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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.

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Method Signature

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Synchronous

def list_dataset_logs(
    dataset_id: str,
    limit: Optional[int] = None,
    offset: Optional[int] = None
) -> Dict[str, Any]

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Asynchronous

async def list_dataset_logs(
    dataset_id: str,
    limit: Optional[int] = None,
    offset: Optional[int] = None
) -> Dict[str, Any]

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Parameters

ParameterTypeRequiredDescription
dataset_idstrYesThe unique identifier of the dataset
limitintNoMaximum number of logs to return (default: 50)
offsetintNoNumber of logs to skip for pagination (default: 0)

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Returns

Returns a dictionary containing the list of logs and pagination information.

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Examples

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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']}")

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With Pagination

# 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")

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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())

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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}")

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Common Use Cases

  • Reviewing dataset contents before training
  • Analyzing data quality and distribution
  • Exporting dataset logs for external processing
  • Monitoring dataset size and growth