LCORE-900: Added the parallel execution for multi-modal evaluation in…#92
LCORE-900: Added the parallel execution for multi-modal evaluation in…#92tisnik merged 1 commit intolightspeed-core:mainfrom
Conversation
WalkthroughAdds configurable parallel execution for provider-model evaluations via a new Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant CLI as CLI / main()
participant Runner as MultiProviderEvaluationRunner
participant Pool as ProcessPoolExecutor
participant Worker as _run_evaluation_worker
participant Eval as Evaluation Engine
CLI->>Runner: instantiate(..., max_workers)
CLI->>Runner: run_evaluations()
Runner->>Runner: determine max_workers (CLI > config > heuristic)
alt Parallel (max_workers > 1)
Runner->>Pool: create pool(max_workers)
Runner->>Runner: build per-task configs (provider, model, paths)
loop per task
Pool->>Worker: submit(task_config)
Worker->>Worker: create isolated temp system config
Worker->>Eval: run evaluation (provider/model)
Eval-->>Worker: return result dict
Worker->>Worker: cleanup temp config
Worker-->>Pool: return result metadata
end
Pool-->>Runner: collect results and failures
else Sequential (max_workers == 1)
Runner->>Runner: iterate tasks
Runner->>Eval: run evaluation
Eval-->>Runner: return result
end
Runner->>Runner: aggregate results, log durations/errors
Runner-->>CLI: return list[dict] results
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes
Possibly related PRs
Suggested reviewers
Poem
Pre-merge checks and finishing touches✅ Passed checks (2 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Nitpick comments (1)
config/multi_eval_config.yaml (1)
11-11: Update the stale inline note.Now that
gpt-4-turbois part of the active model list, the trailing comment claiming it is “Commented out for faster testing” is misleading. Please either drop the note or rephrase it to reflect the current intent.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
config/multi_eval_config.yaml(2 hunks)docs/multi_provider_evaluation.md(5 hunks)script/run_multi_provider_eval.py(9 hunks)tests/script/test_run_multi_provider_eval.py(1 hunks)
🧰 Additional context used
📓 Path-based instructions (3)
config/**/*.{yaml,yml}
📄 CodeRabbit inference engine (AGENTS.md)
Use YAML for configuration files under config/
Files:
config/multi_eval_config.yaml
tests/**/test_*.py
📄 CodeRabbit inference engine (AGENTS.md)
Name test files as test_*.py
Files:
tests/script/test_run_multi_provider_eval.py
tests/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
tests/**/*.py: Use pytest for mocking (pytest-mock’s mocker), not unittest.mock
Name test functions as test_*
Name test classes as Test*
Add comprehensive tests for new features with mocked LLM calls using pytest
Files:
tests/script/test_run_multi_provider_eval.py
🧠 Learnings (6)
📓 Common learnings
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
📚 Learning: 2025-07-16T12:07:29.169Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 19
File: lsc_agent_eval/tests/core/agent_goal_eval/test_script_runner.py:0-0
Timestamp: 2025-07-16T12:07:29.169Z
Learning: In the lsc_agent_eval package, the ScriptRunner class was modified to use absolute paths internally rather than documenting path normalization behavior, providing more predictable and consistent path handling.
Applied to files:
tests/script/test_run_multi_provider_eval.py
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/system.yaml : Keep system configuration in config/system.yaml (LLM, API, metrics metadata, output, logging)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-08T11:09:18.416Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: config/system.yaml:16-20
Timestamp: 2025-09-08T11:09:18.416Z
Learning: The maintainer asamal4 prefers sample configurations in config/system.yaml to show realistic usage patterns (e.g., API enabled: true) rather than conservative defaults, as teams are expected to customize the configuration for their specific environments.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/evaluation_data.yaml : Keep evaluation data in config/evaluation_data.yaml (conversation groups, turns, overrides, scripts)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-10T15:48:14.671Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: src/lightspeed_evaluation/core/output/generator.py:43-49
Timestamp: 2025-09-10T15:48:14.671Z
Learning: In the lightspeed-evaluation framework, system configuration uses Pydantic data models (SystemConfig, OutputConfig, LoggingConfig, etc.) rather than plain dictionaries. Components like OutputHandler receive properly structured Pydantic models, so direct attribute access (e.g., system_config.output.enabled_outputs) is the correct approach.
