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*   [Providers](/v0.1/docs/integrations/platforms/)
    
    *   [Providers](/v0.1/docs/integrations/platforms/)
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    *   [Text embedding models](/v0.1/docs/integrations/text_embedding/)
        
    *   [Vector stores](/v0.1/docs/integrations/vectorstores/)
        
        *   [Memory](/v0.1/docs/integrations/vectorstores/memory/)
        *   [AnalyticDB](/v0.1/docs/integrations/vectorstores/analyticdb/)
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*   [](/v0.1/)
*   [Components](/v0.1/docs/integrations/components/)
*   [Vector stores](/v0.1/docs/integrations/vectorstores/)
*   Weaviate

Weaviate
========

Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering. LangChain connects to Weaviate via the `weaviate-ts-client` package, the official Typescript client for Weaviate.

LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

Weaviate has their own standalone integration package with LangChain, accessible via [`@langchain/weaviate`](https://www.npmjs.com/package/@langchain/weaviate) on NPM!

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/weaviate @langchain/openai @langchain/community

    yarn add @langchain/weaviate @langchain/openai @langchain/community

    pnpm add @langchain/weaviate @langchain/openai @langchain/community

You'll need to run Weaviate either locally or on a server, see [the Weaviate documentation](https://weaviate.io/developers/weaviate/installation) for more information.

Usage, insert documents[​](#usage-insert-documents "Direct link to Usage, insert documents")
--------------------------------------------------------------------------------------------

    /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate, { ApiKey } from "weaviate-ts-client";import { WeaviateStore } from "@langchain/weaviate";import { OpenAIEmbeddings } from "@langchain/openai";export async function run() {  // Something wrong with the weaviate-ts-client types, so we need to disable  const client = (weaviate as any).client({    scheme: process.env.WEAVIATE_SCHEME || "https",    host: process.env.WEAVIATE_HOST || "localhost",    apiKey: new ApiKey(process.env.WEAVIATE_API_KEY || "default"),  });  // Create a store and fill it with some texts + metadata  await WeaviateStore.fromTexts(    ["hello world", "hi there", "how are you", "bye now"],    [{ foo: "bar" }, { foo: "baz" }, { foo: "qux" }, { foo: "bar" }],    new OpenAIEmbeddings(),    {      client,      indexName: "Test",      textKey: "text",      metadataKeys: ["foo"],    }  );}

#### API Reference:

*   [WeaviateStore](https://api.js.langchain.com/classes/langchain_weaviate.WeaviateStore.html) from `@langchain/weaviate`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

Usage, query documents[​](#usage-query-documents "Direct link to Usage, query documents")
-----------------------------------------------------------------------------------------

    /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate, { ApiKey } from "weaviate-ts-client";import { WeaviateStore } from "@langchain/weaviate";import { OpenAIEmbeddings } from "@langchain/openai";export async function run() {  // Something wrong with the weaviate-ts-client types, so we need to disable  const client = (weaviate as any).client({    scheme: process.env.WEAVIATE_SCHEME || "https",    host: process.env.WEAVIATE_HOST || "localhost",    apiKey: new ApiKey(process.env.WEAVIATE_API_KEY || "default"),  });  // Create a store for an existing index  const store = await WeaviateStore.fromExistingIndex(new OpenAIEmbeddings(), {    client,    indexName: "Test",    metadataKeys: ["foo"],  });  // Search the index without any filters  const results = await store.similaritySearch("hello world", 1);  console.log(results);  /*  [ Document { pageContent: 'hello world', metadata: { foo: 'bar' } } ]  */  // Search the index with a filter, in this case, only return results where  // the "foo" metadata key is equal to "baz", see the Weaviate docs for more  // https://weaviate.io/developers/weaviate/api/graphql/filters  const results2 = await store.similaritySearch("hello world", 1, {    where: {      operator: "Equal",      path: ["foo"],      valueText: "baz",    },  });  console.log(results2);  /*  [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ]  */}

#### API Reference:

*   [WeaviateStore](https://api.js.langchain.com/classes/langchain_weaviate.WeaviateStore.html) from `@langchain/weaviate`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

Usage, maximal marginal relevance[​](#usage-maximal-marginal-relevance "Direct link to Usage, maximal marginal relevance")
--------------------------------------------------------------------------------------------------------------------------

You can use maximal marginal relevance search, which optimizes for similarity to the query AND diversity.

