microsoft--agent-framework/python/samples/02-agents/providers/foundry/foundry_chat_client.py

# Copyright (c) Microsoft. All rights reserved.

import asyncio
import os
from random import randint
from typing import Annotated

from agent_framework import Agent, tool
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

"""
Foundry Chat Client with Project Endpoint Example

This sample demonstrates how to create a FoundryChatClient using a
Foundry project endpoint. Instead of providing a service endpoint
directly, you provide a Foundry project endpoint and the client is created via
the Azure AI Foundry project SDK.

This requires:
- The `FOUNDRY_PROJECT_ENDPOINT` environment variable set to your Foundry project endpoint.
- The `FOUNDRY_MODEL` environment variable set to the model deployment name.
"""


@tool(approval_mode="never_require")
def get_weather(
    location: Annotated[str, "The location to get the weather for."],
) -> str:
    """Get the weather for a given location."""
    conditions = ["sunny", "cloudy", "rainy", "stormy"]
    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."


async def non_streaming_example() -> None:
    """Example of non-streaming response (get the complete result at once)."""
    print("=== Non-streaming Response Example ===")

    # 1. Create the FoundryChatClient using a Foundry project endpoint.
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    credential = AzureCliCredential()
    _client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ["FOUNDRY_MODEL"],
        credential=credential,
    )
    agent = Agent(
        client=_client,
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )

    # 2. Run a query and print the result.
    query = "What's the weather like in Seattle?"
    print(f"User: {query}")
    result = await agent.run(query)
    print(f"Result: {result}\n")


async def streaming_example() -> None:
    """Example of streaming response (get results as they are generated)."""
    print("=== Streaming Response Example ===")

    # 1. Create the FoundryChatClient using a Foundry project endpoint.
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    credential = AzureCliCredential()
    _client = FoundryChatClient(
        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        model=os.environ["FOUNDRY_MODEL"],
        credential=credential,
    )
    agent = Agent(
        client=_client,
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )

    # 2. Stream the response and print each chunk as it arrives.
    query = "What's the weather like in Portland?"
    print(f"User: {query}")
    print("Agent: ", end="", flush=True)
    async for chunk in agent.run(query, stream=True):
        if chunk.text:
            print(chunk.text, end="", flush=True)
    print("\n")


async def main() -> None:
    print("=== Foundry Chat Client with Project Endpoint Example ===")

    await non_streaming_example()
    await streaming_example()


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


"""
Sample output:
=== Foundry Chat Client with Project Endpoint Example ===
=== Non-streaming Response Example ===
User: What's the weather like in Seattle?
Result: The weather in Seattle is cloudy with a high of 18°C.

=== Streaming Response Example ===
User: What's the weather like in Portland?
Agent: The weather in Portland is sunny with a high of 25°C.
"""