feat:新增投顾agent和nl2sqlagent
This commit is contained in:
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"""投顾 Agent 的业务意图实现。"""
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"""将意图计算结果组装为可持久化的 Agent 草稿。"""
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from __future__ import annotations
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from decimal import Decimal
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from agent.advisor_agent.intent.rebalance import build_rebalance_plan
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from agent.advisor_agent.intent.recommend import recommend_candidates
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from common.common_const import (
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AGENT_INTENT_REBALANCE,
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AGENT_INTENT_RECOMMEND,
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DRAFT_STATUS_DRAFT,
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)
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from service.advisor_agent.draft import build_generated_content
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def _recommend_markdown(items: list[dict]) -> str:
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lines = ["# 基金推荐草稿", ""]
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for item in items:
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lines.append(
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f"- {item.get('product_name', item.get('product_code'))}"
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f"（{item.get('product_code')}，风险等级 {item.get('risk_level')}）"
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)
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return "\n".join(lines)
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def build_recommendation_draft(
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*,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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candidates: list[dict],
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relation_status: str,
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memories: list[dict] | None = None,
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) -> dict:
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items = recommend_candidates(
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customer_risk,
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candidates,
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memories=memories or [],
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)
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return {
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"customer_id": customer_id,
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"advisor_id": advisor_id,
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"intent": AGENT_INTENT_RECOMMEND,
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"title": "基金推荐草稿",
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"status": DRAFT_STATUS_DRAFT,
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"structured_data": {
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"customer_risk": customer_risk,
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"items": items,
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"relation_status": relation_status,
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},
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"content": build_generated_content(_recommend_markdown(items)),
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"disclaimer_ok": True,
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}
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def build_rebalance_draft(
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*,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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relation_status: str,
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holdings: list[dict],
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target_allocation: dict[str, int | float | Decimal],
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threshold: Decimal,
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candidates: list[dict],
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) -> dict | None:
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plan = build_rebalance_plan(
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relation_status=relation_status,
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customer_risk=customer_risk,
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holdings=holdings,
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target_allocation=target_allocation,
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threshold=threshold,
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candidates=candidates,
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)
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if plan is None:
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return None
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structured_data = dict(plan)
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structured_data["customer_risk"] = customer_risk
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content = ["# 组合调仓建议草稿", "", "## 赎回清单"]
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content.extend(
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f"- {item['product_code']}：{item['amount']} 元" for item in plan["sell"]
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)
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content.append("\n## 申购清单")
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content.extend(
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f"- {item['product_code']}：{item['amount']} 元" for item in plan["buy"]
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)
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return {
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"customer_id": customer_id,
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"advisor_id": advisor_id,
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"intent": AGENT_INTENT_REBALANCE,
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"title": "组合调仓建议草稿",
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"status": DRAFT_STATUS_DRAFT,
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"structured_data": structured_data,
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"deviation": max((abs(value) for value in plan["deviation"].values()), default=Decimal("0")),
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"content": build_generated_content("\n".join(content)),
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"disclaimer_ok": True,
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}
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"""基金深度分析的数据整理层。"""
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from __future__ import annotations
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from decimal import Decimal
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from typing import Iterable
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def _number(value):
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if isinstance(value, Decimal):
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return float(value)
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return value
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def _display_value(value):
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if value is None:
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return "暂无"
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if isinstance(value, float):
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return f"{value:g}"
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return str(value)
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def _build_analysis_text(fund: dict, metrics: list[dict]) -> str:
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fund_name = fund.get("fund_name") or fund.get("fund_code") or "该基金"
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if not metrics:
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return f"{fund_name}暂无足够业绩数据，暂无法形成完整解读。"
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latest = metrics[-1]
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period = _display_value(latest.get("period"))
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return_rate = _display_value(latest.get("return_rate"))
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max_drawdown = _display_value(latest.get("max_drawdown"))
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sharpe = _display_value(latest.get("sharpe"))
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return (
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f"{fund_name}在{period}的历史收益率为{return_rate}%，"
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f"最大回撤为{max_drawdown}%，夏普比率为{sharpe}。"
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"以上仅基于历史业绩数据，不代表未来收益。"
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)
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def build_fund_analysis(fund: dict, performance_rows: Iterable[dict]) -> dict:
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metrics = []
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for row in performance_rows:
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metrics.append(
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{
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key: _number(value)
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for key, value in row.items()
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}
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)
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return {
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"fund_code": fund.get("fund_code"),
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"fund_name": fund.get("fund_name"),
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"risk_level": fund.get("risk_level"),
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"metrics": metrics,
