712 lines
27 KiB
Python
712 lines
27 KiB
Python
"""投顾 Agent HTTP 契约入口与本地业务编排。"""
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from __future__ import annotations
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import json
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import re
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from typing import Literal
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from fastapi import APIRouter, BackgroundTasks, Depends, Query, Request
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from fastapi.responses import StreamingResponse
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from pydantic import ValidationError
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from sqlalchemy.ext.asyncio import AsyncSession
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from agent.advisor_agent.auth import ensure_customer_access
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from agent.advisor_agent.data_query import execute_advisor_data_query
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from agent.advisor_agent.intent.fund_analysis import build_fund_analysis
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from agent.advisor_agent.intent.classifier import classify_advisor_intent
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from agent.advisor_agent.intent.talk_script import build_talk_script
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from agent.advisor_agent.llm import generate_text
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from agent.advisor_agent.intent.generation_flow import (
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generate_rebalance_draft,
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generate_recommendation_draft,
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)
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from agent.advisor_agent.protocol import agent_failure, agent_success
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from common.common_const import (
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AGENT_INTENT_CASUAL_CHAT,
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AGENT_INTENT_DATA_QUERY,
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AGENT_INTENT_DIALOGUE_SCRIPT,
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AGENT_INTENT_FUND_ANALYSIS,
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AGENT_INTENT_REBALANCE,
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AGENT_INTENT_RECOMMEND,
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CUSTOMER_REL_STATUS_SIGNED,
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ERR_CODE_DRAFT_NOT_FOUND,
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ERR_CODE_FORBIDDEN_CUSTOMER,
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ERR_CODE_LLM_ERROR,
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ERR_CODE_NOT_SIGNED_REBALANCE,
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DRAFT_STATUS_DISCARDED,
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DRAFT_STATUS_DRAFT,
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SSE_EVENT_TYPE_DONE,
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SSE_EVENT_TYPE_ERROR,
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SSE_EVENT_TYPE_META,
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SSE_EVENT_TYPE_TEXT,
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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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from api.deps import audited_advisor
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from config.deps import get_db
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from config.database import mysql, redis as redis_db
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from model.sys_user import SysUser
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from repositories.advisor_draft import AdvisorDraftRepo
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from repositories.customer_relation import CustomerRelationRepo
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from service.advisor_agent.context import (
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load_fund_analysis_context,
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load_customer_risk,
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load_rebalance_context,
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load_recommendation_context,
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)
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from service.event_publisher import publish_event
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from schemas.advisor_agent import (
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AdvisorDraftOperateReq,
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AdvisorDraftSaveReq,
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AdvisorChatReq,
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AdvisorFundAnalysisReq,
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AdvisorRebalanceRunReq,
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AdvisorTalkScriptReq,
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AdvisorDataQueryReq,
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)
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from service.nl2sql.query_service import QueryServiceError
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from service.advisor_agent.draft import (
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detail_draft,
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discard_draft,
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ensure_draft_owner,
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get_draft as get_draft_service,
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list_drafts as list_drafts_service,
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save_draft as save_draft_service,
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)
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from utils.exceptions import ApiError
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from utils.request_id import get_request_id, new_request_id
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from utils.logger import get_logger
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router = APIRouter(prefix="/advisor-agent")
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logger = get_logger("advisor_agent.router")
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_NOT_READY_CODE = ERR_CODE_LLM_ERROR
