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
This commit is contained in:
shangfangjian
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"""多 agent 预测层。"""
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from src.llm.agents.base import AgentReport, AgentSpec, run_agent
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from src.llm.agents.orchestrator import MultiPredictResult, predict_match_multi
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__all__ = [
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"AgentReport",
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"AgentSpec",
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"run_agent",
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"MultiPredictResult",
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"predict_match_multi",
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]
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"""Agent 基础设施: spec 定义 + 执行器。
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执行语义:
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1. 数据切片为空 / 明确 no_data → 跳过 LLM, 直接返回 stub(省 token 防幻觉)
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2. LLM 调用失败 → fail-open, 报告标记 status=error, 不阻断整体
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3. 解析失败(LLM 没输出合法 JSON) → status=parse_error
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"""
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from __future__ import annotations
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import functools
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import logging
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from dataclasses import dataclass, field
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from pathlib import Path
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from src.llm.context_builder import MatchHeader
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from src.llm.provider import LLMProvider, LLMResponse
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logger = logging.getLogger(__name__)
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_PROMPT_DIR = Path(__file__).resolve().parent.parent / "prompts" / "agents"
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NO_DATA_SENTINELS = ("无数据", "no data", "no_data")
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@functools.lru_cache(maxsize=16)
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def load_agent_prompt(name: str, version: str = "v1") -> str:
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"""缓存加载 agent prompt 模板。"""
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path = _PROMPT_DIR / f"{name}_{version}.md"
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if not path.exists():
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raise FileNotFoundError(f"agent prompt 不存在: {path}")
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with open(path, encoding="utf-8") as f:
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return f.read()
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@dataclass
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class AgentSpec:
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"""领域专家 agent 定义。"""
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name: str # h2h / form / standings / injuries / xg
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system_prompt: str # system message
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slice_fn: object # async (header, before) -> str 切片函数
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@dataclass
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class AgentReport:
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"""专家 agent 统一输出契约。"""
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agent: str
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status: str = "ok" # ok | no_data | error | parse_error
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data_sufficiency: str = "medium" # high | medium | low | none
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analysis: str = ""
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home_edge: float | None = None # -1.0 ~ 1.0, 正=利主队
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confidence: float | None = None # 0.0 ~ 1.0
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key_evidence: list[str] = field(default_factory=list)
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# xg agent 专属
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exp_home_goals: float | None = None
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exp_away_goals: float | None = None
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probable_score: str | None = None
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# 元信息
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model: str = ""
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latency_ms: int | None = None
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prompt_tokens: int | None = None
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completion_tokens: int | None = None
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def to_dict(self) -> dict:
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return {
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"agent": self.agent,
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"status": self.status,
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"data_sufficiency": self.data_sufficiency,
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"analysis": self.analysis,
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"home_edge": self.home_edge,
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"confidence": self.confidence,
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"key_evidence": self.key_evidence,
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"exp_home_goals": self.exp_home_goals,
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"exp_away_goals": self.exp_away_goals,
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"probable_score": self.probable_score,
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"model": self.model,
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"latency_ms": self.latency_ms,
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"prompt_tokens": self.prompt_tokens,
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"completion_tokens": self.completion_tokens,
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}
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def _is_no_data(slice_text: str) -> bool:
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"""切片是否全无数据(除了标题行全是无数据)。"""
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body = [ln.strip() for ln in slice_text.splitlines() if ln.strip()]
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# 去掉标题行(── 开头)
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content = [ln for ln in body if not ln.startswith("──")]
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if not content:
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return True
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return all(any(s in ln for s in NO_DATA_SENTINELS) for ln in content)
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def _stub_no_data(agent: str) -> AgentReport:
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return AgentReport(
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agent=agent,
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status="no_data",
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data_sufficiency="none",
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analysis="该维度无数据,跳过分析。",
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)
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def _parse_report(agent: str, parsed: dict, resp: LLMResponse, model: str) -> AgentReport:
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"""把 LLM JSON 输出解析为 AgentReport,字段宽容处理。"""
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def _f(v, default=None):
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try:
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return float(v) if v is not None else default
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except (TypeError, ValueError):
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return default
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suff = str(parsed.get("data_sufficiency", "medium")).lower()
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if suff not in ("high", "medium", "low", "none"):
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suff = "medium"
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evidence = parsed.get("key_evidence") or []
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if isinstance(evidence, str):
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evidence = [evidence]
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score = parsed.get("probable_score")
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if isinstance(score, dict):
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score = f"{score.get('home', '?')}-{score.get('away', '?')}"
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return AgentReport(
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agent=agent,
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status="ok",
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data_sufficiency=suff,
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analysis=str(parsed.get("analysis", ""))[:600],
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home_edge=_f(parsed.get("home_edge")),
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confidence=_f(parsed.get("confidence")),
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key_evidence=[str(e)[:120] for e in evidence[:5]],
