I'm trying to make a simple webApp to access my model deployed on Azure OpenAI. I'm using this library as connector, I got to a nice solution and I made it, but I want to implement a RAG structure, where I can feed all this process with an embedding. My question is about how to use the Azure Search Service as a Vector Database for making vector search. Maybe someone has implement this and can help me a little. Besides, if someone has any useful pattern to follow to implement this structure it would help me so much.
Thank you.
To implement a RAG (Retrieval-Augmented Generation) structure using Azure Search Service as a Vector Database, first create an Azure Cognitive Search index to store vector embeddings (using a field of type Collection(Edm.Single)). Then, generate embeddings for your data (e.g., using OpenAI) and upload them to the search index along with relevant metadata. For vector search, use Azure's vector search capabilities to retrieve similar documents based on embeddings, and implement the retrieval mechanism to fetch the most relevant results. Finally, feed these results into your OpenAI model for generation, combining retrieval and generation to enhance responses.