373 lines
13 KiB
Python
373 lines
13 KiB
Python
"""Pydantic response models for the evaluation console HTTP API."""
|
||
|
||
from __future__ import annotations
|
||
|
||
from datetime import datetime, timezone
|
||
from typing import Any
|
||
|
||
from pydantic import BaseModel, ConfigDict, Field
|
||
|
||
|
||
def _utcnow_iso() -> str:
|
||
return datetime.now(timezone.utc).isoformat()
|
||
|
||
|
||
class RunSummary(BaseModel):
|
||
"""Compact description of a single evaluation run for list views."""
|
||
|
||
run_id: str
|
||
scenario_name: str
|
||
mode: str = ""
|
||
judge_model: str = ""
|
||
embedding_model: str = ""
|
||
started_at: str = ""
|
||
finished_at: str = ""
|
||
dataset: str = ""
|
||
total_samples: int = 0
|
||
valid_samples: int = 0
|
||
invalid_samples: int = 0
|
||
metrics: list[str] = Field(default_factory=list)
|
||
metric_means: dict[str, float | None] = Field(default_factory=dict)
|
||
output_path: str = ""
|
||
|
||
|
||
class GroupStat(BaseModel):
|
||
"""Mean metric values for one slice of samples grouped by a metadata field."""
|
||
|
||
key: str
|
||
count: int
|
||
means: dict[str, float | None] = Field(default_factory=dict)
|
||
|
||
|
||
class DistributionBin(BaseModel):
|
||
"""One histogram bucket of sample counts for a single metric."""
|
||
|
||
label: str
|
||
lower: float
|
||
upper: float
|
||
count: int
|
||
|
||
|
||
class SampleScore(BaseModel):
|
||
"""Per-sample row used for the lowest-score review table."""
|
||
|
||
sample_id: str
|
||
question: str = ""
|
||
contexts: list[str] = Field(default_factory=list)
|
||
answer: str = ""
|
||
ground_truth: str = ""
|
||
language: str = ""
|
||
difficulty: str = ""
|
||
question_type: str = ""
|
||
metrics: dict[str, float | None] = Field(default_factory=dict)
|
||
mean_score: float | None = None
|
||
error: str = ""
|
||
|
||
|
||
class ReportData(BaseModel):
|
||
"""Aggregated report payload rendered by the report detail page."""
|
||
|
||
metrics: list[str] = Field(default_factory=list)
|
||
metric_means: dict[str, float | None] = Field(default_factory=dict)
|
||
distributions: dict[str, list[DistributionBin]] = Field(default_factory=dict)
|
||
groupings: dict[str, list[GroupStat]] = Field(default_factory=dict)
|
||
lowest_samples: list[SampleScore] = Field(default_factory=list)
|
||
summary_markdown: str = ""
|
||
advice_markdown: str = "" # optimization_advice.md content (empty if not generated)
|
||
weighted_score_mean: float | None = Field(
|
||
default=None,
|
||
description="加权综合得分均值（metric_weights × doc_weights 共同作用）。",
|
||
)
|
||
metric_weights: dict[str, float] = Field(
|
||
default_factory=dict,
|
||
description="该次运行使用的指标权重配置（来自 scenario.snapshot.yaml）。",
|
||
)
|
||
doc_weights: dict[str, float] = Field(
|
||
default_factory=dict,
|
||
description="该次运行使用的文档权重配置（来自 scenario.snapshot.yaml）。",
|
||
)
|
||
|
||
|
||
class RunDetail(BaseModel):
|
||
"""Full payload for a single run: summary metadata plus the report."""
|
||
|
||
summary: RunSummary
|
||
report: ReportData
|
||
|
||
|
||
class ScenarioInfo(BaseModel):
|
||
"""One discoverable scenario YAML file that can be evaluated from the UI."""
|
||
|
||
path: str
|
||
scenario_name: str = ""
|
||
mode: str = ""
|
||
dataset: str = ""
|
||
judge_model: str = ""
|
||
metrics: list[str] = Field(default_factory=list)
|
||
error: str = ""
|
||
metric_weights: dict[str, float] = Field(
|
||
default_factory=dict,
|
||
description="从场景 YAML 读取的指标权重配置，供前端权重面板预填。",
|
||
)
|
||
doc_weights: dict[str, float] = Field(
|
||
default_factory=dict,
|
||
description="从场景 YAML 读取的文档权重配置，供前端权重面板预填。",
|
||
)
|
||
|
||
|
||
class TaskStatus(BaseModel):
|
||
"""State of a background evaluation task tracked by the task manager."""
|
||
|
||
task_id: str
|
||
scenario_path: str
|
||
status: str
|
||
logs: list[str] = Field(default_factory=list)
|
||
run_id: str | None = None
|
||
error: str | None = None
|
||
created_at: str = ""
|
||
finished_at: str = ""
|
||
|
||
|
||
class TriggerEvaluationRequest(BaseModel):
|
||
"""Request body for launching an evaluation run from the UI."""
