Qdrant is a vector database and vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.
Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.

Github: https://github.com/qdrant/qdrant

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