test目录下增加了redis原生支持的测试，修正了citu_app.py中任务删除api的问题.

citu_app.py

@@ -4130,8 +4130,10 @@ def delete_task_directory_simple(task_id, delete_database_records=False):
             # 删除目录
             shutil.rmtree(task_dir)
             directory_deleted = True
+            operation_message = "目录删除成功"
         else:
             directory_deleted = False
+            operation_message = "目录不存在，无需删除"
         
         # 2. 更新数据库
         database_records_deleted = False
@@ -4180,7 +4182,8 @@ def delete_task_directory_simple(task_id, delete_database_records=False):
             "database_records_deleted": database_records_deleted,
             "deleted_files_count": deleted_files_count,
             "deleted_size": format_size(deleted_size),
-            "deleted_at": datetime.now().isoformat()
+            "deleted_at": datetime.now().isoformat(),
+            "operation_message": operation_message  # 新增：具体的操作消息
         }
         
     except Exception as e:
@@ -4189,19 +4192,45 @@ def delete_task_directory_simple(task_id, delete_database_records=False):
             "success": False,
             "task_id": task_id,
             "error": str(e),
-            "error_code": "DELETE_FAILED"
+            "error_code": "DELETE_FAILED",
+            "operation_message": f"删除操作失败: {str(e)}"  # 新增：失败消息
         }
 
 @app.flask_app.route('/api/v0/data_pipeline/tasks', methods=['DELETE'])
 def delete_tasks():
     """删除任务目录（支持单个和批量）"""
     try:
-        # 获取请求参数
-        req = request.get_json(force=True)
+        # 智能获取参数：支持JSON body和URL查询参数两种方式
+        def get_request_parameter(param_name, array_param_name=None):
+            """从JSON body或URL查询参数中获取参数值"""
+            # 1. 优先从JSON body获取
+            if request.is_json:
+                try:
+                    json_data = request.get_json()
+                    if json_data and param_name in json_data:
+                        return json_data[param_name]
+                except:
+                    pass
+            
+            # 2. 从URL查询参数获取
+            if param_name in request.args:
+                value = request.args.get(param_name)
+                # 处理布尔值
+                if value.lower() in ('true', '1', 'yes'):
+                    return True
+                elif value.lower() in ('false', '0', 'no'):
+                    return False
+                return value
+            
+            # 3. 处理数组参数（如 task_ids[]）
+            if array_param_name and array_param_name in request.args:
+                return request.args.getlist(array_param_name)
+            
+            return None
         
-        # 验证必需参数
-        task_ids = req.get('task_ids')
-        confirm = req.get('confirm')
+        # 获取参数
+        task_ids = get_request_parameter('task_ids', 'task_ids[]')
+        confirm = get_request_parameter('confirm')
         
         if not task_ids:
             return jsonify(bad_request_response(
@@ -4226,8 +4255,10 @@ def delete_tasks():
             )), 400
         
         # 获取可选参数
-        delete_database_records = req.get('delete_database_records', False)
-        continue_on_error = req.get('continue_on_error', True)
+        delete_database_records = get_request_parameter('delete_database_records') or False
+        continue_on_error = get_request_parameter('continue_on_error')
+        if continue_on_error is None:
+            continue_on_error = True
         
         # 执行批量删除操作
         deleted_tasks = []
@@ -4264,20 +4295,36 @@ def delete_tasks():
             "deleted_at": datetime.now().isoformat()
         }
         
+        # 构建智能响应消息
         if len(task_ids) == 1:
-            # 单个删除
+            # 单个删除：使用具体的操作消息
             if summary["failed"] == 0:
-                message = "任务目录删除成功"
+                # 从deleted_tasks中获取具体的操作消息
+                operation_msg = deleted_tasks[0].get('operation_message', '任务处理完成')
+                message = operation_msg
             else:
-                message = "任务目录删除失败"
+                # 从failed_tasks中获取错误消息
+                error_msg = failed_tasks[0].get('error', '删除失败')
+                message = f"任务删除失败: {error_msg}"
         else:
-            # 批量删除
+            # 批量删除：统计各种操作结果
+            directory_deleted_count = sum(1 for task in deleted_tasks if task.get('directory_deleted', False))
+            directory_not_exist_count = sum(1 for task in deleted_tasks if not task.get('directory_deleted', False))
+            
             if summary["failed"] == 0:
-                message = "批量删除完成"
+                # 全部成功
+                if directory_deleted_count > 0 and directory_not_exist_count > 0:
+                    message = f"批量操作完成：{directory_deleted_count}个目录已删除，{directory_not_exist_count}个目录不存在"
+                elif directory_deleted_count > 0:
+                    message = f"批量删除完成：成功删除{directory_deleted_count}个目录"
+                elif directory_not_exist_count > 0:
+                    message = f"批量操作完成：{directory_not_exist_count}个目录不存在，无需删除"
+                else:
+                    message = "批量操作完成"
             elif summary["successfully_deleted"] == 0:
-                message = "批量删除失败"
+                message = f"批量删除失败：{summary['failed']}个任务处理失败"
             else:
-                message = "批量删除部分完成"
+                message = f"批量删除部分完成：成功{summary['successfully_deleted']}个，失败{summary['failed']}个"
         
         return jsonify(success_response(
             response_text=message,

data_pipeline/training_data/task_20250703_000820/bss_business_day_data.ddl

@@ -1,31 +0,0 @@
--- 中文名: 记录各服务区每日经营数据
--- 描述: 记录各服务区每日经营数据，用于业务统计与运营分析
-create table public.bss_business_day_data (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 数据版本号,
-  create_ts timestamp         -- 创建时间戳,
-  created_by varchar(50)      -- 创建人账号,
-  update_ts timestamp         -- 更新时间戳,
-  updated_by varchar(50)      -- 更新人账号,
-  delete_ts timestamp         -- 删除时间戳,
-  deleted_by varchar(50)      -- 删除人账号,
-  oper_date date              -- 统计日期,
-  service_no varchar(255)     -- 服务区编码,
-  service_name varchar(255)   -- 服务区名称,
-  branch_no varchar(255)      -- 档口编码,
-  branch_name varchar(255)    -- 档口名称,
-  wx numeric(19,4)            -- 微信支付金额,
-  wx_order integer            -- 微信订单数量,
-  zfb numeric(19,4)           -- 支付宝支付金额,
-  zf_order integer            -- 支付宝订单数量,
-  rmb numeric(19,4)           -- 现金支付金额,
-  rmb_order integer           -- 现金订单数量,
-  xs numeric(19,4)            -- 行吧支付金额,
-  xs_order integer            -- 行吧订单数量,
-  jd numeric(19,4)            -- 金豆支付金额,
-  jd_order integer            -- 金豆订单数量,
-  order_sum integer           -- 订单总数,
-  pay_sum numeric(19,4)       -- 支付总金额,
-  source_type integer         -- 数据来源类别,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_business_day_data_detail.md

@@ -1,32 +0,0 @@
-## bss_business_day_data（记录各服务区每日经营数据）
-bss_business_day_data 表记录各服务区每日经营数据，用于业务统计与运营分析
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
-- version (integer) - 数据版本号 [非空] [示例: 1]
-- create_ts (timestamp) - 创建时间戳 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
-- created_by (varchar(50)) - 创建人账号 [示例: xingba]
-- update_ts (timestamp) - 更新时间戳 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
-- updated_by (varchar(50)) - 更新人账号
-- delete_ts (timestamp) - 删除时间戳
-- deleted_by (varchar(50)) - 删除人账号
-- oper_date (date) - 统计日期 [示例: 2023-04-01]
-- service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501]
-- service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区]
-- branch_no (varchar(255)) - 档口编码 [示例: 1, H05016]
-- branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区]
-- wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000]
-- wx_order (integer) - 微信订单数量 [示例: 253, 133]
-- zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000]
-- zf_order (integer) - 支付宝订单数量 [示例: 15, 0]
-- rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000]
-- rmb_order (integer) - 现金订单数量 [示例: 56, 12]
-- xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000]
-- xs_order (integer) - 行吧订单数量 [示例: 0, 1]
-- jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000]
-- jd_order (integer) - 金豆订单数量 [示例: 0]
-- order_sum (integer) - 订单总数 [示例: 324, 146]
-- pay_sum (numeric(19,4)) - 支付总金额 [示例: 6077.5000, 2687.0000]
-- source_type (integer) - 数据来源类别 [示例: 1, 0, 4]
-字段补充说明：
-- id 为主键
-- source_type 为枚举字段，包含取值：0、4、1、2、3

data_pipeline/training_data/task_20250703_000820/bss_car_day_count.ddl

@@ -1,17 +0,0 @@
--- 中文名: 抱歉
--- 描述: 抱歉，我暂时无法回答您的问题。请稍后再试。
-create table public.bss_car_day_count (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 最后更新时间,
-  updated_by varchar(50)      -- 最后更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
-  customer_count bigint       -- 车辆数量,
-  car_type varchar(100)       -- 车辆类别,
-  count_date date             -- 统计日期,
-  service_area_id varchar(32) -- 服务区ID,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_car_day_count_detail.md

@@ -1,18 +0,0 @@
-## bss_car_day_count（抱歉）
-bss_car_day_count 表抱歉，我暂时无法回答您的问题。请稍后再试。
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
-- version (integer) - 版本号 [非空] [示例: 1]
-- create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
-- created_by (varchar(50)) - 创建人
-- update_ts (timestamp) - 最后更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
-- updated_by (varchar(50)) - 最后更新人
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
-- customer_count (bigint) - 车辆数量 [示例: 1114, 295]
-- car_type (varchar(100)) - 车辆类别 [示例: 其他]
-- count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02]
-- service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef]
-字段补充说明：
-- id 为主键
-- car_type 为枚举字段，包含取值：其他、危化品、城际、过境

data_pipeline/training_data/task_20250703_000820/bss_company.ddl

@@ -1,15 +0,0 @@
--- 中文名: 业务支撑系统公司信息表
--- 描述: 业务支撑系统公司信息表，记录公司基础信息及创建/更新/删除操作痕迹。
-create table public.bss_company (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 最后更新时间,
-  updated_by varchar(50)      -- 最后更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
-  company_name varchar(255)   -- 公司名称,
-  company_no varchar(255)     -- 公司编码,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_company_detail.md

@@ -1,16 +0,0 @@
-## bss_company（业务支撑系统公司信息表）
-bss_company 表业务支撑系统公司信息表，记录公司基础信息及创建/更新/删除操作痕迹。
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 30675d85ba5044c31acfa243b9d16334, 47ed0bb37f5a85f3d9245e4854959b81]
-- version (integer) - 版本号 [非空] [示例: 1, 2]
-- create_ts (timestamp) - 创建时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
-- created_by (varchar(50)) - 创建人 [示例: admin]
-- update_ts (timestamp) - 最后更新时间 [示例: 2021-05-20 09:51:58.718000, 2021-05-20 09:42:03.341000]
-- updated_by (varchar(50)) - 最后更新人 [示例: admin]
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
-- company_name (varchar(255)) - 公司名称 [示例: 上饶分公司, 宜春分公司]
-- company_no (varchar(255)) - 公司编码 [示例: H03, H02, H07]
-字段补充说明：
-- id 为主键
-- company_no 为枚举字段，包含取值：H01、H02、H03、H04、H05、H06、H07、H08、Q01

data_pipeline/training_data/task_20250703_000820/bss_section_route.ddl

@@ -1,16 +0,0 @@
--- 中文名: 路段路线关联表
--- 描述: 路段路线关联表，维护路段与路线的对应关系，支持高速公路路线规划与管理。
-create table public.bss_section_route (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 更新时间,
-  updated_by varchar(50)      -- 更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
-  section_name varchar(255)   -- 路段名称,
-  route_name varchar(255)     -- 路线名称,
-  code varchar(255)           -- 路段编号,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_section_route_area_link.ddl

@@ -1,7 +0,0 @@
--- 中文名: 路线与服务区关联表
--- 描述: 路线与服务区关联表，记录路线ID与服务区ID的对应关系，支持路径规划和资源分配。
-create table public.bss_section_route_area_link (
-  section_route_id varchar(32) not null -- 路段路线ID，主键,
-  service_area_id varchar(32) not null -- 服务区ID，主键,
-  primary key (section_route_id, service_area_id)
-);

data_pipeline/training_data/task_20250703_000820/bss_section_route_area_link_detail.md

@@ -1,7 +0,0 @@
-## bss_section_route_area_link（路线与服务区关联表）
-bss_section_route_area_link 表路线与服务区关联表，记录路线ID与服务区ID的对应关系，支持路径规划和资源分配。
-字段列表：
-- section_route_id (varchar(32)) - 路段路线ID [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp]
-- service_area_id (varchar(32)) - 服务区ID [主键, 非空] [示例: 08e01d7402abd1d6a4d9fdd5df855ef8, 091662311d2c737029445442ff198c4c]
-字段补充说明：
-- 复合主键：section_route_id, service_area_id

data_pipeline/training_data/task_20250703_000820/bss_section_route_detail.md

@@ -1,16 +0,0 @@
-## bss_section_route（路段路线关联表）
-bss_section_route 表路段路线关联表，维护路段与路线的对应关系，支持高速公路路线规划与管理。
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
-- version (integer) - 版本号 [非空] [示例: 1, 0]
-- create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16]
-- created_by (varchar(50)) - 创建人 [示例: admin]
-- update_ts (timestamp) - 更新时间
-- updated_by (varchar(50)) - 更新人
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
-- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁, 昌九]
-- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶, /]
-- code (varchar(255)) - 路段编号 [示例: SR0001, SR0002]
-字段补充说明：
-- id 为主键

data_pipeline/training_data/task_20250703_000820/bss_service_area.ddl

@@ -1,19 +0,0 @@
--- 中文名: 存储高速公路服务区基本信息（名称、编码等）
--- 描述: 存储高速公路服务区基本信息（名称、编码等），支持服务区运营管理。
-create table public.bss_service_area (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 最后更新时间,
-  updated_by varchar(50)      -- 最后更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除操作人,
-  service_area_name varchar(255) -- 服务区名称,
-  service_area_no varchar(255) -- 服务区编码,
-  company_id varchar(32)      -- 所属公司ID,
-  service_position varchar(255) -- 地理位置坐标,
-  service_area_type varchar(50) -- 服务区类型,
-  service_state varchar(50)   -- 服务区状态,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_service_area_detail.md

@@ -1,21 +0,0 @@
-## bss_service_area（存储高速公路服务区基本信息（名称、编码等））
-bss_service_area 表存储高速公路服务区基本信息（名称、编码等），支持服务区运营管理。
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
-- version (integer) - 版本号 [非空] [示例: 3, 6]
-- create_ts (timestamp) - 创建时间 [示例: 2021-05-21 13:26:40.589000, 2021-05-20 19:51:46.314000]
-- created_by (varchar(50)) - 创建人 [示例: admin]
-- update_ts (timestamp) - 最后更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000]
-- updated_by (varchar(50)) - 最后更新人 [示例: admin]
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除操作人 [示例: ]
-- service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区, 南昌南服务区]
-- service_area_no (varchar(255)) - 服务区编码 [示例: H0814, H0105]
-- company_id (varchar(32)) - 所属公司ID [示例: b1629f07c8d9ac81494fbc1de61f1ea5, ee9bf1180a2b45003f96e597a4b7f15a]
-- service_position (varchar(255)) - 地理位置坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
-- service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
-- service_state (varchar(50)) - 服务区状态 [示例: 开放, 关闭]
-字段补充说明：
-- id 为主键
-- service_area_type 为枚举字段，包含取值：信息化服务区、智能化服务区
-- service_state 为枚举字段，包含取值：开放、关闭、上传数据

data_pipeline/training_data/task_20250703_000820/bss_service_area_mapper.ddl

@@ -1,18 +0,0 @@
--- 中文名: BSS服务区信息映射表
--- 描述: BSS服务区信息映射表，记录服务区基础信息及状态变更记录，支持服务区全生命周期管理。
-create table public.bss_service_area_mapper (
-  id varchar(32) not null     -- 主键ID，主键,
-  version integer not null    -- 版本号,
-  create_ts timestamp         -- 创建时间,
-  created_by varchar(50)      -- 创建人,
-  update_ts timestamp         -- 更新时间,
-  updated_by varchar(50)      -- 更新人,
-  delete_ts timestamp         -- 删除时间,
-  deleted_by varchar(50)      -- 删除人,
-  service_name varchar(255)   -- 服务区名称,
-  service_no varchar(255)     -- 服务区编码,
-  service_area_id varchar(32) -- 服务区ID,
-  source_system_type varchar(50) -- 数据来源系统,
-  source_type integer         -- 数据来源类别ID,
-  primary key (id)
-);

data_pipeline/training_data/task_20250703_000820/bss_service_area_mapper_detail.md

@@ -1,19 +0,0 @@
-## bss_service_area_mapper（BSS服务区信息映射表）
-bss_service_area_mapper 表BSS服务区信息映射表，记录服务区基础信息及状态变更记录，支持服务区全生命周期管理。
-字段列表：
-- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f]
-- version (integer) - 版本号 [非空] [示例: 1]
-- create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29]
-- created_by (varchar(50)) - 创建人 [示例: admin]
-- update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32]
-- updated_by (varchar(50)) - 更新人
-- delete_ts (timestamp) - 删除时间
-- deleted_by (varchar(50)) - 删除人
-- service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区]
-- service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062]
-- service_area_id (varchar(32)) - 服务区ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
-- source_system_type (varchar(50)) - 数据来源系统 [示例: 驿美, 驿购]
-- source_type (integer) - 数据来源类别ID [示例: 3, 1]
-字段补充说明：
-- id 为主键
-- source_system_type 为枚举字段，包含取值：司乘管理、商业管理、驿购、驿美、手工录入

data_pipeline/training_data/task_20250703_000820/db_query_decision_prompt.txt

@@ -1,13 +0,0 @@
-=== 数据库业务范围 ===
-当前数据库存储的是高速公路服务区运营管理的相关数据，主要涉及经营统计、车流分析、服务区基础信息及路段关联，包含以下业务数据：
-核心业务实体：
-- 服务区：记录服务区基础信息及运营状态，主要字段：service_area_name、service_area_no、company_id、service_state
-- 档口：标识服务区经营单元信息，主要字段：branch_no、branch_name
-- 公司：存储管理分公司信息，主要字段：company_name、company_no
-- 车辆类型：分类车辆统计维度，主要字段：car_type
-- 支付渠道：区分不同支付方式，主要字段：wx、zfb、rmb、xs、jd
-关键业务指标：
-- 营收贡献：通过支付金额(wx/zfb/rmb/xs/jd)和订单数量(wx_order/zf_order/rmb_order/xs_order/jd_order)计算各渠道营收占比
-- 车流量分析：基于customer_count字段统计各服务区不同车型的车流量分布
-- 数据质量：通过source_type、source_system_type字段监控数据采集完整性和更新及时性
-- 运营效率：结合order_sum和pay_sum字段评估单位时间内的交易频次和金额波动

data_pipeline/training_data/task_20250703_000820/ddl_generation_result.json

@@ -1,7 +0,0 @@
-{
-  "total_tables": 7,
-  "processed_successfully": 7,
-  "failed": 0,
-  "files_generated": 14,
-  "duration": 440.8336281776428
-}

data_pipeline/training_data/task_20250703_000820/filename_mapping.txt

@@ -1,10 +0,0 @@
-# 文件名映射报告
-# 格式: 原始表名 -> 实际文件名
-
-public.bss_business_day_data -> bss_business_day_data_detail_1.md
-public.bss_car_day_count -> bss_car_day_count_detail_1.md
-public.bss_company -> bss_company_detail_1.md
-public.bss_section_route -> bss_section_route_detail_1.md
-public.bss_section_route_area_link -> bss_section_route_area_link_detail_1.md
-public.bss_service_area -> bss_service_area_detail_1.md
-public.bss_service_area_mapper -> bss_service_area_mapper_detail_1.md

data_pipeline/training_data/task_20250703_000820/metadata.txt

@@ -1,62 +0,0 @@
--- Schema Tools生成的主题元数据
--- 业务背景: 高速公路服务区管理系统
--- 生成时间: 2025-07-03 00:41:43
--- 数据库: highway_db
-
--- 创建表（如果不存在）
-CREATE TABLE IF NOT EXISTS metadata (
-    id SERIAL PRIMARY KEY,    -- 主键
-    topic_name VARCHAR(100) NOT NULL,  -- 业务主题名称
-    description TEXT,                  -- 业务主体说明
-    related_tables TEXT[],			  -- 相关表名
-    biz_entities TEXT[],               -- 主要业务实体名称
-    biz_metrics TEXT[],                -- 主要业务指标名称
-    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP    -- 插入时间
-);
-
--- 插入主题数据
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '日营收分析',
-  '基于 bss_business_day_data 表分析各服务区/档口每日营收结构、支付方式占比及订单量变化趋势',
-  'bss_business_day_data,bss_service_area,bss_service_area_mapper',
-  '服务区,档口,支付渠道,日期',
-  '营收趋势,渠道占比,区域对比'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '车流监测',
-  '通过 bss_car_day_count 表统计各服务区车流量分布，分析危化品/城际车辆通行规律',
-  'bss_car_day_count,bss_service_area',
-  '服务区,车辆类型,日期,地理坐标',
-  '流量趋势,车型分布,热点排名'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '公司绩效',
-  '结合 bss_company 和 bss_service_area 表评估各分公司管辖服务区的运营效能和服务质量',
-  'bss_company,bss_service_area,bss_business_day_data',
-  '分公司,服务区,服务类型,运营状态',
-  '效能指数,服务达标率,营收贡献'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '路线关联',
-  '通过 bss_section_route 和 bss_section_route_area_link 分析路线与服务区的联动效应',
-  'bss_section_route,bss_section_route_area_link,bss_car_day_count',
-  '路线,路段,服务区,车流量',
-  '路线贡献度,服务区覆盖率,车流密度'
-);
-
-INSERT INTO metadata(topic_name, description, related_tables, biz_entities, biz_metrics) VALUES
-(
-  '数据溯源',
-  '基于 bss_service_area_mapper 分析不同来源系统（驿购/驿美等）的数据覆盖完整性和更新时效性',
-  'bss_service_area_mapper,bss_business_day_data,bss_car_day_count',
-  '数据源系统,服务区,编码类型,更新周期',
-  '数据完整性,更新及时性,来源分布'
-);
-

data_pipeline/training_data/task_20250703_000820/metadata_detail.md

@@ -1,20 +0,0 @@
-## metadata（存储分析主题元数据）
-
-`metadata` 主要描述了当前数据库包含了哪些数据内容，哪些分析主题，哪些指标等等。
-
-字段列表：
-
-- `id` (serial) - 主键ID [主键, 非空]
-- `topic_name` (varchar(100)) - 业务主题名称 [非空]
-- `description` (text) - 业务主题说明
-- `related_tables` (text[]) - 涉及的数据表 [示例: bss_business_day_data, bss_section_route_area_link]
-- `biz_entities` (text[]) - 主要业务实体名称 [示例: 支付渠道, 车辆类型, 分公司]
-- `biz_metrics` (text[]) - 主要业务指标名称 [示例: 更新及时性, 营收贡献, 渠道占比]
-- `created_at` (timestamp) - 插入时间 [默认值: `CURRENT_TIMESTAMP`]
-
-字段补充说明：
-
-- `id` 为主键，自增；
-- `related_tables` 用于建立主题与具体明细表的依赖关系；
-- `biz_entities` 表示主题关注的核心对象，例如服务区、车辆、公司；
-- `biz_metrics` 表示该主题关注的业务分析指标，例如营收对比、趋势变化、占比结构等。

data_pipeline/training_data/task_20250703_000820/qa_generation_result.json

@@ -1,8 +0,0 @@
-{
-  "output_file": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250703_000820\\qs_highway_db_20250703_004143_pair.json",
-  "total_questions": 50,
-  "total_themes": 5,
-  "successful_themes": 5,
-  "failed_themes": [],
-  "duration": 424.5340418815613
-}

data_pipeline/training_data/task_20250703_000820/qs_highway_db_20250703_004143_pair.json

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "统计最近7天各服务区总营收排名（按支付总金额）",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营收 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析2023年4月1日各档口微信支付占比（微信金额/总支付金额）",
-    "sql": "SELECT branch_name AS 档口名称, (SUM(wx)/SUM(pay_sum))*100 AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY branch_name HAVING SUM(pay_sum) > 0;"
-  },
-  {
-    "question": "查询2023年4月各服务区支付宝订单量变化趋势（按月统计）",
-    "sql": "SELECT service_name AS 服务区名称, DATE_TRUNC('month', oper_date) AS 月份, SUM(zf_order) AS 支付宝订单量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY 服务区名称, 月份 ORDER BY 月份;"
-  },
-  {
-    "question": "对比2023年4月1日现金支付与行吧支付金额前5的服务区",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) AS 现金支付金额, SUM(xs) AS 行吧支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付金额 DESC, 行吧支付金额 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各支付渠道（微信/支付宝/现金）的平均订单金额（金额/订单量）",
-    "sql": "SELECT (SUM(wx)/SUM(wx_order)) AS 微信平均订单额, (SUM(zfb)/SUM(zf_order)) AS 支付宝平均订单额, (SUM(rmb)/SUM(rmb_order)) AS 现金平均订单额 FROM bss_business_day_data WHERE delete_ts IS NULL AND wx_order > 0 AND zf_order > 0 AND rmb_order > 0;"
-  },
-  {
-    "question": "查询2023年4月订单总量TOP10的服务区及日均订单量",
-    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单量, AVG(order_sum) AS 日均订单量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总订单量 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析庐山服务区2023年4月每日总营收变化趋势（折线图数据）",
-    "sql": "SELECT oper_date AS 统计日期, pay_sum AS 当日营收 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL ORDER BY oper_date;"
-  },
-  {
-    "question": "统计各服务区微信支付金额占比（wx/(wx+zfb+rmb+xs+jd)）超过30%的数量",
-    "sql": "SELECT COUNT(*) AS 服务区数量 FROM (SELECT service_name, (SUM(wx) / (SUM(wx)+SUM(zfb)+SUM(rmb)+SUM(xs)+SUM(jd))) * 100 AS 微信占比 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name) t WHERE 微信占比 > 30;"
-  },
-  {
-    "question": "查询2023年4月现金支付订单占比超过50%的服务区明细",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb_order) AS 现金订单量, SUM(order_sum) AS 总订单量, (SUM(rmb_order)/SUM(order_sum))*100 AS 现金占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(rmb_order)/SUM(order_sum)) > 0.5;"
-  },
-  {
-    "question": "统计各公司下属服务区2023年4月人均消费（总支付金额/车辆数）TOP5",
-    "sql": "SELECT s.company_id AS 公司ID, b.service_name AS 服务区名称, SUM(b.pay_sum)/MAX(c.customer_count) AS 人均消费 FROM bss_business_day_data b LEFT JOIN bss_service_area s ON b.service_no = s.service_area_no LEFT JOIN bss_car_day_count c ON b.service_name = c.service_area_id WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY s.company_id, b.service_name ORDER BY 人均消费 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月各服务区日均车流量TOP10，并展示地理位置坐标",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_position AS 地理位置坐标, AVG(car.customer_count) AS 日均车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, sa.service_position ORDER BY 日均车流量 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析危化品车辆在各服务区的分布比例，筛选出占比超过5%的服务区",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, (SUM(CASE WHEN car.car_type='危化品' THEN car.customer_count ELSE 0 END)/SUM(car.customer_count)*100)::numeric(5,2) AS 危化品占比 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name HAVING (SUM(CASE WHEN car.car_type='危化品' THEN car.customer_count ELSE 0 END)/SUM(car.customer_count)*100) > 5 ORDER BY 危化品占比 DESC;"
-  },
-  {
-    "question": "展示2023年Q2季度城际车辆通行量环比增长率（按月统计）",
-    "sql": "WITH monthly_data AS (SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总通行量 FROM bss_car_day_count WHERE car_type='城际' AND count_date BETWEEN '2023-04-01' AND '2023-06-30' AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date)) SELECT 月份, 总通行量, (总通行量 - LAG(总通行量,1) OVER(ORDER BY 月份))/LAG(总通行量,1) OVER(ORDER BY 月份)*100 AS 环比增长率 FROM monthly_data ORDER BY 月份;"
-  },
-  {
-    "question": "查询2023年单日车流量超过1000辆的服务区及对应日期",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, car.count_date AS 统计日期, car.customer_count AS 车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.customer_count > 1000 AND car.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY car.customer_count DESC;"
-  },
-  {
-    "question": "对比不同车辆类型在工作日与周末的通行量差异",
-    "sql": "SELECT car_type AS 车辆类型, AVG(CASE WHEN EXTRACT(DOW FROM count_date) IN (0,6) THEN customer_count ELSE NULL END) AS 周末日均流量, AVG(CASE WHEN EXTRACT(DOW FROM count_date) NOT IN (0,6) THEN customer_count ELSE NULL END) AS 工作日日均流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "统计各服务区近30天车流热力分布（按地理坐标聚合）",
-    "sql": "SELECT sa.service_position AS 地理坐标, sa.service_area_name AS 服务区名称, SUM(car.customer_count) AS 总流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.count_date >= CURRENT_DATE - 30 AND car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_position, sa.service_area_name;"
-  },
-  {
-    "question": "分析过境车辆在不同路段服务区的通行规律（按路线名称维度）",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(car.customer_count) AS 过境车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id INNER JOIN bss_section_route_area_link link ON sa.id = link.service_area_id INNER JOIN bss_section_route r ON link.section_route_id = r.id WHERE car.car_type='过境' AND car.delete_ts IS NULL GROUP BY r.route_name ORDER BY 过境车流量 DESC;"
-  },
-  {
-    "question": "计算各季度不同类型车辆的流量占比变化趋势",
-    "sql": "SELECT EXTRACT(QUARTER FROM count_date) AS 季度, car_type AS 车辆类型, SUM(customer_count) AS 总流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY EXTRACT(QUARTER FROM count_date), car_type ORDER BY 季度, 总流量 DESC;"
-  },
-  {
-    "question": "查询危化品车辆通行量最高的3个服务区及其月度峰值数据",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, TO_CHAR(car.count_date, 'YYYY-MM') AS 月份, SUM(car.customer_count) AS 危化品流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car_type='危化品' AND car.delete_ts IS NULL GROUP BY sa.service_area_name, TO_CHAR(car.count_date, 'YYYY-MM') ORDER BY 危化品流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "分析相邻服务区间的车流密度分布（按地理坐标距离计算）",
-    "sql": "SELECT a.service_area_name AS 服务区A, b.service_area_name AS 服务区B, ROUND((SQRT(POWER(SPLIT_PART(a.service_position, ',', 1)::float - SPLIT_PART(b.service_position, ',', 1)::float, 2) + POWER(SPLIT_PART(a.service_position, ',', 2)::float - SPLIT_PART(b.service_position, ',', 2)::float, 2)))::numeric, 2) AS 直线距离, SUM(car.customer_count) AS 总车流量 FROM bss_service_area a INNER JOIN bss_service_area b ON a.id != b.id CROSS JOIN LATERAL (SELECT * FROM bss_car_day_count WHERE service_area_id IN (a.id, b.id) AND delete_ts IS NULL) car GROUP BY a.service_area_name, b.service_area_name, 直线距离 ORDER BY 直线距离 ASC;"
-  },
-  {
-    "question": "统计各分公司的管辖服务区总营收贡献排名（按金额降序）",
-    "sql": "SELECT c.company_name AS 分公司名称, SUM(b.pay_sum) AS 总营收贡献 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 总营收贡献 DESC;"
-  },
-  {
-    "question": "分析信息化与智能化服务区类型的日均订单数量对比",
-    "sql": "SELECT s.service_area_type AS 服务区类型, AVG(b.order_sum) AS 日均订单量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.service_area_type;"
-  },
-  {
-    "question": "查询最近7天各分公司日均营收贡献TOP3",
-    "sql": "SELECT * FROM (SELECT c.company_name AS 分公司名称, AVG(b.pay_sum) AS 日均营收 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date >= CURRENT_DATE - INTERVAL '7 days' AND b.delete_ts IS NULL GROUP BY c.company_name) t ORDER BY 日均营收 DESC LIMIT 3;"
-  },
-  {
-    "question": "统计各分公司的服务区数量及关闭比例",
-    "sql": "SELECT c.company_name AS 分公司名称, COUNT(s.id) AS 服务区总数, SUM(CASE WHEN s.service_state='关闭' THEN 1 ELSE 0 END)*100.0/COUNT(s.id) AS 关闭比例 FROM bss_company c JOIN bss_service_area s ON c.id = s.company_id WHERE c.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "找出上周每日营收前三的服务区及所属分公司",
-    "sql": "WITH ranked_data AS (SELECT b.oper_date AS 统计日期, s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, b.pay_sum AS 营收金额, RANK() OVER (PARTITION BY b.oper_date ORDER BY b.pay_sum DESC) AS 排名 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date BETWEEN CURRENT_DATE - INTERVAL '7 days' AND CURRENT_DATE - INTERVAL '1 days') SELECT 统计日期, 分公司名称, 服务区名称, 营收金额 FROM ranked_data WHERE 排名 <= 3;"
-  },
-  {
-    "question": "分析支付宝+微信支付金额占比超50%的分公司",
-    "sql": "SELECT c.company_name AS 分公司名称, (SUM(b.wx + b.zfb)/SUM(b.pay_sum))*100 AS 支付占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL GROUP BY c.company_name HAVING (SUM(b.wx + b.zfb)/SUM(b.pay_sum)) > 50;"
-  },
-  {
-    "question": "对比各分公司本月与上月营收环比增长率",
-    "sql": "WITH monthly_data AS (SELECT c.company_name AS 分公司名称, DATE_TRUNC('month', b.oper_date) AS 月份, SUM(b.pay_sum) AS 月营收 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '2 months' GROUP BY 分公司名称, 月份) SELECT cur.分公司名称, ((cur.月营收/prev.月营收)-1)*100 AS 环比增长率 FROM monthly_data cur JOIN monthly_data prev ON cur.分公司名称 = prev.分公司名称 AND cur.月份 = prev.月份 + INTERVAL '1 month';"
-  },
-  {
-    "question": "统计连续3天订单量下降的服务区及所属分公司",
-    "sql": "WITH daily_order AS (SELECT s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, b.oper_date AS 日期, b.order_sum AS 订单量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL), order_trend AS (SELECT *, LAG(订单量,1) OVER (PARTITION BY 服务区名称 ORDER BY 日期) AS 前一日订单量 FROM daily_order) SELECT DISTINCT 服务区名称, 分公司名称 FROM (SELECT *, CASE WHEN 订单量 < 前一日订单量 AND 前一日订单量 < LAG(订单量,2) OVER (PARTITION BY 服务区名称 ORDER BY 日期) THEN 1 ELSE 0 END AS 连续下降 FROM order_trend) t WHERE 连续下降 = 1;"
-  },
-  {
-    "question": "查询现金支付比例超过10%的服务区明细",
-    "sql": "SELECT s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, (SUM(b.rmb)/SUM(b.pay_sum))*100 AS 现金比例 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL GROUP BY s.service_area_name, c.company_name HAVING (SUM(b.rmb)/SUM(b.pay_sum)) > 10;"
-  },
-  {
-    "question": "统计各分公司管辖服务区的月均车流TOP5",
-    "sql": "SELECT c.company_name AS 分公司名称, s.service_area_name AS 服务区名称, AVG(car.customer_count) AS 月均车流 FROM bss_car_day_count car JOIN bss_service_area s ON car.service_area_id = s.id JOIN bss_company c ON s.company_id = c.id WHERE car.delete_ts IS NULL GROUP BY c.company_name, s.service_area_name ORDER BY 月均车流 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各路线关联的总车流量及平均车流量（近30天），按总车流量排序",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(c.customer_count) AS 总车流量, AVG(c.customer_count) AS 平均车流量 FROM bss_section_route_area_link l JOIN bss_section_route r ON l.section_route_id = r.id JOIN bss_car_day_count c ON l.service_area_id = c.service_area_id WHERE c.count_date >= CURRENT_DATE - 30 AND c.delete_ts IS NULL GROUP BY r.route_name ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "分析2023年4月各服务区车流量TOP10排名",
-    "sql": "SELECT s.service_area_name AS 服务区名称, SUM(c.customer_count) AS 月总车流量, RANK() OVER (ORDER BY SUM(c.customer_count) DESC) AS 排名 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY s.service_area_name LIMIT 10;"
-  },
-  {
-    "question": "计算每条路线关联服务区数量及最近服务区开通时间",
-    "sql": "SELECT r.route_name AS 路线名称, COUNT(l.service_area_id) AS 关联服务区数, MAX(s.create_ts) AS 最近开通时间 FROM bss_section_route r LEFT JOIN bss_section_route_area_link l ON r.id = l.section_route_id LEFT JOIN bss_service_area s ON l.service_area_id = s.id GROUP BY r.route_name ORDER BY 关联服务区数 DESC;"
-  },
-  {
-    "question": "对比不同公司管辖路段的车流量分布（按城际/过境车辆分类）",
