PDFChat: A Local PDF Q&A Chatbot (github.com)

PDFChat has launched a local PDF Q&A chatbot that allows users to interact with their PDF documents through a web interface, powered by Flask, LlamaIndex, and Ollama. This solution is significant as it operates entirely on local machines without the need for external API calls, ensuring user privacy and data security. The chatbot employs a Retrieval Augmented Generation (RAG) approach utilizing vector similarity search to efficiently identify relevant content within PDFs organized in subdirectories, streamlining the retrieval of information. Notable technical features include real-time responses, a clean UI built with HTMX and Alpine.js, and a robust backend that utilizes SQLite for conversation history and ChromaDB for persistent vector storage. Users can ask various questions about their documents, with the chatbot maintaining conversational context across multiple interactions. The package is designed for easy installation and dependency management, making it accessible to developers interested in leveraging local AI for document processing without complicated setup procedures. This innovative tool enhances the utility of AI/ML in document handling, making it easier for users to extract insights from their PDF data.