Vector DB Design Cheat Sheet

Embedding stores, indexes, and reranking

Last Updated: November 21, 2025

Components

Piece Role
Embedding model Encode text
Vector index ANN search
Metadata Filter results
Reranker Score top docs

Commands

faiss index.train
Build index
search(query_vector)
Fetch candidates
rerank(candidates)
Score with LLM

Advice

Cache embeddings, refresh in batches, and monitor recall.

💡 Pro Tip: Normalize embeddings, keep metadata for filtering, and rerank with hybrid signals.
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