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Python: Rebase durable task feature branch with main (#2806)
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Laveesh Rohra
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@@ -14,6 +14,7 @@ This folder contains examples demonstrating different ways to create and use age
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| [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to search custom search instances and provide responses with relevant results. Requires a Bing Custom Search connection and instance configured in your Azure AI project. |
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| [`azure_ai_with_browser_automation.py`](azure_ai_with_browser_automation.py) | Shows how to use Browser Automation with Azure AI agents to perform automated web browsing tasks and provide responses based on web interactions. Requires a Browser Automation connection configured in your Azure AI project. |
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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 for mathematical problem solving and data analysis. |
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| [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
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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 name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
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| [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
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| [`azure_ai_with_application_endpoint.py`](azure_ai_with_application_endpoint.py) | Demonstrates calling the Azure AI application-scoped endpoint. |
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+111
@@ -0,0 +1,111 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import (
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CitationAnnotation,
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HostedCodeInterpreterTool,
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HostedFileContent,
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TextContent,
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)
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from agent_framework._agents import AgentRunResponseUpdate
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from agent_framework.azure import AzureAIClient
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from azure.identity.aio import AzureCliCredential
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"""
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Azure AI V2 Code Interpreter File Generation Sample
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This sample demonstrates how the V2 AzureAIClient handles file annotations
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when code interpreter generates text files. It shows both non-streaming
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and streaming approaches to verify file ID extraction.
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"""
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QUERY = (
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"Write a simple Python script that creates a text file called 'sample.txt' containing "
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"'Hello from the code interpreter!' and save it to disk."
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)
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async def test_non_streaming() -> None:
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"""Test non-streaming response - should have annotations on TextContent."""
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print("=== Testing Non-Streaming Response ===")
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async with (
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AzureCliCredential() as credential,
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AzureAIClient(credential=credential).create_agent(
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name="V2CodeInterpreterFileAgent",
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instructions="You are a helpful assistant that can write and execute Python code to create files.",
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tools=HostedCodeInterpreterTool(),
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) as agent,
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):
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print(f"User: {QUERY}\n")
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result = await agent.run(QUERY)
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print(f"Agent: {result.text}\n")
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# Check for annotations in the response
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annotations_found: list[str] = []
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# AgentRunResponse has messages property, which contains ChatMessage objects
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for message in result.messages:
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for content in message.contents:
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if isinstance(content, TextContent) and content.annotations:
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for annotation in content.annotations:
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if isinstance(annotation, CitationAnnotation) and annotation.file_id:
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annotations_found.append(annotation.file_id)
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print(f"Found file annotation: file_id={annotation.file_id}")
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if annotations_found:
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print(f"SUCCESS: Found {len(annotations_found)} file annotation(s)")
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else:
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print("WARNING: No file annotations found in non-streaming response")
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async def test_streaming() -> None:
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"""Test streaming response - check if file content is captured via HostedFileContent."""
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print("\n=== Testing Streaming Response ===")
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async with (
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AzureCliCredential() as credential,
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AzureAIClient(credential=credential).create_agent(
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name="V2CodeInterpreterFileAgentStreaming",
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instructions="You are a helpful assistant that can write and execute Python code to create files.",
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tools=HostedCodeInterpreterTool(),
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) as agent,
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):
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print(f"User: {QUERY}\n")
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annotations_found: list[str] = []
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text_chunks: list[str] = []
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file_ids_found: list[str] = []
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async for update in agent.run_stream(QUERY):
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if isinstance(update, AgentRunResponseUpdate):
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for content in update.contents:
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if isinstance(content, TextContent):
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if content.text:
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text_chunks.append(content.text)
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if content.annotations:
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for annotation in content.annotations:
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if isinstance(annotation, CitationAnnotation) and annotation.file_id:
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annotations_found.append(annotation.file_id)
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print(f"Found streaming annotation: file_id={annotation.file_id}")
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elif isinstance(content, HostedFileContent):
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file_ids_found.append(content.file_id)
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print(f"Found streaming HostedFileContent: file_id={content.file_id}")
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print(f"\nAgent response: {''.join(text_chunks)[:200]}...")
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if annotations_found or file_ids_found:
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total = len(annotations_found) + len(file_ids_found)
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print(f"SUCCESS: Found {total} file reference(s) in streaming")
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else:
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print("WARNING: No file annotations found in streaming response")
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async def main() -> None:
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print("AzureAIClient Code Interpreter File Generation Test\n")
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await test_non_streaming()
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await test_streaming()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -9,6 +9,8 @@ This folder contains examples demonstrating different ways to create and use age
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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_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to find real-time information from the web using custom search configurations. Demonstrates how to set up and use HostedWebSearchTool with custom search instances. |
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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_bing_grounding_citations.py`](azure_ai_with_bing_grounding_citations.py) | Demonstrates how to extract and display citations from Bing Grounding search responses. Shows how to collect citation annotations (title, URL, snippet) during streaming responses, enabling users to verify sources and access referenced content. |
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| [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
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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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+86
@@ -0,0 +1,86 @@
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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, CitationAnnotation, HostedWebSearchTool
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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"""
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This sample demonstrates how to create an Azure AI agent that uses Bing Grounding
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search to find real-time information from the web with comprehensive citation support.
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It shows how to extract and display citations (title, URL, and snippet) from Bing
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Grounding responses, enabling users to verify sources and explore referenced content.
