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133 lines
6.6 KiB
Markdown
133 lines
6.6 KiB
Markdown
# Metric Mismatch — Guardrails and Fix Pattern
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **RAG_VectorDB**.
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> To reorient, go back here:
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>
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> - [**RAG_VectorDB** — vector databases for retrieval and grounding](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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Use this page when **nearest neighbors look similar in cosine space but your VectorDB runs L2 or dot**, or the reverse.
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This failure appears often in FAISS, Milvus, pgvector, Weaviate, Redis, Vespa, and similar stores.
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---
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## Open these first
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- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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- End-to-end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- Chunking checklist: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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---
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## Core acceptance
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- ΔS(question, retrieved) ≤ 0.45
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- Coverage ≥ 0.70 for the target section
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- λ remains convergent across three paraphrases and two seeds
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- Store metric matches embedding training metric (cosine ↔ cosine, L2 ↔ L2, dot ↔ dot)
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---
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## Typical breakpoints and the right fix
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- **High cosine similarity in logs but wrong meaning**
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→ [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- **Top-k neighbors inconsistent across runs** (vector drift between L2 and cosine)
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→ [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- **Switching embedding models breaks index** (new default metric not aligned with store)
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→ [predeploy-collapse.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/predeploy-collapse.md)
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- **Hybrid dense+BM25 loses semantic signal** (wrong weighting due to metric scaling)
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→ [hybrid_failure.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/RAG/hybrid_failure.md)
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---
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## Store defaults reference
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| Store | Default metric | Notes |
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|---------------|----------------------|----------------------------------------|
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| FAISS | L2 (can set IP or cosine) | Normalize vectors before cosine search |
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| Milvus | L2 / IP | Cosine requires explicit normalization |
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| pgvector | L2 / cosine / IP | Must choose at index creation |
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| Weaviate | cosine | Dot/IP optional |
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| Redis-Vector | cosine | Normalize mandatory |
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| Vespa | dot | Needs scaling to emulate cosine |
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---
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## Fix in 60 seconds
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1. **Log current metric**
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Run a probe query (`SELECT metric FROM index_metadata`). Verify it matches embedding doc.
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2. **Check normalization**
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If metric=cosine but vectors are raw, ΔS will inflate. Normalize to unit length.
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3. **Re-index with explicit metric**
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Drop and rebuild index with the same metric as embedding training.
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4. **Hybrid sanity check**
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If using BM25+dense, reweight so ΔS ≤ 0.45 and coverage ≥ 0.70.
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---
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## Copy-paste test query
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```sql
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-- Example: pgvector
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SELECT id, embedding <=> query_embedding
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FROM documents
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ORDER BY embedding <=> query_embedding
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LIMIT 5;
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````
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Ensure `<=>` operator matches the chosen metric (`cosine`, `L2`, or `IP`).
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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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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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