> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docsdy-1782337909-2539eb6.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Qdrant integrations

> Integrate with Qdrant using LangChain Python.

> [Qdrant](https://qdrant.tech/documentation/) (read: quadrant) 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. `Qdrant` is tailored to extended filtering support.

## Installation and setup

Install the Python partner package:

<CodeGroup>
  ```bash pip theme={null}
  pip install langchain-qdrant
  ```

  ```bash uv theme={null}
  uv add langchain-qdrant
  ```
</CodeGroup>

## Embedding models

### FastEmbedSparse

```python theme={null}
from langchain_qdrant import FastEmbedSparse
```

### SparseEmbeddings

```python theme={null}
from langchain_qdrant import SparseEmbeddings
```

## Vector store

There exists a wrapper around `Qdrant` indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.

To import this vectorstore:

```python theme={null}
from langchain_qdrant import QdrantVectorStore
```

For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](/oss/python/integrations/vectorstores/qdrant)

***

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