AI / RAG Project
PDF Q&A using RAG
A document question-answering system that lets users upload a PDF and ask questions based on its content. The system retrieves the most relevant parts of the document before generating an answer.
Project Type
RAG Application
What is RAG?
Retrieval first, generation second.
RAG stands for Retrieval-Augmented Generation. Instead of asking a language model to answer from memory only, the system first searches the uploaded document and retrieves the most relevant pieces of text.
The retrieved text is then passed to the model as context. This helps the answer stay closer to the PDF content and makes the system more useful for document search, summaries, and Q&A.
Workflow
How the PDF Q&A pipeline works
This is the high-level flow of the RAG system. Later, you can replace this section with a real diagram or visual image.
Upload PDF
The user uploads a PDF document into the system.
Extract Text
The system extracts readable text from the PDF pages. OCR can be added later for scanned documents.
Chunk Content
The document is split into smaller chunks so the model can search and understand it efficiently.
Create Embeddings
Each chunk is converted into vector embeddings that capture semantic meaning.
Retrieve Context
When the user asks a question, the most relevant chunks are retrieved from the vector database.
Generate Answer
The LLM uses the retrieved context to generate an answer grounded in the PDF content.