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_NOT_READY_MESSAGE = "投顾 Agent 核心能力尚未初始化"
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_DRAFT_NOT_FOUND_CODE = ERR_CODE_DRAFT_NOT_FOUND
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_DRAFT_NOT_FOUND_MESSAGE = "草稿不存在或者已废弃"
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def _trace_id(request: Request) -> str:
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return request.headers.get("X-Trace-Id") or get_request_id() or new_request_id()
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def _not_ready(request: Request):
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return agent_failure(_NOT_READY_CODE, _NOT_READY_MESSAGE, trace_id=_trace_id(request))
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def _advisor_runtime(request: Request):
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app = request.scope.get("app")
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return getattr(getattr(app, "state", None), "advisor_agent_runtime", None)
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def _infer_chat_intent(query: str) -> str | None:
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"""从自然语言问题推断投顾意图；无法确定时保留通用问答。"""
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if any(word in query for word in ("调仓", "再平衡", "组合偏离")):
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return "rebalance"
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if any(word in query for word in ("沟通话术", "怎么和客户说", "解释给客户")):
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return "dialogue-script"
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if any(word in query for word in ("基金分析", "分析这只基金", "分析产品")):
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return "fund_analysis"
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if any(word in query for word in ("推荐", "产品建议", "买什么基金", "适合的基金")):
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return AGENT_INTENT_RECOMMEND
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return None
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async def _resolve_customer_from_query(db, *, advisor_id: int, query: str) -> tuple[int | None, str | None]:
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"""解析问题中的客户编号或姓名，并限制在当前投顾客户范围内。"""
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number_match = re.search(r"(?:客户|用户)\s*[#编号号:]?\s*(\d+)", query)
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relation_repo = CustomerRelationRepo(db)
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if number_match:
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customer_id = int(number_match.group(1))
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relation = await relation_repo.get_active_relation(
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customer_id=customer_id,
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advisor_id=advisor_id,
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)
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if relation is None:
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return None, "问题中的客户不在当前投顾的授权范围内"
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return customer_id, None
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rows = await relation_repo.list_customer_rows(advisor_id=advisor_id, limit=100)
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matched = {
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int(account.id)
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for _relation, account, _profile in rows
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if account.real_name and account.real_name in query
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}
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if len(matched) == 1:
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return next(iter(matched)), None
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if len(matched) > 1:
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return None, "问题中的客户姓名无法唯一确定，请补充客户编号"
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if "客户" in query or "用户" in query:
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return None, "请在问题中补充客户编号或客户姓名"
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return None, None
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async def _recall_advisor_memories(
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request: Request, *, customer_id: int, query: str
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) -> list[dict]:
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"""从应用共享 runtime 读取记忆；未装配或故障时安全降级为空。"""
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app = request.scope.get("app")
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state = getattr(app, "state", None)
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runtime = getattr(state, "advisor_agent_runtime", None)
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provider = getattr(runtime, "memory_provider", None)
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if provider is None:
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return []
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try:
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return await provider.recall(customer_id=customer_id, query=query)
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except Exception:
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logger.warning(
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"advisor memory recall failed: customer_id=%s", customer_id, exc_info=True
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)
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return []
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async def _ensure_draft_access(db, draft, advisor_id: int) -> None:
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ensure_draft_owner(draft, advisor_id=advisor_id)
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await ensure_customer_access(
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db,
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advisor_id=advisor_id,
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customer_id=draft.customer_id,
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)
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async def _run_rebalance_background(
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*, advisor_id: int, customer_id: int, trace_id: str
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) -> None:
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"""后台任务使用独立会话，避免请求返回后复用已关闭的请求会话。"""
