WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md

Metric Mismatch — Guardrails and Fix Pattern

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To reorient, go back here:

• RAG_VectorDB — vector databases for retrieval and grounding
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

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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🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
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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Explore More

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Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
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