WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB

RAG + VectorDB — Global Fix Map

A hub to stabilize retrieval pipelines at the embedding ↔ vector store layer.
Use this folder when retrieval looks healthy on paper but citations, meaning, or stability collapse in practice.
Every fix is structural, measurable, and store-agnostic.

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Quick routes to per-page guides

• Metric mismatch → metric_mismatch.md
• Normalization and scaling → normalization_and_scaling.md
• Tokenization and casing → tokenization_and_casing.md
• Chunking ↔ embedding contract → chunking_to_embedding_contract.md
• Vectorstore fragmentation → vectorstore_fragmentation.md
• Dimension mismatch / projection errors → dimension_mismatch_and_projection.md
• Update drift and index skew → update_and_index_skew.md
• Hybrid retriever weight errors → hybrid_retriever_weights.md
• Duplication / near-duplicate collapse → duplication_and_near_duplicate_collapse.md
• Poisoning and contamination → poisoning_and_contamination.md

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When to use this folder

• High similarity but wrong meaning.
• Correct facts exist in the corpus but never surface.
• Citations inconsistent or missing across runs.
• Token count spikes after deploy without corpus changes.
• Index “looks fine” yet coverage stays flat.
• Retrieval quality drifts after updates or merges.
• Hybrid retrievers underperform single retrievers.
• Duplicate or poisoned vectors destabilize the store.

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Acceptance targets (store-agnostic)

• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ stays convergent across 3 paraphrases and 2 seeds
• E_resonance flat across long windows
• Index skew ≤ 5% after updates

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60-second checklist

1. Lock metric and analyzer (cosine vs L2 vs dot).
2. Normalize embeddings (L2 norm, zero-mean scaling).
3. Align tokenization and casing with retriever.
4. Enforce chunking ↔ embedding contract.
5. Probe ΔS across paraphrases. If ≥0.60, escalate.
6. Audit duplication and contamination (hash and dedupe).
7. Monitor index skew after every update.
8. Verify hybrid retriever weights explicitly.

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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 + ”
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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