Applied to files:
docs/multi_provider_evaluation.md
🪛 GitHub Actions: Black
tests/script/test_run_multi_provider_eval.py
[error] 1-1: Black formatting check failed. The file would be reformatted by Black.
script/run_multi_provider_eval.py
[error] 1-1: Black formatting check failed. The file would be reformatted by Black.
🪛 LanguageTool
docs/multi_provider_evaluation.md
[style] ~13-~13: ‘with success’ might be wordy. Consider a shorter alternative.
Context: ...ut sanitization - Comprehensive summary with success/failure statistics and statistical comp...
(EN_WORDINESS_PREMIUM_WITH_SUCCESS)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (2)
- GitHub Check: mypy
- GitHub Check: tests (3.12)
🔇 Additional comments (2)
tests/script/test_run_multi_provider_eval.py (1)
438-445: The sandbox environment lacks both theuvpackage manager and theblackformatter tool needed to verify or fix the formatting issues. I cannot automatically validate whether the code at lines 438-445 complies with Black's formatting rules.The original review comment is valid and actionable. You'll need to run the Black formatter locally on your machine:
uv run black script/run_multi_provider_eval.py tests/script/test_run_multi_provider_eval.pyOr if you don't have
uvinstalled, you can use:black script/run_multi_provider_eval.py tests/script/test_run_multi_provider_eval.pyscript/run_multi_provider_eval.py (1)
1-10: Run Black locally to resolve formatting issues.The GitHub Actions check reports that
script/run_multi_provider_eval.pyandtests/script/test_run_multi_provider_eval.pywould be reformatted by Black. Run Black locally and commit the resulting formatting updates to keep the pipeline green.
bsatapat-jpg
force-pushed
the
dev
branch
2 times, most recently
from
8652aae to
5c68320
Compare
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (1)
script/run_multi_provider_eval.py (1)
232-243: Critical: Unresolved type coercion issue from previous review.The past review correctly identified that
max_workersloaded from YAML arrives as a string (e.g.,"4"), causingTypeError: '>' not supported between instances of 'str' and 'int'at line 242. This issue remains unaddressed—the code still doesn't coercemax_workerstointbefore using it.Apply the suggested fix from the previous review:
if max_workers is not None: self.max_workers = max_workers elif "max_workers" in self.settings: self.max_workers = self.settings["max_workers"] else: # Default: leave one CPU free for system responsiveness cpu_count = multiprocessing.cpu_count() self.max_workers = max(1, cpu_count - 1) + if not isinstance(self.max_workers, int): + try: + self.max_workers = int(self.max_workers) + except (TypeError, ValueError) as exc: + raise ValueError( + f"max_workers must be an integer, got {self.max_workers!r}" + ) from exc + # Ensure at least 1 worker self.max_workers = max(1, self.max_workers)
🧹 Nitpick comments (1)
script/run_multi_provider_eval.py (1)
40-173: Consider refactoring to reduce code duplication.The
_run_evaluation_workerfunction duplicates substantial logic from_run_single_evaluation(lines 403-492), including sanitization, path creation, temp config handling, evaluation execution, and cleanup. This duplication makes maintenance harder and increases the risk of inconsistencies.Consider extracting shared logic into a common helper function that both methods can call. For example:
def _execute_evaluation_task( self, provider_name: str, provider_id: str, model: str, logger_instance: logging.Logger ) -> dict[str, Any]: """Common evaluation logic shared by worker and sequential paths.""" # Shared sanitization, path creation, evaluation, cleanup logic ...This would centralize the logic and make it easier to maintain consistency between sequential and parallel execution paths.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
config/multi_eval_config.yaml(2 hunks)docs/multi_provider_evaluation.md(5 hunks)script/run_multi_provider_eval.py(9 hunks)tests/script/test_run_multi_provider_eval.py(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- tests/script/test_run_multi_provider_eval.py
- config/multi_eval_config.yaml
🧰 Additional context used
🧠 Learnings (5)
📓 Common learnings
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/system.yaml : Keep system configuration in config/system.yaml (LLM, API, metrics metadata, output, logging)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-08T11:09:18.416Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: config/system.yaml:16-20
Timestamp: 2025-09-08T11:09:18.416Z
Learning: The maintainer asamal4 prefers sample configurations in config/system.yaml to show realistic usage patterns (e.g., API enabled: true) rather than conservative defaults, as teams are expected to customize the configuration for their specific environments.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/evaluation_data.yaml : Keep evaluation data in config/evaluation_data.yaml (conversation groups, turns, overrides, scripts)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-10T15:48:14.671Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: src/lightspeed_evaluation/core/output/generator.py:43-49
Timestamp: 2025-09-10T15:48:14.671Z
Learning: In the lightspeed-evaluation framework, system configuration uses Pydantic data models (SystemConfig, OutputConfig, LoggingConfig, etc.) rather than plain dictionaries. Components like OutputHandler receive properly structured Pydantic models, so direct attribute access (e.g., system_config.output.enabled_outputs) is the correct approach.
