20 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.
Compare Pinecone, Chroma, and Weaviate for vector search and storage.
Master Pinecone from setup to production with indexes, metadata, and hybrid search.
Complete guide to Chroma DB for local vector embeddings and semantic search.
Understand embeddings and semantic search: from word vectors to meaning-based retrieval.
Master OpenAI Embeddings API for semantic search and RAG applications.
Generate embeddings locally using Sentence Transformers without API calls.
Implement hybrid search combining keyword BM25 with semantic embeddings, ranking, and LLM-powered query understanding.
Build recommendation systems using embeddings, two-tower models, and solve cold start with hybrid approaches.
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.
Compare text-embedding-3-small vs 3-large, Cohere embed v3, sentence-transformers, multilingual models, and how to choose embeddings for your stack.
Master multimodal embeddings: CLIP for text-image, ImageBind for audio/3D, cross-modal search, and production storage strategies.
Master pgvector setup, index tuning, hybrid search patterns, and embedding pipelines for production semantic search at scale.
pgai extends PostgreSQL with AI capabilities: auto-embedding, semantic search, and LLM function calls—all in SQL. No external vector database required.
Explore chunking strategies from fixed-size to semantic splitting, including sentence-window retrieval and late chunking techniques that dramatically improve retrieval quality.
Master semantic chunking, recursive splitting, parent-child strategies, and late chunking to maximize RAG retrieval quality and cut retrieval latency.
Explore why dense embeddings alone fail, and how hybrid search combining vector similarity with BM25 sparse retrieval dramatically improves RAG quality.
Understand why vector similarity ranks poorly, how cross-encoder rerankers fix it, and implement production-grade reranking with latency optimization.
Compare the top vector databases in 2026: Pinecone serverless, Weaviate multi-tenancy, Qdrant quantization, pgvector for Postgres, and when to use each.