Documentation Index
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Overview
The list_dataset_logs method allows you to retrieve all logs that are part of a specific dataset. This is useful for reviewing dataset contents and analyzing the data.
Method Signature
Synchronous
def list_dataset_logs(
dataset_id: str,
limit: Optional[int] = None,
offset: Optional[int] = None
) -> Dict[str, Any]
Asynchronous
async def list_dataset_logs(
dataset_id: str,
limit: Optional[int] = None,
offset: Optional[int] = None
) -> Dict[str, Any]
Parameters
| Parameter | Type | Required | Description |
|---|
dataset_id | str | Yes | The unique identifier of the dataset |
limit | int | No | Maximum number of logs to return (default: 50) |
offset | int | No | Number of logs to skip for pagination (default: 0) |
Returns
Returns a dictionary containing the list of logs and pagination information.
Examples
Basic Usage
from keywordsai import KeywordsAI
client = KeywordsAI(api_key="your-api-key")
# List all logs in a dataset
logs = client.datasets.list_dataset_logs(dataset_id="dataset_123")
print(f"Found {len(logs['logs'])} logs in dataset")
for log in logs['logs']:
print(f"Log ID: {log['id']}, Created: {log['created_at']}")
# Get first 10 logs
logs = client.datasets.list_dataset_logs(
dataset_id="dataset_123",
limit=10,
offset=0
)
print(f"Page 1: {len(logs['logs'])} logs")
# Get next 10 logs
next_logs = client.datasets.list_dataset_logs(
dataset_id="dataset_123",
limit=10,
offset=10
)
print(f"Page 2: {len(next_logs['logs'])} logs")
Asynchronous Usage
import asyncio
from keywordsai import AsyncKeywordsAI
async def list_logs_example():
client = AsyncKeywordsAI(api_key="your-api-key")
logs = await client.datasets.list_dataset_logs(
dataset_id="dataset_123",
limit=20
)
print(f"Retrieved {len(logs['logs'])} logs")
return logs
asyncio.run(list_logs_example())
Error Handling
try:
logs = client.datasets.list_dataset_logs(dataset_id="dataset_123")
print(f"Successfully retrieved {len(logs['logs'])} logs")
except Exception as e:
print(f"Error listing dataset logs: {e}")
Common Use Cases
- Reviewing dataset contents before training
- Analyzing data quality and distribution
- Exporting dataset logs for external processing
- Monitoring dataset size and growth