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Building an AI-powered chatbot to chat with PDF document using LangChain and Streamlit
With the launch of powerful AI chatbots like ChatGPT and Gemini, there has been a wave of chatbot applications developed using foundational models behind them. These applications aim to reduce hallucinations in large language models (LLMs) using techniques such as the Retrieval-Augmented Generation (RAG). One such example of these applications is “chat with documents”
Chat with documents helps users to query text-based documents such as PDFs, word files, or sheets through a chat interface. This helps users to have a quick and efficient information retrieval from large documents like legal documents, technical manuals, academic papers and corporate files. In this article, let us understand how to build a chat application using LangChain and Streamlit that can fetch information from pdf documents based on user questions.
About LangChain:
LangChain is an open-source framework developed to simplify the process of building applications powered by large language models (LLMs). It provides pre-built components and functions that make it easier for developers to build applications using…