-    "sql": "SELECT cmp.company_name AS 管辖公司, c.car_type AS 车辆类型, SUM(c.customer_count) AS 总车流量 FROM bss_car_day_count c JOIN bss_service_area sa ON c.service_area_id = sa.id JOIN bss_company cmp ON sa.company_id = cmp.id WHERE c.car_type IN ('城际','过境') GROUP BY cmp.company_name, c.car_type ORDER BY 管辖公司, 车辆类型;"
-  },
-  {
-    "question": "查找过去一周无车流记录的服务区清单",
-    "sql": "SELECT s.service_area_name AS 服务区名称 FROM bss_service_area s LEFT JOIN bss_car_day_count c ON s.id = c.service_area_id AND c.count_date >= CURRENT_DATE - 7 WHERE c.id IS NULL AND s.delete_ts IS NULL ORDER BY s.service_area_name;"
-  },
-  {
-    "question": "分析节假日与工作日车流量差异（以清明节假期为例）",
-    "sql": "SELECT CASE WHEN count_date IN ('2023-04-01','2023-04-02','2023-04-05') THEN '节假日' ELSE '工作日' END AS 日期类型, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-07' GROUP BY 日期类型;"
-  },
-  {
-    "question": "计算各路线车流密度（车流量/路段长度）并排序",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(c.customer_count) / MAX(LENGTH(r.section_name))::numeric AS 车流密度 FROM bss_section_route r JOIN bss_section_route_area_link l ON r.id = l.section_route_id JOIN bss_car_day_count c ON l.service_area_id = c.service_area_id WHERE c.count_date = '2023-04-01' GROUP BY r.route_name ORDER BY 车流密度 DESC;"
-  },
-  {
-    "question": "查询昌九路线各服务区近30天日均车流量变化趋势",
-    "sql": "SELECT c.count_date AS 统计日期, s.service_area_name AS 服务区名称, AVG(c.customer_count) AS 日均车流量 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id JOIN bss_section_route_area_link l ON s.id = l.service_area_id JOIN bss_section_route r ON l.section_route_id = r.id WHERE r.route_name = '昌九' AND c.count_date >= CURRENT_DATE - 30 GROUP BY c.count_date, s.service_area_name ORDER BY 统计日期 DESC;"
-  },
-  {
-    "question": "统计各月份车流量同比增长率（对比2022与2023年）",
-    "sql": "WITH monthly_data AS (SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量, EXTRACT(YEAR FROM count_date) AS 年份 FROM bss_car_day_count GROUP BY 月份, 年份) SELECT m1.月份, m1.总车流量 AS \"2023年流量\", m2.总车流量 AS \"2022年流量\", ROUND((m1.总车流量 - m2.总车流量)/m2.总车流量 * 100, 2) || '%' AS 增长率 FROM monthly_data m1 JOIN monthly_data m2 ON m1.月份 = m2.月份 WHERE m1.年份 = 2023 AND m2.年份 = 2022 ORDER BY 月份;"
-  },
-  {
-    "question": "查找未关联任何路线的服务区清单",
-    "sql": "SELECT service_area_name AS 服务区名称 FROM bss_service_area WHERE id NOT IN (SELECT service_area_id FROM bss_section_route_area_link) AND delete_ts IS NULL ORDER BY service_area_name;"
-  },
-  {
-    "question": "统计各来源系统（驿购/驿美等）映射的服务区数量及占比",
-    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area_mapper WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "对比不同来源系统的最新数据更新时间差异",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, MAX(b.update_ts) AS 最新经营数据更新时间, MAX(c.update_ts) AS 最新车流数据更新时间 FROM bss_service_area_mapper m LEFT JOIN bss_business_day_data b ON m.service_no = b.service_no LEFT JOIN bss_car_day_count c ON m.service_area_id = c.service_area_id WHERE m.delete_ts IS NULL GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "查询存在经营数据但无对应车流数据的服务区及来源系统",
-    "sql": "SELECT m.service_name AS 服务区名称, m.source_system_type AS 数据来源系统 FROM bss_service_area_mapper m LEFT JOIN bss_car_day_count c ON m.service_area_id = c.service_area_id WHERE m.delete_ts IS NULL AND c.id IS NULL AND EXISTS(SELECT 1 FROM bss_business_day_data d WHERE d.service_no = m.service_no);"
-  },
-  {
-    "question": "分析最近一周各来源系统的数据更新频率（按更新天数统计）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, COUNT(DISTINCT DATE(b.update_ts)) AS 更新天数 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.update_ts >= CURRENT_DATE - INTERVAL '7 days' GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "统计不同编码类型（数字/字母开头）的来源系统分布情况",
-    "sql": "SELECT CASE WHEN service_no ~ '^\\d' THEN '数字编码' ELSE '字母编码' END AS 编码类型, source_system_type AS 数据来源系统, COUNT(*) AS 数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY 编码类型, source_system_type ORDER BY 编码类型, 数量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日新增且来源为手工录入的服务区明细",
-    "sql": "SELECT service_name AS 服务区名称, service_no AS 服务区编码, create_ts AS 创建时间 FROM bss_service_area_mapper WHERE DATE(create_ts) = '2023-04-01' AND source_system_type = '手工录入' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "分析各公司下属服务区的来源系统覆盖完整性（按公司统计）",
-    "sql": "SELECT com.company_name AS 公司名称, COUNT(m.id) AS 已覆盖服务区数量, COUNT(s.id) AS 总服务区数量, ROUND(COUNT(m.id) * 100.0 / COUNT(s.id), 2) AS 覆盖率 FROM bss_company com LEFT JOIN bss_service_area s ON com.id = s.company_id LEFT JOIN bss_service_area_mapper m ON s.service_area_no = m.service_no AND m.delete_ts IS NULL WHERE com.delete_ts IS NULL GROUP BY com.company_name HAVING COUNT(s.id) > 0;"
-  },
-  {
-    "question": "计算各来源系统的经营数据更新延迟天数（与经营日期相比）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, AVG(EXTRACT(DAY FROM NOW() - b.oper_date)) AS 平均延迟天数 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.oper_date < CURRENT_DATE GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "查询最近30天未进行任何数据更新的服务区及对应来源系统",
-    "sql": "SELECT m.service_name AS 服务区名称, m.source_system_type AS 数据来源系统 FROM bss_service_area_mapper m LEFT JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL GROUP BY m.id HAVING MAX(b.update_ts) < CURRENT_DATE - INTERVAL '30 days' OR COUNT(b.id) = 0;"
-  },
-  {
-    "question": "分析不同来源系统的经营数据支付金额占比（按微信/支付宝/现金分类）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, SUM(b.wx) AS 微信支付总额, SUM(b.zfb) AS 支付宝支付总额, SUM(b.rmb) AS 现金支付总额 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.oper_date = CURRENT_DATE - INTERVAL '1 day' GROUP BY m.source_system_type;"
-  }
-]

