Research/embedding_models/openai.md

last_updated, tags, source

last_updated	tags	source
2026-07-20	embedding openai cloud farsi insurance rag	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)
• 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)
• 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

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

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

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

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