> ## Documentation Index
> Fetch the complete documentation index at: https://turnwise.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Pipeline Executions
> Understanding evaluation pipeline executions and history
# Pipeline Executions
Pipeline executions track when and how evaluations were run. Understanding executions helps you manage evaluation history, debug issues, and analyze performance.
## What Are Pipeline Executions?
A pipeline execution represents a single run of an evaluation metric:
* **When**: Timestamp of execution
* **What**: Which metric was evaluated
* **Where**: Which dataset and entities
* **Result**: Evaluation results
* **Status**: Success or failure
## Execution Structure
```json theme={null}
{
"id": 123,
"evaluation_pipeline_id": 5,
"dataset_id": 1,
"status": "completed",
"started_at": "2024-01-20T14:30:00Z",
"completed_at": "2024-01-20T14:35:00Z",
"total_evaluations": 100,
"successful_evaluations": 98,
"failed_evaluations": 2,
"meta": {
"model": "openai/gpt-5-nano",
"execution_mode": "async"
}
}
```
## Execution Lifecycle
```mermaid theme={null}
stateDiagram-v2
[*] --> Pending: Created
Pending --> Processing: Started
Processing --> Completed: Success
Processing --> Failed: Error
Processing --> Cancelled: User Cancel
Completed --> [*]
Failed --> [*]
Cancelled --> [*]
```
### States
* **Pending**: Execution created but not started
* **Processing**: Currently running
* **Completed**: Finished successfully
* **Failed**: Encountered errors
* **Cancelled**: User cancelled
## Viewing Executions
### Via UI
1. **Open Dataset**
2. **Click "Executions" Tab**
3. **View Execution List**
* See all executions for this dataset
* Filter by metric, status, date
* Sort by various columns
### Via API
```bash theme={null}
# List executions for dataset
GET /evaluation-pipeline-executions?dataset_id=1
# Get specific execution
GET /evaluation-pipeline-executions/123
# Get execution results
GET /evaluation-pipeline-executions/123/results
```
## Execution Details
Each execution includes:
### Basic Info
* **ID**: Unique execution identifier
* **Pipeline**: Which metric was evaluated
* **Dataset**: Which dataset
* **Status**: Current status
* **Timestamps**: Started, completed times
### Statistics
* **Total Evaluations**: Number of entities evaluated
* **Successful**: Number that succeeded
* **Failed**: Number that failed
* **Duration**: Total execution time
### Results
* **Individual Results**: Each conversation/message/step result
* **Aggregated Results**: Summary statistics
* **Errors**: Any failures with details
## Execution History
TurnWise maintains a history of all executions:
### Why History Matters
* **Track Changes**: See how metrics perform over time
* **Debug Issues**: Identify when problems occurred
* **Compare Results**: Compare different evaluation runs
* **Audit Trail**: Complete record of evaluations
### Viewing History
1. **Open Dataset**
2. **Click "Executions" Tab**
3. **Browse History**
* See all past executions
* Filter by date range
* Search by metric name
## Re-Running Evaluations
### When to Re-Run
* **Metric Updated**: Prompt or configuration changed
* **Data Updated**: Conversations modified
* **Failed Executions**: Retry failed evaluations
* **Model Changed**: Using different LLM
### How to Re-Run
#### Via UI
1. **Select Execution**
2. **Click "Re-Run"**
3. **Confirm**
4. **Monitor Progress**
#### Via API
```bash theme={null}
POST /evaluation-pipeline-executions/run
{
"dataset_id": 1,
"pipeline_node_id": 5,
"entity_type": "conversation",
"entity_id": null
}
```
## Execution Comparison
Compare executions to see:
* **Metric Changes**: How results changed
* **Performance**: Execution time differences
* **Accuracy**: Success rate changes
### Comparing Results
1. **Select Two Executions**
2. **Click "Compare"**
3. **View Differences**
* Side-by-side comparison
* Highlighted changes
* Statistical analysis
## Execution Metadata
Executions store metadata:
```json theme={null}
{
"meta": {
"model": "openai/gpt-5-nano",
"execution_mode": "async",
"batch_size": 10,
"template_variables": ["@HISTORY", "@GOAL"],
"user_id": "user_123"
}
}
```
Useful for:
* **Debugging**: Understand execution context
* **Analysis**: Filter by execution parameters
* **Auditing**: Track who ran what
## Execution Results
### Individual Results
Each evaluation produces a result:
```json theme={null}
{
"conversation_id": 123,
"pipeline_node_id": 5,
"result": {
"score": 0.85,
"reasoning": "Response is helpful"
},
"status": "completed",
"execution_time": 2.5,
"created_at": "2024-01-20T14:30:00Z"
}
```
### Aggregated Results
Summary statistics:
```json theme={null}
{
"total": 100,
"average_score": 0.82,
"min_score": 0.3,
"max_score": 1.0,
"std_dev": 0.15,
"distribution": {
"0-0.5": 5,
"0.5-0.7": 20,
"0.7-0.9": 50,
"0.9-1.0": 25
}
}
```
## Exporting Results
### Via UI
1. **Select Execution**
2. **Click "Export"**
3. **Choose Format**
* CSV
* JSON
* Excel
### Via API
```bash theme={null}
GET /evaluation-pipeline-executions/123/export?format=csv
```
## Execution Performance
### Monitoring Performance
Track execution metrics:
* **Duration**: How long it took
* **Throughput**: Evaluations per second
* **Success Rate**: Percentage successful
* **Cost**: LLM API costs
### Optimizing Performance
Enable concurrent execution
Optimize batch sizes
Use faster models when possible
Track performance metrics
## Troubleshooting Executions
### Failed Executions
**Check**:
* Error messages in execution details
* LLM API status
* Data validity
* Prompt correctness
**Solutions**:
* Retry execution
* Fix data issues
* Update prompt
* Check API keys
### Slow Executions
**Check**:
* Execution mode (sync vs async)
* Model selection
* Batch size
* Network latency
**Solutions**:
* Enable async mode
* Use faster model
* Increase batch size
* Check network
## Best Practices
Regularly review execution history
Track execution metrics
Export important results
Note why executions were re-run
## Next Steps
Learn how to run evaluations
Explore the table interface