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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.
- Visual map and recovery: rag-architecture-and-recovery.md
- Why this snippet and how to cite: retrieval-traceability.md
- Snippet schema fence: data-contracts.md
- Embedding vs meaning: embedding-vs-semantic.md
- Chunk boundary sanity: chunking-checklist.md
Related in this folder:
- Tokenization drift: tokenizer_mismatch.md
- Locale and analyzer drift: locale_drift.md
- Multilingual guide hub: multilingual_guide.md
- HyDE behavior by language: hyde_multilingual.md
- 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
| 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 |
-
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.
-
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.
-
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.
- Verify Coverage ≥ 0.70 and ΔS ≤ 0.45 on three paraphrases with different script orders.
- 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_normalizationorpersian_normalization. - Strip bidi control chars in a char filter.
- Set the same analyzer for
indexandsearch_analyzeron the field. - Create a keyword subfield for exact acronyms and model names. Reference: retrieval-playbook.md
- 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
- 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
- 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.
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.
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ΔS remains ≥ 0.60 after normalization and analyzer unification. Re-chunk with stable boundaries and re-embed a gold slice. Open: chunking-checklist.md
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Citations still jump between sections on mixed-script inputs. Enforce snippet schema and forbid cross-section reuse. Open: data-contracts.md, retrieval-traceability.md
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Hybrid retrieval underperforms a single retriever. Align normalization rules before rerank, and make rerank deterministic. Open: rerankers.md
| 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 |
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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