> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# QdrantVectorStore integration
> Integrate with the QdrantVectorStore using LangChain JavaScript.
**Compatibility**: Only available on Node.js.
[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).
This guide provides a quick overview for getting started with Qdrant [vector stores](/oss/javascript/integrations/vectorstores). For detailed documentation of all `QdrantVectorStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-qdrant/QdrantVectorStore).
## Overview
### Integration details
| Class | Package | [PY support](https://python.langchain.com/docs/integrations/vectorstores/qdrant/) | Downloads | Version |
| :--------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :-------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: |
| [`QdrantVectorStore`](https://reference.langchain.com/javascript/langchain-qdrant/QdrantVectorStore) | [`@langchain/qdrant`](https://www.npmjs.com/package/@langchain/qdrant) | ✅ |  |  |
## Setup
To use Qdrant vector stores, set up a Qdrant instance and install `@langchain/qdrant` and `@langchain/core`. The `@langchain/qdrant` package bundles the Qdrant REST client (`@qdrant/js-client-rest`).
This guide uses [OpenAI embeddings](/oss/javascript/integrations/embeddings/openai) as an example. You can use [other supported embeddings models](/oss/javascript/integrations/embeddings) instead.
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm install @langchain/qdrant @langchain/core @langchain/openai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/qdrant @langchain/core @langchain/openai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/qdrant @langchain/core @langchain/openai
```
After installing the required dependencies, run a Qdrant instance with Docker on your computer by following the [Qdrant setup instructions](https://qdrant.tech/documentation/quickstart/). Note the URL your container runs on.
### Credentials
Set a `QDRANT_URL` environment variable:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// e.g. http://localhost:6333
process.env.QDRANT_URL = "your-qdrant-url"
```
If you are using OpenAI embeddings for this guide, set your OpenAI key as well:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
```
If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"
```
## Instantiation
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { QdrantVectorStore } from "@langchain/qdrant";
import { OpenAIEmbeddings } from "@langchain/openai";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const vectorStore = await QdrantVectorStore.fromExistingCollection(embeddings, {
url: process.env.QDRANT_URL,
collectionName: "langchainjs-testing",
});
```
## Manage vector store
### Add items to vector store
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import type { Document } from "@langchain/core/documents";
const document1: Document = {
pageContent: "The powerhouse of the cell is the mitochondria",
metadata: { source: "https://example.com" }
};
const document2: Document = {
pageContent: "Buildings are made out of brick",
metadata: { source: "https://example.com" }
};
const document3: Document = {
pageContent: "Mitochondria are made out of lipids",
metadata: { source: "https://example.com" }
};
const document4: Document = {
pageContent: "The 2024 Olympics are in Paris",
metadata: { source: "https://example.com" }
}
const documents = [document1, document2, document3, document4];
await vectorStore.addDocuments(documents);
```
Top-level document ids and deletion are currently not supported.
## Query vector store
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
### Query directly
Performing a simple similarity search can be done as follows:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const filter = {
"must": [
{ "key": "metadata.source", "match": { "value": "https://example.com" } },
]
};
const similaritySearchResults = await vectorStore.similaritySearch("biology", 2, filter);
for (const doc of similaritySearchResults) {
console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]
```
See [this page](https://qdrant.tech/documentation/concepts/filtering/) for more on Qdrant filter syntax. Note that all values must be prefixed with `metadata.`
If you want to execute a similarity search and receive the corresponding scores you can run:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore("biology", 2, filter)
for (const [doc, score] of similaritySearchWithScoreResults) {
console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);
}
```
```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
* [SIM=0.165] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.148] Mitochondria are made out of lipids [{"source":"https://example.com"}]
```
### Query by turning into retriever
You can also transform the vector store into a [retriever](/oss/javascript/langchain/retrieval) for easier usage in your chains.
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
const retriever = vectorStore.asRetriever({
// Optional filter
filter: filter,
k: 2,
});
await retriever.invoke("biology");
```
```javascript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
[
Document {
pageContent: 'The powerhouse of the cell is the mitochondria',
metadata: { source: 'https://example.com' },
id: undefined
},
Document {
pageContent: 'Mitochondria are made out of lipids',
metadata: { source: 'https://example.com' },
id: undefined
}
]
```
### Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
* [Build a RAG app with LangChain](/oss/javascript/langchain/rag).
* [Agentic RAG](/oss/javascript/langgraph/agentic-rag)
* [Retrieval docs](/oss/javascript/langchain/retrieval)
***
## API reference
For detailed documentation of all `QdrantVectorStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-qdrant/QdrantVectorStore).
***
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