618 lines
23 KiB
Python
618 lines
23 KiB
Python
"""Pydantic response models for the evaluation console HTTP API."""
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from __future__ import annotations
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from datetime import datetime, timezone
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field, field_validator
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def _utcnow_iso() -> str:
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return datetime.now(timezone.utc).isoformat()
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class RunSummary(BaseModel):
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"""Compact description of a single evaluation run for list views."""
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run_id: str
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scenario_name: str
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mode: str = ""
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judge_model: str = ""
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embedding_model: str = ""
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started_at: str = ""
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finished_at: str = ""
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dataset: str = ""
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total_samples: int = 0
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valid_samples: int = 0
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invalid_samples: int = 0
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metrics: list[str] = Field(default_factory=list)
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metric_means: dict[str, float | None] = Field(default_factory=dict)
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output_path: str = ""
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class GroupStat(BaseModel):
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"""Mean metric values for one slice of samples grouped by a metadata field."""
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key: str
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count: int
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means: dict[str, float | None] = Field(default_factory=dict)
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class DistributionBin(BaseModel):
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"""One histogram bucket of sample counts for a single metric."""
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label: str
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lower: float
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upper: float
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count: int
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class SampleScore(BaseModel):
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"""Per-sample row used for the lowest-score review table."""
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sample_id: str
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question: str = ""
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contexts: list[str] = Field(default_factory=list)
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answer: str = ""
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ground_truth: str = ""
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language: str = ""
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difficulty: str = ""
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question_type: str = ""
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metrics: dict[str, float | None] = Field(default_factory=dict)
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mean_score: float | None = None
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error: str = ""
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class ReportData(BaseModel):
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"""Aggregated report payload rendered by the report detail page."""
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metrics: list[str] = Field(default_factory=list)
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metric_means: dict[str, float | None] = Field(default_factory=dict)
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distributions: dict[str, list[DistributionBin]] = Field(default_factory=dict)
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groupings: dict[str, list[GroupStat]] = Field(default_factory=dict)
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lowest_samples: list[SampleScore] = Field(default_factory=list)
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summary_markdown: str = ""
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advice_markdown: str = "" # optimization_advice.md content (empty if not generated)
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weighted_score_mean: float | None = Field(
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default=None,
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description="加权综合得分均值（metric_weights × doc_weights 共同作用）。",
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)
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metric_weights: dict[str, float] = Field(
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default_factory=dict,
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description="该次运行使用的指标权重配置（来自 scenario.snapshot.yaml）。",
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)
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doc_weights: dict[str, float] = Field(
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default_factory=dict,
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description="该次运行使用的文档权重配置（来自 scenario.snapshot.yaml）。",
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)
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class RunDetail(BaseModel):
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"""Full payload for a single run: summary metadata plus the report."""
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summary: RunSummary
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report: ReportData
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class ScenarioInfo(BaseModel):
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"""One discoverable scenario YAML file that can be evaluated from the UI."""
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path: str
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scenario_name: str = ""
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mode: str = ""
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dataset: str = ""
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judge_model: str = ""
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metrics: list[str] = Field(default_factory=list)
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error: str = ""
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metric_weights: dict[str, float] = Field(
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default_factory=dict,
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description="从场景 YAML 读取的指标权重配置，供前端权重面板预填。",
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)
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doc_weights: dict[str, float] = Field(
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default_factory=dict,
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description="从场景 YAML 读取的文档权重配置，供前端权重面板预填。",
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)
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class TaskStatus(BaseModel):
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"""State of a background evaluation task tracked by the task manager."""
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task_id: str
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scenario_path: str
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status: str
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logs: list[str] = Field(default_factory=list)
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run_id: str | None = None
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error: str | None = None
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created_at: str = ""
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finished_at: str = ""
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class TriggerEvaluationRequest(BaseModel):
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"""Request body for launching an evaluation run from the UI."""
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scenario_path: str
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class TriggerEvaluationResponse(BaseModel):
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"""Response returned immediately after queuing an evaluation task."""
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task_id: str
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class LLMProfile(BaseModel):
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"""A named LLM connection configuration that can be reused across tasks."""
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profile_id: str
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name: str
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model: str
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base_url: str
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api_key: str
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timeout_seconds: int = 30
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created_at: str = Field(default_factory=_utcnow_iso)
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updated_at: str = Field(default_factory=_utcnow_iso)
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class CreateProfileRequest(BaseModel):
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"""Request body for creating or updating an LLM profile."""
