Overview
The Datasets API allows you to create, manage, and organize collections of logs for analysis, evaluation, and machine learning workflows. Datasets serve as containers for grouping related conversations and interactions.Key Features
- Create and manage datasets for organizing logs
- Add and remove logs from datasets
- Run evaluations on dataset contents
- Generate evaluation reports and analytics
- List dataset contents with filtering
- Update dataset metadata and descriptions
Quick Start
Available Methods
Core Dataset Operations
Method | Description |
---|---|
create() | Create a new dataset |
list() | List all datasets |
get() | Retrieve a specific dataset |
update() | Update dataset information |
delete() | Delete a dataset |
Log Management
Method | Description |
---|---|
add_logs_to_dataset() | Add logs to a dataset |
remove_logs_from_dataset() | Remove logs from a dataset |
list_dataset_logs() | List logs in a dataset |
Evaluation Operations
Method | Description |
---|---|
run_dataset_evaluation() | Run evaluation on dataset |
get_evaluation_report() | Get evaluation results |
list_evaluation_reports() | List all evaluation reports |
Asynchronous Methods
All methods have asynchronous counterparts with thea
prefix:
acreate()
,alist()
,aget()
,aupdate()
,adelete()
aadd_logs_to_dataset()
,aremove_logs_from_dataset()
,alist_dataset_logs()
arun_dataset_evaluation()
,aget_evaluation_report()
,alist_evaluation_reports()
Dataset Structure
A dataset contains the following information:Common Workflows
1. Dataset Creation and Population
2. Dataset Evaluation
3. Dataset Analysis
Advanced Use Cases
Batch Dataset Operations
Dataset Versioning
Dataset Quality Monitoring
Best Practices
1. Organize Datasets by Purpose
2. Use Metadata for Organization
3. Implement Dataset Validation
4. Regular Dataset Maintenance
Error Handling
Next Steps
- Learn how to create datasets
- Explore adding logs to datasets
- Understand dataset evaluation
- See complete examples for real-world usage