7 articles
Build a production semantic search engine using OpenAI embeddings and cosine similarity in Python. Covers hybrid search, caching, FastAPI deployment, and cost optimization for high-volume use cases.
Scale embeddings search with HNSW vs IVFFlat, batch generation, incremental updates, hybrid search, pre/post-filtering, caching, and dimension reduction.
Master metadata filtering in RAG systems: design schemas, implement self-querying, combine filters with vector similarity, and isolate tenants securely.
Implement semantic caching to reduce LLM API costs by 40-60%, handle similarity thresholds, TTLs, and cache invalidation in production.
Compare pgvector (self-hosted), Pinecone (managed), and Weaviate for production RAG. Index strategies, filtering, cost, and migration patterns.
Understand approximate nearest neighbor algorithms: HNSW internals, IVFFlat trade-offs, quantization impact, and benchmarking strategies.
Master pre-filtering, HNSW payload filtering, pgvector filtering, hybrid scoring, and re-ranking to build fast, accurate semantic search at scale.