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.
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
pip install "docarray[qdrant]"
More information can be found in DocArray’s documentations.
Qdrant might be also used as an embedding backend in 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:
pip install qdrant-txtai
The examples and some more information might be found in qdrant-txtai repository.