A Q&A question answer chatbot powered by LLM RAG. Uses Langchain and Pinecone DB and supports chatting with multiple PDFs.
Sophisticated question-answering (Q&A) chatbots represent one of the most potent applications made possible by Large Language Models (LLMs). These bots excel at providing answers based on particular source material. They leverage a technique called Retrieval Augmented Generation (RAG) to achieve this. RAG involves enhancing the knowledge of LLMs by incorporating additional data. While LLMs possess the ability to reason across a broad spectrum of topics, their knowledge is confined to publicly available data up to a certain training point.
Should one aim to develop AI applications capable of reasoning with private or post-training cut-off date data, it becomes necessary to enrich the model's knowledge with the specific information required. This process, referred to as RetrievalAugmented Generation (RAG), entails bringing in pertinent information and integrating it into the model prompt.