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        *   [Memory](/v0.2/docs/integrations/vectorstores/memory)
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*   [](/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`

* * *

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*   [Setup](#setup)
    *   [Create project](#create-project)
    *   [Add database accessors](#add-database-accessors)
    *   [Configure your schema](#configure-your-schema)
*   [Usage](#usage)
    *   [Ingestion](#ingestion)
    *   [Search](#search)

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