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try:
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async with mysql.get_session_factory()() as task_db:
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relation = await ensure_customer_access(
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task_db, advisor_id=advisor_id, customer_id=customer_id
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)
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if relation.status != CUSTOMER_REL_STATUS_SIGNED:
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return
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context = await load_rebalance_context(task_db, customer_id=customer_id)
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if context is None:
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return
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await generate_rebalance_draft(
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draft_repo=AdvisorDraftRepo(task_db),
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publish=lambda **kwargs: publish_event(redis_db.client(), task_db, **kwargs),
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advisor_id=advisor_id,
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trace_id=trace_id,
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**context,
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)
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except Exception:
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logger.warning("advisor rebalance background task failed", exc_info=True)
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@router.post("/chat/stream")
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async def chat_stream(
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request: Request,
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body: AdvisorChatReq,
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user: SysUser = Depends(audited_advisor),
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db: AsyncSession = Depends(get_db),
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):
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trace_id = _trace_id(request)
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try:
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chat_request = (
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body
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if isinstance(body, AdvisorChatReq)
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else AdvisorChatReq.model_validate(body)
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)
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except ValidationError:
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payload = agent_failure(
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ERR_CODE_FORBIDDEN_CUSTOMER,
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"对话请求缺少有效参数",
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trace_id=trace_id,
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)
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async def validation_error_events():
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_ERROR, **payload}, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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validation_error_events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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runtime = _advisor_runtime(request)
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classification = await classify_advisor_intent(
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chat_request.query,
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getattr(runtime, "llm_client", None),
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explicit_intent=chat_request.intent,
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)
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inferred_intent = classification.intent
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customer_id = chat_request.customer_id
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if not chat_request.query and not chat_request.intent:
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code = ERR_CODE_LLM_ERROR if customer_id is not None else ERR_CODE_FORBIDDEN_CUSTOMER
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message = _NOT_READY_MESSAGE if customer_id is not None else "对话请求缺少有效参数"
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payload = agent_failure(code, message, trace_id=trace_id)
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async def empty_query_events():
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_ERROR, **payload}, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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empty_query_events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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payload = None
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# 请求体只传问题时，从问题中解析客户；解析结果仍必须经过投顾关系授权校验。
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if customer_id is None:
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customer_id, resolve_error = await _resolve_customer_from_query(
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db,
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advisor_id=user.id,
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query=chat_request.query,
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)
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if resolve_error:
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payload = agent_failure(
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ERR_CODE_FORBIDDEN_CUSTOMER,
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resolve_error,
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trace_id=trace_id,
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)
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async def resolve_error_events():
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_ERROR, **payload}, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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resolve_error_events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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else:
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payload = None
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# 不带客户编号时只提供通用基金问答，不读取客户画像，也不生成个性化草稿。
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if customer_id is None:
