An Azure service that provides access to OpenAI’s GPT-3 models with enterprise capabilities.
Hi Kab
Good day.
We have "Store completion " option to record inputs and outputs from model which you can use for evaluation or finetuning purpose
To enable stored completions for your Azure OpenAI deployment set the store parameter to True****. Use the metadata parameter to enrich your stored completion dataset with additional information.
completion = client.chat.completions.create(
model="gpt-4o", # replace with model deployment name
store= True,
metadata = {
"user": "admin",
"category": "docs-test",
},
messages=[
{"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."},
{"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data."}
]
)
Reference used - https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/stored-completions?tabs=python-secure
Hope it eases the work of searching chat completion logs.
Thank you.