Tokenization and Casing — Guardrails and Fix Pattern

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
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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
Chunking checklist: chunking-checklist.md
Retrieval traceability: retrieval-traceability.md
Embedding vs meaning: 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.
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

Check tokenizer logs
Sample corpus and query text, run through the same tokenizer, compare token IDs.
Case-fold
Apply .lower() or Unicode case-fold to both corpus and queries before embedding.
Normalize Unicode
Use unicodedata.normalize("NFKC", text) to ensure consistency.
Re-index if drift found
If tokenization differs, rebuild embedding index after enforcing preprocessing rules.

Copy-paste probe

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	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text 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	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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