Embedding & Indexing
Embedding models and vector indexing for RAG: choosing embeddings, HNSW vs IVF, dimensionality, and index optimization.
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Embedding & Indexing
Where the pipeline turns text into math. The biggest misconception: a vector database does not store your documents — it stores vectors and metadata, and the text lives elsewhere. Get this data layer right and search stays fast and cheap.
Embedding maps each chunk to a point in high-dimensional space where meaning becomes distance — similar text lands nearby. Indexing then makes finding those neighbors fast: at millions of vectors you cannot compare against every one, so an approximate-nearest-neighbor (ANN) index like HNSW trades a sliver of recall for orders-of-magnitude speed. Two rules keep you out of trouble: embed queries and documents with the same model, and store the text separately from the vectors.
Vector Storage Architecture — Where Everything Lives
The biggest misconception about vector databases: they do NOT store your documents. They store vectors (arrays of floats) and metadata. The actual text lives elsewhere. Understanding this architecture is critical for production systems.
What Goes Where
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