Merge pull request 'feat:投顾agent模块优化' (#18) from develop_feature_qianduan into develop

Reviewed-on: #18

api/routers/advisor_agent.py

@@ -2,11 +2,11 @@
 from __future__ import annotations
 
 import json
+import re
 from typing import Literal
 
 from fastapi import APIRouter, BackgroundTasks, Depends, Query, Request
 from fastapi.responses import StreamingResponse
-from pydantic import ValidationError
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from agent.advisor_agent.auth import ensure_customer_access
@@ -37,6 +37,7 @@ from config.deps import get_db
 from config.database import mysql, redis as redis_db
 from model.sys_user import SysUser
 from repositories.advisor_draft import AdvisorDraftRepo
+from repositories.customer_relation import CustomerRelationRepo
 from service.advisor_agent.context import (
     load_fund_analysis_context,
     load_customer_risk,
@@ -87,6 +88,48 @@ def _advisor_runtime(request: Request):
     return getattr(getattr(app, "state", None), "advisor_agent_runtime", None)
 
 
+def _infer_chat_intent(query: str) -> str | None:
+    """从自然语言问题推断投顾意图；无法确定时保留通用问答。"""
+    if any(word in query for word in ("调仓", "再平衡", "组合偏离")):
+        return "rebalance"
+    if any(word in query for word in ("沟通话术", "怎么和客户说", "解释给客户")):
+        return "dialogue-script"
+    if any(word in query for word in ("基金分析", "分析这只基金", "分析产品")):
+        return "fund_analysis"
+    if any(word in query for word in ("推荐", "产品建议", "买什么基金", "适合的基金")):
+        return AGENT_INTENT_RECOMMEND
+    return None
+
+
+async def _resolve_customer_from_query(db, *, advisor_id: int, query: str) -> tuple[int | None, str | None]:
+    """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。"""
+    number_match = re.search(r"(?:客户|用户)\s*[#编号号:]?\s*(\d+)", query)
+    relation_repo = CustomerRelationRepo(db)
+    if number_match:
+        customer_id = int(number_match.group(1))
+        relation = await relation_repo.get_active_relation(
+            customer_id=customer_id,
+            advisor_id=advisor_id,
+        )
+        if relation is None:
+            return None, "问题中的客户不在当前投顾的授权范围内"
+        return customer_id, None
+
+    rows = await relation_repo.list_customer_rows(advisor_id=advisor_id, limit=100)
+    matched = {
+        int(account.id)
+        for _relation, account, _profile in rows
+        if account.real_name and account.real_name in query
+    }
+    if len(matched) == 1:
+        return next(iter(matched)), None
+    if len(matched) > 1:
+        return None, "问题中的客户姓名无法唯一确定，请补充客户编号"
+    if "客户" in query or "用户" in query:
+        return None, "请在问题中补充客户编号或客户姓名"
+    return None, None
+
+
 async def _recall_advisor_memories(
     request: Request, *, customer_id: int, query: str
 ) -> list[dict]:
@@ -143,29 +186,89 @@ async def _run_rebalance_background(
 @router.post("/chat/stream")
 async def chat_stream(
     request: Request,
-    body: dict,
+    body: AdvisorChatReq,
     user: SysUser = Depends(audited_advisor),
     db: AsyncSession = Depends(get_db),
 ):
     trace_id = _trace_id(request)
-    try:
-        chat_request = AdvisorChatReq.model_validate(body)
-    except ValidationError:
-        payload = agent_failure(
-            ERR_CODE_FORBIDDEN_CUSTOMER,
-            "对话请求缺少有效客户范围或参数",
-            trace_id=trace_id,
+    chat_request = body
+    customer_id = chat_request.customer_id
+    inferred_intent = chat_request.intent or _infer_chat_intent(chat_request.query)
+
+    # 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。
+    if customer_id is None:
+        customer_id, resolve_error = await _resolve_customer_from_query(
+            db,
+            advisor_id=user.id,
+            query=chat_request.query,
         )
+        if resolve_error:
+            payload = agent_failure(
+                ERR_CODE_FORBIDDEN_CUSTOMER,
+                resolve_error,
+                trace_id=trace_id,
+            )
+            async def resolve_error_events():
+                yield f"data: {json.dumps({'type': SSE_EVENT_TYPE_ERROR, **payload}, ensure_ascii=False)}\n\n"
+
+            return StreamingResponse(
+                resolve_error_events(),
+                media_type="text/event-stream",
+                headers={"X-Trace-Id": trace_id},
+            )
+        else:
+            payload = None
+
+    # 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。
+    if customer_id is None:
+        if inferred_intent in {
+            AGENT_INTENT_RECOMMEND,
+            "rebalance",
+            "fund_analysis",
+            "dialogue-script",
+        }:
+            if payload is None:
+                payload = agent_failure(
+                    ERR_CODE_FORBIDDEN_CUSTOMER,
+                    "个性化投顾分析需要在问题中明确客户编号或姓名",
+                    trace_id=trace_id,
+                )
+        else:
+            runtime = _advisor_runtime(request)
+            llm_client = getattr(runtime, "llm_client", None)
+            if llm_client is None:
+                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。"
+            else:
+                answer = await generate_text(
+                    llm_client,
+                    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。",
+                    user_prompt=chat_request.query,
+                    fallback=lambda: "当前模型暂时不可用，请稍后重试。",
+                    timeout=5.0,
+                )
+
+            async def events():
+                for event in (
+                    {"type": SSE_EVENT_TYPE_META, "intent": "general_question"},
+                    {"type": SSE_EVENT_TYPE_TEXT, "content": answer},
+                    {"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},
+            )
     else:
-        customer_id = chat_request.customer_id
         relation = await ensure_customer_access(
             db, advisor_id=user.id, customer_id=int(customer_id)
         )
-        if chat_request.intent == AGENT_INTENT_RECOMMEND:
+        if inferred_intent == AGENT_INTENT_RECOMMEND:
             memories = await _recall_advisor_memories(
                 request,
                 customer_id=int(customer_id),
-                query=chat_request.query or "",
+                query=chat_request.query,
             )
             runtime = _advisor_runtime(request)
             context = await load_recommendation_context(

requirements.txt

@@ -12,6 +12,7 @@ pymilvus
 neo4j
 python-dotenv
 pypdf
+sqlglot
 
 fastapi~=0.141.1
 sqlalchemy~=2.0.52
@@ -34,4 +35,4 @@ neo4j~=6.3.0
 redis~=8.1.0
 pymilvus~=3.0.1
 pydantic-settings~=2.15.0
-pyjwt~=2.13.0
+pyjwt~=2.13.0

schemas/advisor_agent.py

@@ -32,14 +32,15 @@ class AdvisorRebalanceRunReq(BaseModel):
 
 
 class AdvisorChatReq(BaseModel):
-    customer_id: int = Field(gt=0)
+    # 通用投顾问答不需要客户上下文；个性化推荐时再传入客户编号。
+    customer_id: int | None = Field(default=None, gt=0)
     intent: Literal[
         AGENT_INTENT_RECOMMEND,
         AGENT_INTENT_REBALANCE,
         AGENT_INTENT_FUND_ANALYSIS,
         AGENT_INTENT_DIALOGUE_SCRIPT,
     ] | None = None
-    query: str | None = Field(default=None, max_length=4000)
+    query: str = Field(min_length=1, max_length=4000)
 
 
 class AdvisorFundAnalysisReq(BaseModel):