> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# QdrantVectorStore 集成
> 使用 LangChain JavaScript 集成 QdrantVectorStore。
**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).
## 概述
### 集成详情
| Class | Package | [PY support](https://python.langchain.com/docs/integrations/vectorstores/qdrant/) | Version |
| :--------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- | :-------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: |
| [`QdrantVectorStore`](https://reference.langchain.com/javascript/langchain-qdrant/QdrantVectorStore) | [`@langchain/qdrant`](https://npmjs.com/@langchain/qdrant) | ✅ |  |
## 设置
To use Qdrant vector stores, you'll need to set up a Qdrant instance and install the `@langchain/qdrant` integration package.
This guide will also use [OpenAI embeddings](/oss/javascript/integrations/embeddings/openai), which require you to install the `@langchain/openai` integration package. You can also use [other supported embeddings models](/oss/javascript/integrations/embeddings) if you wish.
```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.
### 凭证
Once you've done this 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, you'll need to set your OpenAI key as well:
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
process.env.OPENAI_API_KEY = "YOUR_API_KEY";
```
如果你想要自动追踪模型调用，还可以设置你的 [LangSmith](/langsmith/home) API 密钥，取消注释以下内容：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"
```
## 实例化
```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 参考
有关所有 `QdrantVectorStore` 功能和配置的详细文档，请前往 [API 参考](https://reference.langchain.com/javascript/langchain-qdrant/QdrantVectorStore)。
***
[将这些文档连接到](/use-these-docs) Claude、VSCode 等工具，通过 MCP 获取实时答案。
[在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/vectorstores/qdrant.mdx) 或 [提交 issue](https://github.com/langchain-ai/docs/issues/new/choose)。