mirror of
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nastyapashandtrean
d891fa4a38
* update home page * update logo links and logo folder * add links to customers logos --------- Co-authored-by: trean <trean.mi@gmail.com>
61 lines
2.4 KiB
Markdown
61 lines
2.4 KiB
Markdown
---
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title: Build for Production-Grade AI Search
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subtitle: Engineered for real-time retrieval with the speed, accuracy, and scale that modern AI demands.
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pill: WHY QDRANT?
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startFree:
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text: Start Free in Qdrant Cloud
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url: https://cloud.qdrant.io/signup
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featureCards:
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- id: metadata-filters
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title: Expansive Metadata Filters
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description: "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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image:
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src: /img/home/metadata-filters.png
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alt: Metadata filters code illustration
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link:
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text: Learn About Metadata Filters
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url: /documentation/manage-data/payload/
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size: large
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- id: hybrid-search
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title: Native Hybrid Search (Dense + Sparse)
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description: Blend keyword and vector search in one query – use dense or sparse vectors. Supports BM25, SPLADE++, and miniCOIL.
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image:
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src: /img/home/native-hybrid-search.png
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alt: Native hybrid search illustration
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link:
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text: Explore Hybrid Search
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url: /documentation/search/hybrid-queries/
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size: large
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- id: multivector
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title: Built-in Multivector
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description: Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object.
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image:
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src: /img/home/multivector.png
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alt: Multivector illustration
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link:
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text: See Documentation
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url: /documentation/tutorials-search-engineering/using-multivector-representations/
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size: small
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- id: one-stage-filtering
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title: Efficient, One-Stage Filtering
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description: Filters are applied during HNSW traversal — no pre- or post-filtering. High recall with low latency, even under complex conditions.
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image:
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src: /img/home/one-stage-filtering.png
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alt: One-stage filtering illustration
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link:
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text: See Documentation
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url: /articles/filterable-hnsw/
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size: small
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- id: reranking
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title: Full-Spectrum Reranking
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description: 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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image:
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src: /img/home/reranking.png
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alt: Reranking illustration
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link:
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text: See Documentation
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url: /documentation/tutorials-basics/reranking-hybrid-search/
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size: small
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sitemapExclude: true
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---
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