229 lines
9.5 KiB
Python
229 lines
9.5 KiB
Python
"""Conversation-level investment-goal entity extraction and gap handling."""
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import re
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from datetime import UTC, datetime
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from decimal import Decimal
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from typing import Any, Protocol
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.infrastructure.db import SessionFactory
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from app.model.audit import InteractionAudit
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from app.model.conversation import ConversationMessage
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from app.model.goal_conversation import AdvisorGoalConversationExtraction
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from app.model.session import ConversationSession
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EXTRACTION_VERSION = "investment-goal-v1"
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MAX_CLARIFICATION_ROUNDS = 10
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REQUIRED_FIELDS = (
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"annualized_return_lower_pct",
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"annualized_return_upper_pct",
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"max_drawdown_pct",
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"liquidity_requirement",
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"investment_horizon_months",
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"benchmark_name",
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)
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FIELD_LABELS = {
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"annualized_return_lower_pct": "年化收益目标下限",
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"annualized_return_upper_pct": "年化收益目标上限",
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"max_drawdown_pct": "最大回撤容忍度",
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"liquidity_requirement": "资金流动性要求",
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"investment_horizon_months": "投资期限",
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"benchmark_name": "业绩比较基准",
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}
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_NUMBER = r"\d+(?:\.\d+)?"
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class SessionFactoryLike(Protocol):
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def __call__(self) -> AsyncSession: ...
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def _number(value: str) -> float:
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return float(Decimal(value))
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def extract_goal_entities(text: str) -> dict[str, Any]:
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"""Extract explicit goal facts from one user message.
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This parser intentionally accepts only explicit financial statements. It does not
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infer a risk level, expected return, or benchmark from vague language.
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"""
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result: dict[str, Any] = {}
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return_match = re.search(
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rf"(?:年化)?(?:收益目标|收益率|收益)\s*(?:为|在|约为|目标为)?\s*({_NUMBER})\s*%?\s*"
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rf"(?:至|到|[-~～])\s*({_NUMBER})\s*%?",
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text,
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re.IGNORECASE,
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)
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if return_match:
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lower, upper = map(_number, return_match.groups())
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result["annualized_return_lower_pct"] = min(lower, upper)
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result["annualized_return_upper_pct"] = max(lower, upper)
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else:
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single_return = re.search(
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rf"(?:年化)?(?:收益目标|收益率|收益)\s*"
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rf"(?:是|为|约为|目标为)?\s*({_NUMBER})\s*%",
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text,
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re.IGNORECASE,
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)
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if single_return:
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value = _number(single_return.group(1))
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result["annualized_return_lower_pct"] = value
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result["annualized_return_upper_pct"] = value
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drawdown = re.search(rf"(?:最大)?回撤\s*(?:容忍|控制|不超过|为|约为)?\s*({_NUMBER})\s*%?", text)
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if drawdown:
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result["max_drawdown_pct"] = _number(drawdown.group(1))
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if re.search(r"随时|每天|每日|当天|日内", text):
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result["liquidity_requirement"] = "daily"
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else:
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days = re.search(
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r"(?:流动性|用钱|变现|取出).{0,12}?"
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r"([一两两三四五六七八九十0-9]+)\s*天",
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text,
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)
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if days is None:
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days = re.search(
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r"([一两三四五六七八九十0-9]+)\s*天(?:内|可用|能用)", text
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)
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if days:
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count = _chinese_number(days.group(1))
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if count <= 7:
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result["liquidity_requirement"] = "within_7_days"
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elif count <= 30:
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result["liquidity_requirement"] = "within_30_days"
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else:
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result["liquidity_requirement"] = "over_30_days"
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months = re.search(rf"({_NUMBER})\s*(?:年|岁)", text)
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if months:
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result["investment_horizon_months"] = int(_number(months.group(1)) * 12)
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else:
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months = re.search(rf"({_NUMBER})\s*个?月", text)
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if months:
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result["investment_horizon_months"] = int(_number(months.group(1)))
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benchmark = re.search(
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r"(?:业绩比较基准|比较基准|基准)\s*(?:是|为|选)?\s*"
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r"([\u4e00-\u9fffA-Za-z0-9_-]{2,32})",
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text,
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)
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if benchmark:
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result["benchmark_name"] = benchmark.group(1).strip("，。；、")
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else:
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for candidate in ("沪深300指数", "沪深300", "中证500", "中证全指", "上证指数"):
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if candidate in text:
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result["benchmark_name"] = candidate
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break
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return result
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def _chinese_number(value: str) -> int:
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if value.isdigit():
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return int(value)
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digits = {"一": 1, "二": 2, "两": 2, "三": 3, "四": 4, "五": 5,
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"六": 6, "七": 7, "八": 8, "九": 9, "十": 10}
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if value == "十":
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return 10
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if value.startswith("十"):
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return 10 + digits.get(value[1:], 0)
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if value.endswith("十"):
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return digits.get(value[0], 0) * 10
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if "十" in value:
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left, right = value.split("十", 1)
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return digits.get(left, 0) * 10 + digits.get(right, 0)
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return digits.get(value, 0)
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class GoalConversationService:
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"""Merge explicit facts across a session and persist an internal gap snapshot."""
