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ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md
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# Tokenization and Casing — Guardrails and Fix Pattern
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Use this page when **retrieval fails because the text was chunked or embedded with inconsistent tokenization or casing rules**.
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This is common when corpus ingestion applies one tokenizer (e.g. sentencepiece, BPE) and queries use another, or when upper/lowercase mismatches create drift.
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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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- Chunking checklist: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md)
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- Retrieval traceability: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.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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---
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## Core acceptance
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- Corpus and query tokenizers are identical.
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- ΔS(question, retrieved) ≤ 0.45, stable under three paraphrases.
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- λ remains convergent across casing variants.
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- Coverage ≥ 0.70 for the target section.
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---
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## Typical breakpoints and the right fix
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- **Different tokenizers for corpus vs query**
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→ Rebuild index with unified tokenizer. See [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md).
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- **Casing drift** (retrieval fails if query has capitalized or accented terms)
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→ Apply consistent lowercasing or case-fold normalization before embedding.
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- **Unicode variants** (fullwidth vs halfwidth, accents vs base letters)
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→ Normalize text with NFC/NFKC before chunking.
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- **Mixed language tokenization** (CJK vs Latin vs Indic split differently)
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→ Align multilingual tokenizer to match model embedding assumptions.
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---
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## Fix in 60 seconds
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1. **Check tokenizer logs**
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Sample corpus and query text, run through the same tokenizer, compare token IDs.
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2. **Case-fold**
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Apply `.lower()` or Unicode case-fold to both corpus and queries before embedding.
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3. **Normalize Unicode**
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Use `unicodedata.normalize("NFKC", text)` to ensure consistency.
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4. **Re-index if drift found**
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If tokenization differs, rebuild embedding index after enforcing preprocessing rules.
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---
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## Copy-paste probe
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```python
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import unicodedata
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def normalize_and_lower(text):
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return unicodedata.normalize("NFKC", text).lower()
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sample = "Résumé vs Resume"
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print(normalize_and_lower(sample))
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# → "resume vs resume"
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````
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Target: queries and corpus map to the same normalized form.
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---
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## Common gotchas
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* Chunked with sentencepiece but queries fed through default BPE → mismatch.
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* Different language casing (Turkish dotted i, German ß) → normalize before embed.
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* Multilingual queries that mix scripts → ensure same tokenizer config across corpora.
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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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### 🧭 Explore More
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| Module | Description | Link |
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| ------------------------ | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
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| 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) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| 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) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| 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) |
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| 🧙♂️ 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) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <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).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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