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exp_home_goals=_f(parsed.get("exp_home_goals")),
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exp_away_goals=_f(parsed.get("exp_away_goals")),
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probable_score=score if isinstance(score, str) else None,
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model=model,
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latency_ms=resp.latency_ms,
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prompt_tokens=resp.prompt_tokens,
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completion_tokens=resp.completion_tokens,
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)
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async def run_agent(
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spec: AgentSpec,
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header: MatchHeader,
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provider: LLMProvider,
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*,
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before=None,
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version: str = "v1",
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) -> AgentReport:
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"""执行单个专家 agent: 切片 → no_data 门控 → 调 LLM → 解析报告。"""
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# 1. 数据切片
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try:
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slice_text = await spec.slice_fn(header, before=before)
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except Exception as e:
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logger.exception("agent %s slice failed", spec.name)
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return AgentReport(agent=spec.name, status="error", analysis=f"数据切片失败: {e}")
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# 2. no_data 门控: 切片无数据 → 不调 LLM
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if _is_no_data(slice_text):
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logger.debug("agent %s: slice is no_data, skipping LLM", spec.name)
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return _stub_no_data(spec.name)
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# 3. 拼 prompt(模板中 {{context}} 为切片占位)
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template = load_agent_prompt(spec.name, version)
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user_prompt = template.replace("{{context}}", slice_text)
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# 4. 调 LLM
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resp = await provider.chat(
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system=spec.system_prompt,
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user=user_prompt,
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json_mode=True,
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temperature=0.2,
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max_tokens=600,
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)
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if resp.error:
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logger.warning("agent %s LLM failed: %s", spec.name, resp.error)
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return AgentReport(agent=spec.name, status="error", analysis=f"LLM 调用失败: {resp.error}")
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# 5. 解析
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if not resp.parsed:
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return AgentReport(
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agent=spec.name,
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status="parse_error",
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analysis=f"LLM 输出无法解析为 JSON: {resp.content[:200]}",
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)
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return _parse_report(spec.name, resp.parsed, resp, provider.model)
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"""多 agent 预测编排: 并行专家 → 终裁 → 存库。"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import time
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from dataclasses import dataclass
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from src.core.config import settings
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from src.db.base import AsyncSessionLocal
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from src.db.models import Match, Prediction
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from src.llm.agents.base import AgentReport, AgentSpec, load_agent_prompt
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from src.llm.context_builder import (
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MatchHeader,
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form_slice,
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h2h_slice,
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header_text,
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home_away_slice,
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injuries_slice,
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load_match_header,
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stats_slice,
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)
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from src.llm.provider import LLMProvider, get_default_provider
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logger = logging.getLogger(__name__)
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# ── 5 个专家 agent 定义 ──
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# A=近期状态 B=攻防数据 C=主客因素 D=阵容完整性 E=历史交锋
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SPECIALIST_SPECS: list[AgentSpec] = [
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AgentSpec(
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name="form",
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system_prompt="你是足球近期状态分析专家。分析比分与关键事件,输出近期走势判断。只输出 JSON。",
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slice_fn=form_slice,
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),
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AgentSpec(
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name="stats",
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system_prompt="你是足球攻防数据分析专家。评估进球、射门与控球,输出攻防强度。只输出 JSON。",
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slice_fn=stats_slice,
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),
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AgentSpec(
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name="home_away",
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system_prompt="你是足球主客因素分析专家。对比主场与客场表现,评估地理优势影响。只输出 JSON。",
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slice_fn=home_away_slice,
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),
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AgentSpec(
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name="injuries",
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system_prompt="你是足球阵容完整性分析专家。汇总伤停与停赛名单,输出战力缺失程度。只输出 JSON。",
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slice_fn=injuries_slice,
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),
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AgentSpec(
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name="h2h",
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system_prompt="你是足球历史交锋分析专家。分析过去数年以及近期的交手数据,提取交手规律。只输出 JSON。",
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slice_fn=h2h_slice,
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),
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]
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AGGREGATOR_SYSTEM = "你是足球预测终裁专家。综合各领域报告输出最终预测。只输出 JSON。"
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@dataclass
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class MultiPredictResult:
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prediction_id: int
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provider: str
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model: str
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prompt_version: str
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mode: str
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pred_home_goals: float | None
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pred_away_goals: float | None
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pred_1x2: str | None
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confidence: float | None
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reasoning: str | None
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agent_outputs: list[dict]
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agent_weights: dict | None
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context: str
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latency_ms: int | None
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raw: dict | None
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def _get_specialist_provider() -> LLMProvider:
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"""专家模型: LLM_SPECIALIST_MODEL 回落 LLM_MODEL。"""
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p = get_default_provider()
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if settings.LLM_SPECIALIST_MODEL:
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p.model = settings.LLM_SPECIALIST_MODEL
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return p
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def _get_aggregator_provider() -> LLMProvider:
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"""终裁模型: LLM_AGGREGATOR_MODEL 回落 LLM_MODEL。"""
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p = get_default_provider()
