Remove Logs from Dataset

Remove logs from an existing dataset

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 This is useful for cleaning up datasets or removing outdated or irrelevant logs.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

Parameter	Type	Required	Description
dataset_id	str	Yes	The unique identifier of the dataset
log_ids	List[str]	Yes	List 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• Removing outdated or incorrect data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