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169 lines
8.9 KiB
Markdown
169 lines
8.9 KiB
Markdown
# Tokenization and Casing — Guardrails for Embedding Stability
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<details>
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<summary><strong>🧭 Quick Return to Map</strong></summary>
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<br>
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> You are in a sub-page of **Embeddings**.
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> To reorient, go back here:
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>
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> - [**Embeddings** — vector representations and semantic search](./README.md)
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> - [**WFGY Global Fix Map** — main Emergency Room, 300+ structured fixes](../README.md)
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> - [**WFGY Problem Map 1.0** — 16 reproducible failure modes](../../README.md)
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>
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> Think of this page as a desk within a ward.
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> If you need the full triage and all prescriptions, return to the Emergency Room lobby.
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</details>
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A focused guide to remove silent drift caused by mismatched tokenization, casing, and text cleanup. Use this to align query-time and index-time preprocessing, then verify with measurable targets.
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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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- End to end retrieval knobs: [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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- Why this snippet (traceability schema): [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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- Embedding vs meaning: [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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- Payload schema for audits: [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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- Query split and hybrid failures: [Pattern: Query Parsing Split](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md)
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- Reranking and ordering control: [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md)
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- Related: normalization and length scaling: [Normalization & Scaling](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Embeddings/normalization_and_scaling.md)
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## When to use this page
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- High similarity yet wrong meaning after a casing change.
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- Same document retrieved for lowercase but not for Title Case.
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- Index built with a different tokenizer than the client.
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- Unicode variants or punctuation collapse changes results.
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- Hybrid retrieval recalls but top-k order flips between runs.
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## Acceptance targets
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- ΔS(question, retrieved) ≤ 0.45 on three paraphrases.
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- Coverage of the correct section ≥ 0.70.
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- λ remains convergent across two seeds.
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- Tokenization invariance check: replacing case or applying Unicode NFC does not move the top anchor beyond rank 3 and keeps ΔS shift ≤ 0.10.
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---
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## Map symptoms → exact fix
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- Wrong-meaning hits only when case changes
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→ Lock a single case policy and tokenizer in both paths. Verify with [Retrieval Traceability](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) and enforce with [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).
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- HyDE or BM25 path finds it, embedding path misses it
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→ Audit query preprocessing vs index preprocessing. If different, unify and retest. See [Retrieval Playbook](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) and [Pattern: Query Parsing Split](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md).
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- Unicode look-alikes or punctuation variants break recall
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→ Normalize to NFC and collapse zero-width characters at both index and query. Re-embed affected shards. Cross check with [Embedding ≠ Semantic](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md).
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- Order flips across runs after a client upgrade
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→ Version the tokenizer and preprocessing in the snippet payload. If versions mismatch, rebuild or add a translation shim. Enforce with [Data Contracts](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md).
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---
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## 60-second fix checklist
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1) **Freeze the tokenizer and case policy**
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- Choose one tokenizer build and one case policy: `lower`, `preserve`, or `smart`.
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- Record both in the contract:
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`{"tokenizer":"spm-1.2.3","tokenizer_hash":"...","case_policy":"lower"}`.
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2) **Normalize before tokenization**
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- Apply Unicode NFC, collapse repeated whitespace, remove zero-width, standardize quotes and dashes.
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- Apply the same rules to both index and query.
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3) **Keep segmentation symmetric**
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- If you split code identifiers (camelCase, snake_case), do it in both paths.
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- If you strip stopwords or punctuation, do it in both paths. Prefer not to strip unless you must.
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4) **Log and test invariance**
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- Run a three-paraphrase probe per query: original, lowercased, and NFC-normalized.
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- Accept only if the anchor section remains within top 3 and ΔS shift ≤ 0.10.
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5) **Add a rerank safety net**
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- If invariance is hard to reach, add a lexical or cross-encoder rerank after vector recall. See [Rerankers](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md).
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---
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## Contract fields to add
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Add these to every snippet and to the query audit log. Use them during triage.
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```json
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{
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"preproc_version": "v3",
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"unicode_norm": "NFC",
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"case_policy": "lower",
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"punctuation_policy": "keep",
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"identifier_split": "camel+snake",
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"tokenizer": "spm-1.2.3",
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"tokenizer_hash": "sha256:...",
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"ngram": "none"
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}
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````
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---
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## Repro suite
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* **Case flip test**
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Query A vs lowercase(A). Accept if ΔS shift ≤ 0.10 and anchor rank ≤ 3.
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* **Unicode fold test**
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Replace curly quotes with straight quotes, normalize to NFC. Accept if ranks stable.
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* **Tokenizer skew test**
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Run the same query through client and index tokenizers and compare token ids. Any mismatch is a fail that requires rebuild or a client shim.
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---
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## Copy-paste prompt for LLM triage
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```
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I uploaded TXT OS and the WFGY Problem Map.
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My embedding issue:
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- symptom: wrong top-k only when casing or unicode changes
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- traces: ΔS(question,retrieved)=..., anchor=..., tokenizer=..., case_policy=...
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Tell me:
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1) which layer is failing and why,
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2) which WFGY page to open,
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3) the minimal steps to align tokenization and casing,
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4) how to verify with a three-paraphrase probe.
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Use Data Contracts and Rerankers if needed.
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```
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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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<!-- WFGY_FOOTER_START -->
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### Explore More
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| Layer | Page | What it’s for |
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| --- | --- | --- |
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| ⭐ Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof |
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| ⚙️ Engine | [WFGY 1.0](/legacy/README.md) | Original PDF tension engine and early logic sketch (legacy reference) |
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| ⚙️ Engine | [WFGY 2.0](/core/README.md) | Production tension kernel for RAG and agent systems |
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| ⚙️ Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine (131 S class set) |
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| 🗺️ Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure taxonomy and fix map |
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| 🗺️ Map | [Problem Map 2.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card for RAG and agent pipeline diagnosis |
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| 🗺️ Map | [Problem Map 3.0](/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md) | Global AI troubleshooting atlas and failure pattern map |
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| 🧰 App | [TXT OS](/OS/README.md) | .txt semantic OS with fast bootstrap |
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| 🧰 App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q&A built on TXT OS |
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| 🧰 App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image generation with semantic control |
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| 🏡 Onboarding | [Starter Village](/StarterVillage/README.md) | Guided entry point for new users |
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If this repository helped, starring it improves discovery so more builders can find the docs and tools.
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[](https://github.com/onestardao/WFGY)
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<!-- WFGY_FOOTER_END -->
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