A developer building a RAG pipeline for SRE runbooks shares seven vector search tips based on production experience. Key recommendations include using domain-specific embedding models like BAAI/bge-sm…
A developer reports that 80% of RAG system failures stem from poor document chunking, not the LLM or embedding model. A controlled study of 36 methods across 6 domains found content-aware chunking sig…
Metadata filtering before vector search is a cheap but often overlooked recall win. By applying a hard predicate on metadata like customer_id, the search space shrinks from millions to hundreds of chu…
Weaviate has released Engram, a fully managed memory and context service built on its vector database, designed to structure raw interaction data for AI agents. The service addresses long-context degr…
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…
A developer benchmarked ChromaDB, Qdrant, Weaviate, and pgvector for RAG pipelines, finding that most teams over-optimize for future scale while underestimating day-one operational costs. ChromaDB off…
To build a production-ready Retrieval-Augmented Generation (RAG) pipeline in Python using BM25 retrieval via Meilisearch instead of a vector database, arguing that BM25 achieves 85–95% of the recall o…
"RAG Readiness," a tool designed to eliminate the vague "it depends" answers that plague Retrieval-Augmented Generation (RAG) architecture decisions. Instead of providing comparison tables, the tool u…
To implement retrieval-augmented generation (RAG) using the pgvector extension within an existing PostgreSQL database, combined with Amazon Bedrock's Titan embedding model and Claude for answer genera…