Évaluer la justesse des réponses des agents IA avec OpenAI et la méthode RAGAS

• •Peut nécessiter les informations d'identification d'authentification de l'API cible
• •Clé API OpenAI

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        "content": "## 1. Setup Your AI Workflow to Use Evaluations\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nThe Evaluations Trigger is a separate execution which does not affect your production workflow in any way. It is manually triggered and automatically pulled datasets from the assigned Google Sheet."
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        "content": "## 2. Answer Correctness: Is the agent getting its facts correct?\n[Learn more about the Evaluations Trigger](https://docs.n8n.io/integrations/builtin/?utm_source=n8n_app&utm_medium=node_settings_modal-credential_link&utm_campaign=n8n-nodes-base.evaluationTrigger)\n\nThis evaluation measures answer correctness compared to ground truth as a combination of factuality and semantic similarity.\nWhen the agent is without tools, this test may check for accuracy in the agent's training data. For best results, the agent's response should be verbose and conversational. "
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        "content": "## Try It Out!\n### This n8n template demonstrates how to calculate the evaluation metric \"Correctness\" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths.\n\nThe scoring approach is adapted from [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py)\n\n### How it works\n* This evaluation works best where the agent's response is allowed to be more verbose and conversational.\n* For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not ground truth) and False Negative (not in answer but in ground truth).\n* We also calculate an average similarity score on the agent's response against all ground truths.\n* The classification and the similarity score is then averaged to give the final score. \n* A high score indicates the agent is accurate whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer.\n\n### Requirements\n* n8n version 1.94+\n* Check out this Google Sheet for a sample data [https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)\n\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
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