AgenticAI - Document Q&A System

A RAG (Retrieval-Augmented Generation) system that allows users to upload documents, process them into chunks, store embeddings in a vector database, and query them using LLMs.

This project is currently in active development. Core RAG functionality is implemented and testable.

Prerequisites

• Python 3.13+
• OpenAI API key

Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install langchain
pip install langchain-openai
pip install chromadb
pip install pypdf
pip install python-docx

Configuration

Set your OpenAI API key:

import os
os.environ["OPENAI_API_KEY"] = "your-api-key-here"

Or use a .env file (recommended):

OPENAI_API_KEY=your-api-key-here

Project Structure

AgenticAi/
├── llms.ipynb           # Main Jupyter notebook for testing
├── chroma_db/           # Vector database storage (gitignored)
├── .env                 # Environment variables (gitignored)
├── .gitignore
└── README.md

How It Works

1. Document Loading: User selects a file (PDF, text, etc.)
2. Chunking: Document is split into manageable chunks with overlap
3. Embedding: Each chunk is converted to vector embeddings
4. Storage: Embeddings are stored in ChromaDB vector database
5. Query: User asks a question
6. Retrieval: Most relevant chunks are retrieved via similarity search
7. Generation: LLM generates answer based on retrieved context

Current Limitations

• Only supports PDF and text files
• No user interface yet (Jupyter notebook only)
• Vector database is local (not cloud-based)
• Single document processing at a time