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if inferred_intent in {
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AGENT_INTENT_RECOMMEND,
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AGENT_INTENT_REBALANCE,
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AGENT_INTENT_FUND_ANALYSIS,
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AGENT_INTENT_DIALOGUE_SCRIPT,
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AGENT_INTENT_DATA_QUERY,
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}:
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if payload is None:
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payload = agent_failure(
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ERR_CODE_FORBIDDEN_CUSTOMER,
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"个性化投顾分析需要在问题中明确客户编号或姓名",
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trace_id=trace_id,
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)
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else:
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llm_client = getattr(runtime, "llm_client", None)
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if inferred_intent == AGENT_INTENT_CASUAL_CHAT:
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answer = "您好，我是投顾助手，请选择客户后使用个性化分析。"
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elif llm_client is None:
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answer = "已收到问题。当前未配置通用投顾模型，请选择客户后使用个性化分析，或联系管理员配置 Agent 服务。"
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else:
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answer = await generate_text(
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llm_client,
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system_prompt="你是基金投顾助手，只回答通用基金知识和产品分析问题，不读取或推断任何客户信息，不承诺收益，不代客交易。",
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user_prompt=chat_request.query,
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fallback=lambda: "当前模型暂时不可用，请稍后重试。",
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timeout=5.0,
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)
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async def events():
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for event in (
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{"type": SSE_EVENT_TYPE_META, "intent": inferred_intent or "general_question"},
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{"type": SSE_EVENT_TYPE_TEXT, "content": answer},
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{"type": SSE_EVENT_TYPE_DONE},
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):
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yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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else:
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relation = await ensure_customer_access(
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db, advisor_id=user.id, customer_id=int(customer_id)
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)
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if inferred_intent == AGENT_INTENT_RECOMMEND:
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memories = await _recall_advisor_memories(
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request,
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customer_id=int(customer_id),
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query=chat_request.query,
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)
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runtime = _advisor_runtime(request)
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context = await load_recommendation_context(
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db, customer_id=int(customer_id)
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)
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if context is not None:
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try:
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draft = await generate_recommendation_draft(
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draft_repo=AdvisorDraftRepo(db),
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advisor_id=user.id,
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relation_status=relation.status,
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trace_id=trace_id,
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memories=memories,
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llm_client=getattr(runtime, "llm_client", None),
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**context,
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)
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except Exception:
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logger.warning("advisor recommendation generation failed", exc_info=True)
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payload = agent_failure(
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ERR_CODE_LLM_ERROR,
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"推荐方案生成失败，请稍后重试",
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trace_id=trace_id,
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)
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else:
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async def events():
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for event in (
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{
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"type": SSE_EVENT_TYPE_META,
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"draft_id": draft.draft_id,
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"intent": draft.intent,
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"status": draft.status,
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},
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{"type": SSE_EVENT_TYPE_TEXT, "content": draft.content},
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{"type": SSE_EVENT_TYPE_DONE, "draft_id": draft.draft_id},
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):
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yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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if inferred_intent == AGENT_INTENT_DATA_QUERY:
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if not chat_request.query or not chat_request.query.strip():
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payload = agent_failure(
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ERR_CODE_FORBIDDEN_CUSTOMER,
