第一次提交

2026-08-01 16:50:55 +08:00
commit c8f9e39039
68 changed files with 6016 additions and 0 deletions
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+"""统一的 OpenAI 兼容 LLM client：文本 + 多模态。
+
+配置来自用户的 UserSettings（base_url / api_key / 模型名）。api_key 在库里
+是 Fernet 加密的，这里解密后使用。
+"""
+import base64
+from dataclasses import dataclass
+
+from openai import AsyncOpenAI
+
+from app.models.user import UserSettings
+from app.services.crypto import decrypt
+
+
+class LLMConfigError(RuntimeError):
+    """用户尚未正确配置 AI 接口。"""
+
+
+@dataclass
+class LLMResult:
+    content: str
+    model: str | None
+    prompt_tokens: int | None
+    completion_tokens: int | None
+
+
+class LLMClient:
+    def __init__(self, settings: UserSettings):
+        if not settings or not settings.llm_base_url or not settings.llm_api_key_encrypted:
+            raise LLMConfigError("请先在设置里配置 AI 接口（base_url 与 api_key）")
+        api_key = decrypt(settings.llm_api_key_encrypted)
+        if not api_key:
+            raise LLMConfigError("api_key 解密失败，请在设置里重新填写")
+        self._client = AsyncOpenAI(base_url=settings.llm_base_url, api_key=api_key)
+        self._text_model = settings.llm_text_model
+        self._vision_model = settings.llm_vision_model or settings.llm_text_model
+
+    async def chat_text(
+        self,
+        system: str,
+        user: str,
+        *,
+        json_mode: bool = False,
+        model: str | None = None,
+    ) -> LLMResult:
+        m = model or self._text_model
+        if not m:
+            raise LLMConfigError("未配置文本模型名")
+        kwargs = {}
+        if json_mode:
+            kwargs["response_format"] = {"type": "json_object"}
+        resp = await self._client.chat.completions.create(
+            model=m,
+            messages=[
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            **kwargs,
+        )
+        return self._to_result(resp, m)
+
+    async def chat_vision(
+        self,
+        system: str,
+        user_text: str,
+        image_bytes: bytes,
+        mime: str = "image/jpeg",
+        *,
+        json_mode: bool = False,
+        model: str | None = None,
+    ) -> LLMResult:
+        m = model or self._vision_model
+        if not m:
+            raise LLMConfigError("未配置多模态模型名")
+        data_url = f"data:{mime};base64,{base64.b64encode(image_bytes).decode()}"
+        kwargs = {}
+        if json_mode:
+            kwargs["response_format"] = {"type": "json_object"}
+        resp = await self._client.chat.completions.create(
+            model=m,
+            messages=[
+                {"role": "system", "content": system},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": user_text},
+                        {"type": "image_url", "image_url": {"url": data_url}},
+                    ],
+                },
+            ],
+            **kwargs,
+        )
+        return self._to_result(resp, m)
+
+    @staticmethod
+    def _to_result(resp, model: str) -> LLMResult:
+        usage = getattr(resp, "usage", None)
+        return LLMResult(
+            content=resp.choices[0].message.content or "",
+            model=model,
+            prompt_tokens=getattr(usage, "prompt_tokens", None),
+            completion_tokens=getattr(usage, "completion_tokens", None),
+        )