Build a RAG chatbot for your personal ebook collection

The tutorial outlines the process of building an ETL pipeline to transform a personal book collection into a knowledge base for a chatbot application, using MongoDB Atlas and the Unstructured Serverless API. It involves extracting content from books in the EPUB format, partitioning the documents, chunking the text, embedding the chunks with a model, and loading the results into a vector store for retrieval. The ETL pipeline is constructed using various configurations that define its behavior, such as ProcessorConfig for general parameters, and MongoDBConnectionConfig for authentication and data upload. After preprocessing, users can create a vector search index in their MongoDB account to facilitate data retrieval, followed by setting up a retriever integrated with LangChain, which orchestrates the entire process using the Llama3.1:8b model by Meta AI. The final step involves creating a user interface with Streamlit to enable interaction with the app, allowing for multi-turn conversations with the AI Librarian.