Embedding Strategies

This one walks you through choosing and implementing embedding models for vector search, from OpenAI's text-embedding-3 variants to local options like BGE and sentence-transformers. It includes actual comparison tables showing dimensions and token limits, plus battle-tested chunking strategies: token-based, sentence-based, semantic sections, and recursive splitting. The code templates are production-ready, handling batching, normalization, and model-specific quirks like BGE's query prefixes. Useful if you're building RAG systems and need to move past "just use the default embeddings" into understanding tradeoffs between accuracy, cost, and speed. The chunking section alone will save you from reinventing overlap logic.