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
@@ -0,0 +1,224 @@
|
||||
"""多 agent 预测编排: 并行专家 → 终裁 → 存库。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
|
||||
from src.core.config import settings
|
||||
from src.db.base import AsyncSessionLocal
|
||||
from src.db.models import Match, Prediction
|
||||
from src.llm.agents.base import AgentReport, AgentSpec, load_agent_prompt
|
||||
from src.llm.context_builder import (
|
||||
MatchHeader,
|
||||
form_slice,
|
||||
h2h_slice,
|
||||
header_text,
|
||||
home_away_slice,
|
||||
injuries_slice,
|
||||
load_match_header,
|
||||
stats_slice,
|
||||
)
|
||||
from src.llm.provider import LLMProvider, get_default_provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ── 5 个专家 agent 定义 ──
|
||||
# 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
|
||||
SPECIALIST_SPECS: list[AgentSpec] = [
|
||||
AgentSpec(
|
||||
name="form",
|
||||
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。",
|
||||
slice_fn=form_slice,
|
||||
),
|
||||
AgentSpec(
|
||||
name="stats",
|
||||
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。",
|
||||
slice_fn=stats_slice,
|
||||
),
|
||||
AgentSpec(
|
||||
name="home_away",
|
||||
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。",
|
||||
slice_fn=home_away_slice,
|
||||
),
|
||||
AgentSpec(
|
||||
name="injuries",
|
||||
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。",
|
||||
slice_fn=injuries_slice,
|
||||
),
|
||||
AgentSpec(
|
||||
name="h2h",
|
||||
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。",
|
||||
slice_fn=h2h_slice,
|
||||
),
|
||||
]
|
||||
|
||||
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。"
|
||||
|
||||
|
||||
@dataclass
|
||||
class MultiPredictResult:
|
||||
prediction_id: int
|
||||
provider: str
|
||||
model: str
|
||||
prompt_version: str
|
||||
mode: str
|
||||
pred_home_goals: float | None
|
||||
pred_away_goals: float | None
|
||||
pred_1x2: str | None
|
||||
confidence: float | None
|
||||
reasoning: str | None
|
||||
agent_outputs: list[dict]
|
||||
agent_weights: dict | None
|
||||
context: str
|
||||
latency_ms: int | None
|
||||
raw: dict | None
|
||||
|
||||
|
||||
def _get_specialist_provider() -> LLMProvider:
|
||||
"""专家模型: LLM_SPECIALIST_MODEL 回落 LLM_MODEL。"""
|
||||
p = get_default_provider()
|
||||
if settings.LLM_SPECIALIST_MODEL:
|
||||
p.model = settings.LLM_SPECIALIST_MODEL
|
||||
return p
|
||||
|
||||
|
||||
def _get_aggregator_provider() -> LLMProvider:
|
||||
"""终裁模型: LLM_AGGREGATOR_MODEL 回落 LLM_MODEL。"""
|
||||
p = get_default_provider()
|
||||
if settings.LLM_AGGREGATOR_MODEL:
|
||||
p.model = settings.LLM_AGGREGATOR_MODEL
|
||||
return p
|
||||
|
||||
|
||||
async def run_specialists(
|
||||
header: MatchHeader,
|
||||
*,
|
||||
provider: LLMProvider,
|
||||
version: str = "v1",
|
||||
) -> list[AgentReport]:
|
||||
"""并行执行 5 个专家 agent。fail-open: 单个失败不影响其他。"""
|
||||
tasks = [
|
||||
_run_one(spec, header, provider, version=version)
|
||||
for spec in SPECIALIST_SPECS
|
||||
]
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
reports: list[AgentReport] = []
|
||||
for spec, r in zip(SPECIALIST_SPECS, results):
|
||||
if isinstance(r, Exception):
|
||||
logger.warning("agent %s raised: %s", spec.name, r)
|
||||
reports.append(AgentReport(agent=spec.name, status="error", analysis=str(r)[:200]))
|
||||
else:
|
||||
reports.append(r)
|
||||
return reports
|
||||
|
||||
|
||||
async def _run_one(spec, header, provider, *, version) -> AgentReport:
|
||||
from src.llm.agents.base import run_agent
|
||||
|
||||
return await run_agent(spec, header, provider, before=header.match_dt, version=version)
