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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.

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Method Signature

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Synchronous

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

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Asynchronous

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

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Parameters

ParameterTypeRequiredDescription
dataset_idstrYesThe unique identifier of the target dataset
log_requestUnion[Dict[str, Any], LogManagementRequest]YesLog selection criteria containing start_time, end_time, and filters

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Returns

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

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Examples

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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")

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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())

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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}")

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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