Parallelize scenarios

[yuval-qf]

Summary by CodeRabbit

• Refactor

  • Improved the performance of policy scenario evaluations by enabling concurrent processing, resulting in faster update streaming and more responsive status updates during evaluations.

• Chores

  • Updated configuration for pre-commit checks to improve compatibility with argument parsing.

[coderabbitai]

Walkthrough

The updates refactor the policy scenario evaluation process to run each scenario concurrently using asynchronous tasks and a shared queue for status updates, replacing the previous sequential approach. Additionally, the mypy pre-commit hook configuration is adjusted to split the config file argument into two separate entries.

Changes

File(s)	Change Summary
.pre-commit-config.yaml	Modified mypy hook arguments to split --config-file .mypy.ini into two separate arguments.
rogue/services/scenario_evaluation_service.py	Refactored scenario evaluation to run scenarios concurrently via async tasks and a queue; added _evaluate_single_policy_scenario method and updated _evaluate_policy_scenarios method signature and implementation.

Sequence Diagram(s)

sequenceDiagram
    participant Client
    participant ScenarioEvaluationService
    participant asyncio.Queue
    participant arun_evaluator_agent

    Client->>ScenarioEvaluationService: _evaluate_policy_scenarios()
    loop For each scenario
        ScenarioEvaluationService->>ScenarioEvaluationService: _evaluate_single_policy_scenario(scenario, queue) (async task)
        ScenarioEvaluationService->>asyncio.Queue: Put status update ("in_progress")
        ScenarioEvaluationService->>arun_evaluator_agent: Evaluate scenario (async)
        arun_evaluator_agent-->>ScenarioEvaluationService: Yield updates
        ScenarioEvaluationService->>asyncio.Queue: Put update
        ScenarioEvaluationService->>asyncio.Queue: Put result
        alt Exception
            ScenarioEvaluationService->>asyncio.Queue: Put error status update
        end
    end
    ScenarioEvaluationService->>Client: Yield updates from queue_watcher
    ScenarioEvaluationService->>ScenarioEvaluationService: Await all tasks (asyncio.gather)

Loading

Poem

In the warren of code, tasks now hop with delight,
Scenarios run side by side, not just through the night.
Updates queue up, swift as a hare,
While mypy’s config splits with care.
With async and order, our work multiplies—
A rabbit’s proud of this clever surprise!
🐇✨

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📜 Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Lite

📥 Commits

Reviewing files that changed from the base of the PR and between de71cf7 and 2231b6c.

📒 Files selected for processing (2)

• .pre-commit-config.yaml (1 hunks)
• rogue/services/scenario_evaluation_service.py (2 hunks)

🧰 Additional context used

🧠 Learnings (1)

📓 Common learnings

Learnt from: CR
PR: qualifire-dev/qualifire#0
File: .cursor/rules/self_improve.mdc:0-0
Timestamp: 2025-06-23T12:36:40.835Z
Learning: Update rules after major refactors to ensure they remain relevant and accurate.

Learnt from: CR
PR: qualifire-dev/qualifire#0
File: .cursor/rules/dev_workflow.mdc:0-0
Timestamp: 2025-06-23T12:36:21.756Z
Learning: After completing a subtask, review all code changes and chat history to identify new or modified code patterns, conventions, or best practices, and update or create rules accordingly.

🔇 Additional comments (3)

.pre-commit-config.yaml (1)

21-21: LGTM! Correct argument format for mypy.

Splitting the config file argument into separate list elements is the proper way to pass command-line arguments in pre-commit hooks.

rogue/services/scenario_evaluation_service.py (2)

37-65: Well-implemented async scenario evaluation with proper error handling.

The method correctly handles individual scenario evaluation with:

• Status updates via the shared queue
• Proper async iteration over evaluation results
• Exception handling with contextual logging
• Error status reporting on failures

---

66-84: Excellent parallelization implementation with proper queue handling.

The refactoring successfully parallelizes scenario evaluation with:

• Concurrent task execution for each scenario
• Non-blocking queue monitoring with timeout
• Correct handling of the race condition between task completion and queue emptiness
• Proper task synchronization using asyncio.gather

This should significantly improve performance when evaluating multiple scenarios.

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