RAG System for 'Leave No Context Behind' Paper

Inspiration

The inspiration behind this project was to leverage the power of language models and natural language processing techniques to create an interactive system capable of answering questions based on a specific document or corpus of text.

What it does

The RAG (Retrieval-Augmented Generation) System for the 'Leave No Context Behind' Paper is a Streamlit application designed to answer user questions by utilizing a combination of document retrieval and generative AI techniques. It processes a PDF document containing the "Leave No Context Behind" paper and splits the text into chunks. Users can input questions related to the content of the paper, and the system generates answers based on the information extracted from the document.

How we built it

We built this system using Python and several libraries and frameworks, including Streamlit for the user interface, Langchain for accessing GenAI models, NLTK for text processing, and PyPDF2 for PDF extraction. The core of the system relies on the RAG framework, which combines retrieval-based and generative AI approaches to generate contextually relevant responses.

Challenges we ran into

Integrating the Langchain GenAI model with Streamlit posed some initial challenges due to the setup and configuration process. Preprocessing the PDF document and splitting it into manageable chunks required careful handling to ensure accuracy and efficiency. Designing an intuitive user interface that allows for seamless interaction while maintaining responsiveness was another challenge.

Accomplishments that we're proud of

Successfully integrating the Langchain GenAI model into the Streamlit application and leveraging its capabilities to provide accurate and contextually relevant responses. Overcoming the challenges related to PDF processing and text chunking, resulting in a robust system capable of handling large documents effectively. Creating a user-friendly interface that makes it easy for users to input questions and receive answers in real-time.

What we learned

We gained a deeper understanding of the RAG framework and its applications in natural language processing tasks. Working with Langchain and Streamlit provided valuable experience in integrating AI models into web applications and building interactive user interfaces. Handling PDF documents programmatically and implementing text processing techniques for document analysis expanded our knowledge in document processing and information retrieval.

What's next for RAG System for 'Leave No Context Behind' Paper

Enhancing the system's capabilities by incorporating more advanced language models and fine-tuning the existing ones for better performance. Implementing features such as summarization and document search to provide users with a more comprehensive understanding of the document's content. Exploring opportunities to deploy the system in educational or research settings to assist users in accessing and analyzing scholarly literature more efficiently.

Built With

• faiss
• libraries
• models
• nltk
• pypdf2
• pypdfloader
• python
• rag
• toolkit)