mirror of https://github.com/langgenius/dify.git
455 lines
17 KiB
Python
455 lines
17 KiB
Python
import io
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import json
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import logging
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from typing import Any
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from celery import shared_task
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from openpyxl import Workbook
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from openpyxl.styles import Alignment, Border, Font, PatternFill, Side
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from openpyxl.utils import get_column_letter
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from configs import dify_config
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from core.evaluation.base_evaluation_instance import BaseEvaluationInstance
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from core.evaluation.entities.evaluation_entity import (
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EvaluationCategory,
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EvaluationDatasetInput,
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EvaluationItemResult,
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EvaluationRunData,
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)
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from core.evaluation.entities.judgment_entity import JudgmentConfig
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from core.evaluation.evaluation_manager import EvaluationManager
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from core.evaluation.runners.agent_evaluation_runner import AgentEvaluationRunner
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from core.evaluation.runners.base_evaluation_runner import BaseEvaluationRunner
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from core.evaluation.runners.llm_evaluation_runner import LLMEvaluationRunner
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from core.evaluation.runners.retrieval_evaluation_runner import RetrievalEvaluationRunner
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from core.evaluation.runners.snippet_evaluation_runner import SnippetEvaluationRunner
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from core.evaluation.runners.workflow_evaluation_runner import WorkflowEvaluationRunner
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from core.workflow.node_events.base import NodeRunResult
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from extensions.ext_database import db
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from libs.datetime_utils import naive_utc_now
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from models.enums import CreatorUserRole
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from models.evaluation import EvaluationRun, EvaluationRunStatus
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from models.model import UploadFile
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from services.evaluation_service import EvaluationService
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logger = logging.getLogger(__name__)
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@shared_task(queue="evaluation")
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def run_evaluation(run_data_dict: dict[str, Any]) -> None:
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"""Celery task for running evaluations asynchronously.
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Workflow:
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1. Deserialize EvaluationRunData
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2. Update status to RUNNING
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3. Select appropriate Runner based on evaluation_category
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4. Execute runner.run() which handles target execution + metric computation
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5. Generate result XLSX
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6. Update EvaluationRun status to COMPLETED
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"""
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run_data = EvaluationRunData.model_validate(run_data_dict)
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with db.engine.connect() as connection:
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from sqlalchemy.orm import Session
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session = Session(bind=connection)
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try:
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_execute_evaluation(session, run_data)
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except Exception as e:
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logger.exception("Evaluation run %s failed", run_data.evaluation_run_id)
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_mark_run_failed(session, run_data.evaluation_run_id, str(e))
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finally:
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session.close()
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def _execute_evaluation(session: Any, run_data: EvaluationRunData) -> None:
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"""Core evaluation execution logic."""
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evaluation_run = session.query(EvaluationRun).filter_by(id=run_data.evaluation_run_id).first()
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if not evaluation_run:
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logger.error("EvaluationRun %s not found", run_data.evaluation_run_id)
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return
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# Check if cancelled
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if evaluation_run.status == EvaluationRunStatus.CANCELLED:
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logger.info("EvaluationRun %s was cancelled", run_data.evaluation_run_id)
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return
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# Get evaluation instance
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evaluation_instance = EvaluationManager.get_evaluation_instance()
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if evaluation_instance is None:
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raise ValueError("Evaluation framework not configured")
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if run_data.target_type == "dataset":
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results: list[EvaluationItemResult] = _execute_retrieval_test(
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session=session,
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evaluation_run=evaluation_run,
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run_data=run_data,
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evaluation_instance=evaluation_instance,
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)
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else:
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evaluation_service = EvaluationService()
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node_run_result_mapping_list, workflow_run_ids = evaluation_service.execute_targets(
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tenant_id=run_data.tenant_id,
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target_type=run_data.target_type,
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target_id=run_data.target_id,
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input_list=run_data.input_list,
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)
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results = _execute_evaluation_runner(
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session=session,
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run_data=run_data,
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evaluation_instance=evaluation_instance,
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node_run_result_mapping_list=node_run_result_mapping_list,
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)
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_backfill_workflow_run_ids(
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session=session,
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evaluation_run_id=run_data.evaluation_run_id,
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input_list=run_data.input_list,
