📚 Document Question Answering System

A document-based question answering system using vector embeddings and retrieval-augmented generation(RAG) powered by OpenAI and ChromaDB.

✨ Features

• 📂 Document ingestion from directory
• ✂️ Smart text chunking with overlap
• 🔍 Semantic search with ChromaDB
• 🤖 AI-powered answers using GPT-3.5
• 💾 Persistent vector storage
• 🔧 Configurable chunking parameters

🚀 Quick Start

Prerequisites

• Python 3.8+
• OpenAI API key

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/AskDocs_AI.git
   cd AskDocs_AI
   

2. Set up virtual environment:

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows   

3. Install dependencies:

   pip install -r requirements.txt

4. Create .env file:

   echo "OPENAI_API_KEY=your_api_key_here" > .env
   

Usage

1. Add your text documents to ./news_articles directory
2. Run the system:

   python main.py

3. Enter your questions when prompted

🛠 Configuration

Modify these in main.py:

# Document processing
CHUNK_SIZE = 1000      # Characters per chunk
CHUNK_OVERLAP = 20     # Overlap between chunks

# Query settings
N_RESULTS = 2          # Number of chunks to retrieve

# Model settings
EMBEDDING_MODEL = "text-embedding-3-small"
LLM_MODEL = "gpt-3.5-turbo"