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 | 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 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 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x 2x | import { getTurnRelevancyTemplate } from '@api/connectors/evaluations/turn-relevancy/templates/main';
import { TurnRelevancyEvaluationParameters } from '@api/connectors/evaluations/turn-relevancy/types';
import { createLLMJudge } from '@api/evaluations/llm-judge';
import type { UserDataStorageConnector } from '@api/types/connector';
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';
function pickTurnRelevancyData(
log: Log,
params: TurnRelevancyEvaluationParameters,
): {
conversation_history: string;
current_turn: string;
instructions?: string;
} {
// Extract conversation history using standard utilities if not provided in params
let conversation_history = params.conversation_history;
if (!conversation_history) {
try {
const saRequestData = produceSuperAgentsRequestData(
log.ai_provider_request_log.method,
log.ai_provider_request_log.request_url,
{},
log.ai_provider_request_log.request_body,
);
const messages = extractMessagesFromRequestData(
saRequestData as
| ChatCompletionRequestData
| StreamChatCompletionRequestData
| ResponsesRequestData,
);
conversation_history = formatMessagesForExtraction(messages);
} catch {
// Fallback to metadata if parsing fails
conversation_history =
(log.metadata?.conversation_history as string) || '';
}
}
// Extract current turn using standard utilities if not provided in params
let current_turn = params.current_turn;
if (!current_turn) {
try {
const responseBody = SuperAgentsResponseBody.parse(
log.ai_provider_request_log.response_body,
);
current_turn = extractOutputFromResponseBody(responseBody);
} catch {
// Fallback to metadata if parsing fails
current_turn =
(typeof log.metadata?.ground_truth === 'string'
? (log.metadata.ground_truth as string)
: log.metadata?.ground_truth
? JSON.stringify(log.metadata.ground_truth)
: (log.metadata?.current_turn as string) || '') || '';
}
}
const instructions =
params.instructions || (log.metadata?.instructions as string);
return { conversation_history, current_turn, instructions };
}
export async function evaluateLog(
c: AppContext,
evaluation: SkillOptimizationEvaluation,
log: Log,
storageConnector: UserDataStorageConnector,
): Promise<SkillOptimizationEvaluationResult> {
const params = TurnRelevancyEvaluationParameters.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 start_time = Date.now();
const { conversation_history, current_turn, instructions } =
pickTurnRelevancyData(log, params);
const tpl = getTurnRelevancyTemplate({
conversation_history,
current_turn,
strict_mode: params.strict_mode || false,
verbose_mode: params.verbose_mode ?? true,
include_reason: params.include_reason ?? true,
});
const judgeResult = await llmJudge.evaluate({
text: `${tpl.systemPrompt}\n\n${tpl.userPrompt}`,
outputFormat: 'json',
});
let final_score = judgeResult.score;
if (params.strict_mode) {
final_score = final_score === 1.0 ? 1.0 : 0.0;
}
const execution_time = Date.now() - start_time;
const judgeModelName = modelConfig?.model ?? null;
const judgeModelProvider = modelConfig?.provider ?? null;
const evaluationResult: SkillOptimizationEvaluationResult = {
evaluation_id: evaluation.id,
method: EvaluationMethodName.TURN_RELEVANCY,
score: final_score,
extra_data: {
reasoning: judgeResult.reasoning,
conversation_history,
current_turn,
instructions,
strict_mode: params.strict_mode,
metadata: judgeResult.metadata,
execution_time,
execution_time_ms: execution_time,
evaluated_at: new Date().toISOString(),
},
display_info: [
{
label: 'Reasoning',
content: judgeResult.reasoning,
},
{
label: 'Current Turn',
content: current_turn,
},
...(instructions
? [
{
label: 'Additional Instructions',
content: instructions,
},
]
: []),
{
label: 'Conversation History',
content: conversation_history,
},
],
judge_model_name: judgeModelName,
judge_model_provider: judgeModelProvider,
};
return evaluationResult;
}
|