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name: str
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model: str
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base_url: str
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api_key: str
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timeout_seconds: int = 30
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class ProfileApplyRequest(BaseModel):
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"""Request body to patch LLM profile selections into a scenario YAML."""
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scenario_path: str
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judge_profile_id: str | None = None
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answer_profile_id: str | None = None
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dataset_profile_id: str | None = None
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metric_weights: dict[str, float] | None = Field(
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default=None,
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description="指标权重映射，如 {\"faithfulness\": 0.35}。为 null 时不修改 YAML。",
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)
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doc_weights: dict[str, float] | None = Field(
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default=None,
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description="文档权重映射，如 {\"doc.pdf\": 2.0}。为 null 时不修改 YAML。",
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)
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class ProfileApplyResponse(BaseModel):
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"""Response after patching a scenario YAML with profile settings."""
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scenario_path: str
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patched_fields: list[str] = Field(default_factory=list)
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class ProfileProbeRequest(BaseModel):
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"""Inline credentials for testing LLM connectivity without saving a profile."""
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model: str
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base_url: str
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api_key: str
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timeout_seconds: int = 30
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class ProfileTestResponse(BaseModel):
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"""Result of a LLM connectivity test."""
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ok: bool
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message: str
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latency_ms: int | None = None
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def jsonable(value: Any) -> Any:
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"""Convert NaN/inf floats into None so the payload stays valid JSON."""
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import math
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if isinstance(value, float):
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if math.isnan(value) or math.isinf(value):
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return None
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return value
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if isinstance(value, dict):
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return {key: jsonable(item) for key, item in value.items()}
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if isinstance(value, list):
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return [jsonable(item) for item in value]
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return value
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# ---------------------------------------------------------------------------
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# Full pipeline (build + eval) job models
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# ---------------------------------------------------------------------------
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class PipelineJobRequest(BaseModel):
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"""Request body for launching an end-to-end build + evaluation pipeline job."""
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model_config = ConfigDict(
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json_schema_extra={
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"examples": [
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{
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"summary": "西门子 CT 文档评估（完整参数）",
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"value": {
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"docs_path": "datasets/siemens-pdfs",
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"job_name": "siemens-ct-eval-2026",
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"generation_model": "qwen3.6-plus",
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"answer_model": "deepseek-v4-flash",
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"judge_model": "deepseek-v4-flash",
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"embedding_model": "text-embedding-v3",
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"max_questions_per_document": 10,
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"max_source_chunks_per_question": 3,
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"max_documents": None,
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"max_samples": None,
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"metrics": [
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"faithfulness",
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"answer_relevancy",
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"context_recall",
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"context_precision",
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],
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"optimization_advisor": False,
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"failure_mode": "skip",
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},
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},
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{
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"summary": "快速冒烟测试（仅 2 份文档、5 道题）",
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"value": {
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"docs_path": "datasets/siemens-pdfs",
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"job_name": "smoke-test",
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"generation_model": "qwen3.6-plus",
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"answer_model": "deepseek-v4-flash",
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"judge_model": "deepseek-v4-flash",
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"embedding_model": "text-embedding-v3",
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"max_questions_per_document": 5,
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"max_source_chunks_per_question": 3,
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"max_documents": 2,
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"max_samples": 10,
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"metrics": ["faithfulness", "answer_relevancy"],
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"optimization_advisor": False,
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"failure_mode": "skip",
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},
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},
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]
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}
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)
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docs_path: str = Field(
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description="PDF 文档所在文件夹的绝对路径或相对于仓库根目录的相对路径。"
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)
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job_name: str = Field(
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default="",
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description="任务显示名称；留空时系统自动生成唯一标识。",
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)
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generation_model: str = Field(
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default="qwen3.6-plus",
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description="用于从文档片段生成草稿题库的 LLM 模型名称。",
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)
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answer_model: str = Field(
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default="deepseek-v4-flash",
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description="在线评估时调用的答题 LLM 模型名称（siemens_pdf_qa adapter）。",
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)
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judge_model: str = Field(
