# Embeddings — 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.

A hub to stabilize the **embedding layer** before retrieval begins. Use this folder if your vectors look fine at a glance but retrieval keeps drifting, coverage stays low, or store queries fail silently. No infra change needed. --- ## Orientation: what each page covers | Page | What it solves | Typical symptom | |---|---|---| | [Metric Mismatch](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/metric_mismatch.md) | Store metric (L2, cosine, dot) differs from model assumption | High similarity but wrong neighbors | | [Normalization & Scaling](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/normalization_and_scaling.md) | Embeddings not normalized or scaled | Results unstable across runs | | [Tokenization & Casing](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/tokenization_and_casing.md) | Tokenizer mismatch, casing differences | Same text gives different vectors | | [Chunking → Embedding Contract](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/chunking_to_embedding_contract.md) | Chunk cuts misaligned with semantic windows | Snippets cut mid-thought, anchors lost | | [Vectorstore Fragmentation](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/vectorstore_fragmentation.md) | Index silently fragmented | Recall too low even with large k | | [Dimension Mismatch & Projection](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/dimension_mismatch_and_projection.md) | Store dimension vs embedding dimension mismatch | Index errors or silent truncation | | [Update & Index Skew](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/update_and_index_skew.md) | Old vectors remain in index | Results point to stale data | | [Hybrid Retriever Weights](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/hybrid_retriever_weights.md) | BM25 + ANN weights unbalanced | Hybrid worse than single retriever | | [Duplication & Near-Duplicate Collapse](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/duplication_and_near_duplicate_collapse.md) | Duplicate data overwhelms recall | Same doc retrieved repeatedly | | [Poisoning & Contamination](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/poisoning_and_contamination.md) | Embeddings polluted by adversarial/noisy vectors | Retrieval looks “randomized” | --- ## When to use this folder - Retrieval looks **fine by eye** but metrics drift across runs. - Coverage stays low despite healthy-looking indexes. - Citations pull from **stale** or duplicated data. - Same query yields different answers depending on casing or seed. - Hybrid retrievers collapse into noise. --- ## Acceptance targets - ΔS(question, retrieved) ≤ 0.45 - Coverage ≥ 0.70 for target section - λ_observe convergent across 3 paraphrases and 2 seeds - No index skew between write/read --- ## 60-second fix checklist 1. **Lock metrics** One model family, one distance metric. Guide: [Metric Mismatch](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/metric_mismatch.md) 2. **Normalize** Apply L2 norm to embeddings at both write and query. Guide: [Normalization & Scaling](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/normalization_and_scaling.md) 3. **Unify tokenization** Same tokenizer + casing across ingestion and query. Guide: [Tokenization & Casing](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/tokenization_and_casing.md) 4. **Audit chunking** Verify semantic alignment, no mid-thought splits. Guide: [Chunking → Embedding Contract](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/chunking_to_embedding_contract.md) 5. **Rebuild index if skewed** Drop old embeddings, rebuild with correct dimension. Guide: [Update & Index Skew](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/update_and_index_skew.md) --- ## FAQ for newcomers **Why is metric mismatch so common?** Because vector DBs default differently: FAISS often L2, Pinecone cosine, Redis dot. If your embedding model expects cosine, L2 will silently break recall. **Why normalize embeddings?** Without normalization, embeddings vary in magnitude. Distance stops reflecting meaning. **Why do tokenizers matter?** “Apple” vs “apple” may yield different vectors if one side lowercases, the other doesn’t. **What if coverage stays low after all fixes?** Check for fragmentation and duplication collapse. The issue may not be the embedding model itself, but how the index is populated. --- - [Retrieval (Global Fix Map)](https://github.com/onestardao/WFGY/tree/main/ProblemMap/GlobalFixMap/Retrieval/README.md) - [Vector DBs & Stores](https://github.com/onestardao/WFGY/tree/main/ProblemMap/GlobalFixMap/VectorDBs_and_Stores/README.md) --- ### 🔗 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 | Layer | Page | What it’s for | | --- | --- | --- | | Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof | | Engine | [WFGY 1.0](/legacy/README.md) | Original PDF based tension engine | | Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents | | Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine, 131 S class set | | Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure checklist and fix map | | Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline | | Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer | | Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix | | Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 | | Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers | | App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot | | App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS | | App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control | | App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo | If this repository helped, starring it improves discovery so more builders can find the docs and tools. [](https://github.com/onestardao/WFGY)