Document Q&A Agent (OpenAI + Ollama + Gradio)

A lightweight document-aware chatbot that can answer questions from PDF, DOCX, or text files using:

• Sentence-Transformer embeddings** for context retrieval

• LLM models (OpenAI and Ollama here) for answer generation

• A clean Gradio UI for interaction

This project demonstrates real-world LLM engineering: retrieval pipelines, dual-model support, and an interactive user interface.

Features

• Load PDFs, DOCX, or raw text files

• Automatic document chunking + embedding (MiniLM-L6-v2)

• Switch between OpenAI and Ollama models

• Real-time Q&A with context injection

• Gradio interface for easy testing

• Basic unit tests included

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Installation

Clone the repo

• git clone:

https://github.com/ucheorjins/doc-qa-agent.git

• Enter project folder:

cd doc-qa-agent

Create and activate virtual environment

  python3 -m venv .venv

source:

.venv/bin/activate

Install dependencies

pip install -r requirements.txt

NB:

• Install Ollama separately at

  https://ollama.ai

• Put your OpenAI API key in a .env file.

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Environment Variables

Create a .env file:

OPENAI_API_KEY=your_key_here

LLM_PROVIDER= "openai" or "ollama"

OLLAMA_HOST=http://localhost:11434

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Running the App

  cd src

  python main.py

• Gradio will start and print a local URL: Running on

http://127.0.0.1:7860/

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Running Tests

pytest tests

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Future Improvements

• Add user file upload for QA process

• Add vector database support (FAISS / Chroma)

• Add multiple document support

• Deploy online (HuggingFace Spaces or Docker)

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License

MIT License. Free to use and modify.