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Eduard van Valkenburg
95fd5ec658
* renamed AzureAIINferenceEmbeddings and lazy load azure-cosmos and env var rename * updated coverage * fix readme
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# Run with: uv run samples/02-agents/embeddings/azure_openai_embeddings.py
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import asyncio
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import os
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from agent_framework.openai import OpenAIEmbeddingClient
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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"""This sample demonstrates Azure OpenAI embedding generation with ``OpenAIEmbeddingClient``.
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Prerequisites:
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Set the following environment variables or add them to a local ``.env`` file:
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- ``AZURE_OPENAI_ENDPOINT``: Your Azure OpenAI endpoint URL
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- ``AZURE_OPENAI_EMBEDDING_MODEL``: The embedding deployment name
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- ``AZURE_OPENAI_API_VERSION``: Optional API version override
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Sign in with ``az login`` before running the sample.
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"""
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load_dotenv()
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async def main() -> None:
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"""Generate embeddings with Azure OpenAI."""
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async with AzureCliCredential() as credential:
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client = OpenAIEmbeddingClient(
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model=os.getenv("AZURE_OPENAI_EMBEDDING_MODEL"),
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azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
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api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
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credential=credential,
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)
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# 1. Generate a single embedding.
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result = await client.get_embeddings(["Hello, world!"])
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print(f"Single embedding dimensions: {result[0].dimensions}")
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print(f"First 5 values: {result[0].vector[:5]}")
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print(f"Model: {result[0].model}")
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print(f"Usage: {result.usage}")
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print()
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# 2. Generate embeddings for multiple inputs.
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texts = [
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"The weather is sunny today.",
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"It is raining outside.",
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"Machine learning is fascinating.",
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]
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result = await client.get_embeddings(texts)
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print(f"Batch of {len(result)} embeddings, each with {result[0].dimensions} dimensions")
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print(f"First embedding vector: {result[0].vector[:5]}")
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print()
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# 3. Generate embeddings with custom dimensions.
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result = await client.get_embeddings(["Custom dimensions example"], options={"dimensions": 256})
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print(f"Custom dimensions: {result[0].dimensions}")
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output:
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Single embedding dimensions: 1536
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First 5 values: [0.012, -0.034, 0.056, -0.078, 0.090]
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Model: text-embedding-3-small
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Usage: {'prompt_tokens': 4, 'total_tokens': 4}
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Batch of 3 embeddings, each with 1536 dimensions
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Custom dimensions: 256
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"""
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