feat:修改投顾agent和nl2sql的功能

agent/advisor_agent/data_query.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 
 from dataclasses import asdict
 from typing import Any
+from uuid import uuid4
 
 from agent.advisor_agent.auth import ensure_customer_access
 from agent.data_query.agent import DataQueryAgent
@@ -15,6 +16,51 @@ from nl2sql.schema import load_authoritative_schema
 from service.nl2sql.permission_service import load_query_permission
 from service.nl2sql.query_service import QueryServiceError
 from tool.llm import llm as default_llm
+from repositories.fin_holdings import FinHoldingsRepo
+from repositories.fin_product import FinProductRepo
+
+
+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"))
+
+
+async def _query_current_holdings(db, *, customer_id: int, trace_id: str) -> dict[str, Any]:
+    holdings = await FinHoldingsRepo(db).list_by_customer(customer_id, status="持有中")
+    product_repo = FinProductRepo(db)
+    rows: list[dict[str, Any]] = []
+    total_value = 0
+    for holding in holdings:
+        product = await product_repo.get(holding.product_id)
+        rows.append(
+            {
+                "产品代码": product.product_code if product else None,
+                "产品名称": product.product_name if product else None,
+                "风险等级": product.risk_level if product else None,
+                "持有份额": f"{holding.shares:.4f}",
+                "成本金额": f"{holding.cost_amount:.2f}",
+                "当前市值": f"{holding.current_value:.2f}",
+                "盈亏": f"{holding.profit_loss:.2f}",
+                "收益率": f"{holding.profit_ratio:.4f}",
+                "状态": holding.status,
+            }
+        )
+        total_value += holding.current_value
+    names = [row["产品名称"] for row in rows if row["产品名称"]]
+    return {
+        "query_id": f"holdings-{uuid4().hex}",
+        "trace_id": trace_id,
+        "columns": list(rows[0].keys()) if rows else ["产品代码", "产品名称", "风险等级", "持有份额", "成本金额", "当前市值", "盈亏", "收益率", "状态"],
+        "rows": rows,
+        "row_count": len(rows),
+        "truncated": False,
+        "summary": f"当前持仓共 {len(rows)} 条记录。",
+        "answer": (
+            f"当前持有 {len(rows)} 只基金，总市值约 {total_value:.2f} 元。"
+            + (f"包括：{'、'.join(names[:6])}。" if names else "")
+        ),
+        "sql": None,
+    }
 
 
 async def execute_advisor_data_query(
@@ -42,6 +88,12 @@ async def execute_advisor_data_query(
     ``customer_id``，避免投顾借助 NL2SQL 查询其他客户数据。
     """
     await ensure_customer_access(db, advisor_id=advisor_id, customer_id=customer_id)
+    if _is_current_holdings_query(question):
+        return await _query_current_holdings(
+            db,
+            customer_id=customer_id,
+            trace_id=trace_id,
+        )
     permission = await load_query_permission(db, advisor_id)
     if not permission.get("can_query", False):
         raise QueryServiceError("当前投顾没有 NL2SQL 查询权限")

