mirror of
https://github.com/langgenius/dify.git
synced 2026-05-02 16:38:04 +08:00
Merge remote-tracking branch 'origin/feat/evaluation' into feat/evaluation
This commit is contained in:
FFXN
@ -17,3 +17,8 @@ class BaseEvaluationConfig(BaseModel):
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class RagasConfig(BaseEvaluationConfig):
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"""RAGAS-specific configuration."""
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pass
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class DeepEvalConfig(BaseEvaluationConfig):
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"""DeepEval-specific configuration."""
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pass
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1
api/core/evaluation/frameworks/deepeval/__init__.py
Normal file
1
api/core/evaluation/frameworks/deepeval/__init__.py
Normal file
@ -0,0 +1 @@
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298
api/core/evaluation/frameworks/deepeval/deepeval_evaluator.py
Normal file
298
api/core/evaluation/frameworks/deepeval/deepeval_evaluator.py
Normal file
@ -0,0 +1,298 @@
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import logging
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from typing import Any
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from core.evaluation.base_evaluation_instance import BaseEvaluationInstance
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from core.evaluation.entities.config_entity import DeepEvalConfig
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from core.evaluation.entities.evaluation_entity import (
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EvaluationCategory,
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EvaluationItemInput,
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EvaluationItemResult,
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EvaluationMetric,
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)
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from core.evaluation.frameworks.ragas.ragas_model_wrapper import DifyModelWrapper
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logger = logging.getLogger(__name__)
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# Metric name mappings per category
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#
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# deepeval metric field requirements (LLMTestCase fields):
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# - faithfulness: input, actual_output, retrieval_context
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# - answer_relevancy: input, actual_output
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# - contextual_precision: input, actual_output, expected_output, retrieval_context
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# - contextual_recall: input, actual_output, expected_output, retrieval_context
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# - contextual_relevancy: input, actual_output, retrieval_context
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# - hallucination: input, actual_output, context
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# - tool_correctness: input, actual_output, expected_tools
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# - task_completion: input, actual_output
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LLM_METRICS = ["faithfulness", "answer_relevancy"]
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RETRIEVAL_METRICS = ["contextual_precision", "contextual_recall", "contextual_relevancy"]
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AGENT_METRICS = ["tool_correctness", "task_completion"]
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WORKFLOW_METRICS = ["faithfulness", "answer_relevancy"]
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class DeepEvalEvaluator(BaseEvaluationInstance):
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"""DeepEval framework adapter for evaluation."""
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def __init__(self, config: DeepEvalConfig):
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self.config = config
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def get_supported_metrics(self, category: EvaluationCategory) -> list[str]:
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match category:
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case EvaluationCategory.LLM:
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return LLM_METRICS
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case EvaluationCategory.RETRIEVAL:
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return RETRIEVAL_METRICS
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case EvaluationCategory.AGENT:
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return AGENT_METRICS
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case EvaluationCategory.WORKFLOW:
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return WORKFLOW_METRICS
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case _:
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return []
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def evaluate_llm(
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self,
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items: list[EvaluationItemInput],
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metric_name: str,
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model_provider: str,
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model_name: str,
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tenant_id: str,
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) -> list[EvaluationItemResult]:
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return self._evaluate(items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.LLM)
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def evaluate_retrieval(
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self,
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items: list[EvaluationItemInput],
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metric_name: str,
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model_provider: str,
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model_name: str,
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tenant_id: str,
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) -> list[EvaluationItemResult]:
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return self._evaluate(
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items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.RETRIEVAL
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)
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def evaluate_agent(
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self,
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items: list[EvaluationItemInput],
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metric_name: str,
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model_provider: str,
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model_name: str,
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tenant_id: str,
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) -> list[EvaluationItemResult]:
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return self._evaluate(
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items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.AGENT
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)
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def evaluate_workflow(
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self,
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items: list[EvaluationItemInput],
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metric_name: str,
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model_provider: str,
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model_name: str,
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tenant_id: str,
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) -> list[EvaluationItemResult]:
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return self._evaluate(
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items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.WORKFLOW
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)
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def _evaluate(
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self,
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items: list[EvaluationItemInput],
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metric_name: str,
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model_provider: str,
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model_name: str,
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tenant_id: str,
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category: EvaluationCategory,
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) -> list[EvaluationItemResult]:
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"""Core evaluation logic using DeepEval."""
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model_wrapper = DifyModelWrapper(model_provider, model_name, tenant_id)
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requested_metrics = (
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[metric_name]
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if metric_name
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else self.get_supported_metrics(category)
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)
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try:
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return self._evaluate_with_deepeval(items, requested_metrics, category)
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except ImportError:
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logger.warning("DeepEval not installed, falling back to simple evaluation")
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return self._evaluate_simple(items, requested_metrics, model_wrapper)
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def _evaluate_with_deepeval(
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self,
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items: list[EvaluationItemInput],
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requested_metrics: list[str],
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category: EvaluationCategory,
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) -> list[EvaluationItemResult]:
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"""Evaluate using DeepEval library.
