The engineering team behind JobFlo, an AI career operating system, detailed their architecture for an enterprise-grade PostgreSQL database on Supabase, achieving a 99.99% cache hit ratio. They impleme…
Hierarchical Navigable Small Worlds (HNSW), the core data structure behind Elasticsearch, Weaviate, Milvus, QDrant, and Vespa, handles vector updates via delete-then-insertion, but most databases mark…
VectorPrism, an open-source retrieval engine from insightitsGit, claims to cut multi-vector storage costs to a single 1024-dimensional vector per chunk by multiplexing six specialized relevance subspa…
HNSW, the default graph algorithm powering vector search in Faiss, pgvector, Qdrant, and other stores, trades exactness for speed, offering logarithmic retrieval but occasionally returning wrong memor…
Cerebras Systems' knowledge base rebuild, documented across four posts, shows that hybrid retrieval alone never outperformed pure vector search on the company's support corpus, but adding an LLM reran…
Qdrant's filtered vector search benchmark on one million deep-image-96 vectors shows that ACORN-1, a query-time algorithm, repairs graph connectivity gaps left by Filterable HNSW's index-time extra ed…
DuckDB's official vss extension brings HNSW-based approximate nearest neighbor search directly to its SQL engine, allowing developers to run vector similarity queries without a separate vector databas…
A developer rebuilt a logistics company's customer-support agent in Dubai by adding a vector database for long-term memory, enabling the agent to recall customer order history and past tickets in unde…
Gartner projects that by 2028, 80% of GenAI business apps will be developed on existing data management platforms rather than new specialized ones, according to a Redis blog guide on vector search dat…
A developer's guide to vector search fundamentals for RAG design covers ANN algorithms like HNSW, distance metrics, metadata filtering, and BM25. It explains trade-offs between accuracy, speed, and co…
A developer argues that vector databases should adopt tiered storage, similar to traditional data storage, to reduce costs. They cite a startup whose vector index grew from 50 million to 500 million e…
A developer has built a RAG-powered database assistant using PostgreSQL and pgvector, enabling natural-language queries to be answered in seconds. The system retrieves schema metadata via vector simil…
For vector search at scale, on-disk indexing strategies like DiskANN can reduce infrastructure costs by up to 90% compared to in-memory HNSW, according to a technical analysis. The trade-off is a late…
A developer compares Qdrant and Pinecone for production RAG systems, highlighting that Qdrant is an open-source vector search engine that can be self-hosted while Pinecone is a fully managed cloud ser…
ForgeVector offers a practical approach to efficient similarity search by building a vector database on TurboQuant's quantized indices, enabling fast approximate nearest neighbor searches with signifi…
Google Cloud's AlloyDB, a PostgreSQL-compatible database, now delivers up to 4x faster HNSW vector search via its columnar engine, as demonstrated by benchmarks on the GloVe 100 Angular dataset with o…
A new open-source project demonstrates how multimodal vector search works under the hood, using OpenAI CLIP (ViT-B/32) to embed text and images into a 512-dimensional vector space and Qdrant (HNSW ind…
Vector databases use approximate nearest neighbor search, specifically HNSW (Hierarchical Navigable Small World), to find similar vectors without scanning every entry. HNSW builds a multi-layer graph …
A developer's tutorial explains the complete architecture of vector databases, covering schema design, indexing methods like IVF and HNSW, product quantization, distance metrics, metadata indexing, an…
AegisDB, a self-hosted memory database for AI agents, is now available as a single dependency-free C binary that provides durable, searchable long-term memory with multi-tenant auth, encryption at res…