feat:修复投顾agent功能

agent/advisor_agent/data_query.py

@@ -56,7 +56,9 @@ def enrich_customer_rows(
 
 def _is_current_holdings_query(question: str) -> bool:
     text = "".join((question or "").split())
-    return any(term in text for term in ("当前持仓", "目前持仓", "现有持仓", "持仓明细", "持仓情况"))
+    if any(term in text for term in ("当前持仓", "目前持仓", "现有持仓", "持仓明细", "持仓情况")):
+        return True
+    return "持仓" in text and not any(term in text for term in ("历史", "曾经", "已卖出", "交易记录"))
 
 
 async def _query_current_holdings(

agent/advisor_agent/intent/classifier.py

@@ -35,26 +35,58 @@ class IntentClassification:
     reason: str = ""
 
 
-_CLASSIFIER_PROMPT = """你是基金投顾工作台的意图分类器，只负责分类，不回答用户问题。
-只能从以下分类中选择一个：
-- recommend：基金推荐、组合配置或投资方案
-  例：“帮我推荐两只适合稳健型客户的基金”“给客户生成一份组合配置建议”“该买什么基金”
-- rebalance：组合偏离、调仓、再平衡、仓位调整
-  例：“该客户组合偏离目标配置，请给出调仓方案”“组合需要再平衡，降低股票类仓位”
-- fund_analysis：单只基金分析、净值、收益、回撤、波动率、夏普比率
-  例：“分析华夏回报近一年的净值走势和最大回撤”“这只基金的夏普比率和波动率如何”
-- dialogue-script：给客户准备沟通话术、解释、安抚、投诉或风险提醒
-  例：“市场波动时怎么和客户解释”“帮我准备安抚客户的沟通话术”“客户投诉了，话术怎么准备”
-- data_query：查询客户持仓、资产、余额、收益、交易、账户明细、客户名册
-  例：“查询客户48当前持仓和账户余额”“我名下有哪些客户”“统计名下客户数量”
-- casual_chat：问候、闲聊、感谢、身份询问或无法归入业务分类的内容
-  例：“你好”“谢谢”“你是谁”
+_CLASSIFIER_PROMPT = """# 任务：投顾助手意图识别
+你是投顾系统意图分类器，对客户输入文本做意图识别，**只能输出一个类别名称，禁止额外解释**。
+
+## 类别说明
+1. 基金推荐：客户希望推荐、筛选基金产品
+2. 调仓建议：客户询问是否买卖、加减仓、更换基金，寻求调仓交易建议
+3. 客户持仓基金分析：分析客户现有持仓组合、风险、收益情况，不涉及买卖操作建议
+4. 跟客户的沟通话术：投顾需要生成一段发给客户的话术文案。【注意：客户发起提问，不会是该类别】
+5. 数据查询：查询基金客观数据，如净值、基金经理、规模、持仓、费率等事实信息，不做推荐、诊断
+6. 普通聊天：日常问候、单纯情绪吐槽，无明确业务诉求
+
+## 判定优先级（同时存在多个诉求时，取优先级最高）
+调仓建议 > 基金推荐 > 客户持仓基金分析 > 数据查询 > 跟客户的沟通话术 > 普通聊天
+
+## 示例(xx表示我名下的客户名字)
+输入：帮我名下XX推荐几只适合养老的基金
+输出：基金推荐
+
+输入：XX手上的某某基金现在要不要卖出？
+输出：调仓建议
+
+输入：帮我看看xx的基金组合风险高不高
+输出：客户持仓基金分析
+
+输入：帮我查一下XX基金最新规模
+输出：数据查询
+
+输入：帮我写一段话安抚客户，解释近期回撤
+输出：跟客户的沟通话术
+
+输入：今天天气不错
+输出：普通聊天
+
+输入：xx持有的这几只基金波动很大，要不要减仓？
+输出：调仓建议
+
+输入：帮我看下xx持仓的基金，它们的基金经理是谁
+输出：数据查询
+
+现在开始分类
+输入：{{user_query}}
+输出：
 
