TalkingPDFs

This project uses a Retrieval-Augmented Generation (RAG) pipeline to extract relevant information from PDFs. Users can upload PDFs, submit queries, and get accurate responses based on the document content. It combines a retriever model and generative model for intelligent, real-time query handling.

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Retrieval-Augmented Generation (RAG) PDF Query System

This project implements a Retrieval-Augmented Generation (RAG) pipeline to efficiently extract and retrieve relevant information from PDF documents. The system allows users to upload PDFs, submit a query, and retrieve specific answers from the provided documents. This project is particularly useful for document-heavy fields where fast and accurate information retrieval is critical.

Features:

• PDF Input: Upload and process multiple PDF files as the data source.
• Query-based Search: Accepts user queries and extracts the most relevant information from the PDFs.
• RAG Model: Combines the power of a retriever model to find relevant passages and a generative model to answer user queries based on the retrieved content.
• Seamless Interaction: The pipeline provides real-time interaction with the user, similar to a chatbot.

Technologies Used:

• Retrieval-Augmented Generation (RAG)
• Python
• PDF processing libraries
• NLP libraries for text retrieval and generation
• Integration with machine learning models for intelligent search and query response

Future Work:

• Extend the project with more complex NLP techniques
• Add support for additional document types (e.g., DOCX)
• Optimize model performance for large-scale documents