Chunking Strategies + Stores

# Chunking Strategies + Stores (FAISS-Local-First; pgvector Note) Run a 2x2 benchmark: chunk sizes {256, 512} x overlaps {10%, 20%} on the DS program RAG corpus; report P@K / Recall / MRR per cell and pick a winner. ## 1. Why chunking decides retrieval Embeddings compare chunks, not documents. Oversized chunks dilute the signal; slivers lose context. The benchmark harness in the starter fixes the queries and embeddings so only chunking varies — that isolation is the whole experiment. ## 2. Stores: FAISS-local-first Default to local FAISS (zero cost, reproducible). pgvector note: same vectors + metadata in Postgres when the app already lives there; accept the ops cost only at serving time, never for the benchmark. ## 3. Metrics P@K: is a gold chunk in the top K? Recall: fraction of golds retrieved. MRR: 1/rank of first gold, averaged. Report all three per cell; choose by MRR with Recall as tiebreak; note cost/latency per embedding dim (768/1536/3072).