> ## 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.

# Qdrant 集成

> 使用 LangChain Python 与 Qdrant 集成。

> [Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine.
> 它提供 a production-ready service with a convenient API to store, search, and manage
> points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support.

## 安装和设置

Install the Python partner package:

<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>

## 向量嵌入模型

### 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` indexes, 的封装器，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 封装器的更详细演练，请参阅[此笔记本](/oss/python/integrations/vectorstores/qdrant)

***

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