From the course: Build with AI: LLM-Powered Applications with Streamlit

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How the document Q&A chatbot works

How the document Q&A chatbot works - Python Tutorial

From the course: Build with AI: LLM-Powered Applications with Streamlit

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How the document Q&A chatbot works

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- [Narrator] Before you dive into the code, let's define what a document Q&A chatbot actually is. This kind of chatbot allows users to ask questions about documents like tour itineraries, company policies, research papers, or even product manuals. Then it receives relevant answers that are grounded in the content of those documents. These chatbots use modern AI techniques to understand the meaning behind the question and extract meaningful answers from the text using techniques like rag. Let's begin by exploring the pipeline you will slowly create throughout the rest of this course for your document Q&A chatbot. First, you will load and clean the source documents. Second, you will turn the text into mathematical representations called embeddings. Third, you will store those in a vector database so you can search on them later. Fourth, you will build a prompt that includes relevant content. Fifth, you will send that prompt to a large language model to gather an answer. And finally, you…

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