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trean
1d8f16f979
* replaced "vector database" with "vector search" across the website content # Conflicts: # qdrant-landing/content/headless/main/core-features.md # qdrant-landing/content/use-cases/_index.md * upd wording
32 lines
1.3 KiB
Markdown
32 lines
1.3 KiB
Markdown
---
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title: RAG with Qdrant
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description: RAG, powered by Qdrant's efficient data retrieval, elevates AI's capacity to generate rich, context-aware content across text, code, and multimedia, enhancing relevance and precision on a scalable platform. Discover why Qdrant is the perfect choice for your RAG project.
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features:
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- id: 0
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icon:
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src: /icons/outline/speedometer-blue.svg
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alt: Speedometer
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title: Highest RPS
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description: Qdrant leads with top requests-per-second, outperforming alternative vector search in various datasets by up to 4x.
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- id: 1
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icon:
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src: /icons/outline/time-blue.svg
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alt: Time
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title: Fast Retrieval
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description: "Qdrant achieves the lowest latency, ensuring quicker response times in data retrieval: 3ms response for 1M Open AI embeddings."
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- id: 2
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icon:
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src: /icons/outline/vectors-blue.svg
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alt: Vectors
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title: Multi-Vector Support
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description: Integrate the strengths of multiple vectors per document, such as title and body, to create search experiences your customers admire.
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- id: 3
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icon:
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src: /icons/outline/compression-blue.svg
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alt: Compression
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title: Built-in Compression
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description: Significantly reduce memory usage, improve search performance and save up to 30x cost for high-dimensional vectors with Quantization.
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sitemapExclude: true
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---
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