Introduction
Retrieval-Augmented Generation (RAG) combines the power of large language models with your own data sources. This tutorial builds a production-ready chatbot using Microsoft Semantic Kernel.
Step 1: Set Up Azure Resources
Create an Azure OpenAI resource with a GPT-4 deployment and an Azure AI Search instance for vector storage.
Step 2: Initialize the Semantic Kernel Project
dotnet new console -n RagChatbot
cd RagChatbot
dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Connectors.AzureOpenAI
Step 3: Ingest Your Documents
Load documents, chunk them into semantic units, generate embeddings using Azure OpenAI, and store them in Azure AI Search as vectors.
Step 4: Build the Retrieval Pipeline
Configure Semantic Kernel with a memory connector to retrieve relevant document chunks based on user queries using vector similarity search.
Step 5: Create the Chat Loop
Build an interactive chat loop that retrieves context, augments the prompt, and generates grounded responses using GPT-4.
Step 6: Add Conversation Memory
Implement chat history management so the bot can reference previous messages in the conversation for multi-turn interactions.
