# Tokenization and Casing — 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 **retrieval fails because the text was chunked or embedded with inconsistent tokenization or casing rules**.
This is common when corpus ingestion applies one tokenizer (e.g. sentencepiece, BPE) and queries use another, or when upper/lowercase mismatches create drift.
---
## Open these first
- Visual map and recovery: [RAG Architecture & Recovery](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
- Chunking checklist: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
- Retrieval traceability: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
- Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
---
## Core acceptance
- Corpus and query tokenizers are identical.
- ΔS(question, retrieved) ≤ 0.45, stable under three paraphrases.
- λ remains convergent across casing variants.
- Coverage ≥ 0.70 for the target section.
---
## Typical breakpoints and the right fix
- **Different tokenizers for corpus vs query**
→ Rebuild index with unified tokenizer. See [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md).
- **Casing drift** (retrieval fails if query has capitalized or accented terms)
→ Apply consistent lowercasing or case-fold normalization before embedding.
- **Unicode variants** (fullwidth vs halfwidth, accents vs base letters)
→ Normalize text with NFC/NFKC before chunking.
- **Mixed language tokenization** (CJK vs Latin vs Indic split differently)
→ Align multilingual tokenizer to match model embedding assumptions.
---
## Fix in 60 seconds
1. **Check tokenizer logs**
Sample corpus and query text, run through the same tokenizer, compare token IDs.
2. **Case-fold**
Apply `.lower()` or Unicode case-fold to both corpus and queries before embedding.
3. **Normalize Unicode**
Use `unicodedata.normalize("NFKC", text)` to ensure consistency.
4. **Re-index if drift found**
If tokenization differs, rebuild embedding index after enforcing preprocessing rules.
---
## Copy-paste probe
```python
import unicodedata
def normalize_and_lower(text):
return unicodedata.normalize("NFKC", text).lower()
sample = "Résumé vs Resume"
print(normalize_and_lower(sample))
# → "resume vs resume"
````
Target: queries and corpus map to the same normalized form.
---
## Common gotchas
* Chunked with sentencepiece but queries fed through default BPE → mismatch.
* Different language casing (Turkish dotted i, German ß) → normalize before embed.
* Multilingual queries that mix scripts → ensure same tokenizer config across corpora.
---
### 🔗 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 | Canonical framework entry point | [View](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
| Problem Map | Diagnostic map and navigation hub | [View](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
| Tension Universe Experiments | MVP experiment field | [View](https://github.com/onestardao/WFGY/tree/main/TensionUniverse/Experiments) |
| Recognition | Where WFGY is referenced or adopted | [View](https://github.com/onestardao/WFGY/blob/main/recognition/README.md) |
| AI Guide | Anti-hallucination reading protocol for tools | [View](https://github.com/onestardao/WFGY/blob/main/AI_GUIDE.md) |
> If this repository helps, starring it improves discovery for other builders.
> [](https://github.com/onestardao/WFGY)