|
||||
|
||||
|
||||
def _reports_to_json(reports: list[AgentReport]) -> str:
|
||||
return json.dumps([r.to_dict() for r in reports], ensure_ascii=False, indent=1)
|
||||
|
||||
|
||||
async def run_aggregator(
|
||||
header: MatchHeader,
|
||||
reports: list[AgentReport],
|
||||
*,
|
||||
provider: LLMProvider,
|
||||
version: str = "v1",
|
||||
) -> tuple[dict, int, int]:
|
||||
"""终裁: 汇总报告 → 最终 JSON。返回 (解析结果, prompt_tokens, completion_tokens)。"""
|
||||
template = load_agent_prompt("aggregator", version)
|
||||
user_prompt = (
|
||||
template
|
||||
.replace("{{match_header}}", header_text(header))
|
||||
.replace("{{agent_reports}}", _reports_to_json(reports))
|
||||
)
|
||||
resp = await provider.chat(
|
||||
system=AGGREGATOR_SYSTEM,
|
||||
user=user_prompt,
|
||||
json_mode=True,
|
||||
temperature=0.2,
|
||||
max_tokens=1000,
|
||||
)
|
||||
if resp.error:
|
||||
raise RuntimeError(f"aggregator LLM error: {resp.error}")
|
||||
if not resp.parsed:
|
||||
raise RuntimeError(f"aggregator 输出无法解析: {resp.content[:200]}")
|
||||
return resp.parsed, resp.prompt_tokens or 0, resp.completion_tokens or 0
|
||||
|
||||
|
||||
async def predict_match_multi(
|
||||
match_id: int,
|
||||
*,
|
||||
provider: LLMProvider | None = None,
|
||||
version: str = "v1",
|
||||
) -> MultiPredictResult:
|
||||
"""多 agent 端到端预测: 切片 → 并行专家 → 终裁 → 存库。"""
|
||||
start = time.perf_counter()
|
||||
|
||||
# 1. 比赛头(各 agent 共享;不存在则 404)
|
||||
header = await load_match_header(match_id)
|
||||
|
||||
# 2. 并行专家
|
||||
specialist_provider = _get_specialist_provider()
|
||||
reports = await run_specialists(header, provider=specialist_provider, version=version)
|
||||
|
||||
# 3. 终裁
|
||||
aggregator_provider = _get_aggregator_provider()
|
||||
final, agg_prompt_tokens, agg_completion_tokens = await run_aggregator(
|
||||
header, reports, provider=aggregator_provider, version=version
|
||||
)
|
||||
|
||||
latency_ms = int((time.perf_counter() - start) * 1000)
|
||||
|
||||
# 4. 存库
|
||||
async with AsyncSessionLocal() as db:
|
||||
m = await db.get(Match, match_id)
|
||||
if m is None:
|
||||
raise ValueError(f"match {match_id} not found")
|
||||
|
||||
agent_weights = final.get("agent_weights")
|
||||
pred = Prediction(
|
||||
match_id=match_id,
|
||||
provider=settings.LLM_PROVIDER,
|
||||
model=aggregator_provider.model,
|
||||
prompt_version=f"multi_{version}",
|
||||
mode="multi",
|
||||
prompt_tokens=sum(r.prompt_tokens or 0 for r in reports) + agg_prompt_tokens,
|
||||
completion_tokens=sum(r.completion_tokens or 0 for r in reports) + agg_completion_tokens,
|
||||
latency_ms=latency_ms,
|
||||
pred_home_goals=final.get("pred_home_goals"),
|
||||
pred_away_goals=final.get("pred_away_goals"),
|
||||
pred_1x2=final.get("1x2"),
|
||||
confidence=final.get("confidence"),
|
||||
reasoning=final.get("reasoning"),
|
||||
raw_response=final,
|
||||
agent_outputs=[r.to_dict() for r in reports],
|
||||
)
|
||||
db.add(pred)
|
||||
await db.commit()
|
||||
await db.refresh(pred)
|
||||
|
||||
return MultiPredictResult(
|
||||
prediction_id=pred.id,
|
||||
provider=pred.provider,
|
||||
model=pred.model,
|
||||
prompt_version=pred.prompt_version,
|
||||
mode="multi",
|
||||
pred_home_goals=pred.pred_home_goals,
|
||||
pred_away_goals=pred.pred_away_goals,
|
||||
pred_1x2=pred.pred_1x2,
|
||||
confidence=pred.confidence,
|
||||
reasoning=pred.reasoning,
|
||||
agent_outputs=pred.agent_outputs,
|
||||
agent_weights=agent_weights,
|
||||
context=_reports_to_json(reports),
|
||||
latency_ms=latency_ms,
|
||||
raw=final,
|
||||
)
|
||||
Reference in New Issue
Block a user