agent/advisor_agent/intent/classifier.py

@@ -0,0 +1,132 @@
+"""投顾聊天意图分类：LLM 优先，规则识别兜底。"""
+from __future__ import annotations
+
+import asyncio
+import json
+import re
+from dataclasses import dataclass
+
+from common.common_const import (
+    AGENT_INTENT_CASUAL_CHAT,
+    AGENT_INTENT_DATA_QUERY,
+    AGENT_INTENT_DIALOGUE_SCRIPT,
+    AGENT_INTENT_FUND_ANALYSIS,
+    AGENT_INTENT_REBALANCE,
+    AGENT_INTENT_RECOMMEND,
+)
+from agent.advisor_agent.intent.recognizer import recognize_advisor_intent
+
+
+VALID_INTENTS = frozenset({
+    AGENT_INTENT_RECOMMEND,
+    AGENT_INTENT_REBALANCE,
+    AGENT_INTENT_FUND_ANALYSIS,
+    AGENT_INTENT_DIALOGUE_SCRIPT,
+    AGENT_INTENT_DATA_QUERY,
+    AGENT_INTENT_CASUAL_CHAT,
+})
+
+
+@dataclass(frozen=True)
+class IntentClassification:
+    intent: str
+    confidence: float
+    source: str
+    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
+- rebalance：组合偏离、调仓、再平衡、仓位调整
+- fund_analysis：单只基金分析、净值、收益、回撤、波动率、夏普比率
+- dialogue-script：给客户准备沟通话术、解释、安抚、投诉或风险提醒
+- data_query：查询客户持仓、资产、余额、收益、交易、账户明细
+- casual_chat：问候、闲聊、感谢、身份询问或无法归入业务分类的内容
+
+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")
+
+
+def _rule_fallback(query: str | None) -> IntentClassification:
+    intent = recognize_advisor_intent(query)
+    if intent:
+        return IntentClassification(intent=intent, confidence=0.72, source="rule", reason="关键词规则匹配")
+    return IntentClassification(intent=AGENT_INTENT_CASUAL_CHAT, confidence=0.0, source="fallback", reason="无法匹配业务意图")
+
+
+def _parse_model_result(raw: str) -> IntentClassification | None:
+    text = raw.strip()
+    fenced = re.search(r"\{.*\}", text, re.DOTALL)
+    if fenced:
+        text = fenced.group(0)
+    try:
+        payload = json.loads(text)
+    except (TypeError, json.JSONDecodeError):
+        return None
+    intent = payload.get("intent")
+    if intent not in VALID_INTENTS:
+        return None
+    try:
+        confidence = max(0.0, min(1.0, float(payload.get("confidence", 0.0))))
+    except (TypeError, ValueError):
+        confidence = 0.0
+    return IntentClassification(
+        intent=intent,
+        confidence=confidence,
+        source="llm",
+        reason=str(payload.get("reason") or "模型分类"),
+    )
+
+
+async def classify_advisor_intent(
+    query: str | None,
+    llm_client=None,
+    *,
+    explicit_intent: str | None = None,
+    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。"""
+    if explicit_intent in VALID_INTENTS:
+        return IntentClassification(explicit_intent, 1.0, "explicit", "客户端显式指定")
+    if not query or not query.strip():
+        return IntentClassification("", 0.0, "fallback", "空输入")
+    if llm_client is not None:
+        try:
+            raw = await asyncio.wait_for(
+                llm_client.chat(
+                    [
+                        {"role": "system", "content": _CLASSIFIER_PROMPT},
+                        {"role": "user", "content": query.strip()},
+                    ],
+                    temperature=0,
+                    max_tokens=120,
+                ),
+                timeout=timeout,
+            )
+            parsed = _parse_model_result(raw)
+            if parsed is not None:
+                # LLM 偶尔会把“查询持仓/资产”等只读请求误判为推荐；
+                # 对明确的查询动作以规则结果为准，避免误进入草稿生成分支。
+                rule_intent = recognize_advisor_intent(query)
+                if (
+                    rule_intent == AGENT_INTENT_DATA_QUERY
+                    and parsed.intent == AGENT_INTENT_RECOMMEND
+                    and not any(term in (query or "") for term in _RECOMMENDATION_TERMS)
+                ):
+                    return IntentClassification(
+                        intent=rule_intent,
+                        confidence=max(parsed.confidence, 0.9),
+                        source="rule_override",
+                        reason="明确查询类关键词覆盖模型误判",
+                    )
+                return parsed
+        except Exception:
+            pass
+    return _rule_fallback(query)
+
+
+__all__ = ["IntentClassification", "classify_advisor_intent"]

agent/advisor_agent/intent/recognizer.py

@@ -3,7 +3,13 @@ from __future__ import annotations
 
 import re
 
-from common.common_const import AGENT_INTENT_DATA_QUERY
+from common.common_const import (
+    AGENT_INTENT_DATA_QUERY,
+    AGENT_INTENT_DIALOGUE_SCRIPT,
+    AGENT_INTENT_FUND_ANALYSIS,
+    AGENT_INTENT_REBALANCE,
+    AGENT_INTENT_RECOMMEND,
+)
 
 
 _QUERY_ACTIONS = (
@@ -30,7 +36,14 @@ _DATA_TERMS = (
     "客户数据",
     "账户",
 )
-_NON_QUERY_INTENTS = ("推荐", "调仓", "再平衡", "话术", "沟通")
+_REBALANCE_TERMS = ("调仓", "再平衡", "组合调整", "配置偏离", "偏离目标", "降低仓位", "增加仓位")
+_FUND_ANALYSIS_TERMS = ("基金分析", "分析基金", "基金表现", "净值走势", "最大回撤", "夏普比率", "年化波动")
+_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")
+_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")
+
+
+def _contains_any(text: str, terms: tuple[str, ...]) -> bool:
+    return any(term in text for term in terms)
 
 
 def recognize_advisor_intent(query: str | None, explicit_intent: str | None = None) -> str | None:
@@ -42,12 +55,23 @@ def recognize_advisor_intent(query: str | None, explicit_intent: str | None = No
     if explicit_intent:
         return explicit_intent
     text = re.sub(r"\s+", "", query or "")
-    if not text or any(term in text for term in _NON_QUERY_INTENTS):
+    if not text:
         return None
+
+    # 先处理最明确的任务词，避免“查询基金收益”被误判成普通数据查询。
+    if _contains_any(text, _REBALANCE_TERMS):
+        return AGENT_INTENT_REBALANCE
+    if _contains_any(text, _FUND_ANALYSIS_TERMS):
+        return AGENT_INTENT_FUND_ANALYSIS
+    if _contains_any(text, _DIALOGUE_TERMS):
+        return AGENT_INTENT_DIALOGUE_SCRIPT
+
     has_action = any(term in text for term in _QUERY_ACTIONS)
     has_data = any(term in text for term in _DATA_TERMS)
     if has_data and (has_action or "客户" in text or "近一年" in text or "本月" in text):
         return AGENT_INTENT_DATA_QUERY
+    if _contains_any(text, _RECOMMEND_TERMS):
+        return AGENT_INTENT_RECOMMEND
     return None