metrics

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.

class pipecat.metrics.metrics.MetricsData(*, processor: str, model: str | None = None)[source]

Bases: 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

class pipecat.metrics.metrics.TTFBMetricsData(*, processor: str, model: str | None = None, value: float)[source]

Bases: MetricsData

Time To First Byte (TTFB) metrics data.

Parameters:: value – TTFB measurement in seconds.

value: float

class pipecat.metrics.metrics.ProcessingMetricsData(*, processor: str, model: str | None = None, value: float)[source]

Bases: MetricsData

General processing time metrics data.

Parameters:: value – Processing time measurement in seconds.

value: float

class pipecat.metrics.metrics.LLMTokenUsage(*, 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)[source]

Bases: 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

cache_creation_input_tokens: int | None

reasoning_tokens: int | None

class pipecat.metrics.metrics.LLMUsageMetricsData(*, processor: str, model: str | None = None, value: LLMTokenUsage)[source]

Bases: MetricsData

LLM token usage metrics data.

Parameters:: value – Token usage statistics for the LLM operation.

value: LLMTokenUsage

class pipecat.metrics.metrics.TTSUsageMetricsData(*, processor: str, model: str | None = None, value: int)[source]

Bases: MetricsData

Text-to-Speech usage metrics data.

Parameters:: value – Number of characters processed by TTS.

value: int

class pipecat.metrics.metrics.TextAggregationMetricsData(*, processor: str, model: str | None = None, value: float)[source]

Bases: 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

class pipecat.metrics.metrics.TurnMetricsData(*, processor: str, model: str | None = None, is_complete: bool, probability: float, e2e_processing_time_ms: float)[source]

Bases: 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

class pipecat.metrics.metrics.SmartTurnMetricsData(*, processor: str, model: str | None = None, is_complete: bool, probability: float, e2e_processing_time_ms: float, inference_time_ms: float = 0.0, server_total_time_ms: float = 0.0)[source]

Bases: TurnMetricsData

Metrics data for smart turn predictions.

Deprecated since version 0.0.104: Use 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

server_total_time_ms: float