This project focuses on utilizing Retrieval-Augmented Generation (RAG) to efficiently interact with multiple PDFs. By leveraging the FAISS vector database for embedding storage, we aim to streamline the retrieval of specific information from various documents.
The digital world is overflowing with information stored in PDF documents. Extracting valuable insights from these static files is often tedious and time-consuming. To address this challenge, we explored RAG technology, enabling dynamic interaction with PDFs to retrieve information seamlessly.
Our approach integrates the FAISS vector database to store embeddings from multiple PDFs. This setup allows for efficient similarity searches and quick information retrieval. The RAG framework forms the backbone of this interaction, facilitating a conversational interface that extracts precise details from the documents.
We conducted a series of experiments by uploading diverse PDFs to test the system's performance. Various query types were posed to measure the speed and accuracy of information retrieval, assessing the efficiency of both the RAG model and the FAISS vector search.
The results demonstrated a significant improvement in retrieval accuracy and response time when interacting with the PDFs using RAG and FAISS. The system successfully fetched relevant information, showcasing its potential in simplifying data extraction from large document sets. To see the output of this project please refer this link.
This project showcases the power of combining RAG with FAISS vector databases for PDF interaction. The solution enables users to access information from multiple documents effortlessly, transforming static PDFs into interactive resources. Future enhancements will focus on scaling the system for broader use cases and improving its conversational capabilities.
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