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175 lines
7.6 KiB
Markdown
175 lines
7.6 KiB
Markdown
# 🌍 Multilingual RAG Guide — CJK, RTL, and Code-Mix Done Right
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Build one pipeline that works across languages without wrecking recall or citations.
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> **Quick Nav**
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> [Retrieval Playbook](./retrieval-playbook.md) ·
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> [Embedding vs Semantic](./embedding-vs-semantic.md) ·
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> [Patterns](./patterns/README.md) ·
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> [Rerankers](./rerankers.md)
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---
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## 0) Principles
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1. **Detect → Segment → Normalize → Dual-index → Fence citations.**
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2. Keep **original text** for display and **normalized text** for search.
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3. Pick **truly multilingual embeddings** or **per-lang encoders**—don’t mix silently.
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---
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## 1) Language detection
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- Use CLD3/fastText or a simple rule-first fallback (CJK regex, RTL markers).
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- Store `lang` on **chunks** and **queries**.
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- If uncertain, mark `lang="und"` and route to **char-level** retrieval.
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```json
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{"chunk_id":"c1","lang":"zh","text":"ΔS 衡量語義張力 ..."}
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````
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---
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## 2) Segmentation & normalization
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| Language | Segmenter | Notes |
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| --------------------- | ----------------- | ----------------------------------------------------- |
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| Chinese (zh) | jieba / pkuseg | Also store **OpenCC** simplified/Traditional variants |
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| Japanese (ja) | MeCab | Preserve readings for search if useful |
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| Korean (ko) | MeCab-ko / khaiii | Keep compound nouns intact when possible |
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| Thai (th) | PyThaiNLP | Sentence boundaries matter for chunking |
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| Arabic/Hebrew (ar/he) | ICU | Handle diacritics/RTL shaping |
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| Code-mix | ICU + heuristic | Fall back to character n-grams if needed |
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Normalization checklist:
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* Unify punctuation (full-width/half-width).
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* Unicode NFC.
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* Lowercase where appropriate (not for proper nouns in citations).
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* Keep both **`text_orig`** and **`text_norm`**.
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---
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## 3) Embeddings & indexing
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**Recommended multilingual encoders**
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* `bge-m3` (multilingual, strong cross-lingual)
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* `LaBSE` (older but solid cross-lingual)
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**Patterns**
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* If queries in lang A frequently need answers in lang B → *cross-lingual retrieval*.
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* For noisy OCR in CJK, consider **char-level dense + BM25** hybrid.
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**Indexing**
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* Add `lang` and `script` fields; route BM25 analyzers per language.
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* For FAISS dense index: **single multilingual vector space** is easiest; if per-lang spaces, keep a **router**.
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---
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## 4) Retrieval & reranking
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* First stage: **hybrid** (dense + BM25) with **RRF**.
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* If candidates include multiple languages, re-rank with **cross-encoder multilingual** models (e.g., `bge-reranker-base` works well).
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* Never drop minority language candidates too early—keep `k_in ≥ 100` when cross-lingual.
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---
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## 5) Prompting & citations (SCU-safe)
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* Show citations with **language labels** and **line spans**.
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* Forbid the LLM from translating citations; allow translations only in the **explanation**.
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* If answer language ≠ citation language, state: “Cited in {lang}, answer in {lang}.”
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---
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## 6) Evaluation
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* Build a multilingual **gold set**:
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* Include cross-lingual Q→A pairs (e.g., query in English, answer/citation in zh).
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* Track **recall\@50**, **nDCG\@10**, and **ΔS** per language.
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* Acceptance: ΔS ≤ 0.45 for top-ctx in each language; stable λ across 3 paraphrases per language.
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---
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## 7) Common multilingual pitfalls → fixes
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| Pitfall | Why it happens | Fix |
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| --------------------- | ----------------------------- | -------------------------------------------------- |
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| Hybrid fails on CJK | Analyzer/tokenizer mismatch | Use ICU analyzers; char-level BM25 |
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| S/T Chinese mismatch | Source vs query script differ | Store both via **OpenCC**; index two variants |
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| Citations translated | Prompt schema unlocked | Fence citations; explain can translate |
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| Cross-lang recall low | Monolingual embeddings | Use `bge-m3`/LaBSE; or translate query then search |
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| Arabic/Hebrew garbled | RTL shaping | ICU normalization; verify rendering layer |
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---
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## 8) Minimal example (Python, FAISS + BM25 + bge-m3)
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```python
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# pip install sentence-transformers rank_bm25 faiss-cpu opencc-python-reimplemented
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from sentence_transformers import SentenceTransformer
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from rank_bm25 import BM25Okapi
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import numpy as np, faiss, opencc
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enc = SentenceTransformer("BAAI/bge-m3")
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def norm(s, lang):
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if lang in ("zh-hant","zh-hans","zh"):
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return opencc.OpenCC('t2s.json').convert(s)
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return s
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chunks = [{"text":"ΔS 衡量語義張力", "lang":"zh"}, {"text":"Delta-S measures semantic stress", "lang":"en"}]
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X = enc.encode([norm(c["text"], c["lang"]) for c in chunks], normalize_embeddings=True)
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index = faiss.IndexFlatIP(X.shape[1]); index.add(X.astype(np.float32))
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bm25 = BM25Okapi([c["text"].split() for c in chunks])
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def search(q, lang="en"):
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qv = enc.encode([norm(q, lang)], normalize_embeddings=True).astype(np.float32)
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_, I = index.search(qv, 50); dense_rank=[(int(i), r+1) for r,i in enumerate(I[0])]
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sparse_rank=[(i, r+1) for r,i in enumerate(bm25.get_top_n(q.split(), list(range(len(chunks))), 50))]
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# RRF fuse
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```
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(Use a proper RRF from the Retrieval Playbook.)
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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 based tension engine |
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| Engine | [WFGY 2.0](/core/README.md) | Production tension kernel and math engine for RAG and agents |
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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 checklist and fix map |
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| Map | [Problem Map 2.0](/ProblemMap/rag-architecture-and-recovery.md) | RAG focused recovery pipeline |
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| Map | [Problem Map 3.0](/ProblemMap/wfgy-rag-16-problem-map-global-debug-card.md) | Global Debug Card, image as a debug protocol layer |
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| Map | [Semantic Clinic](/ProblemMap/SemanticClinicIndex.md) | Symptom to family to exact fix |
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| Map | [Grandma’s Clinic](/ProblemMap/GrandmaClinic/README.md) | Plain language stories mapped to Problem Map 1.0 |
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| Onboarding | [Starter Village](/StarterVillage/README.md) | Guided tour for newcomers |
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| App | [TXT OS](/OS/README.md) | TXT semantic OS, fast boot |
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| App | [Blah Blah Blah](/OS/BlahBlahBlah/README.md) | Abstract and paradox Q and A built on TXT OS |
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| App | [Blur Blur Blur](/OS/BlurBlurBlur/README.md) | Text to image with semantic control |
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| App | [Blow Blow Blow](/OS/BlowBlowBlow/README.md) | Reasoning game engine and memory demo |
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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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