> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# PipelineParams

> Configure pipeline execution with PipelineParams

## Overview

The `PipelineParams` class provides a structured way to configure various aspects of pipeline execution. These parameters control behaviors like audio settings, metrics collection, heartbeat monitoring, and interruption handling.

## Basic Usage

```python theme={null}
from pipecat.pipeline.task import PipelineParams, PipelineTask

# Create with default parameters
params = PipelineParams()

# Or customize specific parameters
params = PipelineParams(
    audio_in_sample_rate=16000,
    enable_metrics=True
)

# Pass to PipelineTask
pipeline = Pipeline([...])
task = PipelineTask(pipeline, params=params)
```

## Available Parameters

<ParamField path="audio_in_sample_rate" type="int" default="16000">
  Input audio sample rate in Hz.

  <Info>
    Setting the `audio_in_sample_rate` as a `PipelineParam` sets the input sample
    rate for all corresponding services in the pipeline.
  </Info>
</ParamField>

<ParamField path="audio_out_sample_rate" type="int" default="24000">
  Output audio sample rate in Hz.

  <Info>
    Setting the `audio_out_sample_rate` as a `PipelineParam` sets the output
    sample rate for all corresponding services in the pipeline.
  </Info>
</ParamField>

<ParamField path="enable_heartbeats" type="bool" default="False">
  Whether to enable heartbeat monitoring to detect pipeline stalls. See
  [Heartbeats](/api-reference/server/pipeline/heartbeats) for details.
</ParamField>

<ParamField path="heartbeats_period_secs" type="float" default="1.0">
  Period between heartbeats in seconds (when heartbeats are enabled).
</ParamField>

<ParamField path="heartbeats_monitor_secs" type="float" default="10.0">
  Timeout in seconds before warning about missed heartbeats.
</ParamField>

<ParamField path="enable_metrics" type="bool" default="False">
  Whether to enable metrics collection for pipeline performance.
</ParamField>

<ParamField path="enable_usage_metrics" type="bool" default="False">
  Whether to enable usage metrics tracking.
</ParamField>

<ParamField path="report_only_initial_ttfb" type="bool" default="False">
  Whether to report only initial time to first byte metric.
</ParamField>

<ParamField path="send_initial_empty_metrics" type="bool" default="True">
  Whether to send initial empty metrics frame at pipeline start.
</ParamField>

<ParamField path="start_metadata" type="Dict[str, Any]" default="{}">
  Additional metadata to include in the StartFrame.
</ParamField>

## Common Configurations

### Audio Processing Configuration

You can set the audio input and output sample rates in the `PipelineParams` to set the sample rate for all input and output services in the pipeline. This acts as a convenience to avoid setting the sample rate for each service individually. Note, if services are set individually, they will supersede the values set in `PipelineParams`.

```python theme={null}
params = PipelineParams(
    audio_in_sample_rate=8000,   # Lower quality input audio
    audio_out_sample_rate=8000  # High quality output audio
)
```

### Performance Monitoring Configuration

Pipeline heartbeats provide a way to monitor the health of your pipeline by sending periodic heartbeat frames through the system. When enabled, the pipeline will send heartbeat frames every second and monitor their progress through the pipeline.

```python theme={null}
params = PipelineParams(
    enable_heartbeats=True,
    heartbeats_period_secs=2.0,     # Send heartbeats every 2 seconds
    heartbeats_monitor_secs=15.0,   # Warn if heartbeat takes more than 15 seconds
    enable_metrics=True
)
```

## How Parameters Are Used

The parameters you set in `PipelineParams` are passed to various components of the pipeline:

1. **StartFrame**: Many parameters are included in the StartFrame that initializes the pipeline
2. **Metrics Collection**: Metrics settings configure what performance data is gathered
3. **Heartbeat Monitoring**: Controls the pipeline's health monitoring system
4. **Audio Processing**: Sample rates affect how audio is processed throughout the pipeline

## Complete Example

```python theme={null}
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.observers.file_observer import FileObserver
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.pipeline.runner import PipelineRunner

# Create comprehensive parameters
params = PipelineParams(
    audio_in_sample_rate=8000,
    audio_out_sample_rate=8000,
    enable_heartbeats=True,
    enable_metrics=True,
    enable_usage_metrics=True,
    heartbeats_period_secs=1.0,
    report_only_initial_ttfb=False,
    start_metadata={
        "conversation_id": "conv-123",
        "session_data": {
            "user_id": "user-456",
            "start_time": "2023-10-25T14:30:00Z"
        }
    }
)

# Create pipeline and task
pipeline = Pipeline([...])
task = PipelineTask(
    pipeline,
    params=params,
    observers=[FileObserver("pipeline_logs.jsonl")]
)

# Run the pipeline
runner = PipelineRunner()
await runner.run(task)
```

## Additional Information

* Parameters are immutable once the pipeline starts
* The `start_metadata` dictionary can contain any serializable data
* For metrics collection to work properly, `enable_metrics` must be set to `True`