Metric Mismatch

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• RAG_VectorDB — vector databases for retrieval and grounding
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Use this page when nearest neighbors look similar in cosine space but your VectorDB runs L2 or dot, or the reverse.
This failure appears often in FAISS, Milvus, pgvector, Weaviate, Redis, Vespa, and similar stores.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Embedding vs meaning: embedding-vs-semantic.md
• Chunking checklist: chunking-checklist.md

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for the target section
• λ remains convergent across three paraphrases and two seeds
• Store metric matches embedding training metric (cosine ↔ cosine, L2 ↔ L2, dot ↔ dot)

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Typical breakpoints and the right fix

• High cosine similarity in logs but wrong meaning
  → embedding-vs-semantic.md

• Top-k neighbors inconsistent across runs (vector drift between L2 and cosine)
  → retrieval-playbook.md

• Switching embedding models breaks index (new default metric not aligned with store)
  → predeploy-collapse.md

• Hybrid dense+BM25 loses semantic signal (wrong weighting due to metric scaling)
  → hybrid_failure.md

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Store defaults reference

Store	Default metric	Notes
FAISS	L2 (can set IP or cosine)	Normalize vectors before cosine search
Milvus	L2 / IP	Cosine requires explicit normalization
pgvector	L2 / cosine / IP	Must choose at index creation
Weaviate	cosine	Dot/IP optional
Redis-Vector	cosine	Normalize mandatory
Vespa	dot	Needs scaling to emulate cosine

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Fix in 60 seconds

1. Log current metric
   Run a probe query (SELECT metric FROM index_metadata). Verify it matches embedding doc.

2. Check normalization
   If metric=cosine but vectors are raw, ΔS will inflate. Normalize to unit length.

3. Re-index with explicit metric
   Drop and rebuild index with the same metric as embedding training.

4. Hybrid sanity check
   If using BM25+dense, reweight so ΔS ≤ 0.45 and coverage ≥ 0.70.

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Copy-paste test query

-- Example: pgvector
SELECT id, embedding <=> query_embedding
FROM documents
ORDER BY embedding <=> query_embedding
LIMIT 5;

Ensure <=> operator matches the chosen metric (cosine, L2, or IP).

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TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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