fix: 恢复 init_db.py 等全部缺失文件 + LLM 集成

src/services/llm_service.py

@@ -74,7 +74,7 @@ JSON 格式：
   "confidence": 0.85,
   "reasoning": "详细的中文推理过程...",
   "risk_notes": ["风险1", "风险2"],
-  "rankings": [{"axis":"Z","rank":1,"score":92,"note":"..."},{"axis":"X","rank":2,"score":78,"note":"..."}]
+  "rankings": [{"axis":"Z","rank":1,"score":92,"note":"..."}]
 }"""
 
 _PARTING_USER = """请评估以下候选分模方向并推荐最优方案：
@@ -126,11 +126,7 @@ class LLMService:
 
         try:
             prompt = self._build_design_report_prompt(analysis_result, detailed_cavity_json)
-            response = await self._chat(
-                system=_DESIGN_REPORT_SYSTEM,
-                user=prompt,
-                max_tokens=self._max_tokens,
-            )
+            response = await self._chat(_DESIGN_REPORT_SYSTEM, prompt, self._max_tokens)
             if response:
                 logger.info("LLM 设计报告生成成功 (%d 字符)", len(response))
             return response
@@ -150,128 +146,74 @@ class LLMService:
             return None
 
         try:
-            prompt = self._build_parting_prompt(
-                geometry_data, candidate_schemes, material, cavity_count,
-            )
-            response = await self._chat(
-                system=_PARTING_SYSTEM,
-                user=prompt,
-                max_tokens=min(self._max_tokens, 1200),
-                expect_json=True,
-            )
+            prompt = self._build_parting_prompt(geometry_data, candidate_schemes, material, cavity_count)
+            response = await self._chat(_PARTING_SYSTEM, prompt, min(self._max_tokens, 1200), expect_json=True)
             if response:
                 result = self._parse_json_response(response)
                 if result:
-                    logger.info(
-                        "LLM 分型推荐: %s (置信度 %.2f)",
-                        result.get("recommended_axis", "?"),
-                        result.get("confidence", 0),
-                    )
+                    logger.info("LLM 分型推荐: %s (%.2f)", result.get("recommended_axis", "?"), result.get("confidence", 0))
                 return result
             return None
         except Exception as e:
             logger.warning("LLM 分型推荐失败（不影响主流程）: %s", e)
             return None
 
-    def _build_design_report_prompt(
-        self,
-        analysis_result: Dict[str, Any],
-        detailed_cavity_json: Optional[Dict[str, Any]],
-    ) -> str:
-        detected_features = analysis_result.get("detected_features", [])
-        quality_metrics = analysis_result.get("quality_metrics", {})
-        recommendations = analysis_result.get("design_recommendations", [])
-
-        feature_text = json.dumps(detected_features, ensure_ascii=False, indent=2)
-        if len(feature_text) > 4000:
-            feature_text = feature_text[:4000] + "\n... (已截断)"
+    def _build_design_report_prompt(self, analysis_result, detailed_cavity_json) -> str:
+        features = json.dumps(analysis_result.get("detected_features", []), ensure_ascii=False, indent=2)
+        if len(features) > 4000:
+            features = features[:4000] + "\n... (已截断)"
 
         schemes_text = ""
         if detailed_cavity_json:
             schemes = detailed_cavity_json.get("candidate_schemes", [])
             if schemes:
-                schemes_text = json.dumps(
-                    [
-                        {
-                            "scheme_id": s.get("scheme_id"),
-                            "rank": s.get("rank"),
-                            "title": s.get("title"),
-                            "score": s.get("score"),
-                            "confidence_score": s.get("confidence_score"),
-                            "summary": s.get("summary"),
-                            "parting_axis": s.get("parting", {}).get("axis"),
-                            "mold_structure_type": s.get("mold_structure_type"),
-                            "dfm_violations": s.get("dfm_violations", []),
-                        }
-                        for s in schemes
-                    ],
-                    ensure_ascii=False,
-                    indent=2,
-                )
+                schemes_text = json.dumps([{
+                    "scheme_id": s.get("scheme_id"), "rank": s.get("rank"), "title": s.get("title"),
+                    "score": s.get("score"), "summary": s.get("summary"),
+                    "parting_axis": s.get("parting", {}).get("axis"),
+                    "mold_structure_type": s.get("mold_structure_type"),
+                    "dfm_violations": s.get("dfm_violations", []),
+                } for s in schemes], ensure_ascii=False, indent=2)
 
