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Python: Integration tests for Azure AI client and fixes in samples (#2387)

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* Added integration tests

* Update python/packages/azure-ai/tests/test_azure_ai_client.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Small fixes in samples

* Small fix

* Small fix

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Dmytro Struk
2025-11-21 16:31:57 -08:00
committed by GitHub
Unverified
parent b7a19b0fa2
commit ee8936d514
6 changed files with 116 additions and 15 deletions
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python/samples/getting_started/agents/azure_ai/README.md
-2
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@@ -20,8 +20,6 @@ This folder contains examples demonstrating different ways to create and use age
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
| [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. |
| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. |
| [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. |
| [`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. |
| [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. |
python/samples/getting_started/agents/azure_ai/azure_ai_with_existing_conversation.py
+2 -2
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@@ -32,7 +32,7 @@ async def example_with_client() -> None:
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
):
# Create a conversation using OpenAI client
openai_client = await project_client.get_openai_client()
openai_client = project_client.get_openai_client()
conversation = await openai_client.conversations.create()
conversation_id = conversation.id
print(f"Conversation ID: {conversation_id}")
@@ -70,7 +70,7 @@ async def example_with_thread() -> None:
) as agent,
):
# Create a conversation using OpenAI client
openai_client = await project_client.get_openai_client()
openai_client = project_client.get_openai_client()
conversation = await openai_client.conversations.create()
conversation_id = conversation.id
print(f"Conversation ID: {conversation_id}")
python/samples/getting_started/agents/azure_ai_agent/README.md
+2
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@@ -20,6 +20,8 @@ This folder contains examples demonstrating different ways to create and use age
| [`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. |
| [`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. |
| [`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. |
| [`azure_ai_with_search_context_agentic.py`](azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. |
| [`azure_ai_with_search_context_semantic.py`](azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. |
| [`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. |
## Environment Variables
python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_agentic.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_search_context_agentic.py
+4 -5
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@@ -3,12 +3,11 @@
import asyncio
import os
from dotenv import load_dotenv
from agent_framework import ChatAgent
from agent_framework_aisearch import AzureAISearchContextProvider
from agent_framework_azure_ai import AzureAIAgentClient
from azure.identity.aio import DefaultAzureCredential
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
@@ -68,7 +67,7 @@ async def main() -> None:
endpoint=search_endpoint,
index_name=index_name,
api_key=search_key, # Use api_key for API key auth, or credential for managed identity
credential=DefaultAzureCredential() if not search_key else None,
credential=AzureCliCredential() if not search_key else None,
mode="agentic", # Advanced mode for multi-hop reasoning
# Agentic mode configuration
azure_ai_project_endpoint=project_endpoint,
@@ -87,7 +86,7 @@ async def main() -> None:
AzureAIAgentClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
async_credential=DefaultAzureCredential(),
async_credential=AzureCliCredential(),
) as client,
ChatAgent(
chat_client=client,
python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_search_context_semantic.py
+4 -5
View File
@@ -3,12 +3,11 @@
import asyncio
import os
from dotenv import load_dotenv
from agent_framework import ChatAgent
from agent_framework_aisearch import AzureAISearchContextProvider
from agent_framework_azure_ai import AzureAIAgentClient
from azure.identity.aio import DefaultAzureCredential
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
@@ -58,7 +57,7 @@ async def main() -> None:
endpoint=search_endpoint,
index_name=index_name,
api_key=search_key, # Use api_key for API key auth, or credential for managed identity
credential=DefaultAzureCredential() if not search_key else None,
credential=AzureCliCredential() if not search_key else None,
mode="semantic", # Default mode
top_k=3, # Retrieve top 3 most relevant documents
)
@@ -69,7 +68,7 @@ async def main() -> None:
AzureAIAgentClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
async_credential=DefaultAzureCredential(),
async_credential=AzureCliCredential(),
) as client,
ChatAgent(
chat_client=client,