PDFChat is an interactive application that allows users to communicate with PDF documents through a natural conversational interface. This application uses Retrieval-Augmented Generation (RAG) architecture to enable users to ask questions in natural language and receive contextual answers based on the uploaded PDF content.
Unlike regular text search, PDFChat understands the context of questions and extracts relevant information from documents, even if questions are asked using different wording than the original text. This system solves common information access problems in lengthy documents such as research reports, legal documents, technical manuals, and academic textbooks.
With PDFChat, users can extract insights from PDF documents without having to read the entire text, saving up to 70% of time when seeking specific information from complex documents. The system also provides page references and source text citations to ensure answers can be verified and trusted.
Processing very large PDFs (hundreds of pages) led to performance issues and token limits with the language model.
Implemented a chunking strategy that divides the document into semantic sections rather than arbitrary chunks. This approach preserves context while enabling efficient processing of even lengthy documents. Additionally, we implemented a similarity-based retrieval system that only fetches the most relevant document sections for each query, significantly reducing the token count sent to the language model.
Users expected the system to remember previous questions in a conversation chain, but initially each query was processed independently.
Developed a conversation memory system using LangChain's memory components to maintain context across multiple queries. This enabled the system to understand follow-up questions and references to previous parts of the conversation, creating a more natural dialogue experience.
The PDF Chat application achieved 85% accuracy in answering questions based on document content, as measured through user feedback and validation tests. Users reported saving an average of 70% of time compared to manually searching through documents, particularly with technical and academic papers.
The system has been successfully used by over 300 users for various document types, from academic research to legal contracts. The most significant impact has been in educational settings, where students use it to quickly extract information from textbooks and research papers.
I plan to enhance this project with several new features and improvements:
Explore the technical details and implementation of this interactive PDF chat application: