions";import { ChatOpenAI } from "@langchain/openai";import { Document } from "@langchain/core/documents";const zodSchema = z.object({ movie_title: z.string(), critic: z.string(), tone: z.enum(["positive", "negative"]), rating: z .optional(z.number()) .describe("The number of stars the critic rated the movie"),});const metadataTagger = createMetadataTaggerFromZod(zodSchema, { llm: new ChatOpenAI({ model: "gpt-3.5-turbo" }),});const documents = [ new Document({ pageContent: "Review of The Bee Movie\nBy Roger Ebert\nThis is the greatest movie ever made. 4 out of 5 stars.", }), new Document({ pageContent: "Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata: { reliable: false }, }),];const taggedDocuments = await metadataTagger.transformDocuments(documents);console.log(taggedDocuments);/* [ Document { pageContent: 'Review of The Bee Movie\n' + 'By Roger Ebert\n' + 'This is the greatest movie ever made. 4 out of 5 stars.', metadata: { movie_title: 'The Bee Movie', critic: 'Roger Ebert', tone: 'positive', rating: 4 } }, Document { pageContent: 'Review of The Godfather\n' + 'By Anonymous\n' + '\n' + 'This movie was super boring. 1 out of 5 stars.', metadata: { movie_title: 'The Godfather', critic: 'Anonymous', tone: 'negative', rating: 1, reliable: false } } ]/ #### API Reference: [createMetadataTaggerFromZod](https://v02.api.js.langchain.com/functions/langchain_document_transformers_openai_functions.createMetadataTaggerFromZod.html) from `langchain/document_transformers/openai_functions` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` There is an additional `createMetadataTagger` method that accepts a valid JSON Schema object as well. ### Customization[](#customization "Direct link to Customization") You can pass the underlying tagging chain the standard LLMChain arguments in the second options parameter. For example, if you wanted to ask the LLM to focus specific details in the input documents, or extract metadata in a certain style, you could pass in a custom prompt: import { z } from "zod";import { createMetadataTaggerFromZod } from "langchain/document_transformers/openai_functions";import { ChatOpenAI } from "@langchain/openai";import { Document } from "@langchain/core/documents";import { PromptTemplate } from "@langchain/core/prompts";const taggingChainTemplate = `Extract the desired information from the following passage.Anonymous critics are actually Roger Ebert.Passage:{input}`;const zodSchema = z.object({ movie_title: z.string(), critic: z.string(), tone: z.enum(["positive", "negative"]), rating: z .optional(z.number()) .describe("The number of stars the critic rated the movie"),});const metadataTagger = createMetadataTaggerFromZod(zodSchema, { llm: new ChatOpenAI({ model: "gpt-3.5-turbo" }), prompt: PromptTemplate.fromTemplate(taggingChainTemplate),});const documents = [ new Document({ pageContent: "Review of The Bee Movie\nBy Roger Ebert\nThis is the greatest movie ever made. 4 out of 5 stars.", }), new Document({ pageContent: "Review of The Godfather\nBy Anonymous\n\nThis movie was super boring. 1 out of 5 stars.", metadata: { reliable: false }, }),];const taggedDocuments = await metadataTagger.transformDocuments(documents);console.log(taggedDocuments);/* [ Document { pageContent: 'Review of The Bee Movie\n' + 'By Roger Ebert\n' + 'This is the greatest movie ever made. 4 out of 5 stars.', metadata: { movie_title: 'The Bee Movie', critic: 'Roger Ebert', tone: 'positive', rating: 4 } }, Document { pageContent: 'Review of The Godfather\n' + 'By Anonymous\n' + '\n' + 'This movie was super boring. 1 out of 5 stars.', metadata: { movie_title: 'The Godfather', critic: 'Roger Ebert', tone: 'negative', rating: 1, reliable: false } } ]/ #### API Reference: [createMetadataTaggerFromZod](https://v02.api.js.langchain.com/functions/langchain_document_transformers_openai_functions.createMetadataTaggerFromZod.html) from `langchain/document_transformers/openai_functions` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous @mozilla/readability ](/v0.2/docs/integrations/document_transformers/mozilla_readability)[ Next Vector stores ](/v0.2/docs/integrations/vectorstores) * [Usage](#usage) * [Customization](#customization) 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/v0.2/docs/integrations/vectorstores/voy	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Memory](/v0.2/docs/integrations/vectorstores/memory) * [AnalyticDB](/v0.2/docs/integrations/vectorstores/analyticdb) * [Astra DB](/v0.2/docs/integrations/vectorstores/astradb) * [Azure AI Search](/v0.2/docs/integrations/vectorstores/azure_aisearch) * [Azure Cosmos DB](/v0.2/docs/integrations/vectorstores/azure_cosmosdb) * [Cassandra](/v0.2/docs/integrations/vectorstores/cassandra) * [Chroma](/v0.2/docs/integrations/vectorstores/chroma) * [ClickHouse](/v0.2/docs/integrations/vectorstores/clickhouse) * [CloseVector](/v0.2/docs/integrations/vectorstores/closevector) * [Cloudflare Vectorize](/v0.2/docs/integrations/vectorstores/cloudflare_vectorize) * [Convex](/v0.2/docs/integrations/vectorstores/convex) * [Couchbase](/v0.2/docs/integrations/vectorstores/couchbase) * [Elasticsearch](/v0.2/docs/integrations/vectorstores/elasticsearch) * [Faiss](/v0.2/docs/integrations/vectorstores/faiss) * [Google Vertex AI Matching Engine](/v0.2/docs/integrations/vectorstores/googlevertexai) * [SAP HANA Cloud Vector Engine](/v0.2/docs/integrations/vectorstores/hanavector) * [HNSWLib](/v0.2/docs/integrations/vectorstores/hnswlib) * [LanceDB](/v0.2/docs/integrations/vectorstores/lancedb) * [Milvus](/v0.2/docs/integrations/vectorstores/milvus) * [Momento Vector Index (MVI)](/v0.2/docs/integrations/vectorstores/momento_vector_index) * [MongoDB