|
||
|
||
scenario_path: str
|
||
|
||
|
||
class TriggerEvaluationResponse(BaseModel):
|
||
"""Response returned immediately after queuing an evaluation task."""
|
||
|
||
task_id: str
|
||
|
||
|
||
class LLMProfile(BaseModel):
|
||
"""A named LLM connection configuration that can be reused across tasks."""
|
||
|
||
profile_id: str
|
||
name: str
|
||
model: str
|
||
base_url: str
|
||
api_key: str
|
||
timeout_seconds: int = 30
|
||
created_at: str = Field(default_factory=_utcnow_iso)
|
||
updated_at: str = Field(default_factory=_utcnow_iso)
|
||
|
||
|
||
class CreateProfileRequest(BaseModel):
|
||
"""Request body for creating or updating an LLM profile."""
|
||
|
||
name: str
|
||
model: str
|
||
base_url: str
|
||
api_key: str
|
||
timeout_seconds: int = 30
|
||
|
||
|
||
class ProfileApplyRequest(BaseModel):
|
||
"""Request body to patch LLM profile selections into a scenario YAML."""
|
||
|
||
scenario_path: str
|
||
judge_profile_id: str | None = None
|
||
answer_profile_id: str | None = None
|
||
dataset_profile_id: str | None = None
|
||
metric_weights: dict[str, float] | None = Field(
|
||
default=None,
|
||
description="指标权重映射，如 {\"faithfulness\": 0.35}。为 null 时不修改 YAML。",
|
||
)
|
||
doc_weights: dict[str, float] | None = Field(
|
||
default=None,
|
||
description="文档权重映射，如 {\"doc.pdf\": 2.0}。为 null 时不修改 YAML。",
|
||
)
|
||
|
||
|
||
class ProfileApplyResponse(BaseModel):
|
||
"""Response after patching a scenario YAML with profile settings."""
|
||
|
||
scenario_path: str
|
||
patched_fields: list[str] = Field(default_factory=list)
|
||
|
||
|
||
class ProfileProbeRequest(BaseModel):
|
||
"""Inline credentials for testing LLM connectivity without saving a profile."""
|
||
|
||
model: str
|
||
base_url: str
|
||
api_key: str
|
||
timeout_seconds: int = 30
|
||
|
||
|
||
class ProfileTestResponse(BaseModel):
|
||
"""Result of a LLM connectivity test."""
|
||
|
||
ok: bool
|
||
message: str
|
||
latency_ms: int | None = None
|
||
|
||
|
||
def jsonable(value: Any) -> Any:
|
||
"""Convert NaN/inf floats into None so the payload stays valid JSON."""
|
||
import math
|
||
|
||
if isinstance(value, float):
|
||
if math.isnan(value) or math.isinf(value):
|
||
return None
|
||
return value
|
||
if isinstance(value, dict):
|
||
return {key: jsonable(item) for key, item in value.items()}
|
||
if isinstance(value, list):
|
||
return [jsonable(item) for item in value]
|
||
return value
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Full pipeline (build + eval) job models
|
||
# ---------------------------------------------------------------------------
|
||
|
||
class PipelineJobRequest(BaseModel):
|
||
"""Request body for launching an end-to-end build + evaluation pipeline job."""
|
||
|
||
model_config = ConfigDict(
|
||
json_schema_extra={
|
||
"examples": [
|
||
{
|
||
"summary": "西门子 CT 文档评估（完整参数）",
|
||
"value": {
|
||
"docs_path": "datasets/siemens-pdfs",
|
||
"job_name": "siemens-ct-eval-2026",
|
||
"generation_model": "qwen3.6-plus",
|
||
"answer_model": "deepseek-v4-flash",
|
||
"judge_model": "deepseek-v4-flash",
|
||
"embedding_model": "text-embedding-v3",
|
||
"max_questions_per_document": 10,
|
||
"max_source_chunks_per_question": 3,
|
||
"max_documents": None,
|
||
"max_samples": None,
|
||
"metrics": [
|
||
"faithfulness",
|
||
"answer_relevancy",
|
||
"context_recall",
|
||
"context_precision",
|
||
],
|
||
"optimization_advisor": False,
|
||
"failure_mode": "skip",
|
||
},
|
||
},
|
||
{
|
||
"summary": "快速冒烟测试（仅 2 份文档、5 道题）",
|
||
"value": {
|
||
"docs_path": "datasets/siemens-pdfs",
|
||
"job_name": "smoke-test",
|
||
"generation_model": "qwen3.6-plus",
|
||
"answer_model": "deepseek-v4-flash",
|
||
"judge_model": "deepseek-v4-flash",
|
||
"embedding_model": "text-embedding-v3",
|
||
"max_questions_per_document": 5,
|
||
"max_source_chunks_per_question": 3,
|
||
"max_documents": 2,
