🧠 RAG Demo Solution Overview

This repository is a modular Retrieval-Augmented Generation (RAG) demo built with C#, Semantic Kernel, Azure OpenAI, and Azure Functions. It demonstrates a modern, agent-based approach to conversational AI, project ingestion, and hybrid search using Azure AI Search.

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📚 Solution Structure

• Backend/ – Contains all backend projects for the solution:
  • AIServices/ – Core AI logic, agent orchestration, and service interfaces (see AIServices README)
  • RagChatBotAPI/ – Azure Function API for chat, powered by Semantic Kernel and agents (see RagChatBotAPI README)
  • DocumentIngestion/ – Azure Function for bulk project ingestion and embedding generation (see DocumentIngestion README)
  • Shared/ – DTOs and interfaces shared across projects (see Shared README)
• docs/ – Example project data for ingestion

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🚀 What This Demo Shows

• Retrieval-Augmented Generation (RAG): Combines LLMs with project/document search for grounded, context-aware answers
• Semantic Kernel: Orchestrates LLM calls and agent workflows
• Agent Architecture: Modular agents for classification, response, and coordination
• Azure OpenAI: Used for both embeddings and completions
• Azure AI Search (pluggable): Ready for hybrid search scenarios

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📖 How to Use

• Each project contains its own detailed README with setup, configuration, and usage instructions.
• Start with the AIServices README for an overview of the agent and service architecture.
• Use the RagChatBotAPI README to run the chat API.
• Use the DocumentIngestion README to ingest project data for search.

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🛠️ Technologies Used

• C# (.NET 8)
• Azure Functions (Isolated Worker)
• Semantic Kernel
• Azure OpenAI
• Azure AI Search (pluggable)

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📄 License

MIT