Document QA Assistant

A robust pipeline for document ingestion, embedding, and question-answering powered by ChromaDB and OpenAI-compatible LLMs.

(Example: Add your architecture diagram here)

Features

• Multi-format Support: Process Markdown, PDF, Word (.doc/.docx)
• Smart Chunking: Configurable text splitting with overlap
• Semantic Search: HNSW-powered vector similarity (ChromaDB)
• LLM Integration: Streaming responses with citation tracking
• Production Ready:
  • Environment variable configuration
  • Structured logging (file + stdout)
  • Type hints & PEP8 compliance
  • PyInstaller executable support

Quick Start

# 1. Clone repo
git clone https://github.com/yourusername/document-qa-system.git
cd document-qa-system

# 2. Set up environment (Linux/macOS)
make install-dev
cp .env.example .env  # Edit with your API keys

# 3. Add documents to ./documents/
# 4. Run!
make run

Configuration

Edit .env file

# Document Processing
CHUNK_SIZE=512      # Token size per chunk
CHUNK_OVERLAP=50    # Context overlap between chunks

# Vector DB
EMBEDDING_MODEL=all-MiniLM-L6-v2  # Sentence Transformer model

# LLM (OpenAI-compatible)
OPENAI_API_KEY=your-key-here
OPENAI_MODEL=gpt-3.5-turbo

Usage

1. Place documents in ./documents/
2. Launch the interactive Q&A interface:

make run

1. Enter questions when prompted:

Enter your question: What's the capital of France?