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workflow_run_ids=workflow_run_ids,
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)
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# Compute summary metrics
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metrics_summary = _compute_metrics_summary(results, run_data.judgment_config)
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# Generate result XLSX
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result_xlsx = _generate_result_xlsx(run_data.input_list, results)
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# Store result file
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result_file_id = _store_result_file(run_data.tenant_id, run_data.evaluation_run_id, result_xlsx, session)
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# Update run to completed
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evaluation_run: EvaluationRun = session.query(EvaluationRun).filter_by(id=run_data.evaluation_run_id).first()
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if evaluation_run:
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evaluation_run.status = EvaluationRunStatus.COMPLETED
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evaluation_run.completed_at = naive_utc_now()
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evaluation_run.metrics_summary = json.dumps(metrics_summary)
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if result_file_id:
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evaluation_run.result_file_id = result_file_id
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session.commit()
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logger.info("Evaluation run %s completed successfully", run_data.evaluation_run_id)
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def _execute_evaluation_runner(
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session: Any,
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run_data: EvaluationRunData,
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evaluation_instance: BaseEvaluationInstance,
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node_run_result_mapping_list: list[dict[str, NodeRunResult]],
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) -> list[EvaluationItemResult]:
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"""Execute the evaluation runner."""
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default_metrics = run_data.default_metrics
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customized_metrics = run_data.customized_metrics
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results: list[EvaluationItemResult] = []
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for default_metric in default_metrics:
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for node_info in default_metric.node_info_list:
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node_run_result_list: list[NodeRunResult] = []
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for node_run_result_mapping in node_run_result_mapping_list:
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node_run_result = node_run_result_mapping.get(node_info.node_id)
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if node_run_result is not None:
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node_run_result_list.append(node_run_result)
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if node_run_result_list:
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runner = _create_runner(EvaluationCategory(node_info.type), evaluation_instance, session)
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results.extend(
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runner.run(
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evaluation_run_id=run_data.evaluation_run_id,
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tenant_id=run_data.tenant_id,
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target_id=run_data.target_id,
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target_type=run_data.target_type,
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default_metric=default_metric,
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customized_metrics=None,
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model_provider=run_data.evaluation_model_provider,
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model_name=run_data.evaluation_model,
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node_run_result_list=node_run_result_list,
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judgment_config=run_data.judgment_config,
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input_list=run_data.input_list,
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)
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)
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if customized_metrics:
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runner = _create_runner(EvaluationCategory.WORKFLOW, evaluation_instance, session)
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results.extend(
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runner.run(
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evaluation_run_id=run_data.evaluation_run_id,
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tenant_id=run_data.tenant_id,
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target_id=run_data.target_id,
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target_type=run_data.target_type,
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default_metric=None,
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customized_metrics=customized_metrics,
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node_run_result_list=None,
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node_run_result_mapping_list=node_run_result_mapping_list,
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judgment_config=run_data.judgment_config,
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input_list=run_data.input_list,
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)
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)
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return results
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def _create_runner(
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category: EvaluationCategory,
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evaluation_instance: BaseEvaluationInstance,
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session: Any,
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) -> BaseEvaluationRunner:
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"""Create the appropriate runner for the evaluation category."""
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match category:
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case EvaluationCategory.LLM:
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return LLMEvaluationRunner(evaluation_instance, session)
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case EvaluationCategory.RETRIEVAL | EvaluationCategory.KNOWLEDGE_BASE:
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return RetrievalEvaluationRunner(evaluation_instance, session)
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case EvaluationCategory.AGENT:
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return AgentEvaluationRunner(evaluation_instance, session)
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case EvaluationCategory.WORKFLOW:
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return WorkflowEvaluationRunner(evaluation_instance, session)
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case EvaluationCategory.SNIPPET:
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return SnippetEvaluationRunner(evaluation_instance, session)
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case _:
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raise ValueError(f"Unknown evaluation category: {category}")
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def _execute_retrieval_test(
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session: Any,
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evaluation_run: EvaluationRun,
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run_data: EvaluationRunData,
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evaluation_instance: BaseEvaluationInstance,
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) -> list[EvaluationItemResult]:
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"""Execute knowledge base retrieval for all items, then evaluate metrics.
