Initial lightweight AI agent admin

backend/app/services/llm.py

@@ -0,0 +1,69 @@
+import json
+from collections.abc import AsyncGenerator
+
+import httpx
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models import Agent, LLMModel, LLMProvider
+
+
+class LLMService:
+    def __init__(self, db: AsyncSession):
+        self.db = db
+
+    async def complete(self, agent: Agent, user_message: str, history: list[dict] | None = None) -> str:
+        if not agent.model_id:
+            return self._fallback_answer(agent, user_message)
+        model = await self.db.get(LLMModel, agent.model_id)
+        if not model:
+            return self._fallback_answer(agent, user_message)
+        provider = await self.db.get(LLMProvider, model.provider_id)
+        if not provider or provider.status != "enabled" or not provider.api_key or not provider.base_url:
+            return self._fallback_answer(agent, user_message)
+
+        messages = [{"role": "system", "content": agent.system_prompt or f"你是 {agent.name}。"}]
+        messages.extend(history or [])
+        messages.append({"role": "user", "content": user_message})
+        url = provider.base_url.rstrip("/") + "/chat/completions"
+        payload = {
+            "model": model.name,
+            "messages": messages,
+            "temperature": agent.temperature,
+            "max_tokens": agent.max_tokens,
+            "stream": False,
+        }
+        async with httpx.AsyncClient(timeout=60) as client:
+            response = await client.post(
+                url,
+                headers={"Authorization": f"Bearer {provider.api_key}", "Content-Type": "application/json"},
+                json=payload,
+            )
+            response.raise_for_status()
+            data = response.json()
+            return data.get("choices", [{}])[0].get("message", {}).get("content") or ""
+
+    async def stream(self, agent: Agent, user_message: str, history: list[dict] | None = None) -> AsyncGenerator[str, None]:
+        answer = await self.complete(agent, user_message, history)
+        for chunk in self._chunk_text(answer):
+            yield chunk
+
+    def _fallback_answer(self, agent: Agent, user_message: str) -> str:
+        prompt = agent.system_prompt.strip() or "轻量 AI Agent"
+        return (
+            f"{agent.name} 已收到任务：{user_message}\n\n"
+            f"当前使用本地占位响应。配置可用的 LLM Provider 和 Model 后，将自动调用真实模型。\n\n"
+            f"系统提示词摘要：{prompt[:160]}"
+        )
+
+    def _chunk_text(self, text: str) -> list[str]:
+        if not text:
+            return [""]
+        return [text[i : i + 24] for i in range(0, len(text), 24)]
+
+
+def sse(data: dict | str) -> str:
+    if isinstance(data, str):
+        payload = data
+    else:
+        payload = json.dumps(data, ensure_ascii=False, default=str)
+    return f"data: {payload}\n\n"