Document AI RAG

An AI-powered Retrieval-Augmented Generation (RAG) application that enables users to upload documents and ask natural language questions. The system retrieves the most relevant document chunks using semantic search and generates context-aware answers using a Large Language Model (LLM).

Features

• Upload PDF and DOCX documents
• Automatic document parsing and text chunking
• Semantic search using vector embeddings
• Retrieval-Augmented Generation (RAG)
• Context-aware question answering
• Interactive Streamlit web interface

Tech Used

• Language: Python
• Framework: Streamlit
• LLM: Groq API
• Vector Database: FAISS
• Embeddings: Sentence Transformers
• Document Processing: PyPDF, python-docx
• Libraries: NumPy

Project Workflow

• Upload a document.
• Extract text from the document.
• Split the text into smaller chunks.
• Generate embeddings for each chunk.
• Store embeddings in a FAISS vector database.
• Retrieve the most relevant chunks based on the user's question.
• Send the retrieved context to the LLM.
• Display an accurate, context-aware answer.

Skills Demonstrated

• Retrieval-Augmented Generation (RAG)
• Large Language Models (LLMs)
• Prompt Engineering
• Semantic Search
• Vector Databases
• Embeddings
• Information Retrieval
• Python Development

Run Project

pip install -r requirements.txt
streamlit run app.py

Live Demo

Try the app here: https://document-ai-rag-dcimty3oprqrzwys4j74ye.streamlit.app/

Screenshots

Home Page

Upload Document

Ask Questions

Another Example