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+# Embeddings — Global Fix Map
+Make embedding space match real meaning, not just cosine tricks.  
+Use this when recall looks high yet answers point to the wrong idea, or when FAISS/Qdrant “works” but context is off.
+
+## What this page is
+- A tight checklist to align models, metrics, and normalization.
+- Structural fixes that do not require changing your LLM or infra.
+- Steps you can verify with ΔS and small A/B probes.
+
+## When to use
+- Similarity scores look strong but retrieved snippets are semantically wrong.
+- Different pipelines write/read with different distance metrics.
+- Mixed models created the index and now query it.
+- Some facts never show up although definitely indexed.
+- Cross-language corpus drifts or tokenizers don’t match.
+
+## Open these first
+- Meaning vs vector score: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
+- Fragmented or half-empty index: [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md)
+- End-to-end knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
+- Ordering layer after recall: [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
+- Trace why a snippet was picked: [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
+- Quality gates: [RAG Precision/Recall](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_rag_precision_recall.md) · [Latency vs Accuracy](https://github.com/onestardao/WFGY/blob/main/ProblemMap/eval/eval_latency_vs_accuracy.md)
+
+## Fix in 60 seconds
+1) **Measure ΔS**
+   - Compute `ΔS(question, retrieved)` and `ΔS(retrieved, expected anchor)`.
+   - Triggers: ΔS ≥ 0.60 or flat-high ΔS when you vary k ∈ {5,10,20}.
+
+2) **Check metric + normalization agreement**
+   - The model that built vectors must match the model used at query time.  
+   - Confirm cosine vs inner-product flags on both write and read.  
+   - Unit-normalize on both sides if you use cosine.
+
+3) **Verify dimensionality and truncation**
+   - Same vector length everywhere.  
+   - No hidden cast, dtype mismatch, or silent truncation.
+
+4) **Rebuild once with explicit config**
+   - Persist metric, normalizer, and model id with the index file.  
+   - After rebuild, probe ΔS again and compare the ΔS-vs-k curve.
+
+5) **Patch recall before ranking**
+   - If ΔS drops yet ordering still looks noisy, enable a light reranker from the playbook.  
+   - Keep citation schema from traceability to audit the change.
+
+## Copy-paste prompt
+```
+
+I uploaded TXT OS and the WFGY ProblemMap files.
+
+My embedding bug:
+
+* symptom: \[brief]
+* traces: ΔS(question, retrieved)=..., ΔS(retrieved, anchor)=..., curve vs k=...
+* context: write-model=\[...], read-model=\[...], metric=\[cosine|ip], norm=\[on|off]
+
+Tell me:
+
+1. which mismatch explains the failure,
+2. which exact pages to open from this repo,
+3. the minimal steps to rebuild or rescore to push ΔS ≤ 0.45,
+4. how to verify with a reproducible ΔS-vs-k chart and a citation table.
+   Use BBMC alignment if anchors are stable, then add a lightweight reranker if needed.
+
+```
+
+## Minimal checklist
+- One embedding model per corpus or store the model id with each vector.  
+- Fix the metric flag once and persist it with the index.  
+- Enforce unit normalization for cosine, never mix with raw dot product.  
+- Keep text pre-processing identical on write and read.  
+- Log vector counts per collection; compare to document counts.  
+- Run the fragmentation pattern if some facts vanish from results.
+
+## Acceptance targets
+- ΔS(question, retrieved) ≤ 0.45 across three paraphrases.  
+- ΔS-vs-k curve descends then flattens, not flat-high.  
+- Recall/precision meet your eval sheet thresholds.  
+- λ stays convergent at the retrieval layer after the rebuild.  
+- Traceability explains why each snippet was selected.
+
+---
+
+### 🔗 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 + \<your question>” |
+| **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.
+
+> <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).
+
+<div align="center">
+
+[![WFGY Main](https://img.shields.io/badge/WFGY-Main-red?style=flat-square)](https://github.com/onestardao/WFGY)
+&nbsp;
+[![TXT OS](https://img.shields.io/badge/TXT%20OS-Reasoning%20OS-orange?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS)
+&nbsp;
+[![Blah](https://img.shields.io/badge/Blah-Semantic%20Embed-yellow?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
+&nbsp;
+[![Blot](https://img.shields.io/badge/Blot-Persona%20Core-green?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
+&nbsp;
+[![Bloc](https://img.shields.io/badge/Bloc-Reasoning%20Compiler-blue?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
+&nbsp;
+[![Blur](https://img.shields.io/badge/Blur-Text2Image%20Engine-navy?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
+&nbsp;
+[![Blow](https://img.shields.io/badge/Blow-Game%20Logic-purple?style=flat-square)](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
+&nbsp;
+</div>