Mahdi Bazrafshan
6c9cebaaa0
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)
98 lines
3.0 KiB
Markdown
98 lines
3.0 KiB
Markdown
---
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last_updated: 2026-07-20
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tags: [embedding, voyage-ai, cloud, farsi, insurance, rag, free-tier]
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source: import-knowledge
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---
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# Voyage AI — voyage-4-large & voyage-4
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## Overview
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Voyage AI's latest embedding models. Founded by OpenAI alumni. 200M free tokens — risk-free testing.
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## voyage-4-large *(recommended)*
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- **Type**: Cloud
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- **Provider**: Voyage AI
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- **Price**: $0.12 per 1M input tokens (200M free tokens included) ([source](https://docs.voyageai.com/docs/pricing))
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- **Max input tokens**: 32,000
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- **Embedding dimensions**: 1024
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- **Language coverage**: Multilingual
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- **Persian benchmark**: No public Persian-specific scores. MTEB multilingual aggregate available.
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- **Notes**: Slightly cheaper than OpenAI text-embedding-3-large ($0.12 vs $0.13). 32K context — 4× OpenAI's 8K. voyage-3-large and voyage-3 are now legacy.
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## voyage-4 *(mid-tier)*
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- **Type**: Cloud
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- **Provider**: Voyage AI
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- **Price**: $0.06 per 1M input tokens (200M free tokens included) ([source](https://docs.voyageai.com/docs/pricing))
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- **Max input tokens**: 32,000
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- **Embedding dimensions**: 1024
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- **Language coverage**: Multilingual
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- **Persian benchmark**: No public Persian-specific scores.
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## Pros
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- $0.12/1M tokens — slightly cheaper than OpenAI
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- 200M tokens free — enough for testing + months of low-volume traffic
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- 32K context — 4× OpenAI's context window
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- Strong MTEB multilingual scores
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- Simple API (OpenAI-compatible format)
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## Cons
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- No published Farsi-specific scores
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- Smaller company — less battle-tested at enterprise scale
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- Requires separate API key management
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## API Test — Curl
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**Endpoint**: `POST https://api.voyageai.com/v1/embeddings`
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**Auth**: `Authorization: Bearer $VOYAGE_API_KEY`
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**Get key**: https://voyage.ai/api-keys
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**Free tier**: 200M tokens
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### voyage-4-large
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```bash
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curl -s https://api.voyageai.com/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $VOYAGE_API_KEY" -d '{
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"model": "voyage-4-large",
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"input": ["بیمه نامه شخص ثالث چیست؟"]
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}' | python3 -m json.tool
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```
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### voyage-4
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```bash
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curl -s https://api.voyageai.com/v1/embeddings -H "Content-Type: application/json" -H "Authorization: Bearer $VOYAGE_API_KEY" -d '{
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"model": "voyage-4",
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"input": ["بیمه نامه شخص ثالث چیست؟"]
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}' | python3 -m json.tool
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```
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**Response shape**: `data[0].embedding` = float array, `usage.total_tokens` = token count
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## API Test — Python
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```python
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import voyageai
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vo = voyageai.Client(api_key="YOUR_VOYAGE_API_KEY") # or uses VOYAGE_API_KEY env var
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result = vo.embed(
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["بیمه نامه شخص ثالث چیست؟"],
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model="voyage-4-large",
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input_type="document"
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)
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embedding = result.embeddings[0]
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print(f"Dimensions: {len(embedding)}, Tokens: {result.total_tokens}")
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```
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## TODO
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- [ ] Run 100-question eval set against voyage-4-large (200M free tokens)
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- [ ] Benchmark on MIRACL-Farsi subset
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- [ ] Compare with OpenAI text-embedding-3-large on Farsi queries
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- [ ] Calculate cost at production volume
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