feat: 足球 LLM 预测服务初始提交

Profeto — 给 LLM 提供数据,让 LLM 预测足球比分。

核心模块:
- FastAPI 后端 + PostgreSQL (SQLAlchemy async)
- 多 Agent LLM 预测 (5 专家 + 终裁)
- 数据采集 (bzzoiro / understat / injuries)
- React 前端 (Vite + Tailwind)

包含:
- 数据源抽象 (DataSource 协议 + 注册表)
- Alembic 数据库迁移
- Prompt 模板 (单/多 Agent)
- 核心路径单元测试

src/llm/provider.py

@@ -0,0 +1,114 @@
+"""多提供商 LLM 抽象(OpenAI-compatible 接口)。
+
+支持: OpenAI / Deepseek / Ollama / 任何 OpenAI-compatible 网关。
+"""
+from __future__ import annotations
+
+import json
+import logging
+import time
+from dataclasses import dataclass, field
+from typing import Any
+
+from src.core.config import settings
+from src.core.http_client import get_client
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class LLMResponse:
+    content: str
+    parsed: dict | None = None
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
+    latency_ms: int | None = None
+    raw: dict | None = None
+    error: str | None = None
+
+
+@dataclass
+class LLMProvider:
+    """OpenAI-compatible async provider。"""
+
+    api_key: str = ""
+    base_url: str = "https://api.openai.com/v1"
+    model: str = "gpt-4o"
+    timeout: int = 60
+    extra_headers: dict = field(default_factory=dict)
+
+    async def chat(
+        self,
+        system: str,
+        user: str,
+        *,
+        json_mode: bool = True,
+        temperature: float = 0.3,
+        max_tokens: int = 1000,
+    ) -> LLMResponse:
+        """发请求,返回结构化响应。"""
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+            **self.extra_headers,
+        }
+        payload: dict[str, Any] = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system},
+                {"role": "user", "content": user},
+            ],
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+        }
+        if json_mode:
+            payload["response_format"] = {"type": "json_object"}
+
+        start = time.perf_counter()
+        try:
+            client = get_client()
+            resp = await client.post(
+                f"{self.base_url}/chat/completions",
+                headers=headers,
+                json=payload,
+                timeout=self.timeout,
+            )
+            resp.raise_for_status()
+            data = resp.json()
+            latency = int((time.perf_counter() - start) * 1000)
+            usage = data.get("usage", {})
+            content = data["choices"][0]["message"]["content"]
+            parsed = None
+            if json_mode:
+                try:
+                    parsed = json.loads(content)
+                except json.JSONDecodeError:
+                    # 尝试从代码块提取
+                    import re
+                    m = re.search(r"```(?:json)?\s*([\s\S]*?)\s*```", content)
+                    if m:
+                        try:
+                            parsed = json.loads(m.group(1))
+                        except json.JSONDecodeError:
+                            pass
+            return LLMResponse(
+                content=content,
+                parsed=parsed,
+                prompt_tokens=usage.get("prompt_tokens"),
+                completion_tokens=usage.get("completion_tokens"),
+                latency_ms=latency,
+                raw=data,
+            )
+        except Exception as e:
+            latency = int((time.perf_counter() - start) * 1000)
+            logger.error("LLM request failed: %s", e)
+            return LLMResponse(content="", error=str(e), latency_ms=latency)
+
+
+def get_default_provider() -> LLMProvider:
+    return LLMProvider(
+        api_key=settings.LLM_API_KEY,
+        base_url=settings.LLM_BASE_URL,
+        model=settings.LLM_MODEL,
+        timeout=settings.LLM_TIMEOUT,
+    )