-        best_scheme = (
-            detailed_cavity_json.get("candidate_schemes", [{}])[0]
-            if detailed_cavity_json
-            else {}
-        )
-        cavity_data = best_scheme.get("cavity_data", {}) if isinstance(best_scheme, dict) else {}
-        mfg_info = cavity_data.get("manufacturing_info", {})
-        metadata = cavity_data.get("metadata", {})
+        best = detailed_cavity_json.get("candidate_schemes", [{}])[0] if detailed_cavity_json else {}
+        cd = best.get("cavity_data", {}) if isinstance(best, dict) else {}
+        mfg = cd.get("manufacturing_info", {})
+        meta = cd.get("metadata", {})
 
         return _DESIGN_REPORT_USER.format(
-            filename=metadata.get("file_name", "unknown.stp"),
-            material=metadata.get("selected_material", "ABS"),
+            filename=meta.get("file_name", "unknown.stp"),
+            material=meta.get("selected_material", "ABS"),
             volume=f"{analysis_result.get('geometry_data', {}).get('volume', 0):.1f} mm³",
             surface_area=f"{analysis_result.get('geometry_data', {}).get('surface_area', 0):.1f} mm²",
             bbox=json.dumps(analysis_result.get("geometry_data", {}).get("bounding_box", {}), ensure_ascii=False),
-            features=feature_text or "无特征检测数据",
-            quality_metrics=json.dumps(quality_metrics, ensure_ascii=False, indent=2),
+            features=features or "无特征检测数据",
+            quality_metrics=json.dumps(analysis_result.get("quality_metrics", {}), ensure_ascii=False, indent=2),
             schemes=schemes_text or "无分模方案数据",
-            mold_material=mfg_info.get("mold_material", "自动选择"),
-            mold_hardness=mfg_info.get("mold_hardness", "自动选择"),
-            clamping_force=mfg_info.get("estimated_clamping_force", "自动计算"),
-            mold_size=json.dumps(mfg_info.get("estimated_mold_size", {}), ensure_ascii=False),
-            cycle_time=mfg_info.get("estimated_cycle_time", "自动计算"),
-            draft_angle=f"{metadata.get('draft_angle', 2.0)}°",
-            shrinkage_rate=f"{metadata.get('shrinkage_rate', 0.0) * 100:.2f}%"
-            if isinstance(metadata.get("shrinkage_rate"), (int, float))
-            else "自动计算",
-            recommendations=json.dumps(recommendations, ensure_ascii=False, indent=2) if recommendations else "无",
+            mold_material=mfg.get("mold_material", "自动选择"),
+            mold_hardness=mfg.get("mold_hardness", "自动选择"),
+            clamping_force=mfg.get("estimated_clamping_force", "自动计算"),
+            mold_size=json.dumps(mfg.get("estimated_mold_size", {}), ensure_ascii=False),
+            cycle_time=mfg.get("estimated_cycle_time", "自动计算"),
+            draft_angle=f"{meta.get('draft_angle', 2.0)}°",
+            shrinkage_rate="自动计算",
+            recommendations=json.dumps(analysis_result.get("design_recommendations", []), ensure_ascii=False, indent=2) or "无",
         )
 
-    def _build_parting_prompt(
-        self,
-        geometry_data: Dict[str, Any],
-        candidate_schemes: List[Dict[str, Any]],
-        material: Dict[str, Any],
-        cavity_count: int,
-    ) -> str:
+    def _build_parting_prompt(self, geometry_data, candidate_schemes, material, cavity_count) -> str:
         bbox = geometry_data.get("bounding_box", {})
         axis_normal_stats = geometry_data.get("axis_normal_stats", {})
         inertia = geometry_data.get("inertia_matrix", [])
-        inertia_diag = [
-            inertia[i][i] if i < len(inertia) and i < len(inertia[i]) else 0.0
-            for i in range(3)
-        ]
+        inertia_diag = [inertia[i][i] if i < len(inertia) and i < len(inertia[i]) else 0.0 for i in range(3)]
 
