---
title: Integrations
weight: 52
---
Qdrant is a vector database performing an approximate nearest neighbours search on neural embeddings. It can work perfectly fine
as a standalone system, yet, in some cases, you may find it easier to implement your semantic search application using some
higher-level libraries. Some of such projects provide ready-to-go integrations and here is a curated list of them.

## DocArray
You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search.

DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc.
It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API.


To install DocArray with Qdrant support, please do

```bash
pip install "docarray[qdrant]"
```

More information can be found in [DocArray's documentations](https://docarray.jina.ai/advanced/document-store/qdrant/).

## txtai
Qdrant might be also used as an embedding backend in [txtai](https://neuml.github.io/txtai/) semantic applications.

txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and their 
properties to encode high-dimensional data in a lower-dimensional space and allows to find similar objects based on their embeddings' 
proximity.

Qdrant is not built-in txtai backend and requires installing an additional dependency:

```bash
pip install qdrant-txtai
```

The examples and some more information might be found in [qdrant-txtai repository](https://github.com/qdrant/qdrant-txtai).