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77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import Agent
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from agent_framework.foundry import AnthropicFoundryClient
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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"""
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Anthropic Foundry Chat Agent Example
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This sample demonstrates using Anthropic with:
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- Setting up an Anthropic-based agent with hosted tools.
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- Using the `thinking` feature.
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- Displaying both thinking and usage information during streaming responses.
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This example requires `anthropic>=0.74.0` and an endpoint in Foundry for Anthropic.
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To use the Foundry integration ensure you have the following environment variables set:
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- ANTHROPIC_FOUNDRY_API_KEY
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Alternatively you can pass in a azure_ad_token_provider function to the AsyncAnthropicFoundry constructor.
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- ANTHROPIC_FOUNDRY_RESOURCE
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Should be the resource name portion of your Foundry Anthropic URL, such as <your-resource-name>.
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- ANTHROPIC_FOUNDRY_BASE_URL
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Optional alternative to ANTHROPIC_FOUNDRY_RESOURCE. Should be something like
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https://<your-resource-name>.services.ai.azure.com/anthropic/
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- ANTHROPIC_CHAT_MODEL
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Should be something like claude-haiku-4-5
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"""
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async def main() -> None:
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"""Example of streaming response (get results as they are generated)."""
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client = AnthropicFoundryClient()
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# Create MCP tool configuration using instance method
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mcp_tool = client.get_mcp_tool(
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name="Microsoft_Learn_MCP",
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url="https://learn.microsoft.com/api/mcp",
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)
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# Create web search tool configuration using instance method
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web_search_tool = client.get_web_search_tool()
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agent = Agent(
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client=client,
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name="DocsAgent",
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instructions="You are a helpful agent for both Microsoft docs questions and general questions.",
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tools=[mcp_tool, web_search_tool],
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default_options={
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# anthropic needs a value for the max_tokens parameter
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# we set it to 1024, but you can override like this:
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"max_tokens": 20000,
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"thinking": {"type": "enabled", "budget_tokens": 10000},
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},
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)
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query = "Can you compare Python decorators with C# attributes?"
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print(f"User: {query}")
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print("Agent: ", end="", flush=True)
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async for chunk in agent.run(query, stream=True):
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for content in chunk.contents:
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if content.type == "text_reasoning":
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print(f"\033[32m{content.text}\033[0m", end="", flush=True)
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if content.type == "usage":
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print(f"\n\033[34m[Usage so far: {content.usage_details}]\033[0m\n", end="", flush=True)
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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if __name__ == "__main__":
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asyncio.run(main())
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