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default="deepseek-v4-flash",
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description="RAGAS 指标评分时使用的 Judge LLM 模型名称。",
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)
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embedding_model: str = Field(
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default="text-embedding-v3",
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description="RAGAS context-recall / context-precision 使用的 Embedding 模型名称。",
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)
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max_questions_per_document: int = Field(
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default=10, gt=0,
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description="每份 PDF 文档最多生成的草稿题目数量。",
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)
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max_source_chunks_per_question: int = Field(
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default=3, gt=0,
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description="每道题目最多引用的文档片段（source chunk）数量。",
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)
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max_documents: int | None = Field(
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default=None, gt=0,
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description="限制处理的 PDF 文件数量上限（冒烟测试时使用）。",
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)
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max_samples: int | None = Field(
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default=None, gt=0,
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description="限制评估的题目数量上限（冒烟测试时使用）。",
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)
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metrics: list[str] = Field(
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default_factory=lambda: [
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"faithfulness",
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"answer_relevancy",
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"context_recall",
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"context_precision",
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],
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description=(
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"需要计算的 RAGAS 指标列表。"
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"可选值：faithfulness, answer_relevancy, context_recall, "
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"context_precision, noise_sensitivity, factual_correctness, semantic_similarity。"
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),
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)
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optimization_advisor: bool = Field(
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default=False,
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description="为 True 时启用 RAGAS 优化建议模块，生成 optimization_advice.md。",
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)
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failure_mode: str = Field(
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default="skip",
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description="PDF 解析失败时的处理策略：skip（跳过继续）或 fail（立即中止）。",
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)
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class PipelineResult(BaseModel):
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"""Artifact locations and statistics for a completed pipeline run."""
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build_artifact_dir: str = Field(description="题库生成阶段的产物根目录路径。")
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dataset_csv: str = Field(description="生成的草稿题库 CSV 文件路径（评估输入）。")
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source_chunks_jsonl: str = Field(description="文档片段索引文件路径（在线评估 adapter 使用）。")
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total_questions: int = Field(description="成功生成的有效题目总数。")
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parse_failures: int = Field(description="文档解析失败的 PDF 数量。")
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eval_run_id: str = Field(description="RAGAS 评估运行 ID。")
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eval_output_dir: str = Field(description="RAGAS 评估产物根目录路径。")
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scores_csv: str = Field(description="每道题目逐项评分的 CSV 文件路径。")
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summary_md: str = Field(description="评估结果摘要 Markdown 文件路径。")
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class PipelineJobStatus(BaseModel):
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"""State of one end-to-end pipeline job."""
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job_id: str = Field(description="任务唯一标识符。")
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job_name: str = Field(description="任务显示名称。")
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status: str = Field(description="任务状态：queued | running | completed | failed。")
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phase: str = Field(default="idle", description="当前执行阶段：idle | parsing_documents | generating_questions | evaluating | done。")
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logs: list[str] = Field(default_factory=list, description="实时日志行列表。")
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result: PipelineResult | None = Field(default=None, description="任务完成后填充的产物路径与统计信息。")
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error: str | None = Field(default=None, description="失败时的错误信息。")
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created_at: str = Field(default="", description="任务创建时间（ISO 8601 UTC）。")
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finished_at: str = Field(default="", description="任务结束时间（ISO 8601 UTC）。")
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class PipelineJobResponse(BaseModel):
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"""Immediate response returned after a pipeline job is queued."""
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job_id: str = Field(description="任务唯一标识符，用于后续轮询状态。")
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job_name: str = Field(description="任务显示名称。")
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status: str = Field(default="queued", description="初始状态，通常为 queued。")
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# ---------------------------------------------------------------------------
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# Dify 实时评分 API 模型
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# ---------------------------------------------------------------------------
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# 需要 ground_truth 才能计算的指标集合
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_GT_DEPENDENT_METRICS: frozenset[str] = frozenset({
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"context_recall",
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"factual_correctness",
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"semantic_similarity",
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"noise_sensitivity",
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})
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# 需要 contexts 才能计算的指标集合
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_CONTEXT_DEPENDENT_METRICS: frozenset[str] = frozenset({
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"faithfulness",
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"context_recall",
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"context_precision",
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"noise_sensitivity",
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})
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# 所有合法指标名称
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_VALID_METRICS: frozenset[str] = frozenset({
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"faithfulness",
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"answer_relevancy",
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"context_recall",
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"context_precision",
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"noise_sensitivity",
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"factual_correctness",
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"semantic_similarity",
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})
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_DEFAULT_SCORE_METRICS: list[str] = [
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"faithfulness",
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"answer_relevancy",
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"context_recall",
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"context_precision",
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]
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class ScoreRequest(BaseModel):
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"""Request body for the real-time single-sample scoring endpoint."""