Applied to files:
docs/multi_provider_evaluation.md
🪛 LanguageTool
docs/multi_provider_evaluation.md
[style] ~13-~13: ‘with success’ might be wordy. Consider a shorter alternative.
Context: ...ut sanitization - Comprehensive summary with success/failure statistics and statistical comp...
(EN_WORDINESS_PREMIUM_WITH_SUCCESS)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (4)
- GitHub Check: mypy
- GitHub Check: tests (3.13)
- GitHub Check: tests (3.11)
- GitHub Check: tests (3.12)
There was a problem hiding this comment.
Actionable comments posted: 2
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
config/multi_eval_config.yaml(2 hunks)docs/multi_provider_evaluation.md(5 hunks)script/run_multi_provider_eval.py(9 hunks)tests/script/test_run_multi_provider_eval.py(1 hunks)
🧰 Additional context used
📓 Path-based instructions (3)
config/**/*.{yaml,yml}
📄 CodeRabbit inference engine (AGENTS.md)
Use YAML for configuration files under config/
Files:
config/multi_eval_config.yaml
tests/**/test_*.py
📄 CodeRabbit inference engine (AGENTS.md)
Name test files as test_*.py
Files:
tests/script/test_run_multi_provider_eval.py
tests/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
tests/**/*.py: Use pytest for mocking (pytest-mock’s mocker), not unittest.mock
Name test functions as test_*
Name test classes as Test*
Add comprehensive tests for new features with mocked LLM calls using pytest
Files:
tests/script/test_run_multi_provider_eval.py
🧠 Learnings (5)
📓 Common learnings
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/system.yaml : Keep system configuration in config/system.yaml (LLM, API, metrics metadata, output, logging)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-08T11:09:18.416Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: config/system.yaml:16-20
Timestamp: 2025-09-08T11:09:18.416Z
Learning: The maintainer asamal4 prefers sample configurations in config/system.yaml to show realistic usage patterns (e.g., API enabled: true) rather than conservative defaults, as teams are expected to customize the configuration for their specific environments.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/evaluation_data.yaml : Keep evaluation data in config/evaluation_data.yaml (conversation groups, turns, overrides, scripts)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-10T15:48:14.671Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: src/lightspeed_evaluation/core/output/generator.py:43-49
Timestamp: 2025-09-10T15:48:14.671Z
Learning: In the lightspeed-evaluation framework, system configuration uses Pydantic data models (SystemConfig, OutputConfig, LoggingConfig, etc.) rather than plain dictionaries. Components like OutputHandler receive properly structured Pydantic models, so direct attribute access (e.g., system_config.output.enabled_outputs) is the correct approach.
Applied to files:
docs/multi_provider_evaluation.md
🪛 LanguageTool
docs/multi_provider_evaluation.md
[style] ~13-~13: ‘with success’ might be wordy. Consider a shorter alternative.
Context: ...ut sanitization - Comprehensive summary with success/failure statistics and statistical comp...
(EN_WORDINESS_PREMIUM_WITH_SUCCESS)
VladimirKadlec
left a comment
VladimirKadlec
left a comment
There was a problem hiding this comment.
Added some nit comment, please test as we discussed on slack.
