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chore: import upstream snapshot with attribution
2026-07-13 12:37:31 +08:00

99 lines
3.0 KiB
Python

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License
"""Metrics middleware to process metrics using a MetricsProcessor."""
import time
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from graphrag_llm.config import ModelConfig
from graphrag_llm.metrics import MetricsProcessor
from graphrag_llm.types import (
AsyncLLMFunction,
LLMFunction,
Metrics,
)
def with_metrics(
*,
model_config: "ModelConfig",
sync_middleware: "LLMFunction",
async_middleware: "AsyncLLMFunction",
metrics_processor: "MetricsProcessor",
) -> tuple[
"LLMFunction",
"AsyncLLMFunction",
]:
"""Wrap model functions with metrics middleware.
Args
----
model_config: ModelConfig
The model configuration.
sync_middleware: LLMFunction
The synchronous model function to wrap.
Either a completion function or an embedding function.
async_middleware: AsyncLLMFunction
The asynchronous model function to wrap.
Either a completion function or an embedding function.
metrics_processor: MetricsProcessor
The metrics processor to use.
Returns
-------
tuple[LLMFunction, AsyncLLMFunction]
The synchronous and asynchronous model functions wrapped with metrics middleware.
"""
def _metrics_middleware(
**kwargs: Any,
):
metrics: Metrics | None = kwargs.get("metrics")
start_time = time.time()
response = sync_middleware(**kwargs)
end_time = time.time()
if metrics is not None:
metrics_processor.process_metrics(
model_config=model_config,
metrics=metrics,
input_args=kwargs,
response=response,
)
if kwargs.get("stream"):
metrics["compute_duration_seconds"] = 0
metrics["streaming_responses"] = 1
else:
metrics["compute_duration_seconds"] = end_time - start_time
metrics["streaming_responses"] = 0
return response
async def _metrics_middleware_async(
**kwargs: Any,
):
metrics: Metrics | None = kwargs.get("metrics")
start_time = time.time()
response = await async_middleware(**kwargs)
end_time = time.time()
if metrics is not None:
metrics_processor.process_metrics(
model_config=model_config,
metrics=metrics,
input_args=kwargs,
response=response,
)
if kwargs.get("stream"):
metrics["compute_duration_seconds"] = 0
metrics["streaming_responses"] = 1
else:
metrics["compute_duration_seconds"] = end_time - start_time
metrics["streaming_responses"] = 0
return response
return (_metrics_middleware, _metrics_middleware_async) # type: ignore