#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Metrics data models for Pipecat framework.
This module defines Pydantic models for various types of metrics data
collected throughout the pipeline, including timing, token usage, and
processing statistics.
"""
from pydantic import BaseModel
[docs]
class MetricsData(BaseModel):
"""Base class for all metrics data.
Parameters:
processor: Name of the processor generating the metrics.
model: Optional model name associated with the metrics.
"""
processor: str
model: str | None = None
[docs]
class TTFBMetricsData(MetricsData):
"""Time To First Byte (TTFB) metrics data.
Parameters:
value: TTFB measurement in seconds.
"""
value: float
[docs]
class ProcessingMetricsData(MetricsData):
"""General processing time metrics data.
Parameters:
value: Processing time measurement in seconds.
"""
value: float
[docs]
class LLMTokenUsage(BaseModel):
"""Token usage statistics for LLM operations.
Parameters:
prompt_tokens: Number of tokens in the input prompt.
completion_tokens: Number of tokens in the generated completion.
total_tokens: Total number of tokens used (prompt + completion).
cache_read_input_tokens: Number of tokens read from cache, if applicable.
cache_creation_input_tokens: Number of tokens used to create cache entries, if applicable.
"""
prompt_tokens: int
completion_tokens: int
total_tokens: int
cache_read_input_tokens: int | None = None
cache_creation_input_tokens: int | None = None
reasoning_tokens: int | None = None
[docs]
class LLMUsageMetricsData(MetricsData):
"""LLM token usage metrics data.
Parameters:
value: Token usage statistics for the LLM operation.
"""
value: LLMTokenUsage
[docs]
class TTSUsageMetricsData(MetricsData):
"""Text-to-Speech usage metrics data.
Parameters:
value: Number of characters processed by TTS.
"""
value: int
[docs]
class TextAggregationMetricsData(MetricsData):
"""Text aggregation time metrics data.
Measures the time from the first LLM token to the first complete sentence,
representing the latency cost of sentence aggregation in the TTS pipeline.
Parameters:
value: Aggregation time in seconds.
"""
value: float
[docs]
class TurnMetricsData(MetricsData):
"""Metrics data for turn detection predictions.
Parameters:
is_complete: Whether the turn is predicted to be complete.
probability: Confidence probability of the turn completion prediction.
e2e_processing_time_ms: End-to-end processing time in milliseconds,
measured from VAD speech-to-silence transition to turn completion.
"""
is_complete: bool
probability: float
e2e_processing_time_ms: float
[docs]
class SmartTurnMetricsData(TurnMetricsData):
"""Metrics data for smart turn predictions.
.. deprecated:: 0.0.104
Use :class:`TurnMetricsData` instead. This class will be removed in a future version.
Parameters:
inference_time_ms: Time taken for inference in milliseconds.
server_total_time_ms: Total server processing time in milliseconds.
"""
inference_time_ms: float = 0.0
server_total_time_ms: float = 0.0