In my previous blog, I have discussed about complete indexing part like how to extract the data from multiple file sources, chunking, embedding and vector store/DB. This is a very important step to prepare knowledge base for our project to inject our company specific non-confidential information to process user query during 'retrieval' process in RAG. Once you prepare and ready with knowledge base then only we can enter into actual RAG implementation which this blog talks about. Incase if you are planning to implement the complete RAG, I recommend you to read below mentioned blog before going through this blog. https://arunsdatasphere.blogspot.com/2026/04/ai-blog17-rag-preparing-knowledge-base.html RAG (Retrieval Augmented Generation) RAG is a technique where an AI model first retrieves relevant information from an external knowledge source(like a vector database) and then uses it to generate more accurate and context-aware responses. Look at above image to understand ...