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Builds LLMTestCase differently per category:
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- LLM/Workflow: input=prompt, actual_output=output, retrieval_context=context
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- Retrieval: input=query, actual_output=output, expected_output, retrieval_context=context
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- Agent: input=query, actual_output=output
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"""
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deepeval_metrics = _build_deepeval_metrics(requested_metrics)
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if not deepeval_metrics:
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logger.warning("No valid DeepEval metrics found for: %s", requested_metrics)
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return [EvaluationItemResult(index=item.index) for item in items]
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results: list[EvaluationItemResult] = []
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for item in items:
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test_case = self._build_test_case(item, category)
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metrics: list[EvaluationMetric] = []
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for metric in deepeval_metrics:
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try:
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metric.measure(test_case)
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if metric.score is not None:
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metrics.append(
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EvaluationMetric(name=metric.__class__.__name__, value=float(metric.score))
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)
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except Exception:
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logger.exception(
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"Failed to compute metric %s for item %d",
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metric.__class__.__name__,
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item.index,
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)
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results.append(EvaluationItemResult(index=item.index, metrics=metrics))
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return results
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@staticmethod
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def _build_test_case(item: EvaluationItemInput, category: EvaluationCategory) -> Any:
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"""Build a deepeval LLMTestCase with the correct fields per category."""
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from deepeval.test_case import LLMTestCase
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user_input = _format_input(item.inputs, category)
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match category:
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case EvaluationCategory.LLM | EvaluationCategory.WORKFLOW:
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# faithfulness needs: input, actual_output, retrieval_context
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# answer_relevancy needs: input, actual_output
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return LLMTestCase(
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input=user_input,
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actual_output=item.output,
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expected_output=item.expected_output or None,
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retrieval_context=item.context or None,
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)
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case EvaluationCategory.RETRIEVAL:
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# contextual_precision/recall needs: input, actual_output, expected_output, retrieval_context
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return LLMTestCase(
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input=user_input,
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actual_output=item.output or "",
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expected_output=item.expected_output or "",
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retrieval_context=item.context or [],
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)
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case _:
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return LLMTestCase(
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input=user_input,
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actual_output=item.output,
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)
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def _evaluate_simple(
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self,
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items: list[EvaluationItemInput],
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requested_metrics: list[str],
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model_wrapper: DifyModelWrapper,
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) -> list[EvaluationItemResult]:
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"""Simple LLM-as-judge fallback when DeepEval is not available."""
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results: list[EvaluationItemResult] = []
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for item in items:
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metrics: list[EvaluationMetric] = []
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for m_name in requested_metrics:
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try:
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score = self._judge_with_llm(model_wrapper, m_name, item)
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metrics.append(EvaluationMetric(name=m_name, value=score))
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except Exception:
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logger.exception("Failed to compute metric %s for item %d", m_name, item.index)
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results.append(EvaluationItemResult(index=item.index, metrics=metrics))
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return results
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def _judge_with_llm(
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self,
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model_wrapper: DifyModelWrapper,
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metric_name: str,
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item: EvaluationItemInput,
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) -> float:
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"""Use the LLM to judge a single metric for a single item."""
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prompt = self._build_judge_prompt(metric_name, item)
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response = model_wrapper.invoke(prompt)
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return self._parse_score(response)
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@staticmethod
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def _build_judge_prompt(metric_name: str, item: EvaluationItemInput) -> str:
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"""Build a scoring prompt for the LLM judge."""
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parts = [
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f"Evaluate the following on the metric '{metric_name}' using a scale of 0.0 to 1.0.",
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f"\nInput: {item.inputs}",
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f"\nOutput: {item.output}",
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]
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if item.expected_output:
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parts.append(f"\nExpected Output: {item.expected_output}")
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if item.context:
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parts.append(f"\nContext: {'; '.join(item.context)}")
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parts.append(
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"\nRespond with ONLY a single floating point number between 0.0 and 1.0, nothing else."
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)
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return "\n".join(parts)
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||||
@staticmethod
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||||
def _parse_score(response: str) -> float:
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"""Parse a float score from LLM response."""
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||||
import re
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||||
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cleaned = response.strip()
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try:
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score = float(cleaned)
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return max(0.0, min(1.0, score))
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||||
except ValueError:
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||||
match = re.search(r"(\d+\.?\d*)", cleaned)
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if match:
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score = float(match.group(1))
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return max(0.0, min(1.0, score))
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return 0.0
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def _format_input(inputs: dict[str, Any], category: EvaluationCategory) -> str:
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"""Extract the user-facing input string from the inputs dict."""
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||||
match category:
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||||
case EvaluationCategory.LLM | EvaluationCategory.WORKFLOW:
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||||
return str(inputs.get("prompt", ""))
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||||
case EvaluationCategory.RETRIEVAL:
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||||
return str(inputs.get("query", ""))
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||||
case _:
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||||
return str(next(iter(inputs.values()), "")) if inputs else ""
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||||
|
||||
|
||||
def _build_deepeval_metrics(requested_metrics: list[str]) -> list[Any]:
|
||||
"""Build DeepEval metric instances from metric names."""
|
||||
try:
|
||||
from deepeval.metrics import (
|
||||
AnswerRelevancyMetric,
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||||
ContextualPrecisionMetric,
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||||
ContextualRecallMetric,
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||||
ContextualRelevancyMetric,
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||||
FaithfulnessMetric,
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||||
TaskCompletionMetric,
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||||
ToolCorrectnessMetric,
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||||
)
|
||||
|
||||
metric_map: dict[str, Any] = {
|
||||
"faithfulness": FaithfulnessMetric,
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||||
"answer_relevancy": AnswerRelevancyMetric,
|
||||
"contextual_precision": ContextualPrecisionMetric,
|
||||
"contextual_recall": ContextualRecallMetric,
|
||||
"contextual_relevancy": ContextualRelevancyMetric,
|
||||
"tool_correctness": ToolCorrectnessMetric,
|
||||
"task_completion": TaskCompletionMetric,
|
||||
}
|
||||
|
||||
metrics = []
|
||||
for name in requested_metrics:
|
||||
metric_class = metric_map.get(name)
|
||||
if metric_class:
|
||||
metrics.append(metric_class(threshold=0.5))
|
||||
else:
|
||||
logger.warning("Unknown DeepEval metric: %s", name)
|
||||
return metrics
|
||||
except ImportError:
|
||||
logger.warning("DeepEval metrics not available")
|
||||
return []
|
||||
@ -203,7 +203,7 @@ tools = ["cloudscraper~=1.2.71", "nltk~=3.9.1"]
|
||||
# [ Evaluation ] dependency group
|
||||
# Required for evaluation frameworks
|
||||
############################################################
|
||||
evaluation = ["ragas>=0.2.0"]
|
||||
evaluation = ["ragas>=0.2.0", "deepeval>=2.0.0"]
|
||||
|
||||
############################################################
|
||||
# [ VDB ] dependency group
|
||||
|
||||
503
api/uv.lock
generated
503
api/uv.lock
generated
@ -368,6 +368,24 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anthropic"
|
||||
version = "0.49.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "distro" },
|
||||
{ name = "httpx" },
|
||||
{ name = "jiter" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
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sdist = { url = "https://files.pythonhosted.org/packages/86/e3/a88c8494ce4d1a88252b9e053607e885f9b14d0a32273d47b727cbee4228/anthropic-0.49.0.tar.gz", hash = "sha256:c09e885b0f674b9119b4f296d8508907f6cff0009bc20d5cf6b35936c40b4398", size = 210016, upload-time = "2025-02-28T19:35:47.01Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/76/74/5d90ad14d55fbe3f9c474fdcb6e34b4bed99e3be8efac98734a5ddce88c1/anthropic-0.49.0-py3-none-any.whl", hash = "sha256:bbc17ad4e7094988d2fa86b87753ded8dce12498f4b85fe5810f208f454a8375", size = 243368, upload-time = "2025-02-28T19:35:44.963Z" },
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]
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||||
|
||||
[[package]]
|
||||
name = "anyio"
|
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version = "4.11.0"
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@ -512,6 +530,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
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]
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|
||||
[[package]]
|
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name = "backports-tarfile"
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version = "1.2.0"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/86/72/cd9b395f25e290e633655a100af28cb253e4393396264a98bd5f5951d50f/backports_tarfile-1.2.0.tar.gz", hash = "sha256:d75e02c268746e1b8144c278978b6e98e85de6ad16f8e4b0844a154557eca991", size = 86406, upload-time = "2024-05-28T17:01:54.731Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/b9/fa/123043af240e49752f1c4bd24da5053b6bd00cad78c2be53c0d1e8b975bc/backports.tarfile-1.2.0-py3-none-any.whl", hash = "sha256:77e284d754527b01fb1e6fa8a1afe577858ebe4e9dad8919e34c862cb399bc34", size = 30181, upload-time = "2024-05-28T17:01:53.112Z" },
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]
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|
||||
[[package]]
|
||||
name = "basedpyright"
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version = "1.31.7"
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@ -601,6 +628,33 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/b3/cc/38b6f87170908bd8aaf9e412b021d17e85f690abe00edf50192f1a4566b9/billiard-4.2.3-py3-none-any.whl", hash = "sha256:989e9b688e3abf153f307b68a1328dfacfb954e30a4f920005654e276c69236b", size = 87042, upload-time = "2025-11-16T17:47:29.005Z" },
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]
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||||
|
||||
[[package]]
|
||||
name = "black"
|
||||
version = "25.12.0"
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||||
source = { registry = "https://pypi.org/simple" }
|
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dependencies = [
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{ name = "click" },
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{ name = "mypy-extensions" },
|
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{ name = "packaging" },
|
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{ name = "pathspec" },
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{ name = "platformdirs" },
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{ name = "pytokens" },
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]
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/e8/dd/a237e9f565f3617a88b49284b59cbca2a4f56ebe68676c1aad0ce36a54a7/black-25.12.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1a2f578ae20c19c50a382286ba78bfbeafdf788579b053d8e4980afb079ab9be", size = 1712639, upload-time = "2025-12-08T01:52:46.756Z" },
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Reference in New Issue
Block a user