Atlas](/v0.2/docs/integrations/vectorstores/mongodb_atlas) * [MyScale](/v0.2/docs/integrations/vectorstores/myscale) * [Neo4j Vector Index](/v0.2/docs/integrations/vectorstores/neo4jvector) * [Neon Postgres](/v0.2/docs/integrations/vectorstores/neon) * [OpenSearch](/v0.2/docs/integrations/vectorstores/opensearch) * [PGVector](/v0.2/docs/integrations/vectorstores/pgvector) * [Pinecone](/v0.2/docs/integrations/vectorstores/pinecone) * [Prisma](/v0.2/docs/integrations/vectorstores/prisma) * [Qdrant](/v0.2/docs/integrations/vectorstores/qdrant) * [Redis](/v0.2/docs/integrations/vectorstores/redis) * [Rockset](/v0.2/docs/integrations/vectorstores/rockset) * [SingleStore](/v0.2/docs/integrations/vectorstores/singlestore) * [Supabase](/v0.2/docs/integrations/vectorstores/supabase) * [Tigris](/v0.2/docs/integrations/vectorstores/tigris) * [Turbopuffer](/v0.2/docs/integrations/vectorstores/turbopuffer) * [TypeORM](/v0.2/docs/integrations/vectorstores/typeorm) * [Typesense](/v0.2/docs/integrations/vectorstores/typesense) * [Upstash Vector](/v0.2/docs/integrations/vectorstores/upstash) * [USearch](/v0.2/docs/integrations/vectorstores/usearch) * [Vectara](/v0.2/docs/integrations/vectorstores/vectara) * [Vercel Postgres](/v0.2/docs/integrations/vectorstores/vercel_postgres) * [Voy](/v0.2/docs/integrations/vectorstores/voy) * [Weaviate](/v0.2/docs/integrations/vectorstores/weaviate) * [Xata](/v0.2/docs/integrations/vectorstores/xata) * [Zep](/v0.2/docs/integrations/vectorstores/zep) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Vector stores](/v0.2/docs/integrations/vectorstores) * Voy On this page Voy === [Voy](https://github.com/tantaraio/voy) is a WASM vector similarity search engine written in Rust. It's supported in non-Node environments like browsers. You can use Voy as a vector store with LangChain.js. ### Install Voy[](#install-voy "Direct link to Install Voy") tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai voy-search @langchain/community yarn add @langchain/openai voy-search @langchain/community pnpm add @langchain/openai voy-search @langchain/community Usage[](#usage "Direct link to Usage") --------------------------------------- import { VoyVectorStore } from "@langchain/community/vectorstores/voy";import { Voy as VoyClient } from "voy-search";import { OpenAIEmbeddings } from "@langchain/openai";import { Document } from "@langchain/core/documents";// Create Voy client using the library.const voyClient = new VoyClient();// Create embeddingsconst embeddings = new OpenAIEmbeddings();// Create the Voy store.const store = new VoyVectorStore(voyClient, embeddings);// Add two documents with some metadata.await store.addDocuments([ new Document({ pageContent: "How has life been treating you?", metadata: { foo: "Mike", }, }), new Document({ pageContent: "And I took it personally...", metadata: { foo: "Testing", }, }),]);const model = new OpenAIEmbeddings();const query = await model.embedQuery("And I took it personally");// Perform a similarity search.const resultsWithScore = await store.similaritySearchVectorWithScore(query, 1);// Print the results.console.log(JSON.stringify(resultsWithScore, null, 2));/* [ [ { "pageContent": "And I took it personally...", "metadata": { "foo": "Testing" } }, 0 ] ]/ #### API Reference: [VoyVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_voy.VoyVectorStore.html) from `@langchain/community/vectorstores/voy` * [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai` * [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Vercel Postgres ](/v0.2/docs/integrations/vectorstores/vercel_postgres)[ Next Weaviate ](/v0.2/docs/integrations/vectorstores/weaviate) * [Install Voy](#install-voy) * [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/v0.2/docs/integrations/llms/openai	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [LLMs](/v0.2/docs/integrations/llms/) * [AI21](/v0.2/docs/integrations/llms/ai21) * [AlephAlpha](/v0.2/docs/integrations/llms/aleph_alpha) * [AWS SageMakerEndpoint](/v0.2/docs/integrations/llms/aws_sagemaker) * [Azure OpenAI](/v0.2/docs/integrations/llms/azure) * [Bedrock](/v0.2/docs/integrations/llms/bedrock) * [Cloudflare Workers AI](/v0.2/docs/integrations/llms/cloudflare_workersai) * [Cohere](/v0.2/docs/integrations/llms/cohere) * [Fireworks](/v0.2/docs/integrations/llms/fireworks) * [Friendli](/v0.2/docs/integrations/llms/friendli) * [(Legacy) Google PaLM/VertexAI](/v0.2/docs/integrations/llms/google_palm) * [Google Vertex AI](/v0.2/docs/integrations/llms/google_vertex_ai) * [Gradient AI](/v0.2/docs/integrations/llms/gradient_ai) * [HuggingFaceInference](/v0.2/docs/integrations/llms/huggingface_inference) * [Llama CPP](/v0.2/docs/integrations/llms/llama_cpp) * [NIBittensor](/v0.2/docs/integrations/llms/ni_bittensor) * [Ollama](/v0.2/docs/integrations/llms/ollama) * [OpenAI](/v0.2/docs/integrations/llms/openai) * [PromptLayer OpenAI](/v0.2/docs/integrations/llms/prompt_layer_openai) * [RaycastAI](/v0.2/docs/integrations/llms/raycast) * [Replicate](/v0.2/docs/integrations/llms/replicate) * [Together AI](/v0.2/docs/integrations/llms/togetherai) * [WatsonX AI](/v0.2/docs/integrations/llms/watsonx_ai) * [Writer](/v0.2/docs/integrations/llms/writer) * [YandexGPT](/v0.2/docs/integrations/llms/yandex) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [LLMs](/v0.2/docs/integrations/llms/) * OpenAI On this page OpenAI ====== Here's how you can initialize an `OpenAI` LLM instance: tip See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages). * npm * Yarn * pnpm npm install @langchain/openai yarn add @langchain/openai pnpm add @langchain/openai tip We're unifying model params across all