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"question": "双源CT的时间分辨率是多少?",
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"answer": "双源CT的单扇区时间分辨率为75ms。",
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"contexts": "双源CT采用两套管-探测器系统 |||| 单扇区采集旋转135度",
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"ground_truth": "双源CT单扇区时间分辨率为75ms，需旋转135度。",
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"context_separator": " |||| ",
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"metrics": [
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"faithfulness",
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"answer_relevancy",
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"context_recall",
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"context_precision",
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],
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"judge_model": "gpt-5",
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"embedding_model": "text-embedding-3-small",
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}
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}
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)
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question: str = Field(description="问题文本。")
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answer: str = Field(description="待评分的回答。")
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contexts: str | None = Field(
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default=None,
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description="检索上下文字符串，多段之间用 context_separator 拼接。缺失时自动跳过依赖检索内容的指标（faithfulness、context_recall、context_precision、noise_sensitivity）。",
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)
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ground_truth: str | None = Field(
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default=None,
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description="标准参考答案（可选）。缺失时自动跳过需要它的指标。",
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)
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context_separator: str = Field(
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default=" |||| ",
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description="contexts 字段中段落分隔符，默认为四个竖线两侧各一空格。",
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)
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metrics: list[str] = Field(
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default_factory=lambda: list(_DEFAULT_SCORE_METRICS),
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description="需要计算的 RAGAS 指标列表。",
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)
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judge_model: str | None = Field(
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default=None,
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description="Judge LLM 模型名称；为 null 时使用 .env 中的 RAGAS_JUDGE_MODEL。",
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)
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embedding_model: str | None = Field(
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default=None,
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description="Embedding 模型名称；为 null 时使用 .env 中的 RAGAS_EMBEDDING_MODEL。",
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)
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@field_validator("metrics")
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@classmethod
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def validate_metric_names(cls, value: list[str]) -> list[str]:
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"""Reject any metric name not in the supported registry."""
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invalid = [metric_name for metric_name in value if metric_name not in _VALID_METRICS]
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if invalid:
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raise ValueError(
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f"不支持的指标名称：{invalid}。"
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f"合法值：{sorted(_VALID_METRICS)}"
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)
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if not value:
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raise ValueError("metrics 不能为空列表。")
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return value
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def contexts_as_list(self) -> list[str]:
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"""Split the contexts string into a list of non-empty fragments.
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Returns an empty list when contexts is None or blank.
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"""
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if not self.contexts:
|
||
return []
|
||
separator = self.context_separator or " |||| "
|
||
return [part.strip() for part in self.contexts.split(separator) if part.strip()]
|
||
|
||
def effective_metrics(self) -> list[str]:
|
||
"""Return metrics filtered to exclude GT-dependent or context-dependent ones when inputs are absent."""
|
||
result = list(self.metrics)
|
||
if self.ground_truth is None:
|
||
result = [m for m in result if m not in _GT_DEPENDENT_METRICS]
|
||
if not self.contexts:
|
||
result = [m for m in result if m not in _CONTEXT_DEPENDENT_METRICS]
|
||
return result
|
||
|
||
|
||
class ScoreResponse(BaseModel):
|
||
"""Response payload for the real-time scoring endpoint."""
|
||
|
||
scores: dict[str, float | None] = Field(
|
||
description="各指标得分（NaN 或计算失败时为 null）。"
|
||
)
|
||
weighted_score: float | None = Field(
|
||
default=None,
|
||
description="等权加权综合得分（仅对非 null 指标求均值）。",
|
||
)
|
||
latency_ms: int = Field(description="服务端打分耗时（毫秒）。")
|
||
skipped_metrics: list[str] = Field(
|
||
default_factory=list,
|
||
description="因缺少 ground_truth 而跳过的指标名称列表。",
|
||
)
|
||
error: str | None = Field(
|
||
default=None,
|
||
description="打分异常时的错误信息（HTTP 200 仍返回，scores 为空）。",
|
||
)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 异步评分记录模型
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class AsyncScoreJobResponse(BaseModel):
|
||
"""Immediate 202 response after submitting an async score job."""