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"analysis_text": _build_analysis_text(fund, metrics),
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"chart_data": {
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"periods": [row.get("period") for row in metrics],
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"return_rate": [row.get("return_rate") for row in metrics],
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"annual_volatility": [row.get("annual_volatility") for row in metrics],
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"max_drawdown": [row.get("max_drawdown") for row in metrics],
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"sharpe": [row.get("sharpe") for row in metrics],
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},
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}
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"""投顾意图结果到草稿与事件的编排。"""
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from __future__ import annotations
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from decimal import Decimal
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import json
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from agent.advisor_agent.intent.draft_generation import (
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build_rebalance_draft,
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build_recommendation_draft,
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)
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from common.common_const import EVENT_ADVISOR_REBALANCE_DRAFT_CREATED
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from service.advisor_agent.draft import create_draft
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from agent.advisor_agent.llm import generate_text
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async def generate_rebalance_draft(
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*,
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draft_repo,
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publish,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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relation_status: str,
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holdings: list[dict],
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target_allocation: dict[str, int | float | Decimal],
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threshold: Decimal,
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candidates: list[dict],
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trace_id: str,
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) -> dict | None:
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draft_data = build_rebalance_draft(
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customer_id=customer_id,
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advisor_id=advisor_id,
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customer_risk=customer_risk,
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relation_status=relation_status,
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holdings=holdings,
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target_allocation=target_allocation,
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threshold=threshold,
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candidates=candidates,
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)
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if draft_data is None:
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return None
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draft = await create_draft(draft_repo, draft_data)
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event_id = await publish(
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event_name=EVENT_ADVISOR_REBALANCE_DRAFT_CREATED,
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trace_id=trace_id,
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trigger_user_id=advisor_id,
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customer_id=customer_id,
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payload={
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"draft_id": draft.draft_id,
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"customer_id": customer_id,
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"advisor_id": advisor_id,
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"deviation": float(draft.deviation or 0),
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"created_at": draft.create_time.isoformat()
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if draft.create_time
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else None,
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},
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)
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return {"draft": draft, "event_id": event_id}
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async def generate_recommendation_draft(
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*,
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draft_repo,
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customer_id: int,
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advisor_id: int,
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customer_risk: str,
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relation_status: str,
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candidates: list[dict],
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trace_id: str,
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memories: list[dict] | None = None,
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llm_client=None,
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llm_timeout: float = 5.0,
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):
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draft_data = build_recommendation_draft(
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customer_id=customer_id,
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advisor_id=advisor_id,
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customer_risk=customer_risk,
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candidates=candidates,
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relation_status=relation_status,
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memories=memories,
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)
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if llm_client is not None:
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draft_data["content"] = await generate_text(
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llm_client,
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system_prompt="你是基金投顾助手，只生成内部投顾草稿说明，不下单、不承诺收益。",
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user_prompt=(
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"请根据以下候选基金和客户记忆生成简洁推荐说明："
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+ json.dumps(
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{"candidates": candidates, "memories": memories or []},
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ensure_ascii=False,
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)
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),
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fallback=lambda: draft_data["content"],
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timeout=llm_timeout,
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)
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return await create_draft(draft_repo, draft_data)
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@@ -0,0 +1,107 @@
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"""组合偏离度与调仓建议计算。"""
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from __future__ import annotations
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from collections import defaultdict
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from decimal import Decimal, ROUND_HALF_UP
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from typing import Iterable
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from common.common_const import (
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CUSTOMER_REL_STATUS_SIGNED,
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ERR_CODE_NOT_SIGNED_REBALANCE,
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)
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from common.suitability import check_suitability
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from utils.exceptions import ApiError
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_MONEY = Decimal("0.01")
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_PERCENT = Decimal("100")
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def _money(value: Decimal) -> Decimal:
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return value.quantize(_MONEY, rounding=ROUND_HALF_UP)
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def build_rebalance_plan(
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*,
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relation_status: str,
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customer_risk: str,
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holdings: Iterable[dict],
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target_allocation: dict[str, int | float | Decimal],
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threshold: Decimal,
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candidates: Iterable[dict],
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) -> dict | None:
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if relation_status != CUSTOMER_REL_STATUS_SIGNED:
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raise ApiError(ERR_CODE_NOT_SIGNED_REBALANCE, "客户尚未签约，禁止生成调仓草稿")
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values: dict[str, Decimal] = defaultdict(Decimal)
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holdings_by_class: dict[str, list[dict]] = defaultdict(list)
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for holding in holdings:
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asset_class = str(holding.get("asset_class", ""))
