microsoft--agent-framework/python/samples/02-agents/embeddings/openai_embeddings.py

# Copyright (c) Microsoft. All rights reserved.

# Run with: uv run samples/02-agents/embeddings/openai_embeddings.py

import asyncio
import os

from agent_framework.openai import OpenAIEmbeddingClient
from dotenv import load_dotenv

"""This sample demonstrates OpenAI embedding generation with explicit constructor settings.

Prerequisites:
    Set ``OPENAI_API_KEY`` in your environment or in a local ``.env`` file.
"""

load_dotenv()


async def main() -> None:
    """Generate embeddings with OpenAI."""
    client = OpenAIEmbeddingClient(
        model="text-embedding-3-small",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # 1. Generate a single embedding.
    result = await client.get_embeddings(["Hello, world!"])
    print(f"Single embedding dimensions: {result[0].dimensions}")
    print(f"First 5 values: {result[0].vector[:5]}")
    print(f"Model: {result[0].model}")
    print(f"Usage: {result.usage}")
    print()

    # 2. Generate embeddings for multiple inputs.
    texts = [
        "The weather is sunny today.",
        "It is raining outside.",
        "Machine learning is fascinating.",
    ]
    result = await client.get_embeddings(texts)
    print(f"Batch of {len(result)} embeddings, each with {result[0].dimensions} dimensions")
    print(f"First embedding vector: {result[0].vector[:5]}")
    print()

    # 3. Generate embeddings with custom dimensions.
    result = await client.get_embeddings(["Custom dimensions example"], options={"dimensions": 256})
    print(f"Custom dimensions: {result[0].dimensions}")
    print(f"First 5 values: {result[0].vector[:5]}")


if __name__ == "__main__":
    asyncio.run(main())


"""
Sample output:
Single embedding dimensions: 1536
First 5 values: [0.012, -0.034, 0.056, -0.078, 0.090]
Model: text-embedding-3-small
Usage: {'prompt_tokens': 4, 'total_tokens': 4}

Batch of 3 embeddings, each with 1536 dimensions

Custom dimensions: 256
"""