 只输出 JSON，不要 Markdown，不要额外文字：
 {"intent":"分类值","confidence":0到1之间的数字,"reason":"不超过30字的原因"}
 """
 
 _RECOMMENDATION_TERMS = ("推荐", "组合建议", "配置建议", "买什么", "适合配置", "筛选基金", "投资方案")
+_REBALANCE_TERMS = ("调仓", "再平衡", "组合调整", "配置偏离", "偏离目标")
+_DIALOGUE_TERMS = ("话术", "沟通", "怎么跟客户说", "如何向客户解释", "安抚客户", "投诉处理")
+_CUSTOMER_DATA_TERMS = ("持仓", "资产", "余额", "交易", "账户", "份额", "市值", "盈亏", "资金")
+_REFERENCE_TERMS = ("他", "她", "它", "这个客户", "该客户", "那个客户", "刚才", "上一轮")
 _SCOPE_ERROR_MESSAGE = "投顾范围查询仅支持客户数据查询"
 
 
@@ -89,11 +121,31 @@ def _parse_model_result(raw: str) -> IntentClassification | None:
     )
 
 
+def resolve_advisor_intent(
+    query: str | None,
+    classified_intent: str,
+    *,
+    customer_resolved: bool,
+) -> str:
+    """结合实体解析结果做最终业务路由，处理一句话中的明确优先意图。"""
+    text = re.sub(r"\s+", "", query or "")
+    if any(term in text for term in _REBALANCE_TERMS):
+        return AGENT_INTENT_REBALANCE
+    if any(term in text for term in _RECOMMENDATION_TERMS):
+        return AGENT_INTENT_RECOMMEND
+    if any(term in text for term in _DIALOGUE_TERMS):
+        return AGENT_INTENT_DIALOGUE_SCRIPT
+    if customer_resolved and any(term in text for term in _CUSTOMER_DATA_TERMS):
+        return AGENT_INTENT_DATA_QUERY
+    return classified_intent
+
+
 async def classify_advisor_intent(
     query: str | None,
     llm_client=None,
     *,
     explicit_intent: str | None = None,
+    conversation_context: str = "",
     timeout: float = 2.0,
 ) -> IntentClassification:
     """分类用户意图；显式意图兼容旧客户端，模型失败时安全回退规则。"""
@@ -119,13 +171,28 @@ async def classify_advisor_intent(
             "rule_fast",
             "明显查询类问题",
         )
+    normalized_query = re.sub(r"\s+", "", query)
+    if (
+        any(term in normalized_query for term in _CUSTOMER_DATA_TERMS)
+        and conversation_context
+        and any(term in normalized_query for term in _REFERENCE_TERMS)
+    ):
+        return IntentClassification(
+            AGENT_INTENT_DATA_QUERY,
+            0.9,
+            "rule_context",
+            "上下文中的客户数据追问",
+        )
     if llm_client is not None:
         try:
+            prompt = query.strip()
+            if conversation_context:
+                prompt = f"会话上下文：\n{conversation_context[:2000]}\n\n当前问题：\n{prompt}"
             raw = await asyncio.wait_for(
                 llm_client.chat(
                     [
                         {"role": "system", "content": _CLASSIFIER_PROMPT},
-                        {"role": "user", "content": query.strip()},
+                        {"role": "user", "content": prompt},
                     ],
                     temperature=0,
                     max_tokens=120,
@@ -154,4 +221,4 @@ async def classify_advisor_intent(
     return _rule_fallback(query)
 
 
-__all__ = ["IntentClassification", "classify_advisor_intent"]
+__all__ = ["IntentClassification", "classify_advisor_intent", "resolve_advisor_intent"]

