A developer explains that poor vector search results in RAG systems often stem from the HNSW approximate nearest neighbor algorithm rather than embedding models, and demonstrates how to build, break, …
A developer explains how vector search works under the hood, focusing on the two dominant algorithms: IVF (Inverted File Index) and HNSW (Hierarchical Navigable Small World). The post details why appr…
Evokoa launched Polygres, a managed PostgreSQL platform that integrates native graph traversal, HNSW vector search, and hybrid retrieval APIs to enable agents to query structured records, relationship…
A tutorial shows developers how to give AI agents persistent long-term memory using Postgres with the pgvector extension and OpenAI embeddings, eliminating the need for external vector databases. The …
Qdrant has vectorized 33.7 million Ukrainian court decisions from the Unified State Register of Court Decisions using Voyage AI's voyage-3.5 embedding model. The system, running on a dedicated EC2 ins…
Databricks launched Lakebase Search on Neon, a hybrid vector and BM25 full-text retrieval system built as two Postgres extensions optimized for Neon's storage-compute separation architecture, enabling…
EdgeSync-LLM, a new KV cache fragment engine for on-device LLM inference, stores and retrieves transformer KV tensors via HNSW approximate nearest-neighbor search, enabling exact hits at ~8ms TTFT and…
A developer built a production-grade cross-platform face recognition pipeline with anti-spoofing, using a lightweight RFB-320 face detector, FaceNet-style embeddings, and HNSW indexing for sub-millise…
A developer compared four vector databases—Pinecone, Weaviate, Milvus, and Qdrant—in 2026, analyzing deployment complexity, latency benchmarks, filtering quality, and cost. Qdrant achieved the lowest …
A developer completed Module 2 of the LLM Zoomcamp 2026 and built a hybrid search engine from scratch, combining keyword and vector search. The project implemented vector search using ONNX runtime emb…
A developer optimized pgvector performance for the vibe-memory semantic memory service, reducing query time from 800ms to 15ms. Switching from the default IVFFlat index to HNSW provided a 5x improveme…
A developer built a high-performance semantic caching system for LLM calls using Spring AI and pgvector, intercepting prompts with a CallAroundAdvisor and a local embedding model to generate query emb…
A developer compares four vector databases—pgvector, Qdrant, Pinecone, and Weaviate—for RAG workloads, explaining that the choice depends on specific workload metrics like vector count, filtering need…
A developer explains that vector databases are not magic but rely on approximate nearest neighbor (ANN) search algorithms like HNSW and IVF. The post details how these algorithms trade accuracy for sp…
Manticore Search released version 27.1.5 with built-in authentication and authorization, sharded tables, conversational search, and faster vector search. The update also includes improvements to HNSW …
A Cloudera engineer describes how vector search in Elasticsearch enables semantic search and RAG pipelines by finding documents based on meaning rather than exact keywords. The post explains that vect…
Pinecone uses proprietary indexing algorithms—Ananas, PQFS, and IVF with PQFS—selected automatically per slab based on size, instead of general-purpose methods like HNSW. The adaptive approach optimiz…
Manticore Search has introduced early termination for its HNSW-based vector search, a feature that stops distance computations once the result set has converged, reducing unnecessary work. Benchmarks …
A developer built Vektr, a custom RAG engine implementing HNSW graphs from scratch, achieving recall@10 of 0.984 — meaning 98.4% of queries returned all 10 true nearest neighbors in the top results. T…
ON1 (G116 V8) has introduced a virtual chip ISA that achieves 38-microsecond black-box AI memory retrieval by separating vector search into three observable latency stages: fetch, compute, and ANN sea…