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"查询问题不能为空",
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trace_id=trace_id,
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)
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else:
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try:
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result = await execute_advisor_data_query(
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db,
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advisor_id=user.id,
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customer_id=int(customer_id),
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question=chat_request.query,
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trace_id=trace_id,
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llm_client=getattr(_advisor_runtime(request), "llm_client", None),
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)
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except QueryServiceError:
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payload = agent_failure(
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ERR_CODE_LLM_ERROR,
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"客户数据查询失败，请稍后重试",
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trace_id=trace_id,
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)
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else:
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async def events():
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_META, 'intent': AGENT_INTENT_DATA_QUERY, 'query_id': result.get('query_id'), 'trace_id': trace_id}, ensure_ascii=False)}\n\n"
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answer = result.get("answer") or result.get("summary")
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if answer:
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_TEXT, 'content': answer}, ensure_ascii=False)}\n\n"
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yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_DONE, 'query_id': result.get('query_id')}, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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if inferred_intent == AGENT_INTENT_FUND_ANALYSIS:
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fund_codes = re.findall(r"[A-Za-z]{1,6}\d{3,8}", chat_request.query.upper())
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contexts = await load_fund_analysis_context(db, fund_codes=fund_codes)
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if not contexts:
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payload = agent_failure(
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ERR_CODE_LLM_ERROR,
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"未找到可分析的基金数据",
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trace_id=trace_id,
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)
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else:
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result = build_fund_analysis(
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contexts[0]["fund"], contexts[0]["performance"]
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)
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async def events():
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for event in (
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{"type": SSE_EVENT_TYPE_META, "intent": AGENT_INTENT_FUND_ANALYSIS},
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{"type": SSE_EVENT_TYPE_TEXT, "content": result["analysis_text"]},
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{"type": SSE_EVENT_TYPE_DONE},
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):
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yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
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return StreamingResponse(
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events(),
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media_type="text/event-stream",
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headers={"X-Trace-Id": trace_id},
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)
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if inferred_intent == AGENT_INTENT_DIALOGUE_SCRIPT:
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if "市场" in chat_request.query or "波动" in chat_request.query:
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scene_type = TALK_SCENE_MARKET_FLUCTUATION
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elif "投诉" in chat_request.query:
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scene_type = TALK_SCENE_CUSTOMER_COMPLAINT
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elif "拦截" in chat_request.query or "风控" in chat_request.query:
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scene_type = TALK_SCENE_RISK_BLOCK_ORDER
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else:
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scene_type = TALK_SCENE_PORTFOLIO_DIVERGENCE
|
|
result = build_talk_script(scene_type)
|
|
|
|
async def events():
|
|
for event in (
|
|
{"type": SSE_EVENT_TYPE_META, "intent": AGENT_INTENT_DIALOGUE_SCRIPT},
|
|
{"type": SSE_EVENT_TYPE_TEXT, "content": result["content"]},
|
|
{"type": SSE_EVENT_TYPE_DONE},
|
|
):
|
|
yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
|
|
|
|
return StreamingResponse(
|
|
events(),
|
|
media_type="text/event-stream",
|
|
headers={"X-Trace-Id": trace_id},
|
|
)
|
|
if inferred_intent == AGENT_INTENT_CASUAL_CHAT:
|
|
async def events():
|
|
for event in (
|
|
{"type": SSE_EVENT_TYPE_META, "intent": AGENT_INTENT_CASUAL_CHAT},
|
|
{"type": SSE_EVENT_TYPE_TEXT, "content": "您好，我是投顾助手，可以协助您进行基金分析和投资组合管理。"},
|
|
{"type": SSE_EVENT_TYPE_DONE},
|
|
):
|
|
yield f"data: {json.dumps(event, ensure_ascii=False)}\n\n"
|
|
|
|
return StreamingResponse(
|
|
events(),
|
|
media_type="text/event-stream",
|
|
headers={"X-Trace-Id": trace_id},
|
|
)
|
|
if payload is None:
|
|
payload = agent_failure(_NOT_READY_CODE, _NOT_READY_MESSAGE, trace_id=trace_id)
|
|
|
|
async def events():
|
|
yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_ERROR, **payload}, ensure_ascii=False)}\n\n"
|
|
|
|
return StreamingResponse(
|
|
events(),
|
|
media_type="text/event-stream",
|
|
headers={"X-Trace-Id": trace_id},
|
|
)
|
|
|
|
|
|
@router.post("/data-query")
|
|
async def advisor_data_query(
|
|
request: Request,
|
|
body: AdvisorDataQueryReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
"""查询当前投顾选中客户的数据，不返回 SQL，也不生成草稿。"""