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def __init__(
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self, session_factory: SessionFactoryLike = SessionFactory,
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) -> None:
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self.session_factory = session_factory
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async def process(
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self, *, session_id: str, customer_id: int, trace_id: str, message: str
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) -> dict[str, Any]:
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async with self.session_factory() as session, session.begin():
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session_row = await session.scalar(select(ConversationSession).where(
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ConversationSession.session_id == session_id,
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ConversationSession.user_id == customer_id,
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).with_for_update())
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if session_row is None:
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return self._result({}, list(REQUIRED_FIELDS), "partial")
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messages = list(await session.scalars(select(ConversationMessage).where(
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ConversationMessage.session_id == session_id,
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ConversationMessage.customer_id == customer_id,
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ConversationMessage.role == "user",
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).order_by(ConversationMessage.created_at, ConversationMessage.id)))
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merged: dict[str, Any] = {}
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for row in messages:
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merged.update(extract_goal_entities(row.content))
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merged.update(extract_goal_entities(message))
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missing = [field for field in REQUIRED_FIELDS if field not in merged]
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status = "complete" if not missing else (
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"clarification_limit"
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if session_row.clarification_round >= MAX_CLARIFICATION_ROUNDS
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else "partial"
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)
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if status == "partial":
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session_row.clarification_round += 1
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now = datetime.now(UTC).replace(tzinfo=None)
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message_row = await session.scalar(select(ConversationMessage).where(
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ConversationMessage.session_id == session_id,
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ConversationMessage.customer_id == customer_id,
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ConversationMessage.trace_id == trace_id,
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ConversationMessage.role == "user",
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).order_by(ConversationMessage.id.desc()).limit(1))
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if message_row is not None:
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exists = await session.scalar(select(AdvisorGoalConversationExtraction).where(
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AdvisorGoalConversationExtraction.message_id == message_row.id))
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if exists is None:
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session.add(AdvisorGoalConversationExtraction(
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message_id=message_row.id, session_id=session_id,
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customer_id=customer_id, extraction_version=EXTRACTION_VERSION,
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extracted_fields=merged, missing_fields=missing, status=status,
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created_at=now, updated_at=now,
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))
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session.add(InteractionAudit(
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actor_type="agent", actor_id=customer_id, session_id=session_id,
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portal="agent", action_type="advisor.goal_gap_evaluated",
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detail={"trace_id": trace_id, "status": status,
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"missing_count": len(missing)}, created_at=now,
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))
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return self._result(merged, missing, status)
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@staticmethod
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def _result(fields: dict[str, Any], missing: list[str], status: str) -> dict[str, Any]:
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return {"fields": fields, "missing": missing, "status": status}
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@staticmethod
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def customer_prompt(result: dict[str, Any]) -> str:
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missing = result.get("missing", [])
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if result.get("status") == "clarification_limit":
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return "投资目标信息仍不完整，暂时无法继续采集，请转人工顾问协助确认。"
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if missing:
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labels = [FIELD_LABELS[field] for field in missing if field in FIELD_LABELS]
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return "为了形成投资目标，还需要确认：" + "、".join(labels) + "。请一次补充这些信息。"
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fields = result["fields"]
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return (
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f"投资目标信息已收集完整：年化收益目标 {fields['annualized_return_lower_pct']}%-"
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f"{fields['annualized_return_upper_pct']}%，最大回撤 {fields['max_drawdown_pct']}%，"
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f"流动性 {fields['liquidity_requirement']}，"
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f"期限 {fields['investment_horizon_months']} 个月，"
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f"业绩比较基准 {fields['benchmark_name']}。请确认后再生成目标书。"
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)
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@staticmethod
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def should_collect(text: str) -> bool:
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"""Distinguish goal collection from a request to view an existing goal."""
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return bool(extract_goal_entities(text)) or bool(re.search(
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r"制定|设定|创建|填写|采集|规划|目标书", text
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))
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