|
||
|
||
job_id: str = Field(description="任务唯一标识符，用于后续查询结果。")
|
||
status: str = Field(default="queued", description="初始状态：queued。")
|
||
run_id: str | None = Field(
|
||
default=None,
|
||
description="评分完成后写入的 Run ID，可在「运行列表」中查看完整报告。",
|
||
)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Session async 评分模型
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class SessionScoreRequest(ScoreRequest):
|
||
"""Request body for session-grouped async scoring.
|
||
|
||
All calls sharing the same session_id are accumulated into one report.
|
||
Each call adds a new sample row to the session's scores.csv.
|
||
"""
|
||
|
||
model_config = ConfigDict(
|
||
json_schema_extra={
|
||
"examples": [
|
||
{
|
||
"summary": "Dify 会话批量评分",
|
||
"value": {
|
||
"session_id": "dify-session-001",
|
||
"question": "单源CT与双源CT在球管配置上有何本质区别？",
|
||
"answer": "单源CT只有一套球管-探测器系统，双源CT有两套独立的球管-探测器系统。",
|
||
"contexts": "双源CT采用两套管-探测器系统 |||| 单源CT只有一个球管",
|
||
"context_separator": " |||| ",
|
||
"metrics": ["answer_relevancy", "faithfulness"],
|
||
"judge_model": "gpt-5.5",
|
||
"embedding_model": "text-embedding-3-small",
|
||
},
|
||
}
|
||
]
|
||
}
|
||
)
|
||
|
||
session_id: str = Field(
|
||
description=(
|
||
"会话唯一标识符。相同 session_id 的多次调用合并为同一报告，"
|
||
"每次调用新增一个样本行，指标均值和优化建议在每次调用后增量更新。"
|
||
),
|
||
)
|
||
|
||
|
||
class SessionScoreJobResponse(BaseModel):
|
||
"""Immediate 202 response after submitting a session scoring call."""
|
||
|
||
job_id: str = Field(description="本次调用的任务唯一标识符。")
|
||
session_id: str = Field(description="会话标识符。")
|
||
run_id: str = Field(description="本 session 对应的报告 Run ID，可在「运行列表」中查看。")
|
||
status: str = Field(default="queued", description="初始状态：queued。")
|
||
call_count: int = Field(default=1, description="本 session 当前累计调用次数（包含本次）。")
|
||
|
||
|
||
class SessionStatus(BaseModel):
|
||
"""Aggregate status and metrics for a scoring session."""
|
||
|
||
session_id: str = Field(description="会话标识符。")
|
||
run_id: str = Field(description="对应报告目录的 Run ID。")
|
||
call_count: int = Field(description="本 session 累计调用次数。")
|
||
metric_means: dict[str, float | None] = Field(
|
||
default_factory=dict, description="所有已累积样本的各指标均值。"
|
||
)
|
||
latest_finished_at: str = Field(default="", description="最近一次评分完成时间（ISO 8601 UTC）。")
|
||
jobs: list[AsyncScoreJobStatus] = Field(
|
||
default_factory=list, description="本 session 所有调用记录，按创建时间排序。"
|
||
)
|
||
|
||
|
||
class AsyncScoreJobStatus(BaseModel):
|
||
"""State of one async score job (queued → running → completed/failed)."""
|
||
|
||
job_id: str = Field(description="任务唯一标识符。")
|
||
status: str = Field(description="queued | running | completed | failed")
|
||
created_at: str = Field(default="", description="创建时间（ISO 8601 UTC）。")
|
||
finished_at: str = Field(default="", description="完成时间（ISO 8601 UTC）。")
|
||
run_id: str | None = Field(
|
||
default=None,
|
||
description="完成后对应的 Run ID，可通过 GET /api/runs/{run_id} 查看完整报告。",
|
||
)
|
||
request_summary: dict = Field(
|
||
default_factory=dict,
|
||
description="请求参数快照（question 前80字、metrics、judge_model 等）。",
|
||
)
|
||
scores: dict[str, float | None] = Field(default_factory=dict, description="各指标得分。")
|
||
weighted_score: float | None = Field(default=None, description="加权综合得分。")
|
||
latency_ms: int = Field(default=0, description="评分耗时毫秒。")
|
||
skipped_metrics: list[str] = Field(default_factory=list)
|
||
error: str | None = Field(default=None)
|