> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# Qdrant 集成

> 使用 LangChain Python 与 Qdrant 集成。

> [Qdrant](https://qdrant.tech/documentation/)（读作：quadrant）是一个向量相似度搜索引擎。
> 它提供了一个生产就绪的服务，拥有便捷的 API 用于存储、搜索和管理
> 点——带有额外负载的向量。`Qdrant` 专为扩展过滤支持而设计。

## 安装和设置

安装 Python 合作伙伴包：

<CodeGroup>
  ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  pip install langchain-qdrant
  ```

  ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
  uv add langchain-qdrant
  ```
</CodeGroup>

## 嵌入模型

### FastEmbedSparse

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_qdrant import FastEmbedSparse
```

### SparseEmbeddings

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_qdrant import SparseEmbeddings
```

## 向量存储

存在一个围绕 `Qdrant` 索引的包装器，允许您将其用作向量存储，
无论是用于语义搜索还是示例选择。

要导入此向量存储：

```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_qdrant import QdrantVectorStore
```

有关 Qdrant 包装器的更详细教程，请参阅 [此笔记本](/oss/python/integrations/vectorstores/qdrant)

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