Add Logs to Dataset

Add logs to an existing dataset based on filters and time range

Overview

The add_logs_to_dataset method allows you to expand a dataset by adding more logs that match specific criteria. This is useful for creating comprehensive datasets that include different types of logs or extending the time range of analysis.

Method Signature

Synchronous

def add_logs_to_dataset(
    dataset_id: str,
    log_request: Union[Dict[str, Any], LogManagementRequest]
) -> Dict[str, Any]

Asynchronous

async def aadd_logs_to_dataset(
    dataset_id: str,
    log_request: Union[Dict[str, Any], LogManagementRequest]
) -> Dict[str, Any]

Parameters

Parameter	Type	Required	Description
dataset_id	str	Yes	The unique identifier of the target dataset
log_request	Union[Dict[str, Any], LogManagementRequest]	Yes	Log selection criteria containing start_time, end_time, and filters

Returns

Returns a dictionary containing:

• message (str): Success message
• count (int): Number of logs added
• dataset_id (str): ID of the updated dataset

Examples

Basic Usage

from keywordsai import KeywordsAI
from keywordsai_sdk.keywordsai_types.dataset_types import LogManagementRequest
from datetime import datetime, timedelta

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

# Add error logs from the last week
end_time = datetime.utcnow()
start_time = end_time - timedelta(days=7)

log_request = LogManagementRequest(
    start_time=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    end_time=end_time.strftime("%Y-%m-%d %H:%M:%S"),
    filters={
        "status": {"value": "error", "operator": "equals"}
    }
)

result = client.datasets.add_logs_to_dataset("dataset-123", log_request)
print(f"Added {result['count']} error logs to dataset")

Asynchronous Usage

import asyncio
from keywordsai import AsyncKeywordsAI
from keywordsai_sdk.keywordsai_types.dataset_types import LogManagementRequest
from datetime import datetime, timedelta

async def add_logs_example():
    client = AsyncKeywordsAI(api_key="your-api-key")
    
    end_time = datetime.utcnow()
    start_time = end_time - timedelta(days=7)
    
    log_request = LogManagementRequest(
        start_time=start_time.strftime("%Y-%m-%d %H:%M:%S"),
        end_time=end_time.strftime("%Y-%m-%d %H:%M:%S"),
        filters={
            "status": {"value": "error", "operator": "equals"}
        }
    )
    
    result = await client.datasets.aadd_logs_to_dataset("dataset-123", log_request)
    print(f"Added {result['count']} error logs to dataset")

asyncio.run(add_logs_example())

Error Handling

try:
    result = client.datasets.add_logs_to_dataset(
        dataset_id="dataset-123",
        log_request=log_request
    )
except KeywordsAIError as e:
    if "not found" in str(e):
        print("Dataset not found")
    elif "no logs match" in str(e):
        print("No logs found matching the criteria")
    else:
        print(f"Error adding logs to dataset: {e}")

Common Use Cases

• Expanding datasets with logs from specific time periods
• Adding error logs for failure analysis
• Including logs from specific models or users
• Building comprehensive training datasets with filtered criteria