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Qdrant

Featured

by Qdrant

High-performance open-source vector database. Supports dense, sparse, and multi-vector search. On-premise or cloud. Rust-built for production-scale RAG pipelines.

5
Skills
API Key
Auth
No
Streaming
No
Push

Skills

Index and query dense vectors with HNSW for high-recall approximate search

Index and query dense vectors with HNSW for high-recall approximate search

Support sparse vectors for keyword-style retrieval alongside semantic dense search

Support sparse vectors for keyword-style retrieval alongside semantic dense search

Apply complex payload filters to narrow search results without re-ranking overhead

Apply complex payload filters to narrow search results without re-ranking overhead

Use named multi-vector collections for image, text, and code embedding co-search

Use named multi-vector collections for image, text, and code embedding co-search

Quantize vectors to reduce memory footprint by up to 32x with minimal recall loss

Quantize vectors to reduce memory footprint by up to 32x with minimal recall loss

Vector Databasesvector-databaseopen-sourceragembeddingsrustself-hostedsemantic-search
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qdrant
High-performance open-source vector database. Supports dense, sparse, and multi-vector search. On-premise or cloud. Rust-built for production-scale RAG pipelines.
fields
nameQdrant
providerQdrant
urlhttps://qdrant.tech/documentation
categoriesvector-databases
accessapi · cli
authapiKey
streamingfalse
pushfalse
verifiedtrue
tagsvector-database, open-source, rag, embeddings, rust, self-hosted, semantic-search
skills
Index and query dense vectors with HNSW for high-recall approximate searchIndex and query dense vectors with HNSW for high-recall approximate searchIndex and query dense vectors with HNSW for high-recall…
Support sparse vectors for keyword-style retrieval alongside semantic dense searchSupport sparse vectors for keyword-style retrieval alongside semantic dense searchSupport sparse vectors for keyword-style retrieval alon…
Apply complex payload filters to narrow search results without re-ranking overheadApply complex payload filters to narrow search results without re-ranking overheadApply complex payload filters to narrow search results …
Use named multi-vector collections for image, text, and code embedding co-searchUse named multi-vector collections for image, text, and code embedding co-searchUse named multi-vector collections for image, text, and…
Quantize vectors to reduce memory footprint by up to 32x with minimal recall lossQuantize vectors to reduce memory footprint by up to 32x with minimal recall lossQuantize vectors to reduce memory footprint by up to 32…
→ https://qdrant.tech/documentation