~20 lines of Python. Enterprise-grade RAG. Powered by Foundry IQ.<\/strong><\/p>\n The Azure AI Search Context Provider in the Microsoft Agent Framework brings Foundry IQ<\/strong>\u2014Microsoft’s intelligent knowledge layer\u2014directly to your AI agents. Foundry IQ treats retrieval as a reasoning task, not just a keyword lookup or vector search, enabling agents to plan queries, follow chains of information, and synthesize answers across diverse sources.<\/p>\n Traditional RAG puts a heavy tax on every project.<\/strong> Every team rebuilds data connections, chunking logic, embeddings, routing, and permissions from scratch. It leaves organizations with fragmented, duplicated pipelines all trying to answer the same question: what context does the model need to respond effectively?<\/em><\/p>\n Foundry IQ shifts that work into Knowledge Bases.<\/strong> Instead of wiring retrieval logic into every agent, you define a reusable Knowledge Base and connect. Behind the scenes, Foundry IQ handles indexing, vectorization, query planning, and multi-source routing.<\/p>\n The result: getting started feels like “plug in the domain knowledge this agent should have”<\/em> rather than “rebuild a RAG stack.”<\/em><\/p>\n Prerequisites:<\/strong> Azure AI Search service, Azure AI Foundry project, Python 3.10+. See samples \u2192<\/a><\/p>\n Install the package:<\/p>\n Build a RAG agent with Foundry IQ in ~20 lines. This example uses agentic mode<\/strong>, which leverages Foundry IQ Knowledge Bases for intelligent, multi-hop retrieval:<\/p>\n That’s it. Your agent now has access to enterprise knowledge with intelligent retrieval.<\/p>\n The Microsoft Agent Framework<\/a> is an open-source, model-agnostic engine for building agentic AI applications. It provides:<\/p>\n The framework is available for both Python and .NET, enabling developers to build agents that can reason, plan, and take actions across enterprise data.<\/p>\n Foundry IQ<\/strong> is Microsoft’s intelligent knowledge layer for AI agents, built on Azure AI Search. Instead of traditional RAG where one query hits one index once, Foundry IQ treats retrieval as a reasoning task:<\/p>\n Microsoft’s evaluations show up to 36% improvement in response relevance on complex multi-hop queries compared to traditional RAG approaches. Learn more about Foundry IQ \u2192<\/a><\/p>\n The
\nThe Problem with Traditional RAG<\/h2>\n
\nQuick Start<\/h2>\n
pip install agent-framework-azure-ai-search --pre<\/code><\/pre>\nimport asyncio\r\nfrom agent_framework import ChatAgent\r\nfrom agent_framework.azure import AzureAIAgentClient, AzureAISearchContextProvider\r\nfrom azure.identity.aio import DefaultAzureCredential\r\n\r\nasync def main():\r\n # Use managed identity for secure, keyless authentication\r\n credential = DefaultAzureCredential()\r\n\r\n async with (\r\n # Connect to Foundry IQ Knowledge Base for agentic retrieval\r\n AzureAISearchContextProvider(\r\n endpoint=\"YOUR_SEARCH_ENDPOINT\",\r\n knowledge_base_name=\"YOUR_KNOWLEDGE_BASE\",\r\n credential=credential,\r\n mode=\"agentic\",\r\n ) as search,\r\n # Connect to Azure AI Foundry for model inference\r\n AzureAIAgentClient(\r\n project_endpoint=\"YOUR_PROJECT_ENDPOINT\",\r\n model_deployment_name=\"YOUR_MODEL\",\r\n async_credential=credential,\r\n ) as client,\r\n # Create an agent grounded in your Knowledge Base\r\n ChatAgent(chat_client=client, context_providers=[search]) as agent,\r\n ):\r\n print((await agent.run(\"What's in the knowledge base?\")).text)\r\n\r\nasyncio.run(main())<\/code><\/pre>\n
\nWhat is the Microsoft Agent Framework?<\/h2>\n
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\nWhat is Foundry IQ?<\/h2>\n
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\nTwo Retrieval Modes<\/h2>\n
AzureAISearchContextProvider<\/code> supports two modes, so you can choose the right approach for your use case:<\/p>\n