LGTM
… the evaluation framework
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (1)
script/run_multi_provider_eval.py (1)
65-70: Restore--verbosebehavior in worker processes.
logging.basicConfig(... level=logging.INFO, force=True)inside the worker overrides the log level selected by the CLI. As soon as we enter parallel mode, every worker downgrades to INFO, so--verboseno longer produces DEBUG logs where you need them most. Please bubble the parent’s effective log level into the worker before reconfiguring logging.-def _run_evaluation_worker( +def _run_evaluation_worker( provider_name: str, provider_id: str, model: str, system_config_path: str, eval_data_path: str, - output_base: str, + output_base: str, + log_level: int = logging.INFO, ) -> dict[str, Any]: @@ - logging.basicConfig( - level=logging.INFO, + logging.basicConfig( + level=log_level, format=f"%(asctime)s - [Worker-{os.getpid()}] - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", force=True, # Override existing configuration ) @@ - with ProcessPoolExecutor(max_workers=self.max_workers) as executor: + effective_level = logging.getLogger().getEffectiveLevel() + with ProcessPoolExecutor(max_workers=self.max_workers) as executor: @@ - future = executor.submit( + future = executor.submit( _run_evaluation_worker, config["provider_name"], config["provider_id"], config["model"], str(self.system_config_path), str(self.eval_data_path), str(self.output_base), + effective_level, )Also applies to: 573-586
🧹 Nitpick comments (1)
docs/multi_provider_evaluation.md (1)
377-382: Specify the fenced block language for lint compliance.The warning snippet is missing a language hint, tripping MD040. Please mark it as plain text.
-``` +```text WARNING - High resource usage detected: 4 workers × 50 threads = 200 concurrent threads on 8 CPUs. Consider reducing max_workers or core.max_threads in system.yaml to avoid resource contention.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
config/multi_eval_config.yaml(2 hunks)docs/multi_provider_evaluation.md(5 hunks)script/run_multi_provider_eval.py(9 hunks)tests/script/test_run_multi_provider_eval.py(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- config/multi_eval_config.yaml
🧰 Additional context used
📓 Path-based instructions (2)
tests/**/test_*.py
📄 CodeRabbit inference engine (AGENTS.md)
Name test files as test_*.py
Files:
tests/script/test_run_multi_provider_eval.py
tests/**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
tests/**/*.py: Use pytest for mocking (pytest-mock’s mocker), not unittest.mock
Name test functions as test_*
Name test classes as Test*
Add comprehensive tests for new features with mocked LLM calls using pytest
Files:
tests/script/test_run_multi_provider_eval.py
🧠 Learnings (7)
📓 Common learnings
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/system.yaml : Keep system configuration in config/system.yaml (LLM, API, metrics metadata, output, logging)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-08T11:09:18.416Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: config/system.yaml:16-20
Timestamp: 2025-09-08T11:09:18.416Z
Learning: The maintainer asamal4 prefers sample configurations in config/system.yaml to show realistic usage patterns (e.g., API enabled: true) rather than conservative defaults, as teams are expected to customize the configuration for their specific environments.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/evaluation_data.yaml : Keep evaluation data in config/evaluation_data.yaml (conversation groups, turns, overrides, scripts)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-10T15:48:14.671Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: src/lightspeed_evaluation/core/output/generator.py:43-49
Timestamp: 2025-09-10T15:48:14.671Z
Learning: In the lightspeed-evaluation framework, system configuration uses Pydantic data models (SystemConfig, OutputConfig, LoggingConfig, etc.) rather than plain dictionaries. Components like OutputHandler receive properly structured Pydantic models, so direct attribute access (e.g., system_config.output.enabled_outputs) is the correct approach.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/**/*.py : Use structured logging with appropriate levels
Applied to files:
script/run_multi_provider_eval.py
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
Applied to files:
script/run_multi_provider_eval.py
🧬 Code graph analysis (1)
tests/script/test_run_multi_provider_eval.py (1)
script/run_multi_provider_eval.py (1)
MultiProviderEvaluationRunner(176-1189)
🪛 GitHub Actions: Tests
tests/script/test_run_multi_provider_eval.py
[warning] 264-264: High resource usage detected: 2 workers × 10 threads = 20 concurrent threads on 4 CPUs. Consider reducing max_workers or core.max_threads to avoid resource contention.
script/run_multi_provider_eval.py
[warning] 264-264: High resource usage detected: 2 workers × 10 threads = 20 concurrent threads on 4 CPUs. Consider reducing max_workers or core.max_threads in system.yaml to avoid resource contention.