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value = Decimal(str(holding.get("market_value", 0) or 0))
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values[asset_class] += value
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holdings_by_class[asset_class].append(holding)
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total = sum(values.values(), Decimal("0"))
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if total <= 0:
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return None
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target = {
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asset_class: Decimal(str(weight)) for asset_class, weight in target_allocation.items()
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}
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deviation: dict[str, Decimal] = {}
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for asset_class in target:
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actual = values.get(asset_class, Decimal("0")) / total * _PERCENT
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deviation[asset_class] = (actual - target[asset_class]).quantize(
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Decimal("0.01"), rounding=ROUND_HALF_UP
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)
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if not any(abs(value) > threshold for value in deviation.values()):
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return None
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sell: list[dict] = []
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buy: list[dict] = []
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for asset_class, drift in deviation.items():
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if drift > threshold:
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target_value = total * target[asset_class] / _PERCENT
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excess = _money(values.get(asset_class, Decimal("0")) - target_value)
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remaining = excess
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for holding in holdings_by_class.get(asset_class, []):
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amount = min(
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remaining,
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_money(Decimal(str(holding.get("market_value", 0) or 0))),
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)
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if amount > 0:
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sell.append(
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{
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"product_code": holding.get("product_code"),
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"asset_class": asset_class,
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"amount": amount,
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}
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)
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remaining -= amount
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if remaining <= 0:
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break
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elif drift < -threshold:
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target_value = total * target[asset_class] / _PERCENT
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amount = _money(target_value - values.get(asset_class, Decimal("0")))
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for candidate in candidates:
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if candidate.get("asset_class") != asset_class:
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continue
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if not check_suitability(
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customer_risk, candidate.get("risk_level", "")
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).ok:
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continue
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buy.append(
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{
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"product_code": candidate.get("product_code"),
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"asset_class": asset_class,
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"amount": amount,
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}
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)
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break
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return {
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"deviation": deviation,
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"sell": sell,
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"buy": buy,
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}
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@@ -0,0 +1,45 @@
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"""基金推荐候选过滤与排序。"""
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from __future__ import annotations
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from collections import defaultdict
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from typing import Iterable
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from common.common_const import MEMORY_INFO_TYPE_OPINION
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from common.suitability import check_suitability
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def _opinion_bonuses(memories: Iterable[dict]) -> dict[str, float]:
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bonuses: dict[str, float] = defaultdict(float)
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for memory in memories:
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if memory.get("info_type") != MEMORY_INFO_TYPE_OPINION:
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continue
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product_code = memory.get("product_code")
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if product_code:
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bonuses[str(product_code)] += float(memory.get("score", 0.0) or 0.0)
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return bonuses
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def recommend_candidates(
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customer_risk: str,
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candidates: Iterable[dict],
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*,
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memories: Iterable[dict] = (),
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) -> list[dict]:
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"""硬过滤不适当产品，再按业绩和主观观点排序。"""
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bonuses = _opinion_bonuses(memories)
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result = []
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for candidate in candidates:
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suitability = check_suitability(customer_risk, candidate.get("risk_level", ""))
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if not suitability.ok:
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continue
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item = dict(candidate)
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base_score = float(item.get("performance_score", 0.0) or 0.0)
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item["recommendation_score"] = base_score + bonuses.get(
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str(item.get("product_code")), 0.0
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)
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result.append(item)
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return sorted(
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result,
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key=lambda item: item["recommendation_score"],
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reverse=True,
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)
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@@ -0,0 +1,24 @@
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"""投顾沟通话术安全兜底模板。"""
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from __future__ import annotations
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from common.common_const import (
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TALK_SCENE_CUSTOMER_COMPLAINT,
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TALK_SCENE_MARKET_FLUCTUATION,
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TALK_SCENE_PORTFOLIO_DIVERGENCE,
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TALK_SCENE_RISK_BLOCK_ORDER,
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)
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_TEMPLATES = {
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TALK_SCENE_RISK_BLOCK_ORDER: "{name}，这笔交易正在进行风险审核。请先查看审核结果，后续是否交易由您结合自身情况自行决定。",
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TALK_SCENE_MARKET_FLUCTUATION: "{name}，近期市场波动可能放大短期净值变化。建议先关注组合风险和自身资金安排，再审慎决定是否调整。",
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TALK_SCENE_PORTFOLIO_DIVERGENCE: "{name}，当前组合与既定配置基准存在偏离。我可以向您说明偏离来源和可选调整方向，具体决定请结合您的风险承受能力。",
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TALK_SCENE_CUSTOMER_COMPLAINT: "{name}，很抱歉给您带来不好的体验。我会记录您的问题并协助核实处理进展，具体结果以核查信息为准。",
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}
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def build_talk_script(scene_type: str, *, customer_name: str = "客户") -> dict:
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template = _TEMPLATES.get(scene_type)
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if template is None:
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raise ValueError("不支持的话术场景")
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return {"scene_type": scene_type, "content": template.format(name=customer_name)}
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Block a user