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Python: renamed ai search and cleanup of samples and unified import logic (#2369)
* renamed ai search and cleanup of samples and unified import logic * fixed error messages * fixed folder name * remove old samples from readme
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Eduard van Valkenburg
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@@ -20,6 +20,8 @@ This folder contains examples demonstrating different ways to create and use age
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| [`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. |
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| [`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. |
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| [`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. |
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| [`azure_ai_with_search_context_agentic.py`](../../context_providers/azure_ai_search/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. |
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| [`azure_ai_with_search_context_semantic.py`](../../context_providers/azure_ai_search/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. |
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| [`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. |
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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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| [`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. |
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@@ -20,8 +20,6 @@ This folder contains examples demonstrating different ways to create and use age
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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_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. |
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| [`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. |
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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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-117
@@ -1,117 +0,0 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import ChatAgent
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from agent_framework_aisearch import AzureAISearchContextProvider
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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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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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"""
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This sample demonstrates how to use Azure AI Search with agentic mode for RAG
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(Retrieval Augmented Generation) with Azure AI agents.
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**Agentic mode** is recommended for most scenarios:
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- Uses Knowledge Bases in Azure AI Search for query planning
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- Performs multi-hop reasoning across documents
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- Provides more accurate results through intelligent retrieval
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- Slightly slower with more token consumption for query planning
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- See: https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/foundry-iq-boost-response-relevance-by-36-with-agentic-retrieval/4470720
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For simple queries where speed is critical, use semantic mode instead (see azure_ai_with_search_context_semantic.py).
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Prerequisites:
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1. An Azure AI Search service with a search index
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2. An Azure AI Foundry project with a model deployment
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3. An Azure OpenAI resource (for Knowledge Base model calls)
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4. Set the following environment variables:
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- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
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- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID
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- AZURE_SEARCH_INDEX_NAME: Your search index name
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- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
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- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
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- AZURE_SEARCH_KNOWLEDGE_BASE_NAME: Your Knowledge Base name
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- AZURE_OPENAI_RESOURCE_URL: Your Azure OpenAI resource URL (e.g., "https://myresource.openai.azure.com")
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Note: This is different from AZURE_AI_PROJECT_ENDPOINT - Knowledge Base needs the OpenAI endpoint for model calls
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"""
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# Sample queries to demonstrate agentic RAG
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USER_INPUTS = [
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"What information is available in the knowledge base?",
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"Analyze and compare the main topics from different documents",
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"What connections can you find across different sections?",
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]
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async def main() -> None:
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"""Main function demonstrating Azure AI Search agentic mode."""
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# Get configuration from environment
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search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
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search_key = os.environ.get("AZURE_SEARCH_API_KEY")
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index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
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project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
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knowledge_base_name = os.environ["AZURE_SEARCH_KNOWLEDGE_BASE_NAME"]
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azure_openai_resource_url = os.environ["AZURE_OPENAI_RESOURCE_URL"]
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# Create Azure AI Search context provider with agentic mode (recommended for accuracy)
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print("Using AGENTIC mode (Knowledge Bases with query planning, recommended)\n")
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print("ℹ️ This mode is slightly slower but provides more accurate results.\n")
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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index_name=index_name,
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api_key=search_key, # Use api_key for API key auth, or credential for managed identity
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credential=AzureCliCredential() if not search_key else None,
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mode="agentic", # Advanced mode for multi-hop reasoning
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# Agentic mode configuration
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azure_ai_project_endpoint=project_endpoint,
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azure_openai_resource_url=azure_openai_resource_url,
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model_deployment_name=model_deployment,
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knowledge_base_name=knowledge_base_name,
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# Optional: Configure retrieval behavior
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knowledge_base_output_mode="extractive_data", # or "answer_synthesis"
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retrieval_reasoning_effort="minimal", # or "medium", "low"
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top_k=3, # Note: In agentic mode, the server-side Knowledge Base determines final retrieval
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)
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# Create agent with search context provider
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async with (
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search_provider,
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AzureAIAgentClient(
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project_endpoint=project_endpoint,
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model_deployment_name=model_deployment,
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async_credential=AzureCliCredential(),
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) as client,
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ChatAgent(
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chat_client=client,
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name="SearchAgent",
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instructions=(
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"You are a helpful assistant with advanced reasoning capabilities. "
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"Use the provided context from the knowledge base to answer complex "
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"questions that may require synthesizing information from multiple sources."
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),
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context_providers=[search_provider],
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) as agent,
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):
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print("=== Azure AI Agent with Search Context (Agentic Mode) ===\n")
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for user_input in USER_INPUTS:
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print(f"User: {user_input}")
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print("Agent: ", end="", flush=True)
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# Stream response
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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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print("\n")
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if __name__ == "__main__":
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asyncio.run(main())
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-98
@@ -1,98 +0,0 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import ChatAgent
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from agent_framework_aisearch import AzureAISearchContextProvider
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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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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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"""
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This sample demonstrates how to use Azure AI Search with semantic mode for RAG
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(Retrieval Augmented Generation) with Azure AI agents.
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**Semantic mode** is the recommended default mode:
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- Fast hybrid search combining vector and keyword search
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- Uses semantic ranking for improved relevance
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- Returns raw search results as context
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- Best for most RAG use cases
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Prerequisites:
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1. An Azure AI Search service with a search index
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2. An Azure AI Foundry project with a model deployment
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3. Set the following environment variables:
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- AZURE_SEARCH_ENDPOINT: Your Azure AI Search endpoint
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- AZURE_SEARCH_API_KEY: (Optional) Your search API key - if not provided, uses DefaultAzureCredential for Entra ID
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- AZURE_SEARCH_INDEX_NAME: Your search index name
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- AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
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- AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name (e.g., "gpt-4o")
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"""
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# Sample queries to demonstrate RAG
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USER_INPUTS = [
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"What information is available in the knowledge base?",
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"Summarize the main topics from the documents",
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"Find specific details about the content",
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]
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async def main() -> None:
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"""Main function demonstrating Azure AI Search semantic mode."""
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# Get configuration from environment
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search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
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search_key = os.environ.get("AZURE_SEARCH_API_KEY")
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index_name = os.environ["AZURE_SEARCH_INDEX_NAME"]
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project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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model_deployment = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", "gpt-4o")
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# Create Azure AI Search context provider with semantic mode (recommended, fast)
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print("Using SEMANTIC mode (hybrid search + semantic ranking, fast)\n")
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search_provider = AzureAISearchContextProvider(
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endpoint=search_endpoint,
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index_name=index_name,
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api_key=search_key, # Use api_key for API key auth, or credential for managed identity
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credential=AzureCliCredential() if not search_key else None,
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mode="semantic", # Default mode
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top_k=3, # Retrieve top 3 most relevant documents
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)
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# Create agent with search context provider
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async with (
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search_provider,
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AzureAIAgentClient(
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project_endpoint=project_endpoint,
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model_deployment_name=model_deployment,
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async_credential=AzureCliCredential(),
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) as client,
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ChatAgent(
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chat_client=client,
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name="SearchAgent",
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instructions=(
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"You are a helpful assistant. Use the provided context from the "
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"knowledge base to answer questions accurately."
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),
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context_providers=[search_provider],
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) as agent,
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):
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print("=== Azure AI Agent with Search Context (Semantic Mode) ===\n")
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for user_input in USER_INPUTS:
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print(f"User: {user_input}")
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print("Agent: ", end="", flush=True)
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# Stream response
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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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print("\n")
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if __name__ == "__main__":
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asyncio.run(main())
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