packages. We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. import { OpenAI } from "@langchain/openai";const model = new OpenAI({ model: "gpt-3.5-turbo-instruct", // Defaults to "gpt-3.5-turbo-instruct" if no model provided. temperature: 0.9, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY});const res = await model.invoke( "What would be a good company name a company that makes colorful socks?");console.log({ res }); If you're part of an organization, you can set `process.env.OPENAI_ORGANIZATION` to your OpenAI organization id, or pass it in as `organization` when initializing the model. Custom URLs[](#custom-urls "Direct link to Custom URLs") --------------------------------------------------------- You can customize the base URL the SDK sends requests to by passing a `configuration` parameter like this: const model = new OpenAI({ temperature: 0.9, configuration: { baseURL: "https://your_custom_url.com", },}); You can also pass other `ClientOptions` parameters accepted by the official SDK. If you are hosting on Azure OpenAI, see the [dedicated page instead](/v0.2/docs/integrations/llms/azure). * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Ollama ](/v0.2/docs/integrations/llms/ollama)[ Next PromptLayer OpenAI ](/v0.2/docs/integrations/llms/prompt_layer_openai) * [Custom URLs](#custom-urls) 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/v0.2/docs/integrations/chat/openai	!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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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We now suggest using `model` instead of `modelName`, and `apiKey` for API keys. import { ChatOpenAI } from "@langchain/openai";import { HumanMessage } from "@langchain/core/messages";const model = new ChatOpenAI({ temperature: 0.9, apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY});// You can also pass tools or functions to the model, learn more here// https://platform.openai.com/docs/guides/gpt/function-callingconst modelForFunctionCalling = new ChatOpenAI({ model: "gpt-4", temperature: 0,});await modelForFunctionCalling.invoke( [new HumanMessage("What is the weather in New York?")], { functions: [ { name: "get_current_weather", description: "Get the current weather in a given location", parameters: { type: "object", properties: { location: { type: "string", description: "The city and state, e.g. San Francisco, CA", }, unit: { type: "string", enum: ["celsius", "fahrenheit"] }, }, required: ["location"], }, }, ], // You can set the `function_call` arg to force the model to use a function function_call: { name: "get_current_weather", }, });/AIMessage { text: '', name: undefined, additional_kwargs: { function_call: { name: 'get_current_weather', arguments: '{\n "location": "New York"\n}' } }}/// Coerce response type with JSON mode.// Requires "gpt-4-1106-preview" or laterconst jsonModeModel = new ChatOpenAI({ model: "gpt-4-1106-preview", maxTokens: 128,}).bind({ response_format: { type: "json_object", },});// Must be invoked with a system message containing the string "JSON":// https://platform.openai.com/docs/guides/text-generation/json-modeconst res = await jsonModeModel.invoke([ ["system", "Only return JSON"], ["human", "Hi there!"],]);console.log(res);/* AIMessage { content: '{\n "response": "How can I assist you today?"\n}', name: undefined, additional_kwargs: { function_call: undefined, tool_calls: undefined } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [HumanMessage](https://v02.api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` If you're part of an organization, you can set `process.env.OPENAI_ORGANIZATION` with your OpenAI organization id, or pass it in as `organization` when initializing the model. Multimodal messages[](#multimodal-messages "Direct link to Multimodal messages") --------------------------------------------------------------------------------- info This feature is currently in preview. The message schema may change in future releases. OpenAI supports interleaving images with text in input messages with their `gpt-4-vision-preview`. Here's an example of how this looks: import * as fs from "node:fs/promises";import { ChatOpenAI } from "@langchain/openai";import { HumanMessage } from "@langchain/core/messages";const imageData = await fs.readFile("./hotdog.jpg");const chat = new ChatOpenAI({ model: "gpt-4-vision-preview", maxTokens: 1024,});const message = new HumanMessage({ content: [ { type: "text", text: "What's in this image?", }, { type: "image_url", image_url: { url: `data:image/jpeg;base64,${imageData.toString("base64")}`, }, }, ],});const res = await chat.invoke([message]);console.log({ res });/* { res: AIMessage { content: 'The image shows a hot dog, which consists of a grilled or steamed sausage served in the slit of a partially sliced bun. This particular hot dog appears to be plain, without any visible toppings or condiments.', additional_kwargs: { function_call: undefined } } }/const hostedImageMessage = new HumanMessage({ content: [ { type: "text", text: "What does this image say?", }, { type: "image_url", image_url: "https://www.freecodecamp.org/news/content/images/2023/05/Screenshot-2023-05-29-at-5.40.38-PM.png", }, ],});const res2 = await chat.invoke([hostedImageMessage]);console.log({ res2 });/ { res2: AIMessage { content: 'The image contains the text "LangChain" with a graphical depiction of a parrot on the left and two interlocked rings on the left side of the text.', additional_kwargs: { function_call: undefined } } }/const lowDetailImage = new HumanMessage({ content: [ { type: "text", text: "Summarize the contents of this image.", }, { type: "image_url", image_url: { url: "https://blog.langchain.dev/content/images/size/w1248/format/webp/2023/10/Screenshot-2023-10-03-at-4.55.29-PM.png", detail: "low", }, }, ],});const res3 = await chat.invoke([lowDetailImage]);console.log({ res3 });/ { res3: AIMessage { content: 'The image shows a user interface for a service named "WebLangChain," which appears to be powered by "Twalv." It includes a text box with the prompt "Ask me anything about anything!" suggesting that users can enter questions on various topics. Below the text box, there are example questions that users might ask, such as "what is langchain?", "history of mesopotamia," "how to build a discord bot," "leonardo dicaprio girlfriend," "fun gift ideas for software engineers," "how does a prism separate light," and "what beer is best." The interface also includes a round blue button with a paper plane icon, presumably to submit the question. The overall theme of the image is dark with blue accents.', additional_kwargs: { function_call: undefined } } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [HumanMessage](https://v02.api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` Tool calling[](#tool-calling "Direct link to Tool calling") ------------------------------------------------------------ info This feature is currently only available for `gpt-3.5-turbo-1106` and `gpt-4-1106-preview` models. More recent OpenAI chat models support calling multiple functions to get all required data to answer a question. Here's an example how a conversation turn with this functionality might look: import { ChatOpenAI } from "@langchain/openai";import { ToolMessage } from "@langchain/core/messages";// Mocked out function, could be a database/API call in productionfunction getCurrentWeather(location: string, _unit?: string) { if (location.toLowerCase().includes("tokyo")) { return JSON.stringify({ location, temperature: "10", unit: "celsius" }); } else if (location.toLowerCase().includes("san francisco")) { return JSON.stringify({ location, temperature: "72", unit: "fahrenheit", }); } else { return JSON.stringify({ location, temperature: "22", unit: "celsius" }); }}// Bind function to the model as a toolconst chat = new ChatOpenAI({ model: "gpt-3.5-turbo-1106", maxTokens: 128,}).bind({ tools: [ { type: "function", function: { name: "get_current_weather", description: "Get the current weather in a given location", parameters: { type: "object", properties: { location: { type: "string", description: "The city and state, e.g. San Francisco, CA", }, unit: { type: "string", enum: ["celsius", "fahrenheit"] }, }, required: ["location"], }, }, }, ], tool_choice: "auto",});// Ask initial question that requires multiple tool callsconst res = await chat.invoke([ ["human", "What's the weather like in San Francisco, Tokyo, and Paris?"],]);console.log(res.additional_kwargs.tool_calls);/* [ { id: 'call_IiOsjIZLWvnzSh8iI63GieUB', type: 'function', function: { name: 'get_current_weather', arguments: '{"location": "San Francisco", "unit": "celsius"}' } }, { id: 'call_blQ3Oz28zSfvS6Bj6FPEUGA1', type: 'function', function: { name: 'get_current_weather', arguments: '{"location": "Tokyo", "unit": "celsius"}' } }, { id: 'call_Kpa7FaGr3F1xziG8C6cDffsg', type: 'function', function: { name: 'get_current_weather', arguments: '{"location": "Paris", "unit": "celsius"}' } } ]/// Format the results from calling the tool calls back to OpenAI as ToolMessagesconst toolMessages = res.additional_kwargs.tool_calls?.map((toolCall) => { const toolCallResult = getCurrentWeather( JSON.parse(toolCall.function.arguments).location ); return new ToolMessage({ tool_call_id: toolCall.id, name: toolCall.function.name, content: toolCallResult, });});// Send the results back as the next step in the conversationconst finalResponse = await chat.invoke([ ["human", "What's the weather like in San Francisco, Tokyo, and Paris?"], res, ...(toolMessages ?? []),]);console.log(finalResponse);/ AIMessage { content: 'The current weather in:\n' + '- San Francisco is 72°F\n' + '- Tokyo is 10°C\n' + '- Paris is 22°C', additional_kwargs: { function_call: undefined, tool_calls: undefined } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ToolMessage](https://v02.api.js.langchain.com/classes/langchain_core_messages_tool.ToolMessage.html) from `@langchain/core/messages` ### `.withStructuredOutput({ ... })`[](#withstructuredoutput-- "Direct link to withstructuredoutput--") info The `.withStructuredOutput` method is in beta. It is actively being worked on, so the API may change. You can also use the `.withStructuredOutput({ ... })` method to coerce `ChatOpenAI` into returning a structured output. The method allows for passing in either a Zod object, or a valid JSON schema (like what is returned from [`zodToJsonSchema`](https://www.npmjs.com/package/zod-to-json-schema)). Using the method is simple. Just define your LLM and call `.withStructuredOutput({ ... })` on it, passing the desired schema. Here is an example using a Zod schema and the `functionCalling` mode (default mode): import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI } from "@langchain/openai";import { z } from "zod";const model = new ChatOpenAI({ temperature: 0, model: "gpt-4-turbo-preview",});const calculatorSchema = z.object({ operation: z.enum(["add", "subtract", "multiply", "divide"]), number1: z.number(), number2: z.number(),});const modelWithStructuredOutput = model.withStructuredOutput(calculatorSchema);const prompt = ChatPromptTemplate.fromMessages([ ["system", "You are VERY bad at math and must always use a calculator."], ["human", "Please help me!! What is 2 + 2?"],]);const chain = prompt.pipe(modelWithStructuredOutput);const result = await chain.invoke({});console.log(result);/{ operation: 'add', number1: 2, number2: 2 } //** * You can also specify 'includeRaw' to return the parsed * and raw output in the result. /const includeRawModel = model.withStructuredOutput(calculatorSchema, { name: "calculator", includeRaw: true,});const includeRawChain = prompt.pipe(includeRawModel);const includeRawResult = await includeRawChain.invoke({});console.log(JSON.stringify(includeRawResult, null, 2));/{ "raw": { "kwargs": { "content": "", "additional_kwargs": { "tool_calls": [ { "id": "call_A8yzNBDMiRrCB8dFYqJLhYW7", "type": "function", "function": { "name": "calculator", "arguments": "{\"operation\":\"add\",\"number1\":2,\"number2\":2}" } } ] } } }, "parsed": { "operation": "add", "number1": 2, "number2": 2 }} / #### API Reference: [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` Additionally, you can pass in an OpenAI function definition or JSON schema directly: info If using `jsonMode` as the `method` you must include context in your prompt about the structured output you want. This _must_ include the keyword: `JSON`. import { ChatPromptTemplate } from "@langchain/core/prompts";import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({ temperature: 0, model: "gpt-4-turbo-preview",});const calculatorSchema = { type: "object", properties: { operation: { type: "string", enum: ["add", "subtract", "multiply", "divide"], }, number1: { type: "number" }, number2: { type: "number" }, }, required: ["operation", "number1", "number2"],};// Default mode is "functionCalling"const modelWithStructuredOutput = model.withStructuredOutput(calculatorSchema);const prompt = ChatPromptTemplate.fromMessages([ [ "system", `You are VERY bad at math and must always use a calculator.Respond with a JSON object containing three keys:'operation': the type of operation to execute, either 'add', 'subtract', 'multiply' or 'divide','number1': the first number to operate on,'number2': the second number to operate on.`, ], ["human", "Please help me!! What is 2 + 2?"],]);const chain = prompt.pipe(modelWithStructuredOutput);const result = await chain.invoke({});console.log(result);/{ operation: 'add', number1: 2, number2: 2 } //** * You can also specify 'includeRaw' to return the parsed * and raw output in the result, as well as a "name" field * to give the LLM additional context as to what you are generating. /const includeRawModel = model.withStructuredOutput(calculatorSchema, { name: "calculator", includeRaw: true, method: "jsonMode",});const includeRawChain = prompt.pipe(includeRawModel);const includeRawResult = await includeRawChain.invoke({});console.log(JSON.stringify(includeRawResult, null, 2));/{ "raw": { "kwargs": { "content": "{\n \"operation\": \"add\",\n \"number1\": 2,\n \"number2\": 2\n}", "additional_kwargs": {} } }, "parsed": { "operation": "add", "number1": 2, "number2": 2 }} / #### API Reference: [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` Custom URLs[](#custom-urls "Direct link to Custom URLs") --------------------------------------------------------- You can customize the base URL the SDK sends requests to by passing a `configuration` parameter like this: import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({ temperature: 0.9, configuration: { baseURL: "https://your_custom_url.com", },});const message = await model.invoke("Hi there!");console.log(message);/* AIMessage { content: 'Hello! How can I assist you today?', additional_kwargs: { function_call: undefined } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` You can also pass other `ClientOptions` parameters accepted by the official SDK. If you are hosting on Azure OpenAI, see the [dedicated page instead](/v0.2/docs/integrations/chat/azure). Calling fine-tuned models[](#calling-fine-tuned-models "Direct link to Calling fine-tuned models") --------------------------------------------------------------------------------------------------- You can call fine-tuned OpenAI models by passing in your corresponding `modelName` parameter. This generally takes the form of `ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}`. For example: import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({ temperature: 0.9, model: "ft:gpt-3.5-turbo-0613:{ORG_NAME}::{MODEL_ID}",});const message = await model.invoke("Hi there!");console.log(message);/* AIMessage { content: 'Hello! How can I assist you today?', additional_kwargs: { function_call: undefined } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` Generation metadata[](#generation-metadata "Direct link to Generation metadata") --------------------------------------------------------------------------------- If you need additional information like logprobs or token usage, these will be returned directly in the `.invoke` response. tip Requires `@langchain/core` version >=0.1.48. import { ChatOpenAI } from "@langchain/openai";// See https://cookbook.openai.com/examples/using_logprobs for detailsconst model = new ChatOpenAI({ logprobs: true, // topLogprobs: 5,});const responseMessage = await model.invoke("Hi there!");console.log(JSON.stringify(responseMessage, null, 2));/* { "lc": 1, "type": "constructor", "id": [ "langchain_core", "messages", "AIMessage" ], "kwargs": { "content": "Hello! How can I assist you today?", "additional_kwargs": {}, "response_metadata": { "tokenUsage": { "completionTokens": 9, "promptTokens": 10, "totalTokens": 19 }, "finish_reason": "stop", "logprobs": { "content": [ { "token": "Hello", "logprob": -0.0006793116, "bytes": [ 72, 101, 108, 108, 111 ], "top_logprobs": [] }, { "token": "!", "logprob": -0.00011725161, "bytes": [ 33 ], "top_logprobs": [] }, { "token": " How", "logprob": -0.000038457987, "bytes": [ 32, 72, 111, 119 ], "top_logprobs": [] }, { "token": " can", "logprob": -0.00094290765, "bytes": [ 32, 99, 97, 110 ], "top_logprobs": [] }, { "token": " I", "logprob": -0.0000013856493, "bytes": [ 32, 73 ], "top_logprobs": [] }, { "token": " assist", "logprob": -0.14702488, "bytes": [ 32, 97, 115, 115, 105, 115, 116 ], "top_logprobs": [] }, { "token": " you", "logprob": -0.000001147242, "bytes": [ 32, 121, 111, 117 ], "top_logprobs": [] }, { "token": " today", "logprob": -0.000067901296, "bytes": [ 32, 116, 111, 100, 97, 121 ], "top_logprobs": [] }, { "token": "?", "logprob": -0.000014974867, "bytes": [ 63 ], "top_logprobs": [] } ] } } } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` ### With callbacks[](#with-callbacks "Direct link to With callbacks") You can also use the callbacks system: import { ChatOpenAI } from "@langchain/openai";// See https://cookbook.openai.com/examples/using_logprobs for detailsconst model = new ChatOpenAI({ logprobs: true, // topLogprobs: 5,});const result = await model.invoke("Hi there!", { callbacks: [ { handleLLMEnd(output) { console.log("GENERATION OUTPUT:", JSON.stringify(output, null, 2)); }, }, ],});console.log("FINAL OUTPUT", result);/* GENERATION OUTPUT: { "generations": [ [ { "text": "Hello! How can I assist you today?", "message": { "lc": 1, "type": "constructor", "id": [ "langchain_core", "messages", "AIMessage" ], "kwargs": { "content": "Hello! How can I assist you today?", "additional_kwargs": {} } }, "generationInfo": { "finish_reason": "stop", "logprobs": { "content": [ { "token": "Hello", "logprob": -0.0010195904, "bytes": [ 72, 101, 108, 108, 111 ], "top_logprobs": [] }, { "token": "!", "logprob": -0.0004447316, "bytes": [ 33 ], "top_logprobs": [] }, { "token": " How", "logprob": -0.00006682846, "bytes": [ 32, 72, 111, 119 ], "top_logprobs": [] }, ... ] } } } ] ], "llmOutput": { "tokenUsage": { "completionTokens": 9, "promptTokens": 10, "totalTokens": 19 } } } FINAL OUTPUT AIMessage { content: 'Hello! How can I assist you today?', additional_kwargs: { function_call: undefined, tool_calls: undefined } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` ### With `.generate()`[](#with-generate "Direct link to with-generate") import { ChatOpenAI } from "@langchain/openai";import { HumanMessage } from "@langchain/core/messages";// See https://cookbook.openai.com/examples/using_logprobs for detailsconst model = new ChatOpenAI({ logprobs: true, // topLogprobs: 5,});const generations = await model.invoke([new HumanMessage("Hi there!")]);console.log(JSON.stringify(generations, null, 2));/* { "generations": [ [ { "text": "Hello! How can I assist you today?", "message": { "lc": 1, "type": "constructor", "id": [ "langchain_core", "messages", "AIMessage" ], "kwargs": { "content": "Hello! How can I assist you today?", "additional_kwargs": {} } }, "generationInfo": { "finish_reason": "stop", "logprobs": { "content": [ { "token": "Hello", "logprob": -0.0011337858, "bytes": [ 72, 101, 108, 108, 111 ], "top_logprobs": [] }, { "token": "!", "logprob": -0.00044127836, "bytes": [ 33 ], "top_logprobs": [] }, { "token": " How", "logprob": -0.000065994034, "bytes": [ 32, 72, 111, 119 ], "top_logprobs": [] }, ... ] } } } ] ], "llmOutput": { "tokenUsage": { "completionTokens": 9, "promptTokens": 10, "totalTokens": 19 } } }/ #### API Reference: [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [HumanMessage](https://v02.api.js.langchain.com/classes/langchain_core_messages.HumanMessage.html) from `@langchain/core/messages` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Ollama Functions ](/v0.2/docs/integrations/chat/ollama_functions)[ Next PremAI ](/v0.2/docs/integrations/chat/premai) * [Multimodal messages](#multimodal-messages) * [Tool calling](#tool-calling) * [`.withStructuredOutput({ ... })`](#withstructuredoutput--) * [Custom URLs](#custom-urls) * [Calling fine-tuned models](#calling-fine-tuned-models) * [Generation metadata](#generation-metadata) * [With callbacks](#with-callbacks) * [With `.generate()`](#with-generate) 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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://v02.api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [RequestsGetTool](https://v02.api.js.langchain.com/classes/langchain_tools.RequestsGetTool.html) from `langchain/tools` * [RequestsPostTool](https://v02.api.js.langchain.com/classes/langchain_tools.RequestsPostTool.html) from `langchain/tools` * [AIPluginTool](https://v02.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] ] }} * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Tools ](/v0.2/docs/integrations/tools)[ Next Connery Action Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). 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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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.DallEAPIWrapper.html) from `@langchain/openai` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Connery Action Tool ](/v0.2/docs/integrations/tools/connery)[ Next Discord Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_community_tools_connery.ConneryService.html) from `@langchain/community/tools/connery` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://v02.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. * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous ChatGPT Plugins ](/v0.2/docs/integrations/tools/aiplugin-tool)[ Next Dall-E Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_community_tools_discord.DiscordGetMessagesTool.html) from `@langchain/community/tools/discord` * [DiscordChannelSearchTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_discord.DiscordChannelSearchTool.html) from `@langchain/community/tools/discord` * [DiscordSendMessagesTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_discord.DiscordSendMessagesTool.html) from `@langchain/community/tools/discord` * [DiscordGetGuildsTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_discord.DiscordGetGuildsTool.html) from `@langchain/community/tools/discord` * [DiscordGetTextChannelsTool](https://v02.