Principle:Explodinggradients Ragas Experiment Orchestration
| Knowledge Sources | Domains | Last Updated |
|---|---|---|
| explodinggradients/ragas | LLM Evaluation, Experiment Management | 2026-02-10 |
Overview
Experiment Orchestration is the principle of separating evaluation logic from the mechanics of dataset iteration, progress tracking, and result persistence through a decorator-based experiment pattern.
Description
Evaluating LLM applications at scale requires running an evaluation function across every entry in a dataset, tracking progress, handling failures gracefully, and persisting results. Experiment Orchestration addresses these cross-cutting concerns by providing a decorator that wraps a user-defined async evaluation function with all the orchestration machinery.
Separation of Concerns: The user writes a pure evaluation function that takes a single dataset row and returns a result. The orchestration layer handles everything else: iterating over the dataset, scheduling concurrent async tasks, displaying progress bars, catching per-row exceptions, and saving the collected results. This separation means evaluation logic remains clean, testable, and reusable.
Decorator Pattern: The @experiment decorator transforms an async function into an experiment-capable object. The decorated function retains its original callable behavior but gains an arun() method that executes the function across an entire dataset. This dual nature allows the function to be tested on individual rows or run as a full experiment.
Automatic Naming: Experiments receive memorable auto-generated names (via a memorable name generator) unless a custom name is provided. An optional prefix can be configured at decorator time to namespace experiments by project or team.
Result Persistence: After all dataset rows have been processed, the experiment results are automatically saved to the configured backend. The experiment can use the same backend as the source dataset or a different one, providing flexibility in how inputs and outputs are stored.
Error Isolation: Individual row failures are caught and logged as warnings rather than aborting the entire experiment. This ensures that a single problematic input does not prevent results from being collected for all other inputs.
Usage
Use the Experiment Orchestration principle when:
- Running evaluation functions across entire datasets with progress tracking
- Orchestrating concurrent async evaluations for performance
- Persisting experiment results alongside the source dataset
- Needing reproducible experiment naming for tracking and comparison
- Building evaluation pipelines where evaluation logic should be independent of execution mechanics
Theoretical Basis
The theoretical foundation rests on the Decorator Pattern combined with the Command Pattern, where the evaluation function is wrapped with execution context:
PROCEDURE experiment_orchestration(func, dataset, backend):
1. Generate a unique experiment name (or use the provided one)
2. Resolve the backend for result storage
3. Create an empty Experiment container with the name and backend
4. Create async tasks:
FOR each row in dataset:
Schedule func(row) as an async task
5. Initialize progress bar with total = len(dataset)
6. Process results as tasks complete:
FOR each completed task:
TRY:
result = await task
IF result is not None:
Append result to experiment container
CATCH Exception as e:
Log warning: "Task failed with error: {e}"
FINALLY:
Update progress bar
7. Save the experiment container to the backend
8. Return the experiment container
This pattern ensures that the user's evaluation function remains a simple, focused unit of logic while the orchestrator manages all the complexity of parallel execution, error handling, and persistence.