# Script Mixing — Guardrails and Fix Patterns
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Keep retrieval stable when a single query or snippet mixes scripts and directions. Common cases: CJK + Latin acronyms, Arabic or Hebrew with numbers and English terms, Devanagari with Latin product names, and datasets where full-width digits appear beside half-width ASCII. --- ## Open these first * Visual map and recovery: [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) * Why this snippet and how to cite: [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) * Snippet schema fence: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md) * Embedding vs meaning: [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md) * Chunk boundary sanity: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) Related in this folder: * Tokenization drift: [tokenizer\_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/tokenizer_mismatch.md) * Locale and analyzer drift: [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md) * Multilingual guide hub: [multilingual\_guide.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/multilingual_guide.md) * HyDE behavior by language: [hyde\_multilingual.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/hyde_multilingual.md) --- ## Core acceptance targets * ΔS(question, retrieved) ≤ 0.45 for mixed-script queries * Coverage of the target section ≥ 0.70 after repair * λ remains convergent across three paraphrases that include different script orderings * E\_resonance flat on long windows with numerals, punctuation, and brand names mixed in --- ## What this failure looks like | Symptom | Likely cause | Where to fix | | ---------------------------------------------------------------- | --------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | Arabic or Hebrew queries return partial hits or broken citations | Bidirectional marks and numerals flip visual order; analyzer not bidi-aware | Normalize directionality and digits before indexing and querying | | CJK text with Latin acronyms splits unpredictably | Mixed width digits, zero-width chars, or inconsistent spacing rules | Pre-normalize width, strip zero-width, add script-boundary spacing for embedding | | English brand + Thai sentence retrieves far sections | Different analyzers per stage cause token joins and drops | Unify analyzer and pre-segment at script transitions | | High similarity but wrong meaning on acronyms | Casing and width normalization inconsistent between corpus and query | Apply the same ASCII, width, and case rules in both pipelines | --- ## Fix in 60 seconds 1. **Measure ΔS** Run the original mixed-script query and a variant where scripts are separated by spaces. If ΔS improves by ≥ 0.10, you have a script-mixing normalization gap. 2. **Probe λ\_observe** Swap the order of scripts in the query, keep semantics identical. If λ flips or citations jump, lock prompt headers and fix normalization and analyzer alignment first. 3. **Apply the smallest structural change** * Normalize Unicode to NFC, convert full-width to half-width for digits and ASCII. * Remove zero-width characters, directional isolates from raw text. * Ensure the same analyzer is used for both index and query, or pre-segment before embedding. 4. **Verify** Coverage ≥ 0.70 and ΔS ≤ 0.45 on three paraphrases with different script orders. --- ## Minimal repair recipes by stack ### Elasticsearch / OpenSearch * Use ICU chain for mixed scripts. Typical pipeline: `icu_normalizer` (NFC) → `icu_transform` (full-width to half-width) → `icu_folding` → optional CJK bigram filter. * For Arabic or Persian add `arabic_normalization` or `persian_normalization`. * Strip bidi control chars in a char filter. * Set the same analyzer for `index` and `search_analyzer` on the field. * Create a keyword subfield for exact acronyms and model names. Reference: [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md) ### BM25 in code or light stores * Preprocess text with a normalization step that performs: Unicode NFC, width fold for digits and ASCII, lowercasing where safe, removal of zero-width and bidi marks. * For CJK, insert temporary spaces at script boundaries or use character bigrams for both index and query. * Keep identical punctuation rules across stages. Open: [pattern\_query\_parsing\_split.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_query_parsing_split.md) ### Vector stores (FAISS, Milvus, Qdrant, Weaviate, pgvector) * Normalize text before embedding with the same script rules for corpus and queries. * Add lightweight lexical recall (BM25) to catch brand names and numerals, then rerank deterministically. * Re-embed only a gold slice to validate, then batch the full rebuild. Open: [vectorstore-fragmentation.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/patterns/pattern_vectorstore_fragmentation.md) --- ## Diagnostic checklist * The same normalization code runs for ingest and query. * Width folding, casing, digit policy are identical across stages. * Bidi control marks removed or isolated consistently. * Chunk boundaries do not split inside script transitions that carry meaning. * Rerank stage views the normalized text, not raw captures. --- ## Copy-paste tests **Script order probe** ``` Q0: original mixed-script query Q1: same words, scripts reordered Q2: same words, add spaces at script boundaries Return a table with ΔS per query, λ_state, and whether citations stayed in the same section. ``` **Bidi and width sanity** ``` Given a sentence with Arabic text, ASCII digits, and an English acronym: 1) Remove bidi marks and normalize widths. 2) Show the token sequence used by the retriever. 3) Verify that numbers appear in logical order and acronyms stay intact. ``` --- ## When to escalate * ΔS remains ≥ 0.60 after normalization and analyzer unification. Re-chunk with stable boundaries and re-embed a gold slice. Open: [chunking-checklist.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/chunking-checklist.md) * Citations still jump between sections on mixed-script inputs. Enforce snippet schema and forbid cross-section reuse. Open: [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md), [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) * Hybrid retrieval underperforms a single retriever. Align normalization rules before rerank, and make rerank deterministic. Open: [rerankers.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rerankers.md) --- ### 🔗 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 | Layer | Page | What it’s for | | --- | --- | --- | | Proof | [WFGY Recognition Map](/recognition/README.md) | External citations, integrations, and ecosystem proof | | Engine | [WFGY 1.0](/legacy/README.md) | Original PDF based tension engine | | Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents | | Engine | [WFGY 3.0](/TensionUniverse/EventHorizon/README.md) | TXT based Singularity tension engine, 131 S class set | | Map | [Problem Map 1.0](/ProblemMap/README.md) | Flagship 16 problem RAG failure checklist and fix map | | Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline | | Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer | | Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix | | Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 | | Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers | | App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot | | App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS | | App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control | | App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo | If this repository helped, starring it improves discovery so more builders can find the docs and tools. 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