|
|
trace_id = _trace_id(request)
|
|
try:
|
|
result = await execute_advisor_data_query(
|
|
db,
|
|
advisor_id=user.id,
|
|
customer_id=body.customer_id,
|
|
question=body.question,
|
|
trace_id=trace_id,
|
|
session_id=body.session_id,
|
|
max_rows=body.max_rows,
|
|
page=body.page,
|
|
page_size=body.page_size,
|
|
sort_by=body.sort_by,
|
|
sort_order=body.sort_order,
|
|
)
|
|
except QueryServiceError:
|
|
return agent_failure(
|
|
ERR_CODE_LLM_ERROR,
|
|
"客户数据查询失败，请稍后重试",
|
|
trace_id=trace_id,
|
|
)
|
|
result.pop("sql", None)
|
|
return agent_success(result, trace_id=trace_id)
|
|
|
|
|
|
@router.get("/draft/list")
|
|
async def list_drafts(
|
|
request: Request,
|
|
advisor_id: int | None = Query(default=None),
|
|
customer_id: int | None = Query(default=None),
|
|
status: Literal[DRAFT_STATUS_DRAFT, DRAFT_STATUS_DISCARDED] | None = Query(default=None),
|
|
page: int = Query(default=1, ge=1),
|
|
page_size: int = Query(default=10, ge=1, le=100),
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
if advisor_id is not None and advisor_id != user.id:
|
|
raise ApiError(ERR_CODE_FORBIDDEN_CUSTOMER, "无权操作该客户数据")
|
|
if customer_id is not None:
|
|
await ensure_customer_access(db, advisor_id=user.id, customer_id=customer_id)
|
|
result = await list_drafts_service(
|
|
AdvisorDraftRepo(db),
|
|
advisor_id=user.id,
|
|
customer_id=customer_id,
|
|
status=status,
|
|
page=page,
|
|
page_size=page_size,
|
|
)
|
|
return agent_success(result, trace_id=_trace_id(request))
|
|
|
|
|
|
@router.get("/draft/{draft_id}")
|
|
async def get_draft(
|
|
draft_id: str,
|
|
request: Request,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
draft = await get_draft_service(AdvisorDraftRepo(db), draft_id)
|
|
await _ensure_draft_access(db, draft, user.id)
|
|
return agent_success(detail_draft(draft), trace_id=_trace_id(request))
|
|
|
|
|
|
@router.put("/draft/{draft_id}/save")
|
|
async def save_draft(
|
|
draft_id: str,
|
|
request: Request,
|
|
body: AdvisorDraftSaveReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
repo = AdvisorDraftRepo(db)
|
|
draft = await get_draft_service(repo, draft_id)
|
|
await _ensure_draft_access(db, draft, user.id)
|
|
customer_risk = await load_customer_risk(db, customer_id=draft.customer_id)
|
|
result = await save_draft_service(
|
|
repo,
|
|
draft_id,
|
|
title=body.title,
|
|
content=body.content,
|
|
structured_data=body.structured_data,
|
|
customer_risk=customer_risk,
|
|
)
|
|
return agent_success(result, trace_id=_trace_id(request))
|
|
|
|
|
|
@router.post("/draft/{draft_id}/operate")
|
|
async def operate_draft(
|
|
draft_id: str,
|
|
request: Request,
|
|
body: AdvisorDraftOperateReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
if body.operation != "discard":
|
|
raise ApiError(_DRAFT_NOT_FOUND_CODE, "不支持的草稿操作")
|
|
repo = AdvisorDraftRepo(db)
|
|
draft = await get_draft_service(repo, draft_id)
|
|
await _ensure_draft_access(db, draft, user.id)
|
|
result = await discard_draft(repo, draft_id)
|
|
return agent_success(detail_draft(result), trace_id=_trace_id(request))
|
|
|
|
|
|
@router.post("/rebalance/run")
|
|
async def run_rebalance(
|
|
request: Request,
|
|
body: AdvisorRebalanceRunReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
background_tasks: BackgroundTasks = None,
|
|
):
|
|
customer_id = body.customer_id
|
|
relation = await ensure_customer_access(
|
|
db, advisor_id=user.id, customer_id=int(customer_id)
|
|
)
|
|
if relation.status != CUSTOMER_REL_STATUS_SIGNED:
|
|
return agent_failure(
|
|
ERR_CODE_NOT_SIGNED_REBALANCE,
|
|
"客户尚未签约，禁止生成调仓草稿",
|
|
trace_id=_trace_id(request),
|
|
)
|
|
if background_tasks is None:
|
|
background_tasks = BackgroundTasks()
|
|
background_tasks.add_task(
|
|
_run_rebalance_background,
|
|
advisor_id=user.id,
|
|
customer_id=customer_id,
|
|
trace_id=_trace_id(request),
|
|
)
|
|
return agent_success(
|
|
{"accepted": True, "status": "queued"},
|
|
trace_id=_trace_id(request),
|
|
)
|
|
|
|
|
|
@router.post("/fund-analysis")
|
|
async def fund_analysis(
|
|
request: Request,
|
|
body: AdvisorFundAnalysisReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
memories: list[dict] = []
|
|
if body.customer_id is not None:
|
|
await ensure_customer_access(
|
|
db, advisor_id=user.id, customer_id=body.customer_id
|
|
)
|
|
memories = await _recall_advisor_memories(
|
|
request,
|
|
customer_id=body.customer_id,
|
|
query=f"基金分析 {','.join(body.fund_codes)}",
|
|
)
|
|
fund = body.fund
|
|
performance = body.performance
|
|
if fund is None or performance is None:
|
|
contexts = await load_fund_analysis_context(
|
|
db,
|
|
fund_codes=[str(code) for code in body.fund_codes],
|
|
)
|
|
if not contexts:
|
|
return _not_ready(request)
|
|
if len(contexts) == 1:
|
|
fund = contexts[0]["fund"]
|
|
performance = contexts[0]["performance"]
|
|
else:
|
|
return agent_success(
|
|
{"items": [build_fund_analysis(item["fund"], item["performance"]) for item in contexts]},
|
|
trace_id=_trace_id(request),
|
|
)
|
|
result = build_fund_analysis(fund, performance)
|
|
runtime = _advisor_runtime(request)
|
|
if getattr(runtime, "llm_client", None) is not None:
|
|
result["analysis_text"] = await generate_text(
|
|
runtime.llm_client,
|
|
system_prompt="你是基金投顾助手，只基于给定历史数据生成谨慎的内部分析，不承诺收益。",
|
|
user_prompt=json.dumps(
|
|
{"analysis": result, "customer_memories": memories},
|
|
ensure_ascii=False,
|
|
),
|
|
fallback=lambda: result["analysis_text"],
|
|
timeout=5.0,
|
|
)
|
|
return agent_success(result, trace_id=_trace_id(request))
|
|
|
|
|
|
@router.post("/generate-talk-script")
|
|
async def generate_talk_script(
|
|
request: Request,
|
|
body: AdvisorTalkScriptReq,
|
|
user: SysUser = Depends(audited_advisor),
|
|
db: AsyncSession = Depends(get_db),
|
|
):
|
|
await ensure_customer_access(db, advisor_id=user.id, customer_id=body.customer_id)
|
|
memories = await _recall_advisor_memories(
|
|
request,
|
|
customer_id=body.customer_id,
|
|
query=f"沟通话术 {body.scene_type}",
|
|
)
|
|
try:
|
|
result = build_talk_script(
|
|
body.scene_type,
|
|
customer_name=body.customer_name,
|
|
)
|
|
except ValueError as exc:
|
|
raise ApiError(ERR_CODE_LLM_ERROR, str(exc)) from exc
|
|
runtime = _advisor_runtime(request)
|
|
if getattr(runtime, "llm_client", None) is not None:
|
|
result["content"] = await generate_text(
|
|
runtime.llm_client,
|
|
system_prompt="你是合规的基金投顾助手，只生成谨慎沟通话术，不承诺收益、不代客交易。",
|
|
user_prompt=json.dumps(
|
|
{"script": result, "customer_memories": memories},
|
|
ensure_ascii=False,
|
|
),
|
|
fallback=lambda: result["content"],
|
|
timeout=5.0,
|
|
)
|
|
return agent_success(result, trace_id=_trace_id(request))
|