WFGY/ProblemMap/GlobalFixMap/RAG_VectorDB/tokenization_and_casing.md

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.
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

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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.

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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.

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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.

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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.

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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.

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