Press n or j to go to the next uncovered block, b, p or k for the previous block.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 1x 1x 1x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x | import { getArgumentCorrectnessTemplate } from '@api/connectors/evaluations/argument-correctness/templates/main';
import { ArgumentCorrectnessEvaluationParameters } from '@api/connectors/evaluations/argument-correctness/types';
import type { ToolUsage } from '@api/connectors/evaluations/tool-correctness/types';
import { createLLMJudge } from '@api/evaluations/llm-judge';
import type { UserDataStorageConnector } from '@api/types/connector';
import type { AppContext } from '@api/types/hono';
import { extractMessagesFromRequestData } from '@api/utils/embeddings';
import { resolveEvaluationModelConfig } from '@api/utils/evaluation-model-resolver';
import { formatMessagesForExtraction } from '@api/utils/messages';
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';
// Use a template builder to construct prompts
function buildPromptForToolArgs(
input: string,
actual_output: string,
tools_called: ToolUsage[],
): { systemPrompt: string; userPrompt: string } {
const tpl = getArgumentCorrectnessTemplate({
input,
actual_output,
tools_called,
strict_mode: false,
verbose_mode: true,
include_reason: true,
});
return { systemPrompt: tpl.systemPrompt, userPrompt: tpl.userPrompt };
}
export async function evaluateLog(
c: AppContext,
evaluation: SkillOptimizationEvaluation,
log: Log,
storageConnector: UserDataStorageConnector,
): Promise<SkillOptimizationEvaluationResult> {
const params = ArgumentCorrectnessEvaluationParameters.parse(
evaluation.params,
);
const start_time = Date.now();
// Resolve model configuration from evaluation.model_id or system settings
const modelConfig = await resolveEvaluationModelConfig(
c,
evaluation,
storageConnector,
);
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);
let tools_called: ToolUsage[] = [];
if (params.tools_called && Array.isArray(params.tools_called)) {
tools_called = params.tools_called as ToolUsage[];
} else if (log.metadata && typeof log.metadata.tools === 'string') {
try {
tools_called = JSON.parse(log.metadata.tools) as ToolUsage[];
} catch {
tools_called = [];
}
} else if (log.metadata && log.metadata.tools !== undefined) {
const t = log.metadata.tools;
if (Array.isArray(t)) tools_called = t as ToolUsage[];
else if (typeof t === 'object' && t !== null)
tools_called = [t as ToolUsage];
}
const llmJudge = createLLMJudge(
c,
{
temperature: params.temperature,
max_tokens: params.max_tokens,
},
modelConfig ?? undefined,
);
const { systemPrompt, userPrompt } = buildPromptForToolArgs(
input,
output,
tools_called,
);
const judgeResult = await llmJudge.evaluate({
text: `${systemPrompt}\n\n${userPrompt}`,
});
let computed_score: number | null = null;
const meta = judgeResult.metadata as Record<string, unknown> | undefined;
const perTool = Array.isArray(meta?.per_tool)
? (meta?.per_tool as unknown[])
: undefined;
if (perTool && perTool.length > 0) {
const total = perTool.length;
let correctCount = 0;
for (const item of perTool) {
const obj = item as Record<string, unknown>;
if (typeof obj?.correct === 'boolean' && obj.correct) correctCount += 1;
}
computed_score = total > 0 ? correctCount / total : null;
}
const final_score = computed_score ?? judgeResult.score;
const execution_time = Date.now() - start_time;
const judgeModelName = modelConfig?.model ?? null;
const judgeModelProvider = modelConfig?.provider ?? null;
// Format tool correctness information for display
const displayInfoSections = [];
// Add reasoning if available
if (judgeResult.reasoning) {
displayInfoSections.push({
label: 'Reasoning',
content: judgeResult.reasoning,
});
}
// Add per-tool breakdown if available
if (perTool && perTool.length > 0) {
const toolBreakdown = perTool
.map((item) => {
const obj = item as Record<string, unknown>;
const toolName = obj.tool_name || 'Unknown Tool';
const correct = obj.correct ? '✓ Correct' : '✗ Incorrect';
const reason = obj.reason ? `\nReason: ${obj.reason}` : '';
return `${toolName}: ${correct}${reason}`;
})
.join('\n\n');
displayInfoSections.push({
label: 'Tool Arguments Analysis',
content: toolBreakdown,
});
}
// Add tools called summary
if (tools_called.length > 0) {
const toolsSummary = tools_called
.map((tool) => {
return `${tool.name}\nPurpose: ${tool.purpose}\nSuccess: ${tool.success}`;
})
.join('\n\n');
displayInfoSections.push({
label: 'Tools Called',
content: toolsSummary,
});
}
const result: SkillOptimizationEvaluationResult = {
evaluation_id: evaluation.id,
method: EvaluationMethodName.ARGUMENT_CORRECTNESS,
score: final_score,
extra_data: {
tools_called,
execution_time,
execution_time_ms: execution_time,
...(judgeResult.metadata ? { judge_metadata: judgeResult.metadata } : {}),
},
display_info: displayInfoSections,
judge_model_name: judgeModelName,
judge_model_provider: judgeModelProvider,
};
return result;
}
|