A Python library for evaluating LLM responses using Google Gemini as an automated judge.

llm-eval provides a simple way to score LLM responses on five key dimensions: accuracy, relevance, coherence, hallucination risk, and conciseness. It uses Google's Gemini model to perform the evaluation and returns structured JSON results.

1. Clone the repository:

   git clone <repository-url>
   cd llm-eval

2. Install dependencies:

   pip install -r requirements.txt

Create a .env file in the project root with your Gemini API key:

GEMINI_API_KEY=your_api_key_here

Use the command-line interface to evaluate responses:

python cli.py --question "What is the capital of France?" --response "Paris"

Start the FastAPI server:

uvicorn app:app --reload

Send a POST request to /evaluate:

curl -X POST "http://localhost:8000/evaluate" \
     -H "Content-Type: application/json" \
     -d '{"question": "What is the capital of France?", "response": "Paris"}'

{
  "accuracy": {
    "score": 10,
    "reason": "The response is factually correct."
  },
  "relevance": {
    "score": 10,
    "reason": "Directly answers the question."
  },
  "coherence": {
    "score": 9,
    "reason": "Clear and well-structured."
  },
  "hallucination_risk": {
    "score": 10,
    "reason": "No unsupported information."
  },
  "conciseness": {
    "score": 10,
    "reason": "Brief and to the point."
  }
}