azure-ai-evaluation-py

What This Skill Does

The azure-ai-evaluation-py skill provides a comprehensive toolkit for evaluating generative AI applications. It leverages the Azure AI Evaluation SDK to measure critical performance metrics, including quality, safety, and operational efficiency. By integrating this skill into your OpenClaw agent, you can automate the assessment of your LLM responses, ensuring they are grounded, relevant, coherent, and safe. It supports both AI-assisted evaluators (utilizing models like GPT-4o-mini) and traditional NLP-based metrics such as F1, ROUGE, and BLEU scores, allowing for a hybrid evaluation strategy that balances semantic depth with linguistic precision.

Installation

You can install this skill directly via the OpenClaw CLI using the following command: clawhub install openclaw/skills/skills/thegovind/azure-ai-evaluation-py After installation, ensure that your environment variables, specifically AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, and AIPROJECT_CONNECTION_STRING, are configured correctly to enable cloud-based evaluation and safety monitoring.

Use Cases

• Production Monitoring: Automatically evaluate model responses against your ground truth data to detect performance regression after updates.
• Content Safety Auditing: Use built-in safety evaluators (e.g., Violence, Sexual, Self-Harm, Hate) to filter and monitor outputs, ensuring alignment with corporate safety standards.
• RAG Pipeline Optimization: Use the RetrievalEvaluator and GroundednessEvaluator to measure the efficacy of your Retrieval-Augmented Generation systems.
• Comparative Analysis: Run batch evaluations using the evaluate() function to compare multiple model configurations against a single dataset.

Example Prompts

1. "Evaluate the quality of the responses in test_data.jsonl using the Groundedness and Relevance evaluators."
2. "Perform a batch evaluation on the latest chatbot logs and report the mean F1 and BLEU scores."
3. "Check the current safety of my RAG model outputs using the ContentSafetyEvaluator."

Tips & Limitations

• Cost Efficiency: AI-assisted evaluation uses token resources; ensure your AZURE_OPENAI_DEPLOYMENT is set to an efficient model like gpt-4o-mini to manage costs at scale.
• Data Privacy: Ensure that any data passed to the SDK complies with your organization's data protection policies, especially when using external API evaluators.
• Resource Requirements: Batch evaluations for large datasets should be executed in an environment with stable network access to avoid interruption during the analysis phase.