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