    /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate, { ApiKey } from "weaviate-ts-client";import { WeaviateStore } from "@langchain/weaviate";import { OpenAIEmbeddings } from "@langchain/openai";export async function run() {  // Something wrong with the weaviate-ts-client types, so we need to disable  const client = (weaviate as any).client({    scheme: process.env.WEAVIATE_SCHEME || "https",    host: process.env.WEAVIATE_HOST || "localhost",    apiKey: new ApiKey(process.env.WEAVIATE_API_KEY || "default"),  });  // Create a store for an existing index  const store = await WeaviateStore.fromExistingIndex(new OpenAIEmbeddings(), {    client,    indexName: "Test",    metadataKeys: ["foo"],  });  const resultOne = await store.maxMarginalRelevanceSearch("Hello world", {    k: 1,  });  console.log(resultOne);}

#### API Reference:

*   [WeaviateStore](https://api.js.langchain.com/classes/langchain_weaviate.WeaviateStore.html) from `@langchain/weaviate`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

Usage, delete documents[​](#usage-delete-documents "Direct link to Usage, delete documents")
--------------------------------------------------------------------------------------------

    /* eslint-disable @typescript-eslint/no-explicit-any */import weaviate, { ApiKey } from "weaviate-ts-client";import { WeaviateStore } from "@langchain/weaviate";import { OpenAIEmbeddings } from "@langchain/openai";export async function run() {  // Something wrong with the weaviate-ts-client types, so we need to disable  const client = (weaviate as any).client({    scheme: process.env.WEAVIATE_SCHEME || "https",    host: process.env.WEAVIATE_HOST || "localhost",    apiKey: new ApiKey(process.env.WEAVIATE_API_KEY || "default"),  });  // Create a store for an existing index  const store = await WeaviateStore.fromExistingIndex(new OpenAIEmbeddings(), {    client,    indexName: "Test",    metadataKeys: ["foo"],  });  const docs = [{ pageContent: "see ya!", metadata: { foo: "bar" } }];  // Also supports an additional {ids: []} parameter for upsertion  const ids = await store.addDocuments(docs);  // Search the index without any filters  const results = await store.similaritySearch("see ya!", 1);  console.log(results);  /*  [ Document { pageContent: 'see ya!', metadata: { foo: 'bar' } } ]  */  // Delete documents with ids  await store.delete({ ids });  const results2 = await store.similaritySearch("see ya!", 1);  console.log(results2);  /*  []  */  const docs2 = [    { pageContent: "hello world", metadata: { foo: "bar" } },    { pageContent: "hi there", metadata: { foo: "baz" } },    { pageContent: "how are you", metadata: { foo: "qux" } },    { pageContent: "hello world", metadata: { foo: "bar" } },    { pageContent: "bye now", metadata: { foo: "bar" } },  ];  await store.addDocuments(docs2);  const results3 = await store.similaritySearch("hello world", 1);  console.log(results3);  /*  [ Document { pageContent: 'hello world', metadata: { foo: 'bar' } } ]  */  // delete documents with filter  await store.delete({    filter: {      where: {        operator: "Equal",        path: ["foo"],        valueText: "bar",      },    },  });  const results4 = await store.similaritySearch("hello world", 1, {    where: {      operator: "Equal",      path: ["foo"],      valueText: "bar",    },  });  console.log(results4);  /*  []  */}

#### API Reference:

*   [WeaviateStore](https://api.js.langchain.com/classes/langchain_weaviate.WeaviateStore.html) from `@langchain/weaviate`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

* * *

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