llm-quant/app/agents/departments.py

"""Department-level LLM agents coordinating multi-model decisions."""
from __future__ import annotations

import json
from dataclasses import dataclass, field
from typing import Any, Dict, List, Mapping

from app.agents.base import AgentAction
from app.llm.client import run_llm_with_config
from app.llm.prompts import department_prompt
from app.utils.config import DepartmentSettings
from app.utils.logging import get_logger

LOGGER = get_logger(__name__)
LOG_EXTRA = {"stage": "department"}


@dataclass
class DepartmentContext:
    """Structured data fed into a department for decision making."""

    ts_code: str
    trade_date: str
    features: Mapping[str, Any] = field(default_factory=dict)
    market_snapshot: Mapping[str, Any] = field(default_factory=dict)
    raw: Mapping[str, Any] = field(default_factory=dict)


@dataclass
class DepartmentDecision:
    """Result produced by a department agent."""

    department: str
    action: AgentAction
    confidence: float
    summary: str
    raw_response: str
    signals: List[str] = field(default_factory=list)
    risks: List[str] = field(default_factory=list)

    def to_dict(self) -> Dict[str, Any]:
        return {
            "department": self.department,
            "action": self.action.value,
            "confidence": self.confidence,
            "summary": self.summary,
            "signals": self.signals,
            "risks": self.risks,
            "raw_response": self.raw_response,
        }


class DepartmentAgent:
    """Wraps LLM ensemble logic for a single analytical department."""

    def __init__(self, settings: DepartmentSettings) -> None:
        self.settings = settings

    def analyze(self, context: DepartmentContext) -> DepartmentDecision:
        prompt = department_prompt(self.settings, context)
        system_prompt = (
            "你是一个多智能体量化投研系统中的分部决策者，需要根据提供的结构化信息给出买卖意见。"
        )
        try:
            response = run_llm_with_config(self.settings.llm, prompt, system=system_prompt)
        except Exception as exc:  # noqa: BLE001
            LOGGER.exception("部门 %s 调用 LLM 失败：%s", self.settings.code, exc, extra=LOG_EXTRA)
            return DepartmentDecision(
                department=self.settings.code,
                action=AgentAction.HOLD,
                confidence=0.0,
                summary=f"LLM 调用失败：{exc}",
                raw_response=str(exc),
            )

        decision_data = _parse_department_response(response)
        action = _normalize_action(decision_data.get("action"))
        confidence = _clamp_float(decision_data.get("confidence"), default=0.5)
        summary = decision_data.get("summary") or decision_data.get("reason") or ""
        signals = decision_data.get("signals") or decision_data.get("rationale") or []
        if isinstance(signals, str):
            signals = [signals]
        risks = decision_data.get("risks") or decision_data.get("warnings") or []
        if isinstance(risks, str):
            risks = [risks]

        decision = DepartmentDecision(
            department=self.settings.code,
            action=action,
            confidence=confidence,
            summary=summary or "未提供摘要",
            signals=[str(sig) for sig in signals if sig],
            risks=[str(risk) for risk in risks if risk],
            raw_response=response,
        )
        LOGGER.debug(
            "部门 %s 决策：action=%s confidence=%.2f",
            self.settings.code,
            decision.action.value,
            decision.confidence,
            extra=LOG_EXTRA,
        )
        return decision


class DepartmentManager:
    """Orchestrates all departments defined in configuration."""

    def __init__(self, departments: Mapping[str, DepartmentSettings]) -> None:
        self.agents: Dict[str, DepartmentAgent] = {
            code: DepartmentAgent(settings)
            for code, settings in departments.items()
        }

    def evaluate(self, context: DepartmentContext) -> Dict[str, DepartmentDecision]:
        results: Dict[str, DepartmentDecision] = {}
        for code, agent in self.agents.items():
            results[code] = agent.analyze(context)
        return results


def _parse_department_response(text: str) -> Dict[str, Any]:
    """Extract a JSON object from the LLM response if possible."""

    cleaned = text.strip()
    candidate = None
    if cleaned.startswith("{") and cleaned.endswith("}"):
        candidate = cleaned
    else:
        start = cleaned.find("{")
        end = cleaned.rfind("}")
        if start != -1 and end != -1 and end > start:
            candidate = cleaned[start : end + 1]
    if candidate:
        try:
            return json.loads(candidate)
        except json.JSONDecodeError:
            LOGGER.debug("部门响应 JSON 解析失败，返回原始文本", extra=LOG_EXTRA)
    return {"summary": cleaned}


def _normalize_action(value: Any) -> AgentAction:
    if isinstance(value, str):
        upper = value.strip().upper()
        mapping = {
            "BUY": AgentAction.BUY_M,
            "BUY_S": AgentAction.BUY_S,
            "BUY_M": AgentAction.BUY_M,
            "BUY_L": AgentAction.BUY_L,
            "SELL": AgentAction.SELL,
            "HOLD": AgentAction.HOLD,
        }
        if upper in mapping:
            return mapping[upper]
        if "SELL" in upper:
            return AgentAction.SELL
        if "BUY" in upper:
            return AgentAction.BUY_M
    return AgentAction.HOLD


def _clamp_float(value: Any, default: float = 0.5) -> float:
    try:
        num = float(value)
    except (TypeError, ValueError):
        return default
    return max(0.0, min(1.0, num))