Skip to main content

​
Overview

The remove_logs_from_dataset method allows you to remove specific logs from a dataset. This is useful for cleaning up datasets or removing outdated or irrelevant logs.

​
Method Signature

​
Synchronous

def remove_logs_from_dataset(
    dataset_id: str,
    log_ids: List[str]
) -> Dict[str, Any]

​
Asynchronous

async def remove_logs_from_dataset(
    dataset_id: str,
    log_ids: List[str]
) -> Dict[str, Any]

​
Parameters

ParameterTypeRequiredDescription
dataset_idstrYesThe unique identifier of the dataset
log_idsList[str]YesList of log IDs to remove from the dataset

​
Returns

Returns a dictionary containing the operation result and updated dataset information.

​
Examples

​
Basic Usage

from keywordsai import KeywordsAI

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

# Remove logs from dataset
result = client.datasets.remove_logs_from_dataset(
    dataset_id="dataset_123",
    log_ids=["log_456", "log_789"]
)

print(f"Removed {len(result['removed_logs'])} logs from dataset")

​
Asynchronous Usage

import asyncio
from keywordsai import AsyncKeywordsAI

async def remove_logs_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    result = await client.datasets.remove_logs_from_dataset(
        dataset_id="dataset_123",
        log_ids=["log_456", "log_789"]
    )
    
    print(f"Successfully removed {len(result['removed_logs'])} logs")

asyncio.run(remove_logs_example())

​
Error Handling

try:
    result = client.datasets.remove_logs_from_dataset(
        dataset_id="dataset_123",
        log_ids=["log_456"]
    )
except Exception as e:
    print(f"Error removing logs from dataset: {e}")

​
Common Use Cases

  • Cleaning up datasets by removing irrelevant logs
  • Removing outdated or incorrect data
  • Managing dataset size and quality
  • Preparing clean datasets for training
⌘I