Build a Chatbot with Advance RAG System: with LlamaIndex, OpenSource LLM, Flask and LangChain

Image Credit to Author

In the realm of natural language processing (NLP), the evolution of Retrieval-Augmented Generation (RAG) systems represents a significant leap forward in the quest for more intelligent and contextually aware AI models. Building upon the foundation laid by traditional RAG approaches, advanced RAG systems push the boundaries of what is possible, offering unprecedented capabilities and paving the way for transformative applications across various domains.

In my pervious blog I have covered how we can develop a Chatbot by using Opensource LLM and naïve RAG system with Lang Chain and Flask framework.

In this blog, my focus on how to handle limitation of RAG system while developing a Chatbot with Advance RAG system with pre-retriever optimization and post retriever optimization. we’ll delve into the intricacies of advanced RAG systems, There are two primary RAG techniques:

1. Sentence Window Retrieval + Sentence Reranker
2. Auto merging Retrieval + Sentence Reranker

In this blog our focus only on first optimization technique i.e Sentence Window Retrieval + Sentence Reranker.

Recap Naïve RAG:
The Simple RAG research approach emerged as one of the earliest methodologies…