data_pipeline/training_data/task_20250703_000820/qs_highway_db_20250703_004143_pair.json.backup

@@ -1,202 +0,0 @@
-[
-  {
-    "question": "统计最近7天各服务区总营收排名（按支付总金额）",
-    "sql": "SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总营收 FROM bss_business_day_data WHERE oper_date >= CURRENT_DATE - 7 AND delete_ts IS NULL GROUP BY service_name ORDER BY 总营收 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析2023年4月1日各档口微信支付占比（微信金额/总支付金额）",
-    "sql": "SELECT branch_name AS 档口名称, (SUM(wx)/SUM(pay_sum))*100 AS 微信支付占比 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY branch_name HAVING SUM(pay_sum) > 0;"
-  },
-  {
-    "question": "查询2023年4月各服务区支付宝订单量变化趋势（按月统计）",
-    "sql": "SELECT service_name AS 服务区名称, DATE_TRUNC('month', oper_date) AS 月份, SUM(zf_order) AS 支付宝订单量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY 服务区名称, 月份 ORDER BY 月份;"
-  },
-  {
-    "question": "对比2023年4月1日现金支付与行吧支付金额前5的服务区",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb) AS 现金支付金额, SUM(xs) AS 行吧支付金额 FROM bss_business_day_data WHERE oper_date = '2023-04-01' AND delete_ts IS NULL GROUP BY service_name ORDER BY 现金支付金额 DESC, 行吧支付金额 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各支付渠道（微信/支付宝/现金）的平均订单金额（金额/订单量）",
-    "sql": "SELECT (SUM(wx)/SUM(wx_order)) AS 微信平均订单额, (SUM(zfb)/SUM(zf_order)) AS 支付宝平均订单额, (SUM(rmb)/SUM(rmb_order)) AS 现金平均订单额 FROM bss_business_day_data WHERE delete_ts IS NULL AND wx_order > 0 AND zf_order > 0 AND rmb_order > 0;"
-  },
-  {
-    "question": "查询2023年4月订单总量TOP10的服务区及日均订单量",
-    "sql": "SELECT service_name AS 服务区名称, SUM(order_sum) AS 总订单量, AVG(order_sum) AS 日均订单量 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 总订单量 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析庐山服务区2023年4月每日总营收变化趋势（折线图数据）",
-    "sql": "SELECT oper_date AS 统计日期, pay_sum AS 当日营收 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL ORDER BY oper_date;"
-  },
-  {
-    "question": "统计各服务区微信支付金额占比（wx/(wx+zfb+rmb+xs+jd)）超过30%的数量",
-    "sql": "SELECT COUNT(*) AS 服务区数量 FROM (SELECT service_name, (SUM(wx) / (SUM(wx)+SUM(zfb)+SUM(rmb)+SUM(xs)+SUM(jd))) * 100 AS 微信占比 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name) t WHERE 微信占比 > 30;"
-  },
-  {
-    "question": "查询2023年4月现金支付订单占比超过50%的服务区明细",
-    "sql": "SELECT service_name AS 服务区名称, SUM(rmb_order) AS 现金订单量, SUM(order_sum) AS 总订单量, (SUM(rmb_order)/SUM(order_sum))*100 AS 现金占比 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name HAVING (SUM(rmb_order)/SUM(order_sum)) > 0.5;"
-  },
-  {
-    "question": "统计各公司下属服务区2023年4月人均消费（总支付金额/车辆数）TOP5",
-    "sql": "SELECT s.company_id AS 公司ID, b.service_name AS 服务区名称, SUM(b.pay_sum)/MAX(c.customer_count) AS 人均消费 FROM bss_business_day_data b LEFT JOIN bss_service_area s ON b.service_no = s.service_area_no LEFT JOIN bss_car_day_count c ON b.service_name = c.service_area_id WHERE b.oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND b.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY s.company_id, b.service_name ORDER BY 人均消费 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计2023年4月各服务区日均车流量TOP10，并展示地理位置坐标",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, sa.service_position AS 地理位置坐标, AVG(car.customer_count) AS 日均车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.count_date BETWEEN '2023-04-01' AND '2023-04-30' AND car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name, sa.service_position ORDER BY 日均车流量 DESC LIMIT 10;"
-  },
-  {
-    "question": "分析危化品车辆在各服务区的分布比例，筛选出占比超过5%的服务区",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, (SUM(CASE WHEN car.car_type='危化品' THEN car.customer_count ELSE 0 END)/SUM(car.customer_count)*100)::numeric(5,2) AS 危化品占比 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_area_name HAVING (SUM(CASE WHEN car.car_type='危化品' THEN car.customer_count ELSE 0 END)/SUM(car.customer_count)*100) > 5 ORDER BY 危化品占比 DESC;"
-  },
-  {
-    "question": "展示2023年Q2季度城际车辆通行量环比增长率（按月统计）",
-    "sql": "WITH monthly_data AS (SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总通行量 FROM bss_car_day_count WHERE car_type='城际' AND count_date BETWEEN '2023-04-01' AND '2023-06-30' AND delete_ts IS NULL GROUP BY EXTRACT(MONTH FROM count_date)) SELECT 月份, 总通行量, (总通行量 - LAG(总通行量,1) OVER(ORDER BY 月份))/LAG(总通行量,1) OVER(ORDER BY 月份)*100 AS 环比增长率 FROM monthly_data ORDER BY 月份;"
-  },
-  {
-    "question": "查询2023年单日车流量超过1000辆的服务区及对应日期",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, car.count_date AS 统计日期, car.customer_count AS 车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.customer_count > 1000 AND car.delete_ts IS NULL AND sa.delete_ts IS NULL ORDER BY car.customer_count DESC;"
-  },
-  {
-    "question": "对比不同车辆类型在工作日与周末的通行量差异",
-    "sql": "SELECT car_type AS 车辆类型, AVG(CASE WHEN EXTRACT(DOW FROM count_date) IN (0,6) THEN customer_count ELSE NULL END) AS 周末日均流量, AVG(CASE WHEN EXTRACT(DOW FROM count_date) NOT IN (0,6) THEN customer_count ELSE NULL END) AS 工作日日均流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY car_type;"
-  },
-  {
-    "question": "统计各服务区近30天车流热力分布（按地理坐标聚合）",
-    "sql": "SELECT sa.service_position AS 地理坐标, sa.service_area_name AS 服务区名称, SUM(car.customer_count) AS 总流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car.count_date >= CURRENT_DATE - 30 AND car.delete_ts IS NULL AND sa.delete_ts IS NULL GROUP BY sa.service_position, sa.service_area_name;"
-  },
-  {
-    "question": "分析过境车辆在不同路段服务区的通行规律（按路线名称维度）",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(car.customer_count) AS 过境车流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id INNER JOIN bss_section_route_area_link link ON sa.id = link.service_area_id INNER JOIN bss_section_route r ON link.section_route_id = r.id WHERE car.car_type='过境' AND car.delete_ts IS NULL GROUP BY r.route_name ORDER BY 过境车流量 DESC;"
-  },
-  {
-    "question": "计算各季度不同类型车辆的流量占比变化趋势",
-    "sql": "SELECT EXTRACT(QUARTER FROM count_date) AS 季度, car_type AS 车辆类型, SUM(customer_count) AS 总流量 FROM bss_car_day_count WHERE delete_ts IS NULL GROUP BY EXTRACT(QUARTER FROM count_date), car_type ORDER BY 季度, 总流量 DESC;"
-  },
-  {
-    "question": "查询危化品车辆通行量最高的3个服务区及其月度峰值数据",
-    "sql": "SELECT sa.service_area_name AS 服务区名称, TO_CHAR(car.count_date, 'YYYY-MM') AS 月份, SUM(car.customer_count) AS 危化品流量 FROM bss_car_day_count car INNER JOIN bss_service_area sa ON car.service_area_id = sa.id WHERE car_type='危化品' AND car.delete_ts IS NULL GROUP BY sa.service_area_name, TO_CHAR(car.count_date, 'YYYY-MM') ORDER BY 危化品流量 DESC LIMIT 3;"
-  },
-  {
-    "question": "分析相邻服务区间的车流密度分布（按地理坐标距离计算）",
-    "sql": "SELECT a.service_area_name AS 服务区A, b.service_area_name AS 服务区B, ROUND((SQRT(POWER(SPLIT_PART(a.service_position, ',', 1)::float - SPLIT_PART(b.service_position, ',', 1)::float, 2) + POWER(SPLIT_PART(a.service_position, ',', 2)::float - SPLIT_PART(b.service_position, ',', 2)::float, 2)))::numeric, 2) AS 直线距离, SUM(car.customer_count) AS 总车流量 FROM bss_service_area a INNER JOIN bss_service_area b ON a.id != b.id CROSS JOIN LATERAL (SELECT * FROM bss_car_day_count WHERE service_area_id IN (a.id, b.id) AND delete_ts IS NULL) car GROUP BY a.service_area_name, b.service_area_name, 直线距离 ORDER BY 直线距离 ASC;"
-  },
-  {
-    "question": "统计各分公司的管辖服务区总营收贡献排名（按金额降序）",
-    "sql": "SELECT c.company_name AS 分公司名称, SUM(b.pay_sum) AS 总营收贡献 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL AND c.delete_ts IS NULL GROUP BY c.company_name ORDER BY 总营收贡献 DESC;"
-  },
-  {
-    "question": "分析信息化与智能化服务区类型的日均订单数量对比",
-    "sql": "SELECT s.service_area_type AS 服务区类型, AVG(b.order_sum) AS 日均订单量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no WHERE b.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.service_area_type;"
-  },
-  {
-    "question": "查询最近7天各分公司日均营收贡献TOP3",
-    "sql": "SELECT * FROM (SELECT c.company_name AS 分公司名称, AVG(b.pay_sum) AS 日均营收 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date >= CURRENT_DATE - INTERVAL '7 days' AND b.delete_ts IS NULL GROUP BY c.company_name) t ORDER BY 日均营收 DESC LIMIT 3;"
-  },
-  {
-    "question": "统计各分公司的服务区数量及关闭比例",
-    "sql": "SELECT c.company_name AS 分公司名称, COUNT(s.id) AS 服务区总数, SUM(CASE WHEN s.service_state='关闭' THEN 1 ELSE 0 END)*100.0/COUNT(s.id) AS 关闭比例 FROM bss_company c JOIN bss_service_area s ON c.id = s.company_id WHERE c.delete_ts IS NULL GROUP BY c.company_name;"
-  },
-  {
-    "question": "找出上周每日营收前三的服务区及所属分公司",
-    "sql": "WITH ranked_data AS (SELECT b.oper_date AS 统计日期, s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, b.pay_sum AS 营收金额, RANK() OVER (PARTITION BY b.oper_date ORDER BY b.pay_sum DESC) AS 排名 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date BETWEEN CURRENT_DATE - INTERVAL '7 days' AND CURRENT_DATE - INTERVAL '1 days') SELECT 统计日期, 分公司名称, 服务区名称, 营收金额 FROM ranked_data WHERE 排名 <= 3;"
-  },
-  {
-    "question": "分析支付宝+微信支付金额占比超50%的分公司",
-    "sql": "SELECT c.company_name AS 分公司名称, (SUM(b.wx + b.zfb)/SUM(b.pay_sum))*100 AS 支付占比 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL GROUP BY c.company_name HAVING (SUM(b.wx + b.zfb)/SUM(b.pay_sum)) > 50;"
-  },
-  {
-    "question": "对比各分公司本月与上月营收环比增长率",
-    "sql": "WITH monthly_data AS (SELECT c.company_name AS 分公司名称, DATE_TRUNC('month', b.oper_date) AS 月份, SUM(b.pay_sum) AS 月营收 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.oper_date >= DATE_TRUNC('month', CURRENT_DATE) - INTERVAL '2 months' GROUP BY 分公司名称, 月份) SELECT cur.分公司名称, ((cur.月营收/prev.月营收)-1)*100 AS 环比增长率 FROM monthly_data cur JOIN monthly_data prev ON cur.分公司名称 = prev.分公司名称 AND cur.月份 = prev.月份 + INTERVAL '1 month';"
-  },
-  {
-    "question": "统计连续3天订单量下降的服务区及所属分公司",
-    "sql": "WITH daily_order AS (SELECT s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, b.oper_date AS 日期, b.order_sum AS 订单量 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no JOIN bss_company c ON s.company_id = c.id WHERE b.delete_ts IS NULL), order_trend AS (SELECT *, LAG(订单量,1) OVER (PARTITION BY 服务区名称 ORDER BY 日期) AS 前一日订单量 FROM daily_order) SELECT DISTINCT