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/benchmarks/README.md

@@ -0,0 +1,92 @@
+---
+last_updated: 2026-07-20
+tags: [embedding, benchmarks, farsi, persian, miracl, mmarco, famte, mteb]
+source: import-knowledge
+---
+
+# Persian Embedding Benchmarks — What Exists and What Doesn't
+
+## Active Benchmarks
+
+| Benchmark | Has Farsi? | Task Type | URL | Status |
+|-----------|-----------|-----------|-----|--------|
+| **MIRACL** | ✅ Yes (1 of 18 languages) | Passage Retrieval (nDCG@10) | [HF: castorini/mirACL](https://huggingface.co/datasets/castorini/mirACL) | Active. Primary Persian retrieval benchmark. |
+| **MTEB** | ✅ Via MIRACL subtask | Embedding benchmark suite | [HF Spaces: mteb/leaderboard](https://huggingface.co/spaces/mteb/leaderboard) | Integrates MIRACL-Farsi. Per-language breakdowns available. |
+| **FaMTE** | ✅ Yes | Multi-task NLU incl. STS | [GitHub: pnlp-co/FaMTE](https://github.com/pnlp-co/FaMTE) | Persian-only. Repo may be relocated (404 during research). Includes STS relevant to embedding quality. |
+
+## Benchmarks That Do NOT Include Farsi
+
+| Benchmark | Languages | Farsi? |
+|-----------|-----------|--------|
+| **mMARCO** | 14 languages (EN, ZH, FR, DE, etc.) | ❌ No |
+| **Mr. TyDi** | 11 languages (AR, EN, FI, DE, etc.) | ❌ No |
+
+## Key Takeaway
+
+**MIRACL (Farsi) is the primary publicly available Persian retrieval benchmark.** Most embedding models do not publish isolated Farsi scores in their model cards — you need to run your own eval.
+
+## Loading MIRACL-Farsi
+
+```python
+from datasets import load_dataset
+
+# Load Farsi subset
+dataset = load_dataset("castorini/mirACL", "fas", split="validation")
+
+print(f"Number of queries: {len(dataset)}")
+print(f"Sample: {dataset[0]}")
+```
+
+## Using MIRACL-Farsi for Evaluation
+
+```python
+from datasets import load_dataset
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+# Load data
+dataset = load_dataset("castorini/mirACL", "fas", split="validation")
+model = SentenceTransformer("BAAI/bge-m3")
+
+# Embed queries and documents
+queries = [item["query"] for item in dataset]
+documents = [item["positive_document"] for item in dataset]
+
+query_embeddings = model.encode(queries, show_progress_bar=True)
+doc_embeddings = model.encode(documents, show_progress_bar=True)
+
+# Compute cosine similarity and MRR
+def mrr(query_embs, doc_embs, k=10):
+    scores = query_embs @ doc_embs.T
+    rr = []
+    for i, row in enumerate(scores):
+        ranked = np.argsort(row)[::-1][:k]
+        for rank, idx in enumerate(ranked):
+            if idx == i:  # assuming 1:1 query-doc mapping
+                rr.append(1 / (rank + 1))
+                break
+        else:
+            rr.append(0)
+    return np.mean(rr)
+
+print(f"MRR@10: {mrr(query_embeddings, doc_embeddings):.4f}")
+```
+
+## Building Your Own Insurance Eval Set
+
+Since Farsi-specific benchmarks are sparse, build your own:
+
+1. Start with your 100-row Q&A CSV
+2. Map each question to the relevant chunk IDs in your corpus
+3. For each question, embed with candidate model, rank all chunks by cosine similarity
+4. Measure Recall@5 (is the right chunk in top-5?) and MRR (rank of the right chunk)
+
+This gives you the most reliable signal for YOUR specific use case.
+
+## TODO
+
+- [ ] Download MIRACL-Farsi validation set
+- [ ] Run baseline eval with OpenAI text-embedding-3-large
+- [ ] Run eval with BGE-M3
+- [ ] Run eval with gte-multilingual-base
+- [ ] Map 100-row Q&A CSV to chunk IDs for insurance-domain eval