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*   Qdrant

On this page

Qdrant
======

[Qdrant](https://qdrant.tech/) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload.

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

1.  Run a Qdrant instance with Docker on your computer by following the [Qdrant setup instructions](https://qdrant.tech/documentation/quick-start/).
    
2.  Install the Qdrant Node.js SDK.
    
    *   npm
    *   Yarn
    *   pnpm
    
        npm install -S @langchain/qdrant
    
        yarn add @langchain/qdrant
    
        pnpm add @langchain/qdrant
    
3.  Setup Env variables for Qdrant before running the code
    
        export OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HEREexport QDRANT_URL=YOUR_QDRANT_URL_HERE # for example http://localhost:6333
    

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

### Create a new index from texts[​](#create-a-new-index-from-texts "Direct link to Create a new index from texts")

tip

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

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

    import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";// text sample from Godel, Escher, Bachconst vectorStore = await QdrantVectorStore.fromTexts(  [    `Tortoise: Labyrinth? Labyrinth? Could it Are we in the notorious LittleHarmonic Labyrinth of the dreaded Majotaur?`,    `Achilles: Yiikes! What is that?`,    `Tortoise: They say-although I person never believed it myself-that an I            Majotaur has created a tiny labyrinth sits in a pit in the middle of            it, waiting innocent victims to get lost in its fears complexity.            Then, when they wander and dazed into the center, he laughs and            laughs at them-so hard, that he laughs them to death!`,    `Achilles: Oh, no!`,    `Tortoise: But it's only a myth. Courage, Achilles.`,  ],  [{ id: 2 }, { id: 1 }, { id: 3 }, { id: 4 }, { id: 5 }],  new OpenAIEmbeddings(),  {    url: process.env.QDRANT_URL,    collectionName: "goldel_escher_bach",  });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },  Document {    pageContent: 'Achilles: Yiikes! What is that?',    metadata: { id: 1 }  }]*/

#### API Reference:

*   [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Create a new index from docs[​](#create-a-new-index-from-docs "Direct link to Create a new index from docs")

    import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();const vectorStore = await QdrantVectorStore.fromDocuments(  docs,  new OpenAIEmbeddings(),  {    url: process.env.QDRANT_URL,    collectionName: "a_test_collection",  });// Search for the most similar documentconst response = await vectorStore.similaritySearch("hello", 1);console.log(response);/*[  Document {    pageContent: 'Foo\nBar\nBaz\n\n',    metadata: { source: 'src/document_loaders/example_data/example.txt' }  }]*/

#### API Reference:

*   [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [TextLoader](https://api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`

### Query docs from existing collection[​](#query-docs-from-existing-collection "Direct link to Query docs from existing collection")

    import { QdrantVectorStore } from "@langchain/qdrant";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await QdrantVectorStore.fromExistingCollection(  new OpenAIEmbeddings(),  {    url: process.env.QDRANT_URL,    collectionName: "goldel_escher_bach",  });const response = await vectorStore.similaritySearch("scared", 2);console.log(response);/*[  Document { pageContent: 'Achilles: Oh, no!', metadata: {} },  Document {    pageContent: 'Achilles: Yiikes! What is that?',    metadata: { id: 1 }  }]*/

#### API Reference:

*   [QdrantVectorStore](https://api.js.langchain.com/classes/langchain_qdrant.QdrantVectorStore.html) from `@langchain/qdrant`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

* * *

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*   [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)

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