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Pull Request Overview
This PR implements the "Evals Ready" feature by refactoring evaluation management to support per-tab evaluation agents and improving error handling with retry mechanisms.
Key changes:
- Replaced global evaluation agent pattern with per-tab EvaluationAgent instances in AIChatPanel
- Implemented robust retry logic with exponential backoff for failed evaluations
- Created comprehensive Python evaluation server implementation with browsecomp benchmark support
Reviewed Changes
Copilot reviewed 54 out of 214 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
| front_end/panels/ai_chat/ui/AIChatPanel.ts | Moved from global to per-tab EvaluationAgent management with automatic reconnection |
| front_end/panels/ai_chat/evaluation/remote/EvaluationAgent.ts | Removed global instance management and added retry logic with partial result handling |
| front_end/panels/ai_chat/evaluation/EvaluationAgent.ts | Enhanced error handling with retry mechanisms and partial result support |
| front_end/panels/ai_chat/common/EvaluationConfig.ts | Simplified to remove global connection management |
| eval-server/python/* | New Python implementation with comprehensive evaluation server and browsecomp benchmark |
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| partial: true, | ||
| lastError: errorMessage, | ||
| attempts: maxAttempts | ||
| }; |
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The error result structure with hardcoded fields like partial: true and attempts: maxAttempts creates a magic object that might be confused with actual tool results. Consider defining a specific error result type or interface to make this pattern more explicit and maintainable.
| }; | |
| const errorResult: ToolExecutionErrorResult = { | |
| error: `Tool execution failed after ${maxAttempts} attempts: ${errorMessage}`, | |
| partial: true, | |
| lastError: errorMessage, | |
| attempts: maxAttempts | |
| }; | |
| toolResult = errorResult; |
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| if (toolExecutionAttempts < maxAttempts) { | ||
| // Wait before retry, with exponential backoff | ||
| const retryDelay = 1000 * Math.pow(2, toolExecutionAttempts - 1); |
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The exponential backoff calculation should use Math.min() to cap the maximum delay to prevent excessively long waits. Consider: const retryDelay = Math.min(30000, 1000 * Math.pow(2, toolExecutionAttempts - 1)); to limit delays to 30 seconds maximum.
| const retryDelay = 1000 * Math.pow(2, toolExecutionAttempts - 1); | |
| const retryDelay = Math.min(30000, 1000 * Math.pow(2, toolExecutionAttempts - 1)); |
| ] | ||
| }); | ||
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| evaluationLogger.info(logEntry); |
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Moving the evaluationLogger creation outside the function is good, but the logger should be properly closed when the application shuts down to prevent resource leaks. Consider adding a cleanup function or using the existing logger's transports.
## Pull Request Overview This PR implements the "Evals Ready" feature by refactoring evaluation management to support per-tab evaluation agents and improving error handling with retry mechanisms. Key changes: - Replaced global evaluation agent pattern with per-tab EvaluationAgent instances in AIChatPanel - Implemented robust retry logic with exponential backoff for failed evaluations - Created comprehensive Python evaluation server implementation with browsecomp benchmark support
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