会员积分改版V1

video-gen-api/app/services/llm.py

@@ -9,6 +9,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.config import settings
 from app.models.model_config import ModelConfig
 from app.services.operation_log_service import build_exception_detail, log_ai_model_event
+from app.services.llm_billing.context import LlmProviderPostprocessError
 from app.utils.id_gen import generate_id
 
 
@@ -74,66 +75,52 @@ async def optimize_prompt(
     log_owner_type: str | None = None,
     log_owner_id: str | None = None,
     generation_attempt_no: int | None = None,
+    fixed_model_config_id: str | None = None,
+    fixed_model_snapshot: dict | None = None,
 ) -> tuple[str, dict]:
     """Optimize user prompt using LLM. Returns (optimized_text, token_usage_dict)."""
 
-    result = await db.execute(
-        select(ModelConfig)
-        .where(ModelConfig.is_active == True, ModelConfig.deleted_at.is_(None))
-        .order_by(ModelConfig.priority.desc())
+    stmt = select(ModelConfig).where(
+        ModelConfig.is_active.is_(True),
+        ModelConfig.deleted_at.is_(None),
+        ModelConfig.provider != "mock",
+    )
+    if fixed_model_config_id:
+        stmt = stmt.where(ModelConfig.id == fixed_model_config_id)
+    stmt = stmt.order_by(ModelConfig.priority.desc(), ModelConfig.id.asc()).limit(1)
+    result = await db.execute(stmt)
+    item = result.scalar_one_or_none()
+    if item is None:
+        raise LLMProviderCallError("没有可用的固定LLM模型，本版本禁止Mock、自动切换和降级")
+    snapshot = dict(fixed_model_snapshot or {})
+    selected = SimpleNamespace(
+        id=item.id,
+        name=snapshot.get("name") or item.name,
+        provider=snapshot.get("provider") or item.provider,
+        api_base=snapshot.get("api_base") or item.api_base,
+        api_key=item.api_key,
+        model_name=snapshot.get("model_name") or item.model_name,
+        max_tokens=snapshot.get("max_tokens") if snapshot.get("max_tokens") is not None else item.max_tokens,
+        temperature=snapshot.get("temperature") if snapshot.get("temperature") is not None else item.temperature,
     )
-    configs = [
-        SimpleNamespace(
-            id=item.id,
-            name=item.name,
-            provider=item.provider,
-            api_base=item.api_base,
-            api_key=item.api_key,
-            model_name=item.model_name,
-            max_tokens=item.max_tokens,
-            temperature=item.temperature,
-        )
-        for item in result.scalars().all()
-    ]
-    # Release the read transaction before the external LLM request. Only
-    # plain scalar snapshots are used afterwards, so expire_on_commit does
-    # not trigger an ORM refresh while the provider request is in flight.
     await db.commit()
-
-    if configs:
-        # 按 priority 从大到小依次尝试，跳过 mock，失败则用下一个
-        for selected in configs:
-            if selected.provider == "mock":
-                continue
-            if selected.provider in ("openai_compatible", "sdk"):
-                try:
-                    return await _call_openai_compatible(
-                        selected, original_prompt, db, user_id, industry_key, duration,
-                        references=references,
-                        gen_type=gen_type,
-                        image_size=image_size,
-                        image_proportion=image_proportion,
-                        image_px=image_px,
-                        log_module=log_module,
-                        log_step=log_step,
-                        log_project_id=log_project_id,
-                        log_task_id=log_task_id,
-                        log_owner_type=log_owner_type,
-                        log_owner_id=log_owner_id,
-                        generation_attempt_no=generation_attempt_no,
-                    )
-                except LLMProviderCallError:
-                    continue
-
-        # 所有真实模型都失败，降级到 mock
-        mock_cfg = next((c for c in configs if c.provider == "mock"), None)
-        if mock_cfg:
-            return _mock_optimize(original_prompt, gen_type)
-
-    if settings.LLM_MOCK:
-        return _mock_optimize(original_prompt, gen_type)
-
-    return _get_default_prompt(original_prompt, gen_type)
+    if selected.provider not in ("openai_compatible", "sdk"):
+        raise LLMProviderCallError(f"固定模型供应商不受支持：{selected.provider}")
+    return await _call_openai_compatible(
+        selected, original_prompt, db, user_id, industry_key, duration,
+        references=references,
+        gen_type=gen_type,
+        image_size=image_size,
+        image_proportion=image_proportion,
+        image_px=image_px,
+        log_module=log_module,
+        log_step=log_step,
+        log_project_id=log_project_id,
+        log_task_id=log_task_id,
+        log_owner_type=log_owner_type,
+        log_owner_id=log_owner_id,
+        generation_attempt_no=generation_attempt_no,
+    )
 
 
 def _mock_optimize(prompt: str, gen_type: str = "video") -> tuple[str, dict]:
@@ -435,19 +422,28 @@ async def _call_openai_compatible(
             )
             raise LLMProviderCallError(f"{type(exc).__name__}: {exc}") from exc
 
+        raw_usage = data.get("usage") if isinstance(data, dict) else None
+        usage_reported = bool(
+            isinstance(raw_usage, dict)
+            and any(key in raw_usage for key in ("prompt_tokens", "completion_tokens", "total_tokens"))
+        )
+        usage = raw_usage if isinstance(raw_usage, dict) else {}
+        input_tokens = int(usage.get("prompt_tokens", 0) or 0)
+        output_tokens = int(usage.get("completion_tokens", 0) or 0)
+        total_tokens = int(usage.get("total_tokens", input_tokens + output_tokens) or 0)
+        token_usage = {
+            "model_config_id": config.id,
+            "model_config_name": config.name,
+            "model_provider": config.provider,
+            "model_name": config.model_name,
+            "source_module": log_module,
+            "source_step_code": log_step,
+            "input_tokens": input_tokens,
+            "output_tokens": output_tokens,
+            "total_tokens": total_tokens,
+            "usage_reported": usage_reported,
+        }
         try:
-            raw_usage = data.get("usage")
-            usage_reported = bool(
-                isinstance(raw_usage, dict)
-                and any(
-                    key in raw_usage
-                    for key in ("prompt_tokens", "completion_tokens", "total_tokens")
-                )
-            )
-            usage = raw_usage if isinstance(raw_usage, dict) else {}
-            input_tokens = int(usage.get("prompt_tokens", 0) or 0)
-            output_tokens = int(usage.get("completion_tokens", 0) or 0)
-            total_tokens = int(usage.get("total_tokens", input_tokens + output_tokens) or 0)
             content = data["choices"][0]["message"]["content"].strip()
             if not content:
                 raise ValueError("模型未返回有效提示词")
@@ -461,18 +457,5 @@ async def _call_openai_compatible(
                 error=str(exc),
                 **common_log,
             )
-            raise LLMProviderCallError(f"模型响应解析失败: {exc}") from exc
-
-        token_usage = {
-            "model_config_id": config.id,
-            "model_config_name": config.name,
-            "model_provider": config.provider,
-            "model_name": config.model_name,
-            "source_module": log_module,
-            "source_step_code": log_step,
-            "input_tokens": input_tokens,
-            "output_tokens": output_tokens,
-            "total_tokens": total_tokens,
-            "usage_reported": usage_reported,
-        }
+            raise LlmProviderPostprocessError(f"模型响应解析失败: {exc}", usage=token_usage) from exc
         return content, token_usage