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Qdrant

Qdrant

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High-performance vector similarity search engine and database written in Rust with rich payload filtering.

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Qdrant provides production-grade vector search with support for geo-filtering, text matching, and hybrid sparse-dense embeddings. Available open-source and managed in the cloud.

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In-Depth Analysis & Guides for Qdrant

Editorial deep-dives, architectural breakdowns, and tutorials featuring Qdrant.

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Developer EcosystemNov 20, 2024

Advanced RAG Chunking: Semantic Splitting, Recursive Hierarchies, and Sentence Windowing

An architectural guide to advanced RAG text chunking: comparing fixed-character splits, recursive character boundaries, semantic similarity splitting, parent-child document hierarchies, and sliding sentence windows.

Developer EcosystemNov 12, 2024

Semantic Caching in Production: Slashing API Latency and Spend with Vector Similarity Caches

An engineering blueprint for production semantic caching: using vector embeddings, cosine distance thresholds, and Redis vector stores to intercept redundant LLM queries, slashing API latency from 2,000ms to 15ms while reducing inference costs by 40% to 70%.

Developer EcosystemOct 25, 2024

Hybrid Search in Practice: Combining BM25 Sparse Inverted Indices with Dense Embeddings via RRF

An architectural guide to hybrid information retrieval: combining BM25 lexical inverted indices with dense vector embeddings using Reciprocal Rank Fusion (RRF) to eliminate semantic drift, resolve acronyms, and maximize RAG precision.

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