|
||
"max_samples": 10,
|
||
"metrics": ["faithfulness", "answer_relevancy"],
|
||
"optimization_advisor": False,
|
||
"failure_mode": "skip",
|
||
},
|
||
},
|
||
]
|
||
}
|
||
)
|
||
|
||
docs_path: str = Field(
|
||
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。"
|
||
)
|
||
job_name: str = Field(
|
||
default="",
|
||
description="任务显示名称；留空时系统自动生成唯一标识。",
|
||
)
|
||
generation_model: str = Field(
|
||
default="qwen3.6-plus",
|
||
description="用于从文档片段生成草稿题库的 LLM 模型名称。",
|
||
)
|
||
answer_model: str = Field(
|
||
default="deepseek-v4-flash",
|
||
description="在线评估时调用的答题 LLM 模型名称（siemens_pdf_qa adapter）。",
|
||
)
|
||
judge_model: str = Field(
|
||
default="deepseek-v4-flash",
|
||
description="RAGAS 指标评分时使用的 Judge LLM 模型名称。",
|
||
)
|
||
embedding_model: str = Field(
|
||
default="text-embedding-v3",
|
||
description="RAGAS context-recall / context-precision 使用的 Embedding 模型名称。",
|
||
)
|
||
max_questions_per_document: int = Field(
|
||
default=10, gt=0,
|
||
description="每份 PDF 文档最多生成的草稿题目数量。",
|
||
)
|
||
max_source_chunks_per_question: int = Field(
|
||
default=3, gt=0,
|
||
description="每道题目最多引用的文档片段（source chunk）数量。",
|
||
)
|
||
max_documents: int | None = Field(
|
||
default=None, gt=0,
|
||
description="限制处理的 PDF 文件数量上限（冒烟测试时使用）。",
|
||
)
|
||
max_samples: int | None = Field(
|
||
default=None, gt=0,
|
||
description="限制评估的题目数量上限（冒烟测试时使用）。",
|
||
)
|
||
metrics: list[str] = Field(
|
||
default_factory=lambda: [
|
||
"faithfulness",
|
||
"answer_relevancy",
|
||
"context_recall",
|
||
"context_precision",
|
||
],
|
||
description=(
|
||
"需要计算的 RAGAS 指标列表。"
|
||
"可选值：faithfulness, answer_relevancy, context_recall, "
|
||
"context_precision, noise_sensitivity, factual_correctness, semantic_similarity。"
|
||
),
|
||
)
|
||
optimization_advisor: bool = Field(
|
||
default=False,
|
||
description="为 True 时启用 RAGAS 优化建议模块，生成 optimization_advice.md。",
|
||
)
|
||
failure_mode: str = Field(
|
||
default="skip",
|
||
description="PDF 解析失败时的处理策略：skip（跳过继续）或 fail（立即中止）。",
|
||
)
|
||
|
||
|
||
class PipelineResult(BaseModel):
|
||
"""Artifact locations and statistics for a completed pipeline run."""
|
||
|
||
build_artifact_dir: str = Field(description="题库生成阶段的产物根目录路径。")
|
||
dataset_csv: str = Field(description="生成的草稿题库 CSV 文件路径（评估输入）。")
|
||
source_chunks_jsonl: str = Field(description="文档片段索引文件路径（在线评估 adapter 使用）。")
|
||
total_questions: int = Field(description="成功生成的有效题目总数。")
|
||
parse_failures: int = Field(description="文档解析失败的 PDF 数量。")
|
||
eval_run_id: str = Field(description="RAGAS 评估运行 ID。")
|
||
eval_output_dir: str = Field(description="RAGAS 评估产物根目录路径。")
|
||
scores_csv: str = Field(description="每道题目逐项评分的 CSV 文件路径。")
|
||
summary_md: str = Field(description="评估结果摘要 Markdown 文件路径。")
|
||
|
||
|
||
class PipelineJobStatus(BaseModel):
|
||
"""State of one end-to-end pipeline job."""
|
||
|
||
job_id: str = Field(description="任务唯一标识符。")
|
||
job_name: str = Field(description="任务显示名称。")
|
||
status: str = Field(description="任务状态：queued | running | completed | failed。")
|
||
phase: str = Field(default="idle", description="当前执行阶段：idle | parsing_documents | generating_questions | evaluating | done。")
|
||
logs: list[str] = Field(default_factory=list, description="实时日志行列表。")
|
||
result: PipelineResult | None = Field(default=None, description="任务完成后填充的产物路径与统计信息。")
|
||
error: str | None = Field(default=None, description="失败时的错误信息。")
|
||
created_at: str = Field(default="", description="任务创建时间（ISO 8601 UTC）。")
|
||
finished_at: str = Field(default="", description="任务结束时间（ISO 8601 UTC）。")
|
||
|
||
|
||
class PipelineJobResponse(BaseModel):
|
||
"""Immediate response returned after a pipeline job is queued."""
|
||
|
||
job_id: str = Field(description="任务唯一标识符，用于后续轮询状态。")
|
||
job_name: str = Field(description="任务显示名称。")
|
||
status: str = Field(default="queued", description="初始状态，通常为 queued。")
|