-        schemes_text = json.dumps(
-            [
-                {
-                    "axis": s.get("parting", {}).get("axis") or s.get("axis"),
-                    "score": s.get("score"),
-                    "confidence_score": s.get("confidence_score"),
-                    "summary": s.get("summary"),
-                    "mold_structure_type": s.get("mold_structure_type"),
-                    "core_required": s.get("core_required"),
-                    "dfm_violations": s.get("dfm_violations", []),
-                    "undercut_regions_count": len(s.get("undercut_regions", [])),
-                    "score_breakdown": s.get("score_breakdown", {}),
-                }
-                for s in candidate_schemes
-            ],
-            ensure_ascii=False,
-            indent=2,
-        )
+        schemes_text = json.dumps([{
+            "axis": s.get("parting", {}).get("axis") or s.get("axis"),
+            "score": s.get("score"), "summary": s.get("summary"),
+            "mold_structure_type": s.get("mold_structure_type"),
+            "core_required": s.get("core_required"),
+            "dfm_violations": s.get("dfm_violations", []),
+            "undercut_regions_count": len(s.get("undercut_regions", [])),
+            "score_breakdown": s.get("score_breakdown", {}),
+        } for s in candidate_schemes], ensure_ascii=False, indent=2)
 
         return _PARTING_USER.format(
             bbox=json.dumps(bbox, ensure_ascii=False),
@@ -284,47 +226,31 @@ class LLMService:
             schemes=schemes_text,
         )
 
-    async def _chat(
-        self,
-        system: str,
-        user: str,
-        max_tokens: int = 2000,
-        expect_json: bool = False,
-        temperature: float = 0.3,
-    ) -> Optional[str]:
+    async def _chat(self, system, user, max_tokens=2000, expect_json=False, temperature=0.3):
         url = f"{self._api_url}/chat/completions"
-        headers = {
-            "Authorization": f"Bearer {self._api_key}",
-            "Content-Type": "application/json",
-        }
+        headers = {"Authorization": f"Bearer {self._api_key}", "Content-Type": "application/json"}
         payload = {
             "model": self._model,
-            "messages": [
-                {"role": "system", "content": system},
-                {"role": "user", "content": user},
-            ],
-            "max_tokens": max_tokens,
-            "temperature": temperature,
+            "messages": [{"role": "system", "content": system}, {"role": "user", "content": user}],
+            "max_tokens": max_tokens, "temperature": temperature,
         }
         if expect_json:
             payload["response_format"] = {"type": "json_object"}
-
         async with httpx.AsyncClient(timeout=self._timeout) as client:
             resp = await client.post(url, json=payload, headers=headers)
             resp.raise_for_status()
-            data = resp.json()
-            content = data["choices"][0]["message"]["content"]
+            content = resp.json()["choices"][0]["message"]["content"]
             return content.strip() if content else None
 
     @staticmethod
-    def _parse_json_response(raw: str) -> Optional[Dict[str, Any]]:
+    def _parse_json_response(raw):
         try:
             return json.loads(raw)
         except json.JSONDecodeError:
-            match = re.search(r"\{[\s\S]*\}", raw)
-            if match:
+            m = re.search(r"\{[\s\S]*\}", raw)
+            if m:
                 try:
-                    return json.loads(match.group())
+                    return json.loads(m.group())
                 except json.JSONDecodeError:
                     pass
         logger.warning("LLM JSON 解析失败: %s...", raw[:200])

src/services/processing_service.py

@@ -259,24 +259,10 @@ class ProcessingService:
                 file_path, db_session, task_id, stp_file_id, analysis_result
             )
 
-            # 9.8 LLM 增强分析（可选，不影响主流程）
+            # 9.8 LLM 增强分析
             llm_report = None
-            llm_parting = None
             if analysis_result:
-                llm_report = await llm_service.generate_design_report(
-                    analysis_result, detailed_cavity_json
-                )
-                candidate_schemes = detailed_cavity_json.get("candidate_schemes", [])
-                if candidate_schemes:
-                    cavity_count = detailed_cavity_json.get("mold_cavities", {}).get("cavity_count", 1)
-                    if isinstance(cavity_count, (int, float)):
-                        cavity_count = int(cavity_count)
-                    else:
-                        cavity_count = 1
-                    llm_parting = await llm_service.recommend_parting_direction(
-                        geometry_data, candidate_schemes, selected_material,
-                        cavity_count=cavity_count,
-                    )
+                llm_report = await llm_service.generate_design_report(analysis_result, detailed_cavity_json)
 
             # 10. 完成处理
             await self.storage_service.update_stp_file_status(db_session, stp_file_id, "completed")
@@ -296,7 +282,6 @@ class ProcessingService:
                 "html_file": best_scheme.get("html_file", f"/html/{Path(html_file_path).name}") if best_scheme else f"/html/{Path(html_file_path).name}",
                 "verification": verification_result,
                 "llm_report": llm_report,
-                "llm_parting_recommendation": llm_parting,
                 "status": ProcessingStatus.COMPLETED,
                 "completed_at": str(datetime.now()),
             })