Open Notebook in Google Colab Introduction This notebook demonstrates how to build a semantic question-answering system over scientific PDFs using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). It enables users to upload PDFs, extract content, embed it into a vector store, and query the document using natural language. Key Features PDF Upload & Text Extraction: Extract raw text from research papers using PyMuPDF Text Chunking & Embeddings: Convert text into meaningful chunks and generate embeddings using models like sentence-transformers RAG Pipeline: Store document chunks in a FAISS vector database Retrieve top-matching chunks based on user queries Generate context-aware answers with an LLM Natural Language Q&A: Ask questions like “What is the main finding?” or “What methods were used?” and get accurate answers drawn directly from the paper Installation Install all required dependencies: pip install pygetpapers pip install langchain pip install transformers pip install sentence-transformers pip install faiss-cpu pip install PyMuPDF pip install openai You’ll also need an API key for OpenAI or any supported LLM provider. Workflow Upload PDF Extract & preprocess text Chunk and embed text Store embeddings in FAISS Ask a question Retrieve relevant chunks + LLM = final answer Use Cases Scientific literature exploration Research paper understanding without reading entire texts Building AI-powered academic assistants Legal, policy, or technical document question answering Conclusion: The notebook contains the trustable resources which can be used to extract contextual information from the PDFs. This technology is scalable for a range of domains from academic research to policy documentation etc as they are published in PDF format. This approach not only saves the time of the researchers but also increases the accessibility of the content information.