Data & Infrastructure
Hybrid Search with BM25 & Vector pgvector: Reciprocal Rank Fusion (RRF)
Combine the precision of full-text BM25 keyword matching with dense semantic embeddings using Reciprocal Rank Fusion (RRF).
Why Pure Vector Search Is Insufficient
Vector search fails when queries contain exact serial numbers, SKUs, error codes, or product names. BM25 catches exact lexical tokens, while vector search captures semantic context. RRF merges both result lists flawlessly.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
Web Scraping Data Pipelines: Schema Validation, De-duplication & DB Loading
Design resilient ETL scraping pipelines with Pydantic validation, hash-based de-duplication, and idempotent PostgreSQL upserts.
Supabase pgvector Guide: Vector Search, HNSW Indexing & Cosine Distance in PostgreSQL
Build enterprise semantic search directly inside PostgreSQL using Supabase pgvector, HNSW indexing, and cosine distance operators.
RAG Chunking Strategies: Fixed Size, Semantic Chunking & Markdown Hierarchy
Master document chunking: character splitting, semantic boundary detection, and table/header-aware recursive splitting.