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if settings.LLM_AGGREGATOR_MODEL:
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p.model = settings.LLM_AGGREGATOR_MODEL
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return p
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async def run_specialists(
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header: MatchHeader,
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*,
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provider: LLMProvider,
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version: str = "v1",
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) -> list[AgentReport]:
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"""并行执行 5 个专家 agent。fail-open: 单个失败不影响其他。"""
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tasks = [
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_run_one(spec, header, provider, version=version)
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for spec in SPECIALIST_SPECS
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]
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results = await asyncio.gather(*tasks, return_exceptions=True)
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reports: list[AgentReport] = []
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for spec, r in zip(SPECIALIST_SPECS, results):
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if isinstance(r, Exception):
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logger.warning("agent %s raised: %s", spec.name, r)
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reports.append(AgentReport(agent=spec.name, status="error", analysis=str(r)[:200]))
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else:
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reports.append(r)
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return reports
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async def _run_one(spec, header, provider, *, version) -> AgentReport:
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from src.llm.agents.base import run_agent
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return await run_agent(spec, header, provider, before=header.match_dt, version=version)
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def _reports_to_json(reports: list[AgentReport]) -> str:
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return json.dumps([r.to_dict() for r in reports], ensure_ascii=False, indent=1)
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async def run_aggregator(
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header: MatchHeader,
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reports: list[AgentReport],
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*,
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provider: LLMProvider,
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version: str = "v1",
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) -> tuple[dict, int, int]:
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"""终裁: 汇总报告 → 最终 JSON。返回 (解析结果, prompt_tokens, completion_tokens)。"""
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template = load_agent_prompt("aggregator", version)
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user_prompt = (
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template
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.replace("{{match_header}}", header_text(header))
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.replace("{{agent_reports}}", _reports_to_json(reports))
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)
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resp = await provider.chat(
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system=AGGREGATOR_SYSTEM,
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user=user_prompt,
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json_mode=True,
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temperature=0.2,
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max_tokens=1000,
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)
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if resp.error:
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raise RuntimeError(f"aggregator LLM error: {resp.error}")
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if not resp.parsed:
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raise RuntimeError(f"aggregator 输出无法解析: {resp.content[:200]}")
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return resp.parsed, resp.prompt_tokens or 0, resp.completion_tokens or 0
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async def predict_match_multi(
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match_id: int,
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*,
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provider: LLMProvider | None = None,
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version: str = "v1",
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) -> MultiPredictResult:
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"""多 agent 端到端预测: 切片 → 并行专家 → 终裁 → 存库。"""
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start = time.perf_counter()
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# 1. 比赛头(各 agent 共享;不存在则 404)
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header = await load_match_header(match_id)
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# 2. 并行专家
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specialist_provider = _get_specialist_provider()
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reports = await run_specialists(header, provider=specialist_provider, version=version)
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# 3. 终裁
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aggregator_provider = _get_aggregator_provider()
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final, agg_prompt_tokens, agg_completion_tokens = await run_aggregator(
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header, reports, provider=aggregator_provider, version=version
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)
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latency_ms = int((time.perf_counter() - start) * 1000)
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# 4. 存库
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async with AsyncSessionLocal() as db:
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m = await db.get(Match, match_id)
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if m is None:
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raise ValueError(f"match {match_id} not found")
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agent_weights = final.get("agent_weights")
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pred = Prediction(
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match_id=match_id,
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provider=settings.LLM_PROVIDER,
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model=aggregator_provider.model,
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prompt_version=f"multi_{version}",
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mode="multi",
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prompt_tokens=sum(r.prompt_tokens or 0 for r in reports) + agg_prompt_tokens,
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completion_tokens=sum(r.completion_tokens or 0 for r in reports) + agg_completion_tokens,
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latency_ms=latency_ms,
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pred_home_goals=final.get("pred_home_goals"),
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pred_away_goals=final.get("pred_away_goals"),
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pred_1x2=final.get("1x2"),
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confidence=final.get("confidence"),
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reasoning=final.get("reasoning"),
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raw_response=final,
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agent_outputs=[r.to_dict() for r in reports],
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)
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db.add(pred)
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await db.commit()
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await db.refresh(pred)
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return MultiPredictResult(
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prediction_id=pred.id,
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provider=pred.provider,
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model=pred.model,
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prompt_version=pred.prompt_version,
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mode="multi",
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pred_home_goals=pred.pred_home_goals,
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pred_away_goals=pred.pred_away_goals,
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pred_1x2=pred.pred_1x2,
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confidence=pred.confidence,
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reasoning=pred.reasoning,
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agent_outputs=pred.agent_outputs,
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agent_weights=agent_weights,
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context=_reports_to_json(reports),
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latency_ms=latency_ms,
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raw=final,
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)
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