WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB/normalization_and_scaling.md

Normalization and Scaling — Guardrails and Fix Pattern

🧭 Quick Return to Map

You are in a sub-page of RAG_VectorDB.
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 vector similarity is unstable because embeddings are not normalized or scaling factors differ between training and retrieval.
This failure often appears when cosine distance is requested but vectors are stored raw, or when IP/dot metrics exaggerate magnitude.

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Open these first

• Visual map and recovery: RAG Architecture & Recovery
• Embedding vs meaning: embedding-vs-semantic.md
• Metric mismatch: metric_mismatch.md
• Chunking checklist: chunking-checklist.md

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Core acceptance

• Vectors are L2-normalized when using cosine similarity.
• ΔS(question, retrieved) ≤ 0.45, stable across three paraphrases.
• Coverage ≥ 0.70 on the target section.
• λ remains convergent across seeds.

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Typical breakpoints and the right fix

• Cosine similarity reported but vectors not normalized
  → metric_mismatch.md

• Dot product used without rescaling (large norm vectors dominate retrieval)
  → Normalize or rescale embeddings before indexing.

• Cross-model mixing (embeddings from different checkpoints with different norms)
  → Re-normalize the corpus and queries to unit length.

• Hybrid dense + sparse weighting unstable (scale mismatch between BM25 scores and vector norms)
  → Apply explicit min-max or z-score scaling before weighted sum.

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Fix in 60 seconds

1. Check norms
   Sample 100 embeddings. Compute mean L2 norm. If not ~1.0 under cosine, normalization missing.

2. Normalize queries
   Ensure query_vector = vector / ||vector|| before retrieval when using cosine.

3. Corpus re-index
   Drop and rebuild index with normalized vectors if store does not enforce it.

4. Hybrid scaling
   Normalize dense similarity scores into the same 0–1 range as BM25 before combining.

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Copy-paste probe

import numpy as np

def check_norms(vectors):
    norms = np.linalg.norm(vectors, axis=1)
    return norms.mean(), norms.std()

mean_norm, std_norm = check_norms(sample_vectors)
print("Mean norm:", mean_norm, "Std:", std_norm)

Target: mean ≈ 1.0, std ≤ 0.05 for cosine retrieval.

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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
App	Blow Blow Blow	Reasoning game engine and memory demo

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