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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 BING_CONNECTION_ID environment variable
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Example: BING_CONNECTION_ID="your-bing-connection-id"
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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 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 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(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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# 3. 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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print("Agent: ", end="", flush=True)
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# Stream the response and collect citations
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citations: list[CitationAnnotation] = []
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async for chunk in agent.run_stream(user_input):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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# Collect citations from Bing Grounding responses
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for content in getattr(chunk, "contents", []):
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annotations = getattr(content, "annotations", [])
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if annotations:
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citations.extend(annotations)
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print()
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# Display collected citations
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if citations:
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print("\n\nCitations:")
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for i, citation in enumerate(citations, 1):
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print(f"[{i}] {citation.title}: {citation.url}")
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if citation.snippet:
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print(f" Snippet: {citation.snippet}")
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else:
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print("\nNo citations found in the response.")
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print()
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if __name__ == "__main__":
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asyncio.run(main())
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+102
@@ -0,0 +1,102 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import AgentRunResponseUpdate, ChatAgent, HostedCodeInterpreterTool, HostedFileContent
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from agent_framework.azure import AzureAIAgentClient
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from azure.identity.aio import AzureCliCredential
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"""
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Azure AI Agent Code Interpreter File Generation Example
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This sample demonstrates using HostedCodeInterpreterTool with AzureAIAgentClient
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to generate a text file and then retrieve it.
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The test flow:
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1. Create an agent with code interpreter tool
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2. Ask the agent to generate a txt file using Python code
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3. Capture the file_id from HostedFileContent in the response
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4. Retrieve the file using the agents_client.files API
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"""
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async def main() -> None:
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"""Test file generation and retrieval with code interpreter."""
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async with AzureCliCredential() as credential:
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client = AzureAIAgentClient(credential=credential)
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try:
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async with ChatAgent(
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chat_client=client,
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instructions=(
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"You are a Python code execution assistant. "
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"ALWAYS use the code interpreter tool to execute Python code when asked to create files. "
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"Write actual Python code to create files, do not just describe what you would do."
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),
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tools=[HostedCodeInterpreterTool()],
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) as agent:
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# Be very explicit about wanting code execution and a download link
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query = (
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"Use the code interpreter to execute this Python code and then provide me "
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"with a download link for the generated file:\n"
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"```python\n"
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"with open('/mnt/data/sample.txt', 'w') as f:\n"
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" f.write('Hello, World! This is a test file.')\n"
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"'/mnt/data/sample.txt'\n" # Return the path so it becomes downloadable
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"```"
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)
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print(f"User: {query}\n")
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print("=" * 60)
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# Collect file_ids from the response
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file_ids: list[str] = []
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async for chunk in agent.run_stream(query):
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if not isinstance(chunk, AgentRunResponseUpdate):
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continue
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for content in chunk.contents:
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if content.type == "text":
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print(content.text, end="", flush=True)
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elif content.type == "hosted_file":
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if isinstance(content, HostedFileContent):
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file_ids.append(content.file_id)
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print(f"\n[File generated: {content.file_id}]")
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print("\n" + "=" * 60)
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# Attempt to retrieve discovered files
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if file_ids:
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print(f"\nAttempting to retrieve {len(file_ids)} file(s):")
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for file_id in file_ids:
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try:
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file_info = await client.agents_client.files.get(file_id)
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print(f" File {file_id}: Retrieved successfully")
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print(f" Filename: {file_info.filename}")
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print(f" Purpose: {file_info.purpose}")
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print(f" Bytes: {file_info.bytes}")
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except Exception as e:
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print(f" File {file_id}: FAILED to retrieve - {e}")
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else:
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print("No file IDs were captured from the response.")
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# List all files to see if any exist
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print("\nListing all files in the agent service:")
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try:
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files_list = await client.agents_client.files.list()
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count = 0
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for file_info in files_list.data:
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count += 1
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print(f" - {file_info.id}: {file_info.filename} ({file_info.purpose})")
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if count == 0:
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print(" No files found.")
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except Exception as e:
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print(f" Failed to list files: {e}")
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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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@@ -6,7 +6,11 @@ This folder contains examples demonstrating how to use Ollama models with the Ag
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1. **Install Ollama**: Download and install Ollama from [ollama.com](https://ollama.com/)
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2. **Start Ollama**: Ensure Ollama is running on your local machine
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3. **Pull a model**: Run `ollama pull mistral` (or any other model you prefer that supports function calling)
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3. **Pull a model**: Run `ollama pull mistral` (or any other model you prefer)
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- For function calling examples, use models that support tool calling like `mistral` or `qwen2.5`
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- For reasoning examples, use models that support reasoning like `qwen2.5:8b`
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> **Note**: Not all models support all features. Function calling and reasoning capabilities depend on the specific model you're using.
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## Examples
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@@ -16,15 +20,16 @@ This folder contains examples demonstrating how to use Ollama models with the Ag
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## Configuration
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The examples use environment variables for configuration:
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The examples use environment variables for configuration. Set the appropriate variables based on which example you're running:
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### Environment Variables
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Set the following environment variables before running the examples:
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### For OpenAI Client with Ollama (`ollama_with_openai_chat_client.py`)
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- `OLLAMA_ENDPOINT`: The base URL for your Ollama server
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Set the following environment variables:
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- `OLLAMA_ENDPOINT`: The base URL for your Ollama server with `/v1/` suffix
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- Example: `export OLLAMA_ENDPOINT="http://localhost:11434/v1/"`
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- `OLLAMA_MODEL`: The model name to use
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- Example: `export OLLAMA_MODEL="mistral"`
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- Must be a model you have pulled with Ollama
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- Must be a model you have pulled with Ollama
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Reference in New Issue
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