RAG stands for Retrieval Augmented Generation - an advanced AI technique that combines information retrieval with generative AI to provide accurate, context-aware responses.
RAG works through a sophisticated 7-step process:
The PDF document is loaded and parsed using PyPDFLoader to extract all text content from the document pages.
The extracted text is split into smaller chunks (12,000 characters) with overlaps (1,500 characters) to maintain context across boundaries.
Each text chunk is converted into a dense vector representation (embedding) that captures semantic meaning using Google's Gemini model.
These embeddings are stored in a vector database, allowing for efficient similarity search based on semantic content rather than exact keyword matching.
When you ask a question, your query is converted into an embedding and compared against all stored document embeddings to find the most relevant chunks.
The system retrieves the top-k most semantically similar chunks (top 4 in this implementation) that contain information related to your query.
The retrieved chunks are combined with your question and sent to the Gemini model, which generates an accurate answer based on the document's context.
This application is built using the following technologies:
In this application:
AIML,Global Academy of Technology
Led the development of the RAG model architecture and deployed the application on Google Cloud Run, ensuring scalable and efficient document processing.
AIML,Global Academy of Technology
Designed and implemented the interactive user interface, seamlessly integrating frontend components with backend API calls for a smooth user experience.