Rag-pipeline-pdf is a Python-based project implementing a Retrieval-Augmented Generation (RAG) pipeline specifically for PDF documents. It combines traditional information retrieval techniques with AI-powered generation, allowing users to query large PDF datasets efficiently and get accurate, context-aware answers.
- PDF Text Extraction: Automatically extracts and preprocesses text from PDF documents.
- Vector Database Storage: Uses ChromaDB to store embeddings of the extracted text for fast and efficient retrieval.
- RAG Pipeline: Integrates retrieval with AI models to provide context-aware answers from the documents.
- Interactive Notebooks: Jupyter notebooks included for testing, exploring, and demonstrating the pipeline.
- Scalable & Modular: Can be extended to larger datasets or integrated with other AI applications.
- Built the end-to-end pipeline from PDF ingestion to AI query.
- Implemented vectorization and storage for quick retrieval.
- Enabled AI-assisted querying to fetch precise answers from PDFs.
- Organized the project for easy experimentation and extension.
This project serves as a base for building intelligent document search systems and can be extended to handle multiple document types or integrated into web applications.