# 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md)
>
> 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.
---
## Orientation: what each page means
| Fix Page | What it solves | Typical symptom |
|----------|----------------|-----------------|
| [metric_mismatch.md](./metric_mismatch.md) | Distance metric mismatch (cosine vs L2 vs dot) | High similarity numbers but wrong meaning |
| [normalization_and_scaling.md](./normalization_and_scaling.md) | Missing normalization or scaling issues | Embeddings with larger norms dominate |
| [tokenization_and_casing.md](./tokenization_and_casing.md) | Tokenizer or casing drift | Same text embeds differently across runs |
| [chunking_to_embedding_contract.md](./chunking_to_embedding_contract.md) | Chunking not aligned with embedding model | Citations cut mid-sentence or incoherent snippets |
| [vectorstore_fragmentation.md](./vectorstore_fragmentation.md) | Over-fragmented stores | Retrieval pulls incomplete, scattered sections |
| [dimension_mismatch_and_projection.md](./dimension_mismatch_and_projection.md) | Embedding and index dimension mismatch | Runtime errors or silent drop of vectors |
| [update_and_index_skew.md](./update_and_index_skew.md) | Index not refreshed after updates | Old sections keep showing up |
| [hybrid_retriever_weights.md](./hybrid_retriever_weights.md) | Hybrid weighting not tuned | BM25+ANN underperforms single retriever |
| [duplication_and_near_duplicate_collapse.md](./duplication_and_near_duplicate_collapse.md) | Redundant entries collapse signal | Top-k filled with near-identical chunks |
| [poisoning_and_contamination.md](./poisoning_and_contamination.md) | Malicious or noisy vectors | Hallucinations, unsafe content retrieval |
---
## 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.
---
## 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
---
## 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.
---
## 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](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
3. **Tune hybrid retrievers**
Keep candidate lists from BM25 and ANN. Detect query splits.
→ See [rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
4. **Cold-start fences**
Block traffic until index hash and embedding version match.
→ See [bootstrap-ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md)
5. **Observability**
Log ΔS and λ. Alert if ΔS ≥ 0.60.
---
### 🔗 Quick-Start Downloads (60 sec)
| Tool | Link | 3-Step Setup |
|------|------|--------------|
| **WFGY 1.0 PDF** | [Engine Paper](https://github.com/onestardao/WFGY/blob/main/I_am_not_lizardman/WFGY_All_Principles_Return_to_One_v1.0_PSBigBig_Public.pdf) | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ” |
| **TXT OS (plain-text OS)** | [TXTOS.txt](https://github.com/onestardao/WFGY/blob/main/OS/TXTOS.txt) | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
---
### 🧭 Explore More
| Module | Description | Link |
|-----------------------|----------------------------------------------------------|----------|
| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
| 🧙♂️ Starter Village 🏡 | New here? Lost in symbols? Click here and let the wizard guide you through | [Start →](https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md) |
---
> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
> Engineers, hackers, and open source builders who supported WFGY from day one.
> 
⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
[](https://github.com/onestardao/WFGY)
[](https://github.com/onestardao/WFGY/tree/main/OS)
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