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Unlike the workflow-based path, there are no workflow nodes to traverse.
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Hit testing is run directly for each dataset item and the results are fed
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straight into :class:`RetrievalEvaluationRunner`.
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"""
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node_run_result_list = EvaluationService.execute_retrieval_test_targets(
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dataset_id=run_data.target_id,
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account_id=evaluation_run.created_by,
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input_list=run_data.input_list,
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)
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results: list[EvaluationItemResult] = []
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runner = RetrievalEvaluationRunner(evaluation_instance, session)
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results.extend(
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runner.run(
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evaluation_run_id=run_data.evaluation_run_id,
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tenant_id=run_data.tenant_id,
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target_id=run_data.target_id,
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target_type=run_data.target_type,
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default_metric=None,
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model_provider=run_data.evaluation_model_provider,
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model_name=run_data.evaluation_model,
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node_run_result_list=node_run_result_list,
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judgment_config=run_data.judgment_config,
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input_list=run_data.input_list,
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)
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)
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return results
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def _backfill_workflow_run_ids(
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session: Any,
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evaluation_run_id: str,
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input_list: list[EvaluationDatasetInput],
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workflow_run_ids: list[str | None],
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) -> None:
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"""Set ``workflow_run_id`` on items that were created by the runner."""
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from models.evaluation import EvaluationRunItem
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for item, wf_run_id in zip(input_list, workflow_run_ids):
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if not wf_run_id:
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continue
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run_item = (
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session.query(EvaluationRunItem)
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.filter_by(evaluation_run_id=evaluation_run_id, item_index=item.index)
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.first()
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)
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if run_item:
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run_item.workflow_run_id = wf_run_id
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session.commit()
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def _mark_run_failed(session: Any, run_id: str, error: str) -> None:
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"""Mark an evaluation run as failed."""
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try:
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evaluation_run = session.query(EvaluationRun).filter_by(id=run_id).first()
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if evaluation_run:
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evaluation_run.status = EvaluationRunStatus.FAILED
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evaluation_run.error = error[:2000] # Truncate error
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evaluation_run.completed_at = naive_utc_now()
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session.commit()
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except Exception:
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logger.exception("Failed to mark run %s as failed", run_id)
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def _compute_metrics_summary(
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results: list[EvaluationItemResult],
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judgment_config: JudgmentConfig | None,
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) -> dict[str, Any]:
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"""Compute aggregate metric and judgment summaries for an evaluation run.
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Metric statistics are calculated from successful item results only. When a
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judgment config is present, the summary also reports how many successful
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items passed or failed the configured judgment rules.
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"""
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summary: dict[str, Any] = {}
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if judgment_config is not None and judgment_config.conditions:
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evaluated_results: list[EvaluationItemResult] = [
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result for result in results if result.error is None and result.metrics
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]
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passed_items = sum(1 for result in evaluated_results if result.judgment.passed)
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evaluated_items = len(evaluated_results)
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summary["_judgment"] = {
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"enabled": True,
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"logical_operator": judgment_config.logical_operator,
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"configured_conditions": len(judgment_config.conditions),
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"evaluated_items": evaluated_items,
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"passed_items": passed_items,
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"failed_items": evaluated_items - passed_items,
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"pass_rate": passed_items / evaluated_items if evaluated_items else 0.0,
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}
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return summary
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def _generate_result_xlsx(
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input_list: list[EvaluationDatasetInput],
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results: list[EvaluationItemResult],
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) -> bytes:
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"""Generate result XLSX with input data, actual output, metric scores, and judgment."""
