# Normalization and Scaling — Guardrails and Fix Pattern
🧭 Quick Return to Map
> You are in a sub-page of **RAG_VectorDB**.
> To reorient, go back here:
>
> - [**RAG_VectorDB** — vector databases for retrieval and grounding](./README.md)
> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
>
> Think of this page as a desk within a ward.
> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
Use this page when **vector similarity is unstable because embeddings are not normalized or scaling factors differ** between training and retrieval.
This failure often appears when cosine distance is requested but vectors are stored raw, or when IP/dot metrics exaggerate magnitude.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
- Metric mismatch: [metric_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md)
- Chunking checklist: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
---
## Core acceptance
- Vectors are L2-normalized when using cosine similarity.
- ΔS(question, retrieved) ≤ 0.45, stable across three paraphrases.
- Coverage ≥ 0.70 on the target section.
- λ remains convergent across seeds.
---
## Typical breakpoints and the right fix
- **Cosine similarity reported but vectors not normalized**
→ [metric_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/RAG_VectorDB/metric_mismatch.md)
- **Dot product used without rescaling** (large norm vectors dominate retrieval)
→ Normalize or rescale embeddings before indexing.
- **Cross-model mixing** (embeddings from different checkpoints with different norms)
→ Re-normalize the corpus and queries to unit length.
- **Hybrid dense + sparse weighting unstable** (scale mismatch between BM25 scores and vector norms)
→ Apply explicit min-max or z-score scaling before weighted sum.
---
## Fix in 60 seconds
1. **Check norms**
Sample 100 embeddings. Compute mean L2 norm. If not ~1.0 under cosine, normalization missing.
2. **Normalize queries**
Ensure `query_vector = vector / ||vector||` before retrieval when using cosine.
3. **Corpus re-index**
Drop and rebuild index with normalized vectors if store does not enforce it.
4. **Hybrid scaling**
Normalize dense similarity scores into the same 0–1 range as BM25 before combining.
---
## Copy-paste probe
```python
import numpy as np
def check_norms(vectors):
norms = np.linalg.norm(vectors, axis=1)
return norms.mean(), norms.std()
mean_norm, std_norm = check_norms(sample_vectors)
print("Mean norm:", mean_norm, "Std:", std_norm)
````
Target: mean ≈ 1.0, std ≤ 0.05 for cosine retrieval.
---
### 🔗 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)
[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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