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Qdrant is a high-performance vector database for similarity and semantic search based on embeddings. It provides low latency, scalable replication, persistence, metadata filtering, a REST and gRPC API and client libraries. Common use cases include semantic search, recommendation systems, semantic classification and retrieval-augmented generation; it can be r…
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Qdrant is a vector database for fast similarity and semantic search using embeddings, metadata filtering and integration-ready APIs.
Qdrant originated as an open-source vector database for similarity and semantic search; the project grew from the need to store vectors with payloads and filtering in production.
Embeddings are stored as vectors and searched through an index by distance. Payloads and filters constrain results semantically; collections, shards, and replicas shape schema, scale, and availability.
Vectors represent content in a space.
Distance indexes return nearby points.
Payload filters connect retrieval with domain attributes.
Qdrant fits semantic search and retrieval-augmented generation when vector retrieval must combine with structured filters. Quality depends heavily on the embedding model, chunking, updates, and evaluation.
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