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import getTaskCompletionVerdictTemplate from '@api/connectors/evaluations/task-completion/templates/verdict';
import { TaskCompletionEvaluationParameters } from '@api/connectors/evaluations/task-completion/types';
import { createLLMJudge } from '@api/evaluations/llm-judge';
import type { UserDataStorageConnector } from '@api/types/connector';
import type { LLMJudge } from '@api/types/evaluations/llm-judge';
import type { AppContext } from '@api/types/hono';
import { resolveEvaluationModelConfig } from '@api/utils/evaluation-model-resolver';
import { formatMessagesForExtraction } from '@api/utils/messages';
import { extractMessagesFromRequestData } from '@api/utils/super-agents/requests';
import { extractOutputFromResponseBody } from '@api/utils/super-agents/responses';
import type {
ChatCompletionRequestData,
ResponsesRequestData,
StreamChatCompletionRequestData,
} from '@shared/types/api/request';
import { SuperAgentsResponseBody } from '@shared/types/api/response';
import type {
SkillOptimizationEvaluation,
SkillOptimizationEvaluationResult,
} from '@shared/types/data';
import type { Log } from '@shared/types/data/log';
import { EvaluationMethodName } from '@shared/types/evaluations';
import { produceSuperAgentsRequestData } from '@shared/utils/sa-request-data';
/**
* Generate verdict using universal LLM judge with verdict template
*/
async function generateVerdict(
{ task, outcome }: { task: string; outcome: string },
llm_judge: LLMJudge,
): Promise<{ verdict: number; reason: string }> {
const verdictTemplate = getTaskCompletionVerdictTemplate({ task, outcome });
const verdict_result = await llm_judge.evaluate({
text: `${verdictTemplate.systemPrompt}\n\n${verdictTemplate.userPrompt}`,
});
return {
verdict: verdict_result.score,
reason: verdict_result.reasoning,
};
}
async function getTaskAndOutcome(
c: AppContext,
params: TaskCompletionEvaluationParameters,
log: Log,
connector: UserDataStorageConnector,
): Promise<{ task: string; outcome: string }> {
const saRequestData = produceSuperAgentsRequestData(
log.ai_provider_request_log.method,
log.ai_provider_request_log.request_url,
{},
log.ai_provider_request_log.request_body,
);
const responseBody = SuperAgentsResponseBody.parse(
log.ai_provider_request_log.response_body,
);
const messages = extractMessagesFromRequestData(
saRequestData as
| ChatCompletionRequestData
| StreamChatCompletionRequestData
| ResponsesRequestData,
);
const input = formatMessagesForExtraction(messages);
const output = extractOutputFromResponseBody(responseBody);
const { task, outcome } = await extractTaskAndOutcome(
c,
params,
input,
output,
connector,
);
return { task: params.task || task, outcome };
}
export async function evaluateLog(
c: AppContext,
evaluation: SkillOptimizationEvaluation,
log: Log,
storageConnector: UserDataStorageConnector,
): Promise<SkillOptimizationEvaluationResult> {
const start_time = Date.now();
try {
const params = TaskCompletionEvaluationParameters.parse(evaluation.params);
// Resolve model configuration from evaluation.model_id or system settings
const modelConfig = await resolveEvaluationModelConfig(
c,
evaluation,
storageConnector,
);
const llmJudge = createLLMJudge(
c,
{
temperature: params.temperature,
max_tokens: params.max_tokens,
},
modelConfig ?? undefined,
);
const { task, outcome } = await getTaskAndOutcome(
c,
params,
log,
storageConnector,
);
// Step 2: Generate verdict
const { verdict, reason } = await generateVerdict(
{ task, outcome },
llmJudge,
);
const verdict_llm_output = JSON.stringify({ verdict, reason });
const execution_time = Date.now() - start_time;
const judgeModelName = modelConfig?.model ?? null;
const judgeModelProvider = modelConfig?.provider ?? null;
const result: SkillOptimizationEvaluationResult = {
evaluation_id: evaluation.id,
method: EvaluationMethodName.TASK_COMPLETION,
score: verdict,
extra_data: {
task,
outcome,
strict_mode: params.strict_mode,
extraction_llm_output: {
task,
outcome,
},
verdict_llm_output,
execution_time,
execution_time_ms: execution_time,
evaluated_at: new Date().toISOString(),
},
display_info: [
{
label: 'Task',
content: task,
},
{
label: 'Outcome',
content: outcome,
},
{
label: 'Verdict',
content: `Score: ${verdict}\n\nReason:\n${reason}`,
},
],
judge_model_name: judgeModelName,
judge_model_provider: judgeModelProvider,
};
return result;
} catch (err) {
// Always return a result, even if evaluation fails
// This ensures arm stats and counters are updated
const execution_time = Date.now() - start_time;
const errorMessage = err instanceof Error ? err.message : String(err);
return {
evaluation_id: evaluation.id,
method: EvaluationMethodName.TASK_COMPLETION,
score: 0.5, // Neutral fallback score
extra_data: {
task: '',
outcome: '',
strict_mode: false,
extraction_llm_output: {
task: '',
outcome: '',
},
verdict_llm_output: JSON.stringify({
verdict: 0.5,
reason: `Evaluation failed: ${errorMessage}`,
}),
execution_time,
execution_time_ms: execution_time,
evaluated_at: new Date().toISOString(),
error: errorMessage,
fallback: true,
},
display_info: [
{
label: 'Error',
content: `Evaluation failed: ${errorMessage}`,
},
],
judge_model_name: null,
judge_model_provider: null,
};
}
}
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