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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://v02.api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [DiscordSendMessagesTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_discord.DiscordSendMessagesTool.html) from `@langchain/community/tools/discord` * [DadJokeAPI](https://v02.api.js.langchain.com/classes/langchain_community_tools_dadjokeapi.DadJokeAPI.html) from `@langchain/community/tools/dadjokeapi` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Dall-E Tool ](/v0.2/docs/integrations/tools/dalle)[ Next DuckDuckGoSearch ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.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://v02.api.js.langchain.com/classes/langchain_community_tools_duckduckgo_search.DuckDuckGoSearch.html) from `@langchain/community/tools/duckduckgo_search` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [pull](https://v02.api.js.langchain.com/functions/langchain_hub.pull.html) from `langchain/hub` * [AgentExecutor](https://v02.api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://v02.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) * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Discord Tool ](/v0.2/docs/integrations/tools/discord)[ Next Exa Search ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_exa.ExaSearchResults.html) from `@langchain/exa` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [pull](https://v02.api.js.langchain.com/functions/langchain_hub.pull.html) from `langchain/hub` * [AgentExecutor](https://v02.api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://v02.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`](/v0.2/docs/integrations/retrievers/exa) and the [`createRetrieverTool`](https://v02.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://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [MessagesPlaceholder](https://v02.api.js.langchain.com/classes/langchain_core_prompts.MessagesPlaceholder.html) from `@langchain/core/prompts` * [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://v02.api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [createOpenAIFunctionsAgent](https://v02.api.js.langchain.com/functions/langchain_agents.createOpenAIFunctionsAgent.html) from `langchain/agents` * [createRetrieverTool](https://v02.api.js.langchain.com/functions/langchain_tools_retriever.createRetrieverTool.html) from `langchain/tools/retriever` * [ExaRetriever](https://v02.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). * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous DuckDuckGoSearch ](/v0.2/docs/integrations/tools/duckduckgo_search)[ Next Gmail Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [OpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [GmailCreateDraft](https://v02.api.js.langchain.com/classes/langchain_community_tools_gmail.GmailCreateDraft.html) from `@langchain/community/tools/gmail` * [GmailGetMessage](https://v02.api.js.langchain.com/classes/langchain_community_tools_gmail.GmailGetMessage.html) from `@langchain/community/tools/gmail` * [GmailGetThread](https://v02.api.js.langchain.com/classes/langchain_community_tools_gmail.GmailGetThread.html) from `@langchain/community/tools/gmail` * [GmailSearch](https://v02.api.js.langchain.com/classes/langchain_community_tools_gmail.GmailSearch.html) from `@langchain/community/tools/gmail` * [GmailSendMessage](https://v02.api.js.langchain.com/classes/langchain_community_tools_gmail.GmailSendMessage.html) from `@langchain/community/tools/gmail` * [StructuredTool](https://v02.api.js.langchain.com/classes/langchain_core_tools.StructuredTool.html) from `@langchain/core/tools` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Exa Search ](/v0.2/docs/integrations/tools/exa_search)[ Next Google Calendar Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * [OpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [Calculator](https://v02.api.js.langchain.com/classes/langchain_community_tools_calculator.Calculator.html) from `@langchain/community/tools/calculator` * [GoogleCalendarCreateTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_google_calendar.GoogleCalendarCreateTool.html) from `@langchain/community/tools/google_calendar` * [GoogleCalendarViewTool](https://v02.api.js.langchain.com/classes/langchain_community_tools_google_calendar.GoogleCalendarViewTool.html) from `@langchain/community/tools/google_calendar` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Gmail Tool ](/v0.2/docs/integrations/tools/gmail)[ Next Google Places Tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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); * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Google Places Tool ](/v0.2/docs/integrations/tools/google_places)[ Next Python interpreter tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_community_tools_google_places.GooglePlacesAPI.html) from `@langchain/community/tools/google_places` * [OpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [initializeAgentExecutorWithOptions](https://v02.api.js.langchain.com/functions/langchain_agents.initializeAgentExecutorWithOptions.html) from `langchain/agents` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Google Calendar Tool ](/v0.2/docs/integrations/tools/google_calendar)[ Next Agent