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

# Get Evaluation Report

> Retrieve the results and report of a completed evaluation

## Overview

The `get_evaluation_report` method allows you to retrieve the detailed results and report of a completed evaluation. This provides insights into model performance and data quality metrics.

## Method Signature

### Synchronous

```python theme={"system"}
def get_evaluation_report(
    evaluation_id: str
) -> Dict[str, Any]
```

### Asynchronous

```python theme={"system"}
async def get_evaluation_report(
    evaluation_id: str
) -> Dict[str, Any]
```

## Parameters

| Parameter       | Type  | Required | Description                             |
| --------------- | ----- | -------- | --------------------------------------- |
| `evaluation_id` | `str` | Yes      | The unique identifier of the evaluation |

## Returns

Returns a dictionary containing the evaluation report with metrics, scores, and detailed results.

## Examples

### Basic Usage

```python theme={"system"}
from keywordsai import KeywordsAI

client = KeywordsAI(api_key="your-api-key")

# Get evaluation report
report = client.datasets.get_evaluation_report(
    evaluation_id="eval_123"
)

print(f"Evaluation Status: {report['status']}")
print(f"Overall Score: {report['overall_score']}")
print(f"Metrics: {report['metrics']}")
```

### Detailed Report Analysis

```python theme={"system"}
# Get and analyze detailed report
report = client.datasets.get_evaluation_report(evaluation_id="eval_123")

if report['status'] == 'completed':
    print(f"Evaluation completed successfully")
    print(f"Dataset: {report['dataset_id']}")
    print(f"Evaluators used: {len(report['evaluator_results'])}")
    
    # Print individual evaluator results
    for evaluator_id, result in report['evaluator_results'].items():
        print(f"\n{evaluator_id}:")
        print(f"  Score: {result['score']}")
        print(f"  Details: {result['details']}")
else:
    print(f"Evaluation status: {report['status']}")
```

### Asynchronous Usage

```python theme={"system"}
import asyncio
from keywordsai import AsyncKeywordsAI

async def get_report_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    report = await client.datasets.get_evaluation_report(
        evaluation_id="eval_123"
    )
    
    print(f"Report retrieved for evaluation {report['evaluation_id']}")
    return report

asyncio.run(get_report_example())
```

### Export Report Data

```python theme={"system"}
import json

# Get report and export to file
report = client.datasets.get_evaluation_report(evaluation_id="eval_123")

# Save report to JSON file
with open(f"evaluation_report_{report['evaluation_id']}.json", 'w') as f:
    json.dump(report, f, indent=2)

print(f"Report exported to file")
```

## Error Handling

```python theme={"system"}
try:
    report = client.datasets.get_evaluation_report(
        evaluation_id="eval_123"
    )
    
    if report['status'] == 'failed':
        print(f"Evaluation failed: {report.get('error_message', 'Unknown error')}")
    elif report['status'] == 'running':
        print("Evaluation is still in progress")
    else:
        print(f"Report retrieved successfully")
        
except Exception as e:
    print(f"Error retrieving evaluation report: {e}")
```

## Report Structure

A typical evaluation report contains:

* `evaluation_id`: Unique identifier
* `status`: Current status (running, completed, failed)
* `dataset_id`: ID of the evaluated dataset
* `overall_score`: Aggregate score across all evaluators
* `metrics`: Summary metrics and statistics
* `evaluator_results`: Detailed results for each evaluator
* `created_at`: Evaluation start time
* `completed_at`: Evaluation completion time

## Common Use Cases

* Monitoring model performance over time
* Generating quality reports for stakeholders
* Comparing different model versions
* Identifying areas for improvement
* Compliance and audit reporting