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/bge-m3.md

@@ -0,0 +1,127 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, bge-m3, local, farsi, insurance, rag, triple-mode, recommended]
+source: import-knowledge
+---
+
+# BAAI/bge-m3 — Top Local Recommendation ⭐
+
+## Overview
+
+- **Type**: Local
+- **Provider**: BAAI (Beijing Academy of AI)
+- **Parameters**: ~568M
+- **VRAM (fp16)**: ~1.1 GB
+- **Embedding dimensions**: 1024
+- **Max input tokens**: 8,192
+- **Language coverage**: 100+ languages including Farsi
+- **ColBERT/late interaction**: YES (native multi-vector retrieval)
+- **Sparse vectors**: YES (native lexical weight retrieval)
+- **Serving platform**: FlagEmbedding library, sentence-transformers, TEI, or Ollama (available)
+- **License**: MIT
+- **Model card**: https://huggingface.co/BAAI/bge-m3
+
+## Why It's #1
+
+**Triple-mode retrieval** (dense + sparse + ColBERT) from a single model. This is uniquely suited to LangGraph + Qdrant hybrid search:
+- **Dense**: Standard cosine similarity embeddings
+- **Sparse**: BM25-like lexical retrieval without a separate sparse encoder
+- **ColBERT**: Token-level late interaction for finer-grained matching
+
+Only 1.1 GB VRAM on a 24 GB RTX 3090 — leaves room for other workloads. 8K context handles long insurance policy chunks.
+
+## Pros
+
+- Supports Farsi (100+ languages)
+- Triple-mode: dense + sparse + ColBERT multi-vector
+- Hybrid search ready — no separate sparse encoder needed
+- 8,192 token context (can handle long insurance policy chunks)
+- Only 1.1 GB VRAM — leaves headroom for other workloads
+- MIT license — no commercial restrictions
+- Available on Ollama, TEI, sentence-transformers
+- Active development by BAAI (Beijing Academy of AI)
+
+## Cons
+
+- No published Farsi-specific retrieval benchmark scores
+- Slightly less community adoption than multilingual-E5
+- Triple-mode adds architectural complexity if only dense mode is used
+
+## API Test — Ollama
+
+```bash
+# Pull model
+ollama pull bge-m3
+
+# Test with Farsi insurance query
+curl -s http://localhost:11434/api/embeddings   -d '{
+    "model": "bge-m3",
+    "prompt": "بیمه نامه شخص ثالث چیست؟"
+  }' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["embedding"])}')"
+```
+
+## API Test — Python (sentence-transformers)
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("BAAI/bge-m3")
+
+# Farsi insurance query
+query = "شرایط لغو بیمه نامه چیست؟"
+embedding = model.encode(query)
+print(f"Dimensions: {len(embedding)}, Type: {type(embedding)}")
+# Expected: Dimensions: 1024
+```
+
+## API Test — Python (FlagEmbedding for triple-mode)
+
+```python
+from FlagEmbedding import BGEM3FlagModel
+
+model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
+
+# Encode with all three modes
+sentences = ["بیمه نامه شخص ثالث چیست؟", "شرایط بیمه آتش‌سوزی"]
+output = model.encode(sentences, return_dense=True, return_sparse=True, return_colbert_vecs=True)
+
+print(f"Dense dims: {output['dense_vecs'].shape}")
+print(f"Sparse tokens: {len(output['lexical_weights'][0])}")
+print(f"ColBERT vecs: {len(output['colbert_vecs'][0])}")
+```
+
+## Serving Platform Notes
+
+| Platform | Status | Notes |
+|----------|--------|-------|
+| **Ollama** ✅ | Available | Easiest. `ollama pull bge-m3` |
+| **TEI** ✅ | Available | Best throughput for production |
+| **sentence-transformers** ✅ | Available | Direct Python integration |
+| **vLLM** | Not natively | vLLM is for LLM serving, not embeddings |
+
+## Qdrant Integration
+
+BGE-M3's sparse vectors work natively with Qdrant's sparse vector support:
+
+```python
+from qdrant_client import QdrantClient
+from qdrant_client.models import VectorParams, Distance, SparseVectorParams
+
+client = QdrantClient("localhost", port=6333)
+
+# Create collection with both dense and sparse vectors
+client.create_collection(
+    collection_name="insurance_docs",
+    vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
+    sparse_vectors_config={
+        "sparse": SparseVectorParams()
+    }
+)
+```
+
+## TODO
+
+- [ ] Benchmark on MIRACL-Farsi subset
+- [ ] Compare dense-only vs triple-mode retrieval quality
+- [ ] Test with Qdrant sparse vector indexing
+- [ ] Measure inference latency on RTX 3090