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142 lines
8.3 KiB
Markdown
142 lines
8.3 KiB
Markdown
# RAG + VectorDB — Global Fix Map
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<details>
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<summary><strong>🏥 Quick Return to Emergency Room</strong></summary>
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<br>
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> You are in a specialist desk.
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> For full triage and doctors on duty, return here:
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>
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md)
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>
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> Think of this page as a sub-room.
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> If you want full consultation and prescriptions, go back to the Emergency Room lobby.
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</details>
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This hub covers **typical retrieval bugs caused by vector databases and embeddings**.
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Use this page if your RAG pipeline looks fine but answers keep drifting, citations don’t match, or hybrid retrievers underperform.
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Every page here is a guardrail with copy-paste recipes and acceptance targets.
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---
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## Orientation: what each page means
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| Fix Page | What it solves | Typical symptom |
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|----------|----------------|-----------------|
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| [metric_mismatch.md](./metric_mismatch.md) | Distance metric mismatch (cosine vs L2 vs dot) | High similarity numbers but wrong meaning |
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| [normalization_and_scaling.md](./normalization_and_scaling.md) | Missing normalization or scaling issues | Embeddings with larger norms dominate |
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| [tokenization_and_casing.md](./tokenization_and_casing.md) | Tokenizer or casing drift | Same text embeds differently across runs |
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| [chunking_to_embedding_contract.md](./chunking_to_embedding_contract.md) | Chunking not aligned with embedding model | Citations cut mid-sentence or incoherent snippets |
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| [vectorstore_fragmentation.md](./vectorstore_fragmentation.md) | Over-fragmented stores | Retrieval pulls incomplete, scattered sections |
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| [dimension_mismatch_and_projection.md](./dimension_mismatch_and_projection.md) | Embedding and index dimension mismatch | Runtime errors or silent drop of vectors |
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| [update_and_index_skew.md](./update_and_index_skew.md) | Index not refreshed after updates | Old sections keep showing up |
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| [hybrid_retriever_weights.md](./hybrid_retriever_weights.md) | Hybrid weighting not tuned | BM25+ANN underperforms single retriever |
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| [duplication_and_near_duplicate_collapse.md](./duplication_and_near_duplicate_collapse.md) | Redundant entries collapse signal | Top-k filled with near-identical chunks |
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| [poisoning_and_contamination.md](./poisoning_and_contamination.md) | Malicious or noisy vectors | Hallucinations, unsafe content retrieval |
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---
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## When to use this folder
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- Your answers look **semantically wrong** even though top-k similarity looks high.
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- Citations point to the wrong section or cannot be verified.
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- Hybrid retrieval underperforms vs single retriever.
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- Index seems “healthy” but recall/coverage stays low.
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---
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## Core acceptance targets
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- ΔS(question, retrieved) ≤ 0.45
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- Coverage of target section ≥ 0.70
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- λ_observe convergent across 3 paraphrases
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- E_resonance flat on long windows
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---
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## FAQ for newcomers
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**Why do we need these fixes if VectorDBs are mature?**
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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.
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**What is metric mismatch and why is it deadly?**
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If your index uses `L2` but embeddings were trained for `cosine`, the “closest” neighbors are meaningless. This is the single most common RAG failure.
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**Why do duplicates matter so much?**
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If your corpus has many repeated sentences, the retriever fills top-k with clones. The LLM sees no diversity and hallucinates.
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**Is poisoning really a real-world issue?**
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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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---
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## 60-Second Fix Checklist
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1. **Lock metrics and analyzers**
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One embedding model per field. One distance metric. Same analyzer for read/write.
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2. **Enforce snippet contracts**
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Require `{snippet_id, section_id, source_url, offsets, tokens}`.
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→ See [data-contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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3. **Tune hybrid retrievers**
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Keep candidate lists from BM25 and ANN. Detect query splits.
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→ See [rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
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4. **Cold-start fences**
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Block traffic until index hash and embedding version match.
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→ See [bootstrap-ordering](https://github.com/onestardao/WFGY/blob/main/ProblemMap/bootstrap-ordering.md)
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5. **Observability**
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Log ΔS and λ. Alert if ΔS ≥ 0.60.
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---
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### 🔗 Quick-Start Downloads (60 sec)
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| Tool | Link | 3-Step Setup |
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|------|------|--------------|
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| **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 + <your question>” |
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| **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 |
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---
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### 🧭 Explore More
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| Module | Description | Link |
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|-----------------------|----------------------------------------------------------|----------|
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| 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) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| 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) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| 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) |
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| 🧙♂️ 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) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [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).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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