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Mahdi Bazrafshan 6c9cebaaa0 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)

2026-07-20 15:00:11 +03:30

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Cohere — embed-v4.0

Overview

Type: Cloud
Provider: Cohere
Price: Enterprise only (Model Vault: ~$4.00–$5.00/hr dedicated instance) — legacy embed-multilingual-v3 was ~$0.10/1M tokens (source)
Max input tokens: 512,000
Embedding dimensions: 256, 512, 1024, or 1536 (configurable)
Language coverage: 100+ languages including Farsi
Persian benchmark: Cohere models score well on MIRACL-Farsi on the MTEB leaderboard. No isolated Farsi numbers published by Cohere.

Why It's #1 Cloud Pick

512K context window — largest of any embedding API. Can embed full insurance documents without chunking. Strong Farsi support (100+ languages). MIRACL-Farsi scores on MTEB leaderboard.

Caveat: Enterprise-only pricing. No public per-token API anymore.

Pros

100+ languages with explicit Farsi support
512K context — can embed full documents, no chunking needed
Strong MIRACL-Farsi scores
v2 API with input_type distinction (search_query vs search_document)
Configurable dimensions (256–1536)

Cons

Enterprise-only pricing (Model Vault: ~$4–5/hr dedicated instance)
No public per-token API pricing — requires sales engagement
Smaller ecosystem than OpenAI

API Test — Curl

Endpoint: POST https://api.cohere.ai/v2/embed Auth: Authorization: Bearer $COHERE_API_KEY Get key: https://dashboard.cohere.com/api-keys

curl -s https://api.cohere.ai/v2/embed   -H "Content-Type: application/json"   -H "Authorization: Bearer $COHERE_API_KEY"   -d '{
    "model": "embed-v4.0",
    "texts": ["بیمه نامه شخص ثالث چیست؟"],
    "input_type": "search_query",
    "embedding_types": ["float"]
  }' | python3 -m json.tool

⚠️ Key Differences from OpenAI

Aspect	OpenAI	Cohere
Input field	`"input"` (string/array)	`"texts"` (array)
Input type	N/A	Required: `"search_query"` or `"search_document"`
Output format	`data[0].embedding`	`data.embeddings.float[0]`
Token count	`usage.total_tokens`	`meta.billed_units.input_tokens`

Indexing Documents (use search_document)

curl -s https://api.cohere.ai/v2/embed   -H "Content-Type: application/json"   -H "Authorization: Bearer $COHERE_API_KEY"   -d '{
    "model": "embed-v4.0",
    "texts": ["بیمه نامه شخص ثالث شامل پوشش خسارات مالی و جانی است."],
    "input_type": "search_document",
    "embedding_types": ["float"]
  }' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["data"]["embeddings"]["float"][0])}, tokens={d["meta"]["billed_units"]["input_tokens"]}')"

API Test — Python

import cohere

co = cohere.ClientV2(api_key="YOUR_COHERE_API_KEY")  # or use COHERE_API_KEY env var

response = co.embed(
    model="embed-v4.0",
    input_type="search_query",
    texts=["بیمه نامه شخص ثالث چیست؟"],
    embedding_types=["float"],
    output_dimension=1024
)

embedding = response.embeddings.float[0]
print(f"Dimensions: {len(embedding)}")

TODO

Contact Cohere sales for enterprise pricing
Benchmark on MIRACL-Farsi subset
Compare with OpenAI text-embedding-3-large on Farsi queries
Verify on-premises deployment options

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