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.
Master Pinecone from account setup to production RAG — serverless indexes, metadata filtering, hybrid search, and performance tuning. The only Pinecone tutorial you need for building LLM-powered search.
Introduction to Weaviate: distributed vector database with native hybrid search, multi-tenancy, and GraphQL API. Learn to build scalable semantic search and RAG systems with Python and Weaviate Cloud.
Understand how text embeddings enable semantic search — from word vectors to transformer-based embeddings, cosine similarity, and practical implementation for RAG systems. The foundation of every LLM retrieval application.
Master the OpenAI Embeddings API — text-embedding-3-small vs large, batch embedding, dimensionality reduction, cost optimization, and full RAG integration. Everything you need to build production semantic search.
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.