MCP Server & Agentic RAG: Streamable HTTP Tool with .NET, Exposing Azure Search for Azure Foundry скачать в хорошем качестве

MCP Server & Agentic RAG: Streamable HTTP Tool with .NET, Exposing Azure Search for Azure Foundry

Ready to build a robust, production-ready AI agent from scratch on Azure? 🛠️ This comprehensive, deep-dive tutorial takes you through the entire end-to-end workflow of creating a high-performance RAG (Retrieval-Augmented Generation) solution using Azure AI services. We'll meticulously cover every step, from implementing a grounded search mechanism to fully integrating it with an intelligent agent in the Azure AI Foundry. What We Cover in Detail: Core RAG Implementation with Azure Search and .NET: We start by establishing the RAG backbone. You'll see how to use .NET to securely connect to and query a dedicated Azure AI Search index. This ensures your agent's responses are not only creative but are also grounded in your specific, verifiable data, minimizing hallucinations. We dive into the specific API calls and data mapping required for optimal retrieval. Scenario-Based Testing & Validation with Postman: Before moving to the agent, we stress-test the RAG service. Learn professional techniques for using Postman to execute a full suite of tests, covering various search parameters, edge cases, and security authorization flows. This validation step is crucial for ensuring the reliability and performance of your RAG endpoint under real-world conditions. The Critical Role of the MCP Tool (Managed Cognitive Plane): This is the key integration step. We demonstrate exactly how to utilize the MCP tool to officially expose our high-performance .NET-backed Azure Search functionality. The MCP acts as a secure, managed gateway, transforming your custom API into a service that the Azure AI ecosystem can natively recognize and consume as an external tool for the agent. Agent Assembly in Azure AI Foundry: We then pivot to the Azure AI Foundry, where we provision and configure the intelligent agent. Watch the process of integrating the MCP-exposed search as a primary tool for the agent. This step teaches the agent when and how to use your custom RAG retrieval mechanism to answer complex user queries. Local Agent Development and Testing with VS Code: Finally, we bring the process to your desktop! We use VS Code to locally develop, fine-tune, and perform iterative testing on the agent. You'll see how to simulate conversations and observe the agent's decision-making process, ensuring its logic is sound and its integration with your custom RAG endpoint—via the MCP—is seamless and effective. If you're a developer or AI architect aiming to bridge custom backend development (C#/.NET) with powerful Azure AI Agents, this video offers the complete technical blueprint. Timeline: Building an MCP Server for Azure AI (00:07) - Introduces project: Building an MCP server to connect Azure AI Search to an AI agent. (00:30) - Creates an Azure Blob Storage data source and generates 100 synthetic insurance policy files using the Bogus library. (01:26) - Creates an Azure AI Search service, defining an index and an indexer to load the documents from blob storage. (04:05) - Starts developing a .NET Function App with HTTP triggers for document upload and search. (36:56) - Implements vector search by integrating an Azure OpenAI embedding model to convert text into numerical vectors for semantic search. (01:42:01) - Builds the core MCP server, creating an "Azure Search Tool" that wraps the search API. (01:50:53) - Tests the MCP server locally using the MCP Inspector tool to confirm the search tool works. (01:57:04) - Creates an AI agent in VS Code using the Azure AI Foundry extension. (01:58:21) - Deploys the MCP server to an Azure Web App and configures all necessary API keys and endpoints. (02:02:50) - Connects the deployed MCP server to the AI agent as a usable tool. (02:07:24) - Successfully tests the final integration: the AI agent uses natural language prompts to query the insurance data, and the MCP tool automatically executes the search with proper filters and sorting.