Eduard van Valkenburg
d08200d00e
* Bump Python package versions for 1.12.0 release Bump packages represented in the 1.12.0 changelog, promote Foundry Hosting, Azure Content Understanding, Gemini, Mistral, Monty, and Tools to beta, and apply the requested beta cohort date stamp. Root and core move to 1.12.0, released and RC packages use their selected increments, alpha packages including Hosting MCP use the 260721 stamp, and core floors are raised only for proven consumers. Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf * fix version in readme * Add Responses conversation ID changes to release notes Include the breaking Hosting Responses conversation ID helper changes from #7234 in the Python 1.12.0 changelog. Copilot-Session: 2dd9980a-b869-4c16-8642-75b7a6d6ebdf
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from typing import Annotated, Any, Literal
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from agent_framework import Agent, tool
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from agent_framework.foundry import FoundryChatClient, ResponsesHostServer
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from agent_framework.hyperlight import HyperlightCodeActProvider
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from azure.identity import DefaultAzureCredential
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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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@tool(approval_mode="never_require")
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def compute(
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operation: Annotated[
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Literal["add", "subtract", "multiply", "divide"],
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"Math operation: add, subtract, multiply, or divide.",
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],
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a: Annotated[float, "First numeric operand."],
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b: Annotated[float, "Second numeric operand."],
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) -> float:
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"""Perform a math operation for sandboxed code."""
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operations = {
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"add": a + b,
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"subtract": a - b,
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"multiply": a * b,
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"divide": a / b if b else float("inf"),
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}
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return operations[operation]
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@tool(approval_mode="never_require")
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async def fetch_data(
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table: Annotated[str, "Name of the simulated table to query."],
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) -> list[dict[str, Any]]:
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"""Fetch records from a named table."""
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await asyncio.sleep(0.5)
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data: dict[str, list[dict[str, Any]]] = {
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"users": [
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{"id": 1, "name": "Alice", "role": "admin"},
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{"id": 2, "name": "Bob", "role": "user"},
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{"id": 3, "name": "Charlie", "role": "admin"},
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],
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"products": [
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{"id": 101, "name": "Widget", "price": 9.99},
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{"id": 102, "name": "Gadget", "price": 19.99},
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],
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}
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return data.get(table, [])
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def main():
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# 1. Create the Foundry chat client.
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
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credential=DefaultAzureCredential(),
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function_invocation_configuration={"include_detailed_errors": True},
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)
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# 2. Register sandbox tools on a Hyperlight CodeAct provider. The model only
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# sees `execute_code`; `compute` and `fetch_data` are reachable from
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# inside the sandbox via `call_tool(...)`.
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codeact = HyperlightCodeActProvider(
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tools=[compute, fetch_data],
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approval_mode="never_require",
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)
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# 3. Build the agent. History is managed by the hosting infrastructure, so
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# request the model not to persist server-side conversation state.
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agent = Agent(
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client=client,
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instructions="You are a helpful assistant. Keep your answers brief.",
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context_providers=[codeact],
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default_options={"store": False},
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)
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# 4. Serve the agent over the Foundry Responses protocol.
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server = ResponsesHostServer(agent)
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server.run()
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if __name__ == "__main__":
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main()
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