feat: add versioned backend scoring workflow

backend/wisegrade/llm.py

@@ -0,0 +1,112 @@
+import base64
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+import httpx
+from pydantic import BaseModel, Field, ValidationError, model_validator
+
+from .config import get_settings
+
+
+class EvidenceResult(BaseModel):
+    chunk_id: int
+    page: int = Field(ge=1)
+    quote: str = Field(min_length=1)
+
+
+class ScoreResult(BaseModel):
+    criterion_id: str
+    score: float = Field(ge=0)
+    max_score: float = Field(gt=0)
+    reason: str = Field(min_length=1)
+    evidence: list[EvidenceResult]
+    missing_evidence: list[str] = Field(default_factory=list)
+    suggestion: str = ""
+    confidence: float = Field(ge=0, le=1)
+
+    @model_validator(mode="after")
+    def valid_range(self):
+        if self.score > self.max_score or (self.score > 0 and not self.evidence):
+            raise ValueError("score exceeds maximum or lacks evidence")
+        return self
+
+
+class FactResult(BaseModel):
+    student_no: str = ""
+    name: str = ""
+    topic: str = ""
+    direction: str = ""
+    goals: list[str] = Field(default_factory=list)
+    metrics: list[str] = Field(default_factory=list)
+    hardware: list[str] = Field(default_factory=list)
+    software: list[str] = Field(default_factory=list)
+    experiments: list[str] = Field(default_factory=list)
+    data: list[str] = Field(default_factory=list)
+    schedule: list[str] = Field(default_factory=list)
+    cost: list[str] = Field(default_factory=list)
+    conclusions: list[str] = Field(default_factory=list)
+
+
+class ConsistencyIssue(BaseModel):
+    description: str
+    chunk_ids: list[int] = Field(min_length=2)
+
+
+class ConsistencyResult(BaseModel):
+    issues: list[ConsistencyIssue] = Field(default_factory=list)
+
+
+class ModelError(RuntimeError):
+    pass
+
+
+class DeepSeekClient:
+    def __init__(self, *, base_url=None, model=None, api_key=None, timeout=None, retries=None, transport=None):
+        settings = get_settings()
+        self.base_url = (base_url or settings.deepseek_base_url).rstrip("/")
+        self.model = model or settings.deepseek_model
+        self.api_key = api_key or settings.deepseek_api_key
+        self.timeout = timeout or settings.model_timeout_seconds
+        self.retries = settings.model_retries if retries is None else retries
+        self.transport = transport
+        if not self.api_key:
+            raise ModelError("未配置DeepSeek API Key")
+
+    def json_call(self, prompt: str, payload: dict[str, Any], schema: type[BaseModel], *, images: list[str] | None = None):
+        content: list[dict] = [{"type": "text", "text": json.dumps(payload, ensure_ascii=False)}]
+        for image in images or []:
+            path = Path(image)
+            if not path.is_file():
+                raise ModelError(f"页面图像不存在: {path.name}")
+            content.append({"type": "image_url", "image_url": {"url": "data:image/png;base64," + base64.b64encode(path.read_bytes()).decode()}})
+        body = {"model": self.model, "response_format": {"type": "json_object"},
+                "messages": [{"role": "system", "content": prompt + " 严格输出一个JSON对象。"}, {"role": "user", "content": content}],
+                "temperature": 0}
+        last_error = None
+        for attempt in range(self.retries + 1):
+            try:
+                with httpx.Client(timeout=self.timeout, transport=self.transport) as client:
+                    response = client.post(self.base_url + "/chat/completions", json=body,
+                                           headers={"Authorization": f"Bearer {self.api_key}"})
+                response.raise_for_status()
+                raw = response.json()["choices"][0]["message"]["content"]
+                return schema.model_validate(json.loads(raw))
+            except (httpx.HTTPError, ValueError, KeyError, IndexError, ValidationError) as exc:
+                # Never include response bodies or headers: providers sometimes echo credentials.
+                last_error = type(exc).__name__
+                if attempt < self.retries:
+                    time.sleep(min(2 ** attempt, 5))
+        raise ModelError(f"模型请求或JSON校验失败（{last_error}），已重试{self.retries}次")
+
+
+def validate_score(result: ScoreResult, criterion: dict, chunks: list[dict]):
+    if result.criterion_id != criterion["criterion_id"] or result.max_score != criterion["max_score"]:
+        raise ModelError("模型评分项或满分与规则版本不一致")
+    by_id = {chunk["chunk_id"]: chunk for chunk in chunks}
+    for evidence in result.evidence:
+        chunk = by_id.get(evidence.chunk_id)
+        if not chunk or evidence.page != chunk["page"] or evidence.quote not in chunk["text"]:
+            raise ModelError("模型证据未能匹配检索原文")
+    return result