Hands-on: Azure AI Search & AI Foundry for RAG

Index Introduction Azure Resources Azure AI Search Azure AI Foundry Code Do not forget to Clean the Cloud Conclusion Introduction In this lab, we will build a full RAG pipeline using Azure. RAG is a technique where, instead of relying solely on a language model's training data, we first retrieve relevant documents from an external knowledge base and then pass them to the model to generate a more accurate and grounded answer. To do this, we will use two Azure services: Azure AI Search as the vector database to store and retrieve document embeddings, and Azure AI Foundry to deploy the embedding model and the generation model. By the end of this lab, you will have a working RAG pipeline running on Azure. Azure Resources Azure AI Search Azure AI Search is a cloud search service that supports full-text search, filters, and vector search. In this lab, we are using it as a vector database. We store document embeddings in it and query them using cosine similarity to find the most relevant docu