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evaluation runtime

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This commit is contained in:
jyong 2026-03-11 17:31:11 +08:00
parent 2bd48e62a3
commit a83a28bf70
4 changed files with 477 additions and 84 deletions
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7
api/core/evaluation/base_evaluation_instance.py
View File

@ -5,6 +5,7 @@ from collections.abc import Mapping
from typing import Any
from core.evaluation.entities.evaluation_entity import (
DefaultMetric,
EvaluationCategory,
EvaluationItemInput,
EvaluationItemResult,
@ -21,7 +22,7 @@ class BaseEvaluationInstance(ABC):
def evaluate_llm(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
@ -33,7 +34,7 @@ class BaseEvaluationInstance(ABC):
def evaluate_retrieval(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
@ -45,7 +46,7 @@ class BaseEvaluationInstance(ABC):
def evaluate_agent(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,

44
api/core/evaluation/frameworks/ragas/ragas_evaluator.py
View File

@ -14,8 +14,8 @@ from core.evaluation.frameworks.ragas.ragas_model_wrapper import DifyModelWrappe
logger = logging.getLogger(__name__)
# Metric name mappings per category
LLM_METRICS = ["faithfulness", "answer_relevancy", "answer_correctness", "answer_similarity"]
RETRIEVAL_METRICS = ["context_precision", "context_recall", "context_relevancy"]
LLM_METRICS = ["faithfulness", "answer_relevancy", "answer_correctness", "semantic_similarity"]
RETRIEVAL_METRICS = ["context_precision", "context_recall", "context_relevance"]
AGENT_METRICS = ["tool_call_accuracy", "answer_correctness"]
WORKFLOW_METRICS = ["faithfulness", "answer_correctness"]
@ -42,51 +42,51 @@ class RagasEvaluator(BaseEvaluationInstance):
def evaluate_llm(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
) -> list[EvaluationItemResult]:
return self._evaluate(items, default_metrics, model_provider, model_name, tenant_id, EvaluationCategory.LLM)
return self._evaluate(items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.LLM)
def evaluate_retrieval(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
) -> list[EvaluationItemResult]:
return self._evaluate(
items, default_metrics, model_provider, model_name, tenant_id, EvaluationCategory.RETRIEVAL
items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.RETRIEVAL
)
def evaluate_agent(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
) -> list[EvaluationItemResult]:
return self._evaluate(items, default_metrics, model_provider, model_name, tenant_id, EvaluationCategory.AGENT)
return self._evaluate(items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.AGENT)
def evaluate_workflow(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
) -> list[EvaluationItemResult]:
return self._evaluate(
items, default_metrics, model_provider, model_name, tenant_id, EvaluationCategory.WORKFLOW
items, metric_name, model_provider, model_name, tenant_id, EvaluationCategory.WORKFLOW
)
def _evaluate(
self,
items: list[EvaluationItemInput],
default_metrics: str,
metric_name: str,
model_provider: str,
model_name: str,
tenant_id: str,
@ -98,10 +98,10 @@ class RagasEvaluator(BaseEvaluationInstance):
string similarity if RAGAS import fails.
"""
model_wrapper = DifyModelWrapper(model_provider, model_name, tenant_id)
# Extract metric names from default_metrics string.
# Extract metric names from metric_name string.
requested_metrics = (
[default_metrics]
if default_metrics
[metric_name]
if metric_name
else self.get_supported_metrics(category)
)
@ -159,7 +159,7 @@ class RagasEvaluator(BaseEvaluationInstance):
if metric_name in result_df.columns:
score = result_df.iloc[i][metric_name]
if score is not None and not (isinstance(score, float) and score != score): # NaN check
metrics.append(EvaluationMetric(name=metric_name, score=float(score)))
metrics.append(EvaluationMetric(name=metric_name, value=float(score)))
results.append(EvaluationItemResult(index=item.index, metrics=metrics))
return results
except Exception:
@ -181,7 +181,7 @@ class RagasEvaluator(BaseEvaluationInstance):
for metric_name in requested_metrics:
try:
score = self._judge_with_llm(model_wrapper, metric_name, query, item)
metrics.append(EvaluationMetric(name=metric_name, score=score))
metrics.append(EvaluationMetric(name=metric_name, value=score))
except Exception:
logger.exception("Failed to compute metric %s for item %d", metric_name, item.index)
@ -246,24 +246,26 @@ class RagasEvaluator(BaseEvaluationInstance):
def _build_ragas_metrics(requested_metrics: list[str]) -> list[Any]:
"""Build RAGAS metric instances from metric names."""
try:
from ragas.metrics import (
from ragas.metrics.collections import (
AnswerCorrectness,
AnswerRelevancy,
AnswerSimilarity,
ContextPrecision,
ContextRecall,
ContextRelevancy,
ContextRelevance,
Faithfulness,
SemanticSimilarity,
ToolCallAccuracy,
)
metric_map: dict[str, Any] = {
"faithfulness": Faithfulness,
"answer_relevancy": AnswerRelevancy,
"answer_correctness": AnswerCorrectness,
"answer_similarity": AnswerSimilarity,
"semantic_similarity": SemanticSimilarity,
"context_precision": ContextPrecision,
"context_recall": ContextRecall,
"context_relevancy": ContextRelevancy,
"context_relevance": ContextRelevance,
"tool_call_accuracy": ToolCallAccuracy,
}
metrics = []

5
api/core/evaluation/runners/llm_evaluation_runner.py
View File

@ -13,7 +13,6 @@ from core.evaluation.entities.evaluation_entity import (
)
from core.evaluation.runners.base_evaluation_runner import BaseEvaluationRunner
from core.workflow.node_events import NodeRunResult
from models.model import App, AppMode
logger = logging.getLogger(__name__)
@ -38,9 +37,11 @@ class LLMEvaluationRunner(BaseEvaluationRunner):
# Merge actual_output into items for evaluation
if not node_run_result_list:
return []
if not default_metric:
raise ValueError("Default metric is required for LLM evaluation")
merged_items = self._merge_results_into_items(node_run_result_list)
return self.evaluation_instance.evaluate_llm(
merged_items, default_metrics, model_provider, model_name, tenant_id
merged_items, default_metric.metric, model_provider, model_name, tenant_id
)
@staticmethod

505
api/uv.lock generated
View File

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@ -1524,6 +1567,9 @@ dev = [
{ name = "types-tqdm" },
{ name = "types-ujson" },
]
evaluation = [
{ name = "ragas" },
]
storage = [
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{ name = "bce-python-sdk" },
@ -1724,6 +1770,7 @@ dev = [
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{ name = "types-ujson", specifier = ">=5.10.0" },
]
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