docs(research): add farsi embedding model research knowledge base

Body:
        Why:
        - Insurance RAG pipeline needs Farsi-optimized embeddings
        - Current local baseline (Nomic Embed) is English-only

        Changes:
        - 8 cloud model profiles with curl + python test commands
        - 5 local model profiles with VRAM, serving platform notes
        - Persian benchmark landscape (MIRACL-Fa, mMARCO, FaMTE, MTEB)
        - Decision report with ranked options and recommendations
        - Full comparison table (15 models × 10 columns)

        Impact:
        - Knowledge base for embedding model selection
        - Top picks: BGE-M3 (local), Cohere embed-v4.0 (cloud)
        - Critical: Nomic Embed must be replaced (English-only)

embedding_models/mE5-large.md

@@ -0,0 +1,73 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, multilingual-e5, local, farsi, insurance, rag]
+source: import-knowledge
+---
+
+# intfloat/multilingual-e5-large
+
+## Overview
+
+- **Type**: Local
+- **Provider**: intfloat
+- **Parameters**: ~560M (24-layer, xlm-roberta-large init)
+- **VRAM (fp16)**: ~1.1 GB
+- **Embedding dimensions**: 1024
+- **Max input tokens**: 512
+- **Language coverage**: 100 languages including Farsi
+- **Serving platform**: sentence-transformers, TEI, Ollama (available)
+- **License**: MIT
+- **Model card**: https://huggingface.co/intfloat/multilingual-e5-large
+
+## Pros
+
+- Most widely used multilingual embedding model
+- Strong community support and documentation
+- 100 languages including Farsi
+- MIT license
+- Available on Ollama
+
+## Cons
+
+- **512 token max context** — requires aggressive chunking for insurance documents
+- No ColBERT/sparse support
+- Requires `"query: "` / `"passage: "` prefixes for optimal retrieval
+- Benchmarked on Mr. TyDi but Farsi is NOT one of the 11 Mr. TyDi languages
+
+## ⚠️ 512 Token Limit
+
+Insurance policy clauses and FAQ answers can easily exceed 512 tokens. You will need to chunk aggressively. Consider BGE-M3 (8K context) or gte-multilingual-base (8K context) instead.
+
+## API Test — Ollama
+
+```bash
+ollama pull multilingual-e5-large
+
+curl -s http://localhost:11434/api/embeddings   -d '{
+    "model": "multilingual-e5-large",
+    "prompt": "query: بیمه نامه شخص ثالث چیست؟"
+  }' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["embedding"])}')"
+```
+
+**⚠️ Important**: Ollama may not auto-add the `"query: "` prefix. You must add it manually in the prompt for optimal retrieval quality.
+
+## API Test — Python
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("intfloat/multilingual-e5-large")
+
+# ⚠️ Must add "query: " prefix for queries, "passage: " for documents
+query_embedding = model.encode("query: بیمه نامه شخص ثالث چیست؟")
+doc_embedding = model.encode("passage: بیمه نامه شخص ثالث شامل پوشش خسارات مالی و جانی است.")
+
+print(f"Query dims: {len(query_embedding)}, Doc dims: {len(doc_embedding)}")
+# Expected: Query dims: 1024, Doc dims: 1024
+```
+
+## TODO
+
+- [ ] Benchmark on MIRACL-Farsi subset
+- [ ] Test with aggressive chunking (max 500 tokens)
+- [ ] Compare with BGE-M3 on Farsi retrieval quality