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wb = Workbook()
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ws = wb.active
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if ws is None:
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ws = wb.create_sheet("Evaluation Results")
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ws.title = "Evaluation Results"
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header_font = Font(bold=True, color="FFFFFF")
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header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
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header_alignment = Alignment(horizontal="center", vertical="center")
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thin_border = Border(
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left=Side(style="thin"),
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right=Side(style="thin"),
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top=Side(style="thin"),
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bottom=Side(style="thin"),
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)
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# Collect all metric names
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all_metric_names: list[str] = []
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for result in results:
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for metric in result.metrics:
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if metric.name not in all_metric_names:
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all_metric_names.append(metric.name)
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# Collect all input keys
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input_keys: list[str] = []
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for item in input_list:
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for key in item.inputs:
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if key not in input_keys:
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input_keys.append(key)
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# Include judgment column only when at least one result has judgment conditions evaluated
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has_judgment = any(bool(r.judgment.condition_results) for r in results)
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# Build headers
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judgment_headers = ["judgment"] if has_judgment else []
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headers = (
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["index"] + input_keys + ["expected_output", "actual_output"] + all_metric_names + judgment_headers + ["error"]
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)
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# Write header row
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for col_idx, header in enumerate(headers, start=1):
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cell = ws.cell(row=1, column=col_idx, value=header)
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cell.font = header_font
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cell.fill = header_fill
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cell.alignment = header_alignment
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cell.border = thin_border
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# Set column widths
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ws.column_dimensions["A"].width = 10
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for col_idx in range(2, len(headers) + 1):
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ws.column_dimensions[get_column_letter(col_idx)].width = 25
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# Build result lookup
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result_by_index = {r.index: r for r in results}
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# Write data rows
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for row_idx, item in enumerate(input_list, start=2):
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result = result_by_index.get(item.index)
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col = 1
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# Index
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ws.cell(row=row_idx, column=col, value=item.index).border = thin_border
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col += 1
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# Input values
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for key in input_keys:
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val = item.inputs.get(key, "")
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ws.cell(row=row_idx, column=col, value=str(val)).border = thin_border
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col += 1
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# Expected output
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ws.cell(row=row_idx, column=col, value=item.expected_output or "").border = thin_border
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col += 1
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# Actual output
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ws.cell(row=row_idx, column=col, value=result.actual_output if result else "").border = thin_border
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col += 1
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# Metric scores
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metric_scores = {m.name: m.value for m in result.metrics} if result else {}
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for metric_name in all_metric_names:
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score = metric_scores.get(metric_name)
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ws.cell(row=row_idx, column=col, value=score if score is not None else "").border = thin_border
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col += 1
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# Judgment result
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if has_judgment:
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if result and result.judgment.condition_results:
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judgment_value = "Pass" if result.judgment.passed else "Fail"
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else:
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judgment_value = ""
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ws.cell(row=row_idx, column=col, value=judgment_value).border = thin_border
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col += 1
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# Error
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ws.cell(row=row_idx, column=col, value=result.error if result else "").border = thin_border
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output = io.BytesIO()
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wb.save(output)
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output.seek(0)
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return output.getvalue()
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def _store_result_file(
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tenant_id: str,
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run_id: str,
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xlsx_content: bytes,
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session: Any,
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) -> str | None:
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"""Store result XLSX file and return the UploadFile ID."""
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try:
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from extensions.ext_storage import storage
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from libs.uuid_utils import uuidv7
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filename = f"evaluation-result-{run_id[:8]}.xlsx"
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storage_key = f"evaluation_results/{tenant_id}/{str(uuidv7())}.xlsx"
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storage.save(storage_key, xlsx_content)
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upload_file: UploadFile = UploadFile(
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tenant_id=tenant_id,
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storage_type=dify_config.STORAGE_TYPE,
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key=storage_key,
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name=filename,
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size=len(xlsx_content),
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extension="xlsx",
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mime_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
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created_by_role=CreatorUserRole.ACCOUNT,
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created_by="system",
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created_at=naive_utc_now(),
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used=False,
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)
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session.add(upload_file)
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session.commit()
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return upload_file.id
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except Exception:
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logger.exception("Failed to store result file for run %s", run_id)
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return None
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