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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
2025-11-22 00:31:57 +00:00
committed by GitHub
co-authored by Copilot
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
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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
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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/azure_ai_with_search_context_agentic.py
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@@ -1,118 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
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
# Load environment variables from .env file
load_dotenv()
"""
This sample demonstrates how to use Azure AI Search with agentic mode for RAG
(Retrieval Augmented Generation) with Azure AI agents.
**Agentic mode** is recommended for most scenarios:
- Uses Knowledge Bases in Azure AI Search for query planning
- Performs multi-hop reasoning across documents
- Provides more accurate results through intelligent retrieval
- Slightly slower with more token consumption for query planning
- See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720
For simple queries where speed is critical, use semantic mode instead (see azure_ai_with_search_context_semantic.py).
Prerequisites:
1. An Azure AI Search service with a search index
2. An Azure AI Foundry project with a model deployment
3. An Azure OpenAI resource (for Knowledge Base model calls)
4. Set the following environment variables:
- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID
- AZURE_SEARCH_INDEX_NAME: Your search index name
- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
- AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name
- AZURE_OPENAI_RESOURCE_URL: Your Azure OpenAI resource URL (e.g., "https://myresource.openai.azure.com")
Note: This is different from AZURE_AI_PROJECT_ENDPOINT - Knowledge Base needs the OpenAI endpoint for model calls
"""
# Sample queries to demonstrate agentic RAG
USER_INPUTS = [
"What information is available in the knowledge base?",
"Analyze and compare the main topics from different documents",
"What connections can you find across different sections?",
]
async def main() -> None:
"""Main function demonstrating Azure AI Search agentic mode."""
# Get configuration from environment
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
knowledge_base_name = os.environ["AZURE_SEARCH_KNOWLEDGE_BASE_NAME"]
azure_openai_resource_url = os.environ["AZURE_OPENAI_RESOURCE_URL"]
# Create Azure AI Search context provider with agentic mode (recommended for accuracy)
print("Using AGENTIC mode (Knowledge Bases with query planning, recommended)\n")
print("ℹ️ This mode is slightly slower but provides more accurate results.\n")
search_provider = AzureAISearchContextProvider(
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,
mode="agentic", # Advanced mode for multi-hop reasoning
# Agentic mode configuration
azure_ai_project_endpoint=project_endpoint,
azure_openai_resource_url=azure_openai_resource_url,
model_deployment_name=model_deployment,
knowledge_base_name=knowledge_base_name,
# Optional: Configure retrieval behavior
knowledge_base_output_mode="extractive_data", # or "answer_synthesis"
retrieval_reasoning_effort="minimal", # or "medium", "low"
top_k=3, # Note: In agentic mode, the server-side Knowledge Base determines final retrieval
)
# Create agent with search context provider
async with (
search_provider,
AzureAIAgentClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
async_credential=DefaultAzureCredential(),
) as client,
ChatAgent(
chat_client=client,
name="SearchAgent",
instructions=(
"You are a helpful assistant with advanced reasoning capabilities. "
"Use the provided context from the knowledge base to answer complex "
"questions that may require synthesizing information from multiple sources."
),
context_providers=[search_provider],
) as agent,
):
print("=== Azure AI Agent with Search Context (Agentic Mode) ===\n")
for user_input in USER_INPUTS:
print(f"User: {user_input}")
print("Agent: ", end="", flush=True)
# Stream response
async for chunk in agent.run_stream(user_input):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
if __name__ == "__main__":
asyncio.run(main())
python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py
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@@ -1,99 +0,0 @@
# Copyright (c) Microsoft. All rights reserved.
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
# Load environment variables from .env file
load_dotenv()
"""
This sample demonstrates how to use Azure AI Search with semantic mode for RAG
(Retrieval Augmented Generation) with Azure AI agents.
**Semantic mode** is the recommended default mode:
- Fast hybrid search combining vector and keyword search
- Uses semantic ranking for improved relevance
- Returns raw search results as context
- Best for most RAG use cases
Prerequisites:
1. An Azure AI Search service with a search index
2. An Azure AI Foundry project with a model deployment
3. Set the following environment variables:
- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID
- AZURE_SEARCH_INDEX_NAME: Your search index name
- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
"""
# Sample queries to demonstrate RAG
USER_INPUTS = [
"What information is available in the knowledge base?",
"Summarize the main topics from the documents",
"Find specific details about the content",
]
async def main() -> None:
"""Main function demonstrating Azure AI Search semantic mode."""
# Get configuration from environment
search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
search_key = os.environ.get("AZURE_SEARCH_API_KEY")
index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
# Create Azure AI Search context provider with semantic mode (recommended, fast)
print("Using SEMANTIC mode (hybrid search + semantic ranking, fast)\n")
search_provider = AzureAISearchContextProvider(
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,
mode="semantic", # Default mode
top_k=3, # Retrieve top 3 most relevant documents
)
# Create agent with search context provider
async with (
search_provider,
AzureAIAgentClient(
project_endpoint=project_endpoint,
model_deployment_name=model_deployment,
async_credential=DefaultAzureCredential(),
) as client,
ChatAgent(
chat_client=client,
name="SearchAgent",
instructions=(
"You are a helpful assistant. Use the provided context from the "
"knowledge base to answer questions accurately."
),
context_providers=[search_provider],
) as agent,
):
print("=== Azure AI Agent with Search Context (Semantic Mode) ===\n")
for user_input in USER_INPUTS:
print(f"User: {user_input}")
print("Agent: ", end="", flush=True)
# Stream response
async for chunk in agent.run_stream(user_input):
if chunk.text:
print(chunk.text, end="", flush=True)
print("\n")
if __name__ == "__main__":
asyncio.run(main())