agent/advisor_agent/intent/recognizer.py

@@ -26,6 +26,7 @@ _QUERY_ACTIONS = (
 _DATA_TERMS = (
     "持仓",
     "资产",
+    "资金",
     "收益",
     "交易记录",
     "申购",
@@ -42,6 +43,14 @@ _DIALOGUE_TERMS = ("话术", "沟通", "怎么跟客户说", "如何向客户解
 _RECOMMEND_TERMS = ("推荐", "组合建议", "配置建议", "买什么", "适合配置", "筛选基金", "投资方案")
 _CUSTOMER_IDENTITY_TERMS = ("是谁", "姓名", "实名", "基本信息", "联系方式", "手机号")
 _CUSTOMER_ROSTER_TERMS = ("名单", "列表", "几个")
+_CUSTOMER_RISK_TERMS = (
+    "风险评级", "风险等级", "风险类型", "激进型", "进取型", "平衡型", "稳健型", "保守型",
+    "C1", "C2", "C3", "C4", "C5",
+)
+_IMPLICIT_QUERY_TERMS = (
+    "谁", "哪个", "哪些", "多少", "最大", "最多", "最小", "最少", "最高", "最低",
+    "合计", "总额", "总资产", "排名", "前几", "前十", "超过", "低于", "是否", "有没有",
+)
 _SCOPE_ERROR_MESSAGE = "投顾范围查询仅支持客户数据查询"
 
 
@@ -88,7 +97,15 @@ def recognize_advisor_intent(query: str | None, explicit_intent: str | None = No
     )
     if has_customer_roster:
         return AGENT_INTENT_DATA_QUERY
-    if has_data and (has_action or "客户" in text or "近一年" in text or "本月" in text):
+    if "客户" in text and _contains_any(text, _CUSTOMER_RISK_TERMS) and not _contains_any(text, _RECOMMEND_TERMS):
+        return AGENT_INTENT_DATA_QUERY
+    if has_data and (
+        has_action
+        or "客户" in text
+        or "近一年" in text
+        or "本月" in text
+        or _contains_any(text, _IMPLICIT_QUERY_TERMS)
+    ):
         return AGENT_INTENT_DATA_QUERY
     if _contains_any(text, _RECOMMEND_TERMS):
         return AGENT_INTENT_RECOMMEND

api/routers/advisor_agent.py

@@ -13,7 +13,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from agent.advisor_agent.auth import ensure_customer_access
 from agent.advisor_agent.data_query import execute_advisor_data_query
 from agent.advisor_agent.intent.fund_analysis import build_fund_analysis
-from agent.advisor_agent.intent.classifier import classify_advisor_intent
+from agent.advisor_agent.intent.classifier import classify_advisor_intent, resolve_advisor_intent
 from agent.advisor_agent.intent.talk_script import build_talk_script
 from agent.advisor_agent.llm import generate_text
 from agent.advisor_agent.intent.generation_flow import (
@@ -69,6 +69,7 @@ from schemas.advisor_agent import (
     AdvisorDataQueryReq,
 )
 from service.nl2sql.query_service import QueryServiceError
+from nl2sql.query_rewriter import rewrite_query
 from service.advisor_agent.draft import (
     detail_draft,
     discard_draft,
@@ -248,10 +249,20 @@ async def chat_stream(
         )
 
     runtime = _advisor_runtime(request)
-    classification = await classify_advisor_intent(
+    context_store = SessionContextStore(redis_db.client())
+    conversation_context = build_conversation_context(
+        await context_store.load(user.id, chat_request.session_id)
+    )
+    effective_question = await rewrite_query(
         chat_request.query,
+        conversation_context,
+        llm_client=getattr(runtime, "llm_client", None),
+    )
+    classification = await classify_advisor_intent(
+        effective_question,
         getattr(runtime, "llm_client", None),
         explicit_intent=None,
+        conversation_context=conversation_context,
     )
     inferred_intent = classification.intent
     scope = chat_request.scope
@@ -277,11 +288,16 @@ async def chat_stream(
         await ensure_customer_access(db, advisor_id=user.id, customer_id=int(customer_id))
     else:
         resolved_customer_id, _resolve_error = await _resolve_customer_from_query(
-            db, advisor_id=user.id, query=chat_request.query
+            db, advisor_id=user.id, query=effective_question
         )
         if resolved_customer_id is not None:
             customer_id = resolved_customer_id
             scope = "customer"
+    inferred_intent = resolve_advisor_intent(
+        effective_question,
+        inferred_intent,
+        customer_resolved=customer_id is not None,
+    )
 
