🧠Spe Knowledge Assistant
An intelligent RAG pipeline that understands and answers questions about your documents and images.
Project Overview
Spe is a complete Retrieval-Augmented Generation (RAG) system built from the ground up. It allows users to upload documents (PDFs) and images (screenshots, photos), and then engage in a natural conversation to extract information. The system uses an OCR engine to read text from images, a vector database for efficient searching, and a large language model (Meta-Llama-13b) to generate accurate, context-aware answers.
From Pixels to Answers
OCR-Powered RAG
Key Features
📄 PDF & Image Processing
Extracts text from both PDF documents and images (PNG, JPG) using a robust OCR engine.
🧠Meta-Llama 13b Integration
Leverages Meta's powerful Llama 13b model locally for high-quality text generation and understanding.
Isolated Sessions
Each document upload session is isolated, ensuring answers are strictly based on the provided context.
🚀 High-Performance Backend
Built with FastAPI and served via ngrok, providing a responsive and scalable foundation.
💬 Interactive Chat UI
A dynamic, mobile-responsive interface with streaming responses, stop-generation control, and a fresh start option.
📚 Source Citation
Every answer is backed by the source document's filename, ensuring transparency and trust.
Interactive RAG Pipeline
Click on each component of the pipeline to learn more about its role.
Document Upload
PDFs & Images
OCR & Embedding
Text Extraction
Pinecone Vector Store
Indexing
User Query & RAG
Context Retrieval
Meta-Llama 13b LLM
Answer Generation
Select a component to see details.