🪛 LanguageTool
docs/multi_provider_evaluation.md
[style] ~13-~13: ‘with success’ might be wordy. Consider a shorter alternative.
Context: ...ut sanitization - Comprehensive summary with success/failure statistics and statistical comp...
(EN_WORDINESS_PREMIUM_WITH_SUCCESS)
🪛 markdownlint-cli2 (0.18.1)
docs/multi_provider_evaluation.md
322-322: Emphasis used instead of a heading
(MD036, no-emphasis-as-heading)
377-377: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: mypy
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Nitpick comments (1)
docs/multi_provider_evaluation.md (1)
377-381: Add language hint to warning block.Markdownlint (MD040) flags the warning snippet because the fenced code block is missing a language tag; adding one keeps lint quiet and retains consistent monospace rendering. Annotating it as
text(or similar) should be enough.-``` +```text WARNING - High resource usage detected: 4 workers × 50 threads = 200 concurrent threads on 8 CPUs. Consider reducing max_workers or core.max_threads in system.yaml to avoid resource contention. -``` +```
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (4)
config/multi_eval_config.yaml(2 hunks)docs/multi_provider_evaluation.md(5 hunks)script/run_multi_provider_eval.py(9 hunks)tests/script/test_run_multi_provider_eval.py(2 hunks)
🚧 Files skipped from review as they are similar to previous changes (2)
- config/multi_eval_config.yaml
- tests/script/test_run_multi_provider_eval.py
🧰 Additional context used
🧠 Learnings (7)
📓 Common learnings
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/lightspeed_evaluation/** : Add all new evaluation features under src/lightspeed_evaluation/ (do not add new features elsewhere)
Applied to files:
docs/multi_provider_evaluation.mdscript/run_multi_provider_eval.py
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/system.yaml : Keep system configuration in config/system.yaml (LLM, API, metrics metadata, output, logging)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-08T11:09:18.416Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: config/system.yaml:16-20
Timestamp: 2025-09-08T11:09:18.416Z
Learning: The maintainer asamal4 prefers sample configurations in config/system.yaml to show realistic usage patterns (e.g., API enabled: true) rather than conservative defaults, as teams are expected to customize the configuration for their specific environments.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to config/evaluation_data.yaml : Keep evaluation data in config/evaluation_data.yaml (conversation groups, turns, overrides, scripts)
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-09-10T15:48:14.671Z
Learnt from: asamal4
Repo: lightspeed-core/lightspeed-evaluation PR: 47
File: src/lightspeed_evaluation/core/output/generator.py:43-49
Timestamp: 2025-09-10T15:48:14.671Z
Learning: In the lightspeed-evaluation framework, system configuration uses Pydantic data models (SystemConfig, OutputConfig, LoggingConfig, etc.) rather than plain dictionaries. Components like OutputHandler receive properly structured Pydantic models, so direct attribute access (e.g., system_config.output.enabled_outputs) is the correct approach.
Applied to files:
docs/multi_provider_evaluation.md
📚 Learning: 2025-10-16T11:17:19.324Z
Learnt from: CR
Repo: lightspeed-core/lightspeed-evaluation PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-10-16T11:17:19.324Z
Learning: Applies to src/**/*.py : Use structured logging with appropriate levels
Applied to files:
script/run_multi_provider_eval.py
🪛 LanguageTool
docs/multi_provider_evaluation.md
[style] ~13-~13: ‘with success’ might be wordy. Consider a shorter alternative.
Context: ...ut sanitization - Comprehensive summary with success/failure statistics and statistical comp...
(EN_WORDINESS_PREMIUM_WITH_SUCCESS)
🪛 markdownlint-cli2 (0.18.1)
docs/multi_provider_evaluation.md
322-322: Emphasis used instead of a heading
(MD036, no-emphasis-as-heading)
377-377: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
- GitHub Check: tests (3.11)
3 participants
The implementation should ensure:
Board: https://issues.redhat.com/browse/LCORE-900
Summary by CodeRabbit
New Features
Configuration
Documentation
Tests