with AWS Lambda ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai` * [PythonInterpreterTool](https://v02.api.js.langchain.com/classes/langchain_experimental_tools_pyinterpreter.PythonInterpreterTool.html) from `langchain/experimental/tools/pyinterpreter` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [StringOutputParser](https://v02.api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Agent with AWS Lambda ](/v0.2/docs/integrations/tools/lambda_agent)[ Next SearchApi tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://v02.api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [RunnableSequence](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables` * [AgentFinish](https://v02.api.js.langchain.com/types/langchain_core_agents.AgentFinish.html) from `@langchain/core/agents` * [AgentAction](https://v02.api.js.langchain.com/types/langchain_core_agents.AgentAction.html) from `@langchain/core/agents` * [BaseMessageChunk](https://v02.api.js.langchain.com/classes/langchain_core_messages.BaseMessageChunk.html) from `@langchain/core/messages` * [SearchApi](https://v02.api.js.langchain.com/classes/langchain_community_tools_searchapi.SearchApi.html) from `@langchain/community/tools/searchapi` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous Python interpreter tool ](/v0.2/docs/integrations/tools/pyinterpreter)[ Next Searxng Search tool ](/v0.2/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/v0.2/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) You are viewing the preview v0.2 docs. View the stable v0.1 docs [here](/v0.1/docs/get_started/introduction/). Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchainjs/discussions/5386). [ ![🦜️🔗 Langchain](/v0.2/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.2/img/brand/wordmark-dark.png) ](/v0.2/)[Integrations](/v0.2/docs/integrations/platforms/)[API Reference](https://v02.api.js.langchain.com) [More](#) * [People](/v0.2/docs/people/) * [Community](/v0.2/docs/community) * [Tutorials](/v0.2/docs/additional_resources/tutorials) * [Contributing](/v0.2/docs/contributing) [v0.2](#) * [v0.2](/v0.2/docs/introduction) * [v0.1](https://js.langchain.com/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](/v0.2/docs/integrations/platforms/) * [Providers](/v0.2/docs/integrations/platforms/) * [Anthropic](/v0.2/docs/integrations/platforms/anthropic) * [AWS](/v0.2/docs/integrations/platforms/aws) * [Google](/v0.2/docs/integrations/platforms/google) * [Microsoft](/v0.2/docs/integrations/platforms/microsoft) * [OpenAI](/v0.2/docs/integrations/platforms/openai) * [Components](/v0.2/docs/integrations/components) * [Chat models](/v0.2/docs/integrations/chat/) * [LLMs](/v0.2/docs/integrations/llms/) * [Embedding models](/v0.2/docs/integrations/text_embedding) * [Document loaders](/v0.2/docs/integrations/document_loaders) * [Document transformers](/v0.2/docs/integrations/document_transformers) * [Vector stores](/v0.2/docs/integrations/vectorstores) * [Retrievers](/v0.2/docs/integrations/retrievers) * [Tools](/v0.2/docs/integrations/tools) * [ChatGPT Plugins](/v0.2/docs/integrations/tools/aiplugin-tool) * [Connery Action Tool](/v0.2/docs/integrations/tools/connery) * [Dall-E Tool](/v0.2/docs/integrations/tools/dalle) * [Discord Tool](/v0.2/docs/integrations/tools/discord) * [DuckDuckGoSearch](/v0.2/docs/integrations/tools/duckduckgo_search) * [Exa Search](/v0.2/docs/integrations/tools/exa_search) * [Gmail Tool](/v0.2/docs/integrations/tools/gmail) * [Google Calendar Tool](/v0.2/docs/integrations/tools/google_calendar) * [Google Places Tool](/v0.2/docs/integrations/tools/google_places) * [Agent with AWS Lambda](/v0.2/docs/integrations/tools/lambda_agent) * [Python interpreter tool](/v0.2/docs/integrations/tools/pyinterpreter) * [SearchApi tool](/v0.2/docs/integrations/tools/searchapi) * [Searxng Search tool](/v0.2/docs/integrations/tools/searxng) * [StackExchange Tool](/v0.2/docs/integrations/tools/stackexchange) * [Tavily Search](/v0.2/docs/integrations/tools/tavily_search) * [Web Browser Tool](/v0.2/docs/integrations/tools/webbrowser) * [Wikipedia tool](/v0.2/docs/integrations/tools/wikipedia) * [WolframAlpha Tool](/v0.2/docs/integrations/tools/wolframalpha) * [Agent with Zapier NLA Integration](/v0.2/docs/integrations/tools/zapier_agent) * [Toolkits](/v0.2/docs/integrations/toolkits) * [Stores](/v0.2/docs/integrations/stores/) * [](/v0.2/) * [Components](/v0.2/docs/integrations/components) * [Tools](/v0.2/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](/v0.2/docs/how_to/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://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai` * [AgentExecutor](https://v02.api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents` * [BaseMessageChunk](https://v02.api.js.langchain.com/classes/langchain_core_messages.BaseMessageChunk.html) from `@langchain/core/messages` * [AgentAction](https://v02.api.js.langchain.com/types/langchain_core_agents.AgentAction.html) from `@langchain/core/agents` * [AgentFinish](https://v02.api.js.langchain.com/types/langchain_core_agents.AgentFinish.html) from `@langchain/core/agents` * [RunnableSequence](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables` * [ChatPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts` * [SearxngSearch](https://v02.api.js.langchain.com/classes/langchain_community_tools_searxng_search.SearxngSearch.html) from `@langchain/community/tools/searxng_search` * * * #### Was this page helpful? #### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E). [ Previous SearchApi tool ](/v0.2/docs/integrations/tools/searchapi)[ Next StackExchange Tool ](/v0.2/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.