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https://github.com/hoshikawa2/agent_framework_oci_evaluator.git
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144
evaluator/engine.py
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144
evaluator/engine.py
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from __future__ import annotations
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import inspect, json, random
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from datetime import datetime, timedelta
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from typing import Any, Awaitable, Callable
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from evaluator.collectors.base import ConversationCollector
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from evaluator.collectors.langfuse import LangfuseCollector
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from evaluator.collectors.agent_framework import AgentFrameworkCollector
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from evaluator.collectors.mock import MockCollector
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from evaluator.config.agents import AgentConfig
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from evaluator.config.settings import settings
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from evaluator.core.models import ConversationRecord, RunStatus
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from evaluator.judges.llm_judge import TIMStyleLLMJudge
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from evaluator.output.legacy_exporter import export_legacy_txt_gz
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from evaluator.persistence.repository import EvaluationRepository
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from evaluator.publishers.langfuse_scores import LangfuseScorePublisher
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ProgressCallback = Callable[[dict[str, Any]], Awaitable[None] | None]
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class EvaluationEngine:
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def __init__(self, repository: EvaluationRepository | None=None, progress_callback: ProgressCallback | None=None):
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self.repository = repository or EvaluationRepository(auto_init_schema=False)
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self.progress_callback = progress_callback
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self.judge = TIMStyleLLMJudge()
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self.langfuse_publisher = LangfuseScorePublisher()
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# async def _emit(self, run_id: str, stage: str, message: str='', **details):
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# details.pop('run_id', None)
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# await self.repository.arecord_progress(run_id, stage, message, details)
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# event={'run_id': run_id, 'stage': stage, 'message': message, 'details': details}
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async def _emit(self, progress_run_id: str, stage: str, message: str = "", **details):
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details.pop("run_id", None)
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await self.repository.arecord_progress(
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progress_run_id,
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stage,
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message,
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details,
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)
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event = {
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"run_id": progress_run_id,
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"stage": stage,
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"message": message,
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"details": details,
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}
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if self.progress_callback:
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r = self.progress_callback(event)
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if inspect.isawaitable(r): await r
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def collector_for(self, source: str) -> ConversationCollector:
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if source == 'langfuse': return LangfuseCollector()
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if source == 'agent_framework': return AgentFrameworkCollector()
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if source == 'mock': return MockCollector()
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raise ValueError('source must be langfuse, agent_framework or mock')
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async def run_agent(self, agent: AgentConfig, period_start: datetime, period_end: datetime, source: str='langfuse', limit: int | None=None) -> dict:
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run_id = await self.repository.acreate_run(period_start, period_end, source, agent.agent_id)
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try:
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await self._emit(run_id, 'RUN_CREATED', f'Agent run created: {agent.agent_id}', agent_id=agent.agent_id, source=source)
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collector = self.collector_for(source)
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await self._emit(run_id, 'COLLECTING', 'Collecting conversations')
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records = await collector.collect(period_start, period_end, agent_aliases=agent.aliases, limit=limit)
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await self._emit(run_id, 'COLLECTED', f'Collected {len(records)} records before sampling')
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records = self._sample(records, agent.percentage)
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await self._emit(run_id, 'SAMPLED', f'Kept {len(records)} records', percentage=agent.percentage)
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inserted = await self.repository.ainsert_items(run_id, records)
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await self._emit(run_id, 'ITEMS_INSERTED', f'Inserted {inserted} items')
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summary = await self._process(run_id)
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output_path = export_legacy_txt_gz(self.repository, run_id, agent.agent_id)
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await self._emit(run_id, 'EXPORTED', f'Exported {output_path}', output_file=str(output_path))
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return {**summary, 'agent_id': agent.agent_id, 'output_file': str(output_path), 'uploaded_to': None}
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except Exception as exc:
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await self.repository.amark_run_status(run_id, RunStatus.PARTIAL, str(exc))
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await self._emit(run_id, 'PARTIAL', f'Run failed: {exc}', error=str(exc))
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return {'status':'PARTIAL','run_id':run_id,'agent_id':agent.agent_id,'error':str(exc)}
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async def run(self, period_start: datetime, period_end: datetime, source: str='langfuse', limit: int | None=None) -> dict:
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run_id = await self.repository.acreate_run(period_start, period_end, source, None)
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try:
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collector = self.collector_for(source)
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await self._emit(run_id, 'COLLECTING', 'Collecting conversations')
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records = await collector.collect(period_start, period_end, limit=limit)
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await self._emit(run_id, 'COLLECTED', f'Collected {len(records)} records')
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await self.repository.ainsert_items(run_id, records)
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return await self._process(run_id)
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except Exception as exc:
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await self.repository.amark_run_status(run_id, RunStatus.PARTIAL, str(exc))
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await self._emit(run_id, 'PARTIAL', f'Run failed: {exc}', error=str(exc))
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return {'status':'PARTIAL','run_id':run_id,'error':str(exc)}
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async def _process(self, run_id: str) -> dict:
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processed_records: list[ConversationRecord] = []
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while True:
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items = await self.repository.afetch_next_items(run_id, settings.batch_size)
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if not items: break
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await self._emit(run_id, 'BATCH_STARTED', f'Processing {len(items)} items')
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for item in items:
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item_id=item['item_id']
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await self.repository.amark_item_processing(item_id)
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try:
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raw=item['raw_json']
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if hasattr(raw, 'read'): raw = raw.read()
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record = ConversationRecord.model_validate(json.loads(raw))
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result = await self.judge.judge_trace(record)
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await self.repository.asave_trace_result(run_id, item_id, record, result)
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await self.langfuse_publisher.publish_trace_score(record, result)
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await self.repository.amark_item_completed(run_id, item_id)
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processed_records.append(record)
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#await self._emit(run_id, 'ITEM_COMPLETED', f'Item completed {item_id}', trace_id=record.trace_id)
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loop_result = getattr(result, "loop_result", None)
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await self._emit(
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run_id,
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"ITEM_COMPLETED",
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f"Item completed {item_id}",
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trace_id=record.trace_id,
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session_id=record.session_id,
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judgeScore=result.judgeScore,
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accuracyScore=result.accuracyScore,
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alucinationScore=result.alucinationScore,
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rationale=result.rationale,
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loop=getattr(loop_result, "loop", 0) if loop_result else 0,
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loop_reason=getattr(loop_result, "reason", "") if loop_result else "",
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)
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except Exception as exc:
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await self.repository.amark_item_failed(run_id, item_id, str(exc))
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await self._emit(run_id, 'ITEM_FAILED', f'Item failed {item_id}', error=str(exc))
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if processed_records:
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sessions = await self.judge.judge_sessions(processed_records)
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for sid, result in sessions.items():
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agent_id = next((r.agent_id for r in processed_records if r.session_id == sid), None)
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await self.repository.asave_session_result(run_id, sid, agent_id, result)
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await self._emit(run_id, 'SESSION_JUDGE_COMPLETED', f'Evaluated {len(sessions)} sessions')
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await self.repository.amark_run_status(run_id, RunStatus.COMPLETED)
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summary = await self.repository.asummarize_run(run_id)
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await self._emit(run_id, 'COMPLETED', 'Run completed', **summary)
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return {'status':'COMPLETED', **summary}
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def _sample(self, records: list[ConversationRecord], percentage: float) -> list[ConversationRecord]:
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if percentage >= 1: return records
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rng = random.Random(42)
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return [r for r in records if rng.random() <= percentage]
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