WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB

RAG + VectorDB — Global Fix Map

🏥 Quick Return to Emergency Room

You are in a specialist desk.
For full triage and doctors on duty, return here:

• 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 sub-room.
If you want full consultation and prescriptions, go back to the Emergency Room lobby.

This hub covers typical retrieval bugs caused by vector databases and embeddings.
Use this page if your RAG pipeline looks fine but answers keep drifting, citations don’t match, or hybrid retrievers underperform.
Every page here is a guardrail with copy-paste recipes and acceptance targets.

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Orientation: what each page means

Fix Page	What it solves	Typical symptom
metric_mismatch.md	Distance metric mismatch (cosine vs L2 vs dot)	High similarity numbers but wrong meaning
normalization_and_scaling.md	Missing normalization or scaling issues	Embeddings with larger norms dominate
tokenization_and_casing.md	Tokenizer or casing drift	Same text embeds differently across runs
chunking_to_embedding_contract.md	Chunking not aligned with embedding model	Citations cut mid-sentence or incoherent snippets
vectorstore_fragmentation.md	Over-fragmented stores	Retrieval pulls incomplete, scattered sections
dimension_mismatch_and_projection.md	Embedding and index dimension mismatch	Runtime errors or silent drop of vectors
update_and_index_skew.md	Index not refreshed after updates	Old sections keep showing up
hybrid_retriever_weights.md	Hybrid weighting not tuned	BM25+ANN underperforms single retriever
duplication_and_near_duplicate_collapse.md	Redundant entries collapse signal	Top-k filled with near-identical chunks
poisoning_and_contamination.md	Malicious or noisy vectors	Hallucinations, unsafe content retrieval

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When to use this folder

• Your answers look semantically wrong even though top-k similarity looks high.
• Citations point to the wrong section or cannot be verified.
• Hybrid retrieval underperforms vs single retriever.
• Index seems “healthy” but recall/coverage stays low.

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Core acceptance targets

• ΔS(question, retrieved) ≤ 0.45
• Coverage of target section ≥ 0.70
• λ_observe convergent across 3 paraphrases
• E_resonance flat on long windows

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FAQ for newcomers

Why do we need these fixes if VectorDBs are mature?
Because RAG pipelines often break not at the infra level but at the semantic boundary. Even if FAISS, Milvus, or Pinecone run fine, the contracts between embedding, chunking, and retrieval are fragile.

What is metric mismatch and why is it deadly?
If your index uses L2 but embeddings were trained for cosine, the “closest” neighbors are meaningless. This is the single most common RAG failure.

Why do duplicates matter so much?
If your corpus has many repeated sentences, the retriever fills top-k with clones. The LLM sees no diversity and hallucinates.

Is poisoning really a real-world issue?
Yes. Even a single malicious doc can bias retrieval. This page shows how to detect and quarantine them without retraining the whole pipeline.

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60-Second Fix Checklist

1. Lock metrics and analyzers
   One embedding model per field. One distance metric. Same analyzer for read/write.

2. Enforce snippet contracts
   Require {snippet_id, section_id, source_url, offsets, tokens}.
   → See data-contracts

3. Tune hybrid retrievers
   Keep candidate lists from BM25 and ANN. Detect query splits.
   → See rerankers

4. Cold-start fences
   Block traffic until index hash and embedding version match.
   → See bootstrap-ordering

5. Observability
   Log ΔS and λ. Alert if ΔS ≥ 0.60.

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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

Layer	Page	What it’s for
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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