mirror of
https://github.com/microsoft/agent-framework.git
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Eduard van Valkenburg
95fd5ec658
* renamed AzureAIINferenceEmbeddings and lazy load azure-cosmos and env var rename * updated coverage * fix readme
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "agent-framework-foundry",
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# ]
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# ///
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# Run with: uv run samples/02-agents/embeddings/foundry_embeddings.py
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import pathlib
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from agent_framework import Content
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from agent_framework.foundry import FoundryEmbeddingClient
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from dotenv import load_dotenv
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load_dotenv()
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"""Microsoft Foundry Image Embedding Example
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This sample demonstrates how to generate image embeddings using the
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Foundry embedding client with the Cohere-embed-v3-english model.
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Images are passed as ``Content`` objects created with ``Content.from_data()``.
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Prerequisites:
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Deploy an embedding model to a Foundry-hosted inference endpoint that supports image inputs,
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such as Cohere-embed-v3-english.
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The details page for that model, has a target URI and a Key, which should be set in environment variables or a .env
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file as follows, the target URI should append the `/models` path:
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- FOUNDRY_MODELS_ENDPOINT: Your Foundry models endpoint URL, for instance:
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https://<apim-instance>.azure-api.net/<foundry-instance>/models
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- FOUNDRY_MODELS_API_KEY: Your API key
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- FOUNDRY_EMBEDDING_MODEL: The text embedding model name
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(e.g. "text-embedding-3-small")
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- FOUNDRY_IMAGE_EMBEDDING_MODEL: The image embedding model name
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(e.g. "Cohere-embed-v3-english")
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"""
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SAMPLE_IMAGE_PATH = pathlib.Path(__file__).parent.parent.parent / "shared" / "sample_assets" / "sample_image.jpg"
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async def main() -> None:
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"""Generate image embeddings with Foundry."""
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async with FoundryEmbeddingClient() as client:
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# 1. Generate an image embedding.
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image_bytes = SAMPLE_IMAGE_PATH.read_bytes()
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image_content = Content.from_data(data=image_bytes, media_type="image/jpeg")
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result = await client.get_embeddings([image_content])
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print(f"Image 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 image and text embeddings separately in one call.
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# The client dispatches text to the text endpoint and images to the image
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# endpoint, then reassembles results in the original input order.
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result = await client.get_embeddings(["A half-timbered house in a forested valley", image_content])
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print(f"Text embedding dimensions: {result[0].dimensions}")
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print(f"First 5 values: {result[0].vector[:5]}")
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print(f"Image embedding dimensions: {result[1].dimensions}")
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print(f"First 5 values: {result[1].vector[:5]}")
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print()
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# 3. Generate image embeddings with input_type option.
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result = await client.get_embeddings(
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[image_content],
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options={"input_type": "document"},
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)
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print(f"Document embedding dimensions: {result[0].dimensions}")
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print(f"First 5 values: {result[0].vector[:5]}")
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output (using deployment: Cohere-embed-v3-english, which is Cohere's "embed-english-v3.0-image" model):
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Image embedding dimensions: 1024
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First 5 values: [0.029159546, -0.007926941, -0.0032978058, -0.0030403137, -0.012786865]
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Model: embed-english-v3.0-image
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Usage: {'input_token_count': 1000, 'output_token_count': 0}
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Text embedding dimensions: 1536
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First 5 values: [-0.019439403, 0.015791258, 0.012358093, 0.0028533707, -0.01649483]
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Image embedding dimensions: 1024
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First 5 values: [0.029159546, -0.007926941, -0.0032978058, -0.0030403137, -0.012786865]
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Document embedding dimensions: 1024
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First 5 values: [0.029159546, -0.007926941, -0.0032978058, -0.0030403137, -0.012786865]
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"""
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