     if customer_id is None:
         if inferred_intent == AGENT_INTENT_DATA_QUERY:
@@ -317,16 +333,12 @@ async def chat_stream(
     if inferred_intent == AGENT_INTENT_DATA_QUERY:
         effective_scope = "customer" if customer_id is not None else "advisor"
         try:
-            context_store = SessionContextStore(redis_db.client())
-            conversation_context = build_conversation_context(
-                await context_store.load(user.id, chat_request.session_id)
-            )
             result = await execute_advisor_data_query(
                 db,
                 advisor_id=user.id,
                 customer_id=int(customer_id) if customer_id is not None else None,
                 scope=effective_scope,
-                question=chat_request.query,
+                question=effective_question,
                 trace_id=trace_id,
                 session_id=chat_request.session_id,
                 conversation_context=conversation_context,

nl2sql/query_rewriter.py

@@ -21,6 +21,24 @@ _REFERENCE_TERMS = (
     "这个基金", "该基金", "那只基金", "这只基金", "这个结果", "上一轮", "刚才", "前面",
 )
 
+_CUSTOMER_RISK_LABELS = {
+    "保守型": "C1",
+    "稳健型": "C2",
+    "平衡型": "C3",
+    "进取型": "C4",
+    "激进型": "C5",
+}
+
+
+def _normalize_customer_risk_label(question: str) -> str:
+    """将展示层客户风险标签规范化为数据库 C1-C5 编码。"""
+    normalized = re.sub(r"\s+", "", question)
+    if "客户" not in normalized and "风险" not in normalized:
+        return normalized
+    for label, code in _CUSTOMER_RISK_LABELS.items():
+        normalized = normalized.replace(label, code)
+    return normalized
+
 
 def _rewrite_explicit_customer_identity(question: str) -> str | None:
     normalized = re.sub(r"\s+", "", question)
@@ -63,7 +81,7 @@ async def rewrite_query(
     llm_client=llm,
 ) -> str:
     """使用有限会话上下文补全问题；改写失败时安全返回原问题。"""
-    original = (question or "").strip()
+    original = _normalize_customer_risk_label((question or "").strip())
     context = (conversation_context or "").strip()
     if not original:
         return original

nl2sql/semantic_catalog.json

@@ -18,12 +18,12 @@
       "value_hint": "fin_product.product_type 实际枚举值为：货币型/债券型/混合型/股票型/指数型/QDII；股票型基金必须用 product_type = '股票型'，不要使用 Schema 注释里的'股票基金'"
     },
     {
-      "term": "客户",
+      "term": "客户风险等级",
       "enabled": true,
-      "aliases": ["客户", "投资人", "持有人"],
+      "aliases": ["客户", "投资人", "持有人", "风险评级", "风险等级", "风险类型", "激进型", "进取型", "平衡型", "稳健型", "保守型", "C1", "C2", "C3", "C4", "C5"],
       "tables": ["fin_customer_profile", "fin_holdings", "fin_risk_assessment"],
       "fields": ["customer_id", "risk_level", "customer_level"],
-      "value_hint": "fin_customer_profile.risk_level / fin_risk_assessment.risk_level 实际枚举值为 R1-R5（R1 最保守，R5 最激进，与产品风险等级同一口径）；查'保守型/稳健型客户'对应 R1/R2，'激进型客户'对应 R5"
+      "value_hint": "客户风险字段实际枚举值为 C1-C5（C1 最保守，C5 最激进）；展示层映射为保守型=C1、稳健型=C2、平衡型=C3、进取型=C4、激进型=C5。产品风险等级才使用 R1-R5"
     },
     {
       "term": "持仓",

tool/llm.py

@@ -200,6 +200,10 @@ class LLMClient:
                 "max_tokens": request_max_tokens,
                 "stream": False,
             }
+            # SQL/分类等短输出任务不需要深度思考；DeepSeek-V4 若开启思考，
+            # 可能耗尽 token 预算而返回空的 content。
+            if model.lower().startswith("deepseek-v4"):
+                payload["enable_thinking"] = False
             try:
                 client = self._client_for(backend)
                 r = await client.post(url, headers=backend.headers, json=payload)