41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
from mlflow.anthropic.autolog import async_patched_class_call, patched_class_call
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from mlflow.utils.annotations import experimental
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from mlflow.utils.autologging_utils import autologging_integration, safe_patch
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FLAVOR_NAME = "anthropic"
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@experimental
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@autologging_integration(FLAVOR_NAME)
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def autolog(
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log_traces: bool = True,
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disable: bool = False,
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silent: bool = False,
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):
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"""
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Enables (or disables) and configures autologging from Anthropic to MLflow.
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Only synchronous calls are supported. Asynchnorous APIs and streaming are not recorded.
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Args:
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log_traces: If ``True``, traces are logged for Anthropic models.
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If ``False``, no traces are collected during inference. Default to ``True``.
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disable: If ``True``, disables the Anthropic autologging. Default to ``False``.
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silent: If ``True``, suppress all event logs and warnings from MLflow during Anthropic
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autologging. If ``False``, show all events and warnings.
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"""
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from anthropic.resources import AsyncMessages, Messages
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safe_patch(
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FLAVOR_NAME,
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Messages,
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"create",
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patched_class_call,
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)
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safe_patch(
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FLAVOR_NAME,
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AsyncMessages,
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"create",
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async_patched_class_call,
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)
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