AI-Powered Multi-PDF Document Assistant with RAG

AI-Powered Multi-PDF Document Assistant with RAGAI-Powered Multi-PDF Document Assistant with RAG

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Developed an AI-powered document assistant that allows users to upload multiple PDF files and ask questions about their content. The application retrieves relevant information from uploaded documents and uses a large language model (LLM) to generate context-aware answers.

Key Features

Multi-PDF document upload and processing

Retrieval-Augmented Generation (RAG) for document-based question answering

Semantic search for retrieving relevant information

AI-powered responses using LLM integration

FastAPI backend for API-based interactions

Streamlit interface for user interaction

LangChain and LangGraph for AI workflow development

Technology Stack

Python, LangChain, LangGraph, RAG, LLMs, FastAPI, Streamlit, FAISS/Vector Store, Groq API.

Business Value

Helps users find information across multiple documents without manually searching through every PDF. The solution can be adapted for internal knowledge bases, business reports, technical documentation, and document-heavy workflows.

My Role

Designed and developed the application, integrated document retrieval and LLM-based question answering, and connected the backend API with the user interface.

Available Services

Custom AI chatbot development

RAG pipeline development and customization

PDF-based knowledge assistants

Python backend and API integration

AI application prototyping