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/gte-multilingual.md

@@ -0,0 +1,71 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, gte, alibaba, local, farsi, insurance, rag, lightweight]
+source: import-knowledge
+---
+
+# Alibaba-NLP/gte-multilingual-base
+
+## Overview
+
+- **Type**: Local
+- **Provider**: Alibaba NLP
+- **Parameters**: ~305M
+- **VRAM (fp16)**: ~610 MB
+- **Embedding dimensions**: 768
+- **Max input tokens**: 8,192
+- **Language coverage**: 70+ languages **explicitly including Farsi** (fa)
+- **Serving platform**: sentence-transformers, TEI
+- **License**: Apache-2.0
+- **Model card**: https://huggingface.co/Alibaba-NLP/gte-multilingual-base
+
+## Why Consider It
+
+Lightest model with **confirmed Farsi support**. Only 610 MB VRAM. 8K context. Apache-2.0 license. Benchmarked on MIRACL and PL-MTEB. Simpler than BGE-M3 (dense-only, no ColBERT/sparse overhead).
+
+## Pros
+
+- Explicitly lists Farsi in supported languages
+- Only 610 MB VRAM — lightest meaningful option with Farsi
+- 8,192 token context
+- Benchmarked on MIRACL and PL-MTEB
+- Apache-2.0 license
+- Simpler architecture (dense-only) — easier to deploy and debug
+
+## Cons
+
+- No ColBERT/sparse support — dense-only retrieval
+- No published isolated Farsi retrieval scores
+- Alibaba NLP ecosystem less mature than HuggingFace ecosystem
+- Not available on Ollama (may need manual GGUF conversion)
+
+## API Test — Python
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base")
+
+# Farsi insurance query
+query = "شرایط لغو بیمه نامه چیست？"
+embedding = model.encode(query)
+print(f"Dimensions: {len(embedding)}")
+# Expected: Dimensions: 768
+```
+
+## API Test — TEI (Docker)
+
+```bash
+# Start TEI server
+docker run --gpus all -p 8080:80   -v $PWD/data:/data   ghcr.io/huggingface/text-embeddings-inference:latest   --model-id Alibaba-NLP/gte-multilingual-base
+
+# Test
+curl -s http://localhost:8080/embed   -H "Content-Type: application/json"   -d '{"inputs": "بیمه نامه شخص ثالث چیست؟"}'   | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d[0])}')"
+```
+
+## TODO
+
+- [ ] Benchmark on MIRACL-Farsi subset
+- [ ] Compare with BGE-M3 on Farsi retrieval quality
+- [ ] Test GGUF conversion for Ollama compatibility
+- [ ] Measure inference latency on RTX 3090