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This post is a follow up to my previous one on Querying SQL Server With Natural Language In Semantic Kernel, if you haven’t read it yet, go take a look for a brief introduction to Semantic Kernel and the plugin architecture. The majority of Retrieval Augmented Generation (RAG) systems utilise Vector Databases, such as Azure AI Search, to store chunks of documents that can be accessed via Large Language Models (LLMs)....

With the recent rise of Retrieval Augmented Generation (RAG) alongside Large Language Models (LLMs), we can build AI-powered systems that can search and summarize indexed documents to enhance the knowledge of chatbots. This works great for unstructured data stored in documents, but what if you want to access information stored in a SQL database and summarize accordingly? By combining existing RAG techniques with natural language to SQL processing, we can create enterprise chatbot systems that can fetch and summarize both unstructured and structured data....