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Python: Foundry Agent Completeness (#954)
* foundry completeness * tests + openapi sample * bing grounding sample * options integration tests * merge conflict fix * fix failing test * add mcp approval handling
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@@ -7,12 +7,17 @@ This folder contains examples demonstrating different ways to create and use age
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| File | Description |
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|------|-------------|
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| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAIAgentClient`. It automatically handles all configuration using environment variables. |
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| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIAgentClient` settings, including project endpoint, model deployment, credentials, and agent name. |
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| [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to find real-time information from the web. Demonstrates web search capabilities with proper source citations and comprehensive error handling. |
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| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
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| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents. |
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| [`azure_ai_with_existing_thread.py`](azure_ai_with_existing_thread.py) | Shows how to work with a pre-existing thread by providing the thread ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created threads. |
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| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIAgentClient` settings, including project endpoint, model deployment, credentials, and agent name. |
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| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Demonstrates how to use the HostedFileSearchTool with Azure AI agents to search through uploaded documents. Shows file upload, vector store creation, and querying document content. Includes both streaming and non-streaming examples. |
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| [`azure_ai_with_function_tools.py`](azure_ai_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
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| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure AI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
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| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
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| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate Azure AI agents with hosted Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates remote MCP server connections and tool discovery. |
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| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
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| [`azure_ai_with_multiple_tools.py`](azure_ai_with_multiple_tools.py) | Demonstrates how to use multiple tools together with Azure AI agents, including web search, MCP servers, and function tools. Shows coordinated multi-tool interactions and approval workflows. |
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| [`azure_ai_with_openapi_tools.py`](azure_ai_with_openapi_tools.py) | Demonstrates how to use OpenAPI tools with Azure AI agents to integrate external REST APIs. Shows OpenAPI specification loading, anonymous authentication, thread context management, and coordinated multi-API conversations using weather and countries APIs. |
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| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
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## Environment Variables
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@@ -0,0 +1,61 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import ChatAgent, HostedWebSearchTool
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from agent_framework_azure_ai import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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"""
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The following sample demonstrates how to create an Azure AI agent that
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uses Bing Grounding search to find real-time information from the web.
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Prerequisites:
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1. A connected Grounding with Bing Search resource in your Azure AI project
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2. Set the BING_CONNECTION_ID environment variable to your Bing connection ID
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Example: BING_CONNECTION_ID="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/
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providers/Microsoft.CognitiveServices/accounts/{ai-service-name}/projects/{project-name}/
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connections/{connection-name}"
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To set up Bing Grounding:
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1. Go to Azure AI Foundry portal (https://ai.azure.com)
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2. Navigate to your project's "Connected resources" section
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3. Add a new connection for "Grounding with Bing Search"
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4. Copy the connection ID and set it as the BING_CONNECTION_ID environment variable
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"""
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async def main() -> None:
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"""Main function demonstrating Azure AI agent with Bing Grounding search."""
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# 1. Create Bing Grounding search tool using HostedWebSearchTool
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# The connection_id will be automatically picked up from BING_CONNECTION_ID environment variable
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bing_search_tool = HostedWebSearchTool(
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name="Bing Grounding Search",
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description="Search the web for current information using Bing",
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)
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# 2. Use AzureAIAgentClient as async context manager for automatic cleanup
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async with (
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AzureAIAgentClient(async_credential=AzureCliCredential()) as client,
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ChatAgent(
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chat_client=client,
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name="BingSearchAgent",
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instructions=(
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"You are a helpful assistant that can search the web for current information. "
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"Use the Bing search tool to find up-to-date information and provide accurate, "
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"well-sourced answers. Always cite your sources when possible."
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),
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tools=bing_search_tool,
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) as agent,
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):
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# 4. Demonstrate agent capabilities with web search
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print("=== Azure AI Agent with Bing Grounding Search ===\n")
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user_input = "What is the most popular programming language?"
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print(f"User: {user_input}")
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response = await agent.run(user_input)
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print(f"Agent: {response.text}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,81 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from pathlib import Path
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from agent_framework import ChatAgent, HostedFileSearchTool, HostedVectorStoreContent
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from agent_framework_azure_ai import AzureAIAgentClient
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from azure.ai.agents.models import FileInfo, VectorStore
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from azure.identity.aio import AzureCliCredential
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"""
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The following sample demonstrates how to create a simple, Azure AI agent that
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uses a file search tool to answer user questions.
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"""
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# Simulate a conversation with the agent
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USER_INPUTS = [
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"Who is the youngest employee?",
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"Who works in sales?",
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"I have a customer request, who can help me?",
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]
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async def main() -> None:
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"""Main function demonstrating Azure AI agent with file search capabilities."""
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client = AzureAIAgentClient(async_credential=AzureCliCredential())
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file: FileInfo | None = None
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vector_store: VectorStore | None = None
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try:
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# 1. Upload file and create vector store
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pdf_file_path = Path(__file__).parent.parent / "resources" / "employees.pdf"
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print(f"Uploading file from: {pdf_file_path}")
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file = await client.project_client.agents.files.upload_and_poll(
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file_path=str(pdf_file_path), purpose="assistants"
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)
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print(f"Uploaded file, file ID: {file.id}")
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vector_store = await client.project_client.agents.vector_stores.create_and_poll(
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file_ids=[file.id], name="my_vectorstore"
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)
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print(f"Created vector store, vector store ID: {vector_store.id}")
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# 2. Create file search tool with uploaded resources
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file_search_tool = HostedFileSearchTool(inputs=[HostedVectorStoreContent(vector_store_id=vector_store.id)])
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# 3. Create an agent with file search capabilities
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# The tool_resources are automatically extracted from HostedFileSearchTool
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async with ChatAgent(
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chat_client=client,
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name="EmployeeSearchAgent",
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instructions=(
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"You are a helpful assistant that can search through uploaded employee files "
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"to answer questions about employees."
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),
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tools=file_search_tool,
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) as agent:
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# 4. Simulate conversation with the agent
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for user_input in USER_INPUTS:
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print(f"# User: '{user_input}'")
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response = await agent.run(user_input)
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print(f"# Agent: {response.text}")
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finally:
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# 5. Cleanup: Delete the vector store and file
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try:
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if vector_store is not None:
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await client.project_client.agents.vector_stores.delete(vector_store.id)
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if file is not None:
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await client.project_client.agents.files.delete(file.id)
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except Exception:
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# Ignore cleanup errors to avoid masking issues
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pass
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finally:
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await client.close()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,91 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import json
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from pathlib import Path
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from typing import Any
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from agent_framework import ChatAgent
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from agent_framework_azure_ai import AzureAIAgentClient
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from azure.ai.agents.models import OpenApiAnonymousAuthDetails, OpenApiTool
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from azure.identity.aio import AzureCliCredential
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"""
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The following sample demonstrates how to create a simple, Azure AI agent that
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uses OpenAPI tools to answer user questions.
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"""
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# Simulate a conversation with the agent
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USER_INPUTS = [
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"What is the name and population of the country that uses currency with abbreviation THB?",
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"What is the current weather in the capital city of that country?",
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]
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def load_openapi_specs() -> tuple[dict[str, Any], dict[str, Any]]:
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"""Load OpenAPI specification files."""
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resources_path = Path(__file__).parent.parent / "resources"
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with open(resources_path / "weather.json") as weather_file:
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weather_spec = json.load(weather_file)
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with open(resources_path / "countries.json") as countries_file:
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countries_spec = json.load(countries_file)
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return weather_spec, countries_spec
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async def main() -> None:
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"""Main function demonstrating Azure AI agent with OpenAPI tools."""
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# 1. Load OpenAPI specifications (synchronous operation)
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weather_openapi_spec, countries_openapi_spec = load_openapi_specs()
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# 2. Use AzureAIAgentClient as async context manager for automatic cleanup
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async with AzureAIAgentClient(async_credential=AzureCliCredential()) as client:
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# 3. Create OpenAPI tools using Azure AI's OpenApiTool
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auth = OpenApiAnonymousAuthDetails()
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openapi_weather = OpenApiTool(
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name="get_weather",
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spec=weather_openapi_spec,
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description="Retrieve weather information for a location using wttr.in service",
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auth=auth,
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)
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openapi_countries = OpenApiTool(
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name="get_country_info",
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spec=countries_openapi_spec,
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description="Retrieve country information including population and capital city",
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auth=auth,
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)
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# 4. Create an agent with OpenAPI tools
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# Note: We need to pass the Azure AI native OpenApiTool definitions directly
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# since the agent framework doesn't have a HostedOpenApiTool wrapper yet
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async with ChatAgent(
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chat_client=client,
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name="OpenAPIAgent",
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instructions=(
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"You are a helpful assistant that can search for country information "
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"and weather data using APIs. When asked about countries, use the country "
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"API to find information. When asked about weather, use the weather API. "
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"Provide clear, informative answers based on the API results."
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),
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# Pass the raw tool definitions from Azure AI's OpenApiTool
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tools=[*openapi_countries.definitions, *openapi_weather.definitions],
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) as agent:
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# 5. Simulate conversation with the agent maintaining thread context
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print("=== Azure AI Agent with OpenAPI Tools ===\n")
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# Create a thread to maintain conversation context across multiple runs
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thread = agent.get_new_thread()
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for user_input in USER_INPUTS:
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print(f"User: {user_input}")
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# Pass the thread to maintain context across multiple agent.run() calls
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response = await agent.run(user_input, thread=thread)
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print(f"Agent: {response.text}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -0,0 +1,141 @@
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{
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"openapi": "3.0.1",
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"info": {
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"title": "REST Countries API",
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"description": "Get information about countries of the world",
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"version": "3.1"
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},
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"servers": [
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{
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"url": "https://restcountries.com/v3.1"
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}
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],
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"paths": {
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"/currency/{currency}": {
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"get": {
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"operationId": "getCountriesByCurrency",
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"summary": "Get countries by currency",
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"description": "Search for countries by currency code",
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"parameters": [
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{
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"name": "currency",
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"in": "path",
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"required": true,
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"schema": {
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"type": "string"
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},
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"description": "Currency code (e.g., THB, USD, EUR)"
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}
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],
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"responses": {
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"200": {
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"description": "Successful response",
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"content": {
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"application/json": {
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"schema": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "object",
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"properties": {
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"common": {"type": "string"},
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"official": {"type": "string"}
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}
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},
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"population": {"type": "integer"},
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"region": {"type": "string"},
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"subregion": {"type": "string"},
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"capital": {
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"type": "array",
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"items": {"type": "string"}
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},
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"currencies": {
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"type": "object",
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"additionalProperties": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"symbol": {"type": "string"}
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}
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}
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},
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"languages": {
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"type": "object",
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"additionalProperties": {"type": "string"}
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},
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"latlng": {
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"type": "array",
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"items": {"type": "number"}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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},
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"/name/{name}": {
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"get": {
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"operationId": "getCountryByName",
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"summary": "Get country by name",
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"description": "Search for countries by name",
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"parameters": [
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{
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"name": "name",
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"in": "path",
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"required": true,
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"schema": {
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"type": "string"
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},
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"description": "Country name"
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}
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],
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"responses": {
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"200": {
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"description": "Successful response",
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"content": {
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"application/json": {
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"schema": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "object",
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"properties": {
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"common": {"type": "string"},
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"official": {"type": "string"}
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}
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},
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"population": {"type": "integer"},
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"region": {"type": "string"},
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"subregion": {"type": "string"},
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"capital": {
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"type": "array",
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"items": {"type": "string"}
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},
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"currencies": {
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"type": "object",
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"additionalProperties": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"symbol": {"type": "string"}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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}
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@@ -0,0 +1,76 @@
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%PDF-1.7
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%����
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1 0 obj
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<</Type/Catalog/Pages 2 0 R/Lang(en) /StructTreeRoot 22 0 R/MarkInfo<</Marked true>>/Metadata 132 0 R/ViewerPreferences 133 0 R>>
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endobj
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2 0 obj
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<</Type/Pages/Count 1/Kids[ 4 0 R] >>
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endobj
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3 0 obj
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<</Author(Test Author) /Creator(Test Creator) /Title(Employee Directory) >>
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endobj
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4 0 obj
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<</Type/Page/Parent 2 0 R/MediaBox[0 0 612 792]/Resources<</Font<</F1 5 0 R>>>>/Contents 6 0 R>>
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endobj
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||||
5 0 obj
|
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<</Type/Font/Subtype/Type1/BaseFont/Times-Roman>>
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endobj
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||||
6 0 obj
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<</Length 200>>
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stream
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BT
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/F1 12 Tf
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50 750 Td
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(Employee Directory) Tj
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0 -30 Td
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(Name: John Smith) Tj
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0 -15 Td
|
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(Department: Engineering) Tj
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0 -15 Td
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(Age: 28) Tj
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0 -30 Td
|
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(Name: Alice Johnson) Tj
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0 -15 Td
|
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(Department: Sales) Tj
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0 -15 Td
|
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(Age: 24) Tj
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0 -30 Td
|
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(Name: Bob Wilson) Tj
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||||
0 -15 Td
|
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(Department: Marketing) Tj
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0 -15 Td
|
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(Age: 35) Tj
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||||
ET
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||||
endstream
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||||
endobj
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22 0 obj
|
||||
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|
||||
{
|
||||
"openapi": "3.1.0",
|
||||
"info": {
|
||||
"title": "wttr.in Weather API",
|
||||
"description": "Retrieves current weather data for a location using wttr.in service",
|
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"version": "v1.0.0"
|
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"paths": {
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"get": {
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"operationId": "GetCurrentWeather",
|
||||
"summary": "Get weather information for a specific location",
|
||||
"description": "Get weather information for a specific location",
|
||||
"parameters": [
|
||||
{
|
||||
"name": "location",
|
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"in": "path",
|
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"description": "City or location to retrieve the weather for",
|
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"schema": {
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"type": "string"
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}
|
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},
|
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{
|
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"name": "format",
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|
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|
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|
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"404": {
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"components": {
|
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|
||||
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|
||||
}
|
||||
Reference in New Issue
Block a user