4 articles
Build a production semantic search engine using OpenAI embeddings, cosine similarity, and vector databases. Complete Python guide with real-world examples, performance optimization, and deployment patterns.
Understand embeddings and semantic search: from word vectors to meaning-based retrieval.
Implement exact-match and semantic caching with Redis to dramatically reduce LLM API calls, improving latency and cutting costs by 60% through intelligent cache invalidation.
Master pgvector setup, index tuning, hybrid search patterns, and embedding pipelines for production semantic search at scale.