WFGY/ProblemMap/GlobalFixMap/Embeddings

Embeddings — Global Fix Map

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A hub to stabilize the embedding layer before retrieval begins.
Use this folder if your vectors look fine at a glance but retrieval keeps drifting, coverage stays low, or store queries fail silently. No infra change needed.

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Orientation: what each page covers

Page	What it solves	Typical symptom
Metric Mismatch	Store metric (L2, cosine, dot) differs from model assumption	High similarity but wrong neighbors
Normalization & Scaling	Embeddings not normalized or scaled	Results unstable across runs
Tokenization & Casing	Tokenizer mismatch, casing differences	Same text gives different vectors
Chunking → Embedding Contract	Chunk cuts misaligned with semantic windows	Snippets cut mid-thought, anchors lost
Vectorstore Fragmentation	Index silently fragmented	Recall too low even with large k
Dimension Mismatch & Projection	Store dimension vs embedding dimension mismatch	Index errors or silent truncation
Update & Index Skew	Old vectors remain in index	Results point to stale data
Hybrid Retriever Weights	BM25 + ANN weights unbalanced	Hybrid worse than single retriever
Duplication & Near-Duplicate Collapse	Duplicate data overwhelms recall	Same doc retrieved repeatedly
Poisoning & Contamination	Embeddings polluted by adversarial/noisy vectors	Retrieval looks “randomized”

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When to use this folder

• Retrieval looks fine by eye but metrics drift across runs.
• Coverage stays low despite healthy-looking indexes.
• Citations pull from stale or duplicated data.
• Same query yields different answers depending on casing or seed.
• Hybrid retrievers collapse into noise.

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Acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for target section
• λ_observe convergent across 3 paraphrases and 2 seeds
• No index skew between write/read

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60-second fix checklist

1. Lock metrics
   One model family, one distance metric.
   Guide: Metric Mismatch

2. Normalize
   Apply L2 norm to embeddings at both write and query.
   Guide: Normalization & Scaling

3. Unify tokenization
   Same tokenizer + casing across ingestion and query.
   Guide: Tokenization & Casing

4. Audit chunking
   Verify semantic alignment, no mid-thought splits.
   Guide: Chunking → Embedding Contract

5. Rebuild index if skewed
   Drop old embeddings, rebuild with correct dimension.
   Guide: Update & Index Skew

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FAQ for newcomers

Why is metric mismatch so common?
Because vector DBs default differently: FAISS often L2, Pinecone cosine, Redis dot. If your embedding model expects cosine, L2 will silently break recall.

Why normalize embeddings?
Without normalization, embeddings vary in magnitude. Distance stops reflecting meaning.

Why do tokenizers matter?
“Apple” vs “apple” may yield different vectors if one side lowercases, the other doesn’t.

What if coverage stays low after all fixes?
Check for fragmentation and duplication collapse. The issue may not be the embedding model itself, but how the index is populated.

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• Retrieval (Global Fix Map)
• Vector DBs & Stores

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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

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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