A production comparison of Pinecone, Qdrant, Weaviate, and Milvus in 2026: benchmarks, pricing, hybrid search, filter models, and code samples. Includes a decision matrix and a pgvector fallback for teams under 10M vectors.
Voyage-3-large tops MTEB v2, Cohere Embed v4 handles 128K multimodal, OpenAI is the best default, and Jina wins multilingual. Full 2026 benchmark with code.
How to chunk documents for RAG in 2026: contextual retrieval, late chunking, semantic splitting, plus production Python code, benchmarks, and evaluation tips.
Pure vector search fails on roughly 40% of real-world queries. This 2026 production guide shows how to combine BM25 sparse retrieval, dense embeddings, RRF fusion, and a cross-encoder reranker into a single pipeline that pushes recall@10 from 78% to 91% — with working Python code.