---
last_updated: 2026-07-20
tags: [embedding, openai, cloud, farsi, insurance, rag]
source: import-knowledge
---

# OpenAI — text-embedding-3-large & text-embedding-3-small

## Overview

OpenAI's embedding API is the current baseline for our RAG pipeline. Two models available: text-embedding-3-large (higher quality) and text-embedding-3-small (cheaper).

## text-embedding-3-large *(current baseline)*

- **Type**: Cloud
- **Provider**: OpenAI
- **Price**: $0.13 per 1M input tokens ([source](https://openai.com/api/pricing/))
- **Max input tokens**: 8,191
- **Embedding dimensions**: 256, 1024, or 3072 (configurable via `dimensions` param)
- **Language coverage**: Multilingual (trained on multilingual corpus including Farsi)
- **Persian benchmark**: No public Persian-specific retrieval benchmark score published by OpenAI. MTEB aggregate available; MIRACL-Farsi subtask scores exist on the MTEB leaderboard but not officially published by OpenAI.
- **Notes**: Good general multilingual performance but not specifically optimized for Farsi. Pricing is mid-range among cloud options. Already integrated — zero migration cost.

## text-embedding-3-small *(cheaper alternative)*

- **Type**: Cloud
- **Provider**: OpenAI
- **Price**: $0.02 per 1M input tokens ([source](https://openai.com/api/pricing/))
- **Max input tokens**: 8,191
- **Embedding dimensions**: 512 or 1536 (configurable)
- **Language coverage**: Multilingual
- **Persian benchmark**: No public Persian-specific scores.
- **Notes**: 6.5× cheaper than text-embedding-3-large but generally lower retrieval quality. Useful as a cost-optimized fallback, not as a quality upgrade.

## API Test — Curl

**Endpoint**: `POST https://api.openai.com/v1/embeddings`
**Auth**: `Authorization: Bearer $OPENAI_API_KEY`
**Get key**: https://platform.openai.com/api-keys

### text-embedding-3-large

```bash
curl -s https://api.openai.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "text-embedding-3-large",
    "input": ["بیمه نامه شخص ثالث چیست؟"]
  }' | python3 -m json.tool
```

### text-embedding-3-small

```bash
curl -s https://api.openai.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "text-embedding-3-small",
    "input": ["بیمه نامه شخص ثالث چیست؟"]
  }' | python3 -m json.tool
```

**Response shape**: `data[0].embedding` = float array, `usage.total_tokens` = token count

## API Test — Python

```python
from openai import OpenAI

client = OpenAI()  # uses OPENAI_API_KEY env var

# Test with a Farsi insurance query
response = client.embeddings.create(
    model="text-embedding-3-large",
    input=["بیمه نامه شخص ثالث چیست؟"]
)

embedding = response.data[0].embedding
tokens = response.usage.total_tokens
print(f"Dimensions: {len(embedding)}, Tokens: {tokens}")
# Expected: Dimensions: 3072, Tokens: 6
```

## Python Test — Compare Both Models

```python
from openai import OpenAI
import time

client = OpenAI()
query = "شرایط لغو بیمه نامه چیست؟"

for model in ["text-embedding-3-large", "text-embedding-3-small"]:
    start = time.time()
    resp = client.embeddings.create(model=model, input=[query])
    elapsed = time.time() - start
    dims = len(resp.data[0].embedding)
    tokens = resp.usage.total_tokens
    print(f"{model}: dims={dims}, tokens={tokens}, time={elapsed:.2f}s")
```

## Response Format

| Field | Path |
|-------|------|
| Embedding vector | `data[0].embedding` |
| Token count | `usage.total_tokens` |

## Key Differences from Other Providers

- Uses standard `input` field (string or array)
- Configurable dimensions — can reduce from 3072 to 1024 or 256 to save storage/latency
- 8K context limit — may require aggressive chunking for long insurance documents
- Data sent to US servers — not on-premises

## TODO

- [ ] Benchmark on MIRACL-Farsi subset
- [ ] Test dimension reduction (3072 → 1024) impact on retrieval quality
- [ ] Calculate monthly cost at production volume