| Build for Production-Grade AI Search |
Engineered for real-time retrieval with the speed, accuracy, and scale that modern AI demands. |
WHY QDRANT? |
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| metadata-filters |
Expansive Metadata Filters |
Store metadata in JSON and use advanced filters, such as <code>nested</code>, <code>text</code>, <code>geo</code>, <code>has_vector</code>, and more. |
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Metadata filters code illustration |
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| Learn About Metadata Filters |
/documentation/manage-data/payload/ |
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| hybrid-search |
Native Hybrid Search (Dense + Sparse) |
Blend keyword and vector search in one query – use dense or sparse vectors. Supports BM25, SPLADE++, and miniCOIL. |
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| /img/home/native-hybrid-search.png |
Native hybrid search illustration |
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| Explore Hybrid Search |
/documentation/search/hybrid-queries/ |
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| multivector |
Built-in Multivector |
Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object. |
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| /img/home/multivector.png |
Multivector illustration |
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| See Documentation |
/documentation/tutorials-search-engineering/using-multivector-representations/ |
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| one-stage-filtering |
Efficient, One-Stage Filtering |
Filters are applied during HNSW traversal — no pre- or post-filtering. High recall with low latency, even under complex conditions. |
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| /img/home/one-stage-filtering.png |
One-stage filtering illustration |
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| See Documentation |
/articles/filterable-hnsw/ |
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| reranking |
Full-Spectrum Reranking |
Infuse business logic with score boosting, achieve token-level precision with late interaction models (e.g. ColBERT), diversify results with Maximum Marginal Relevance (MMR) |
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| /img/home/reranking.png |
Reranking illustration |
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| See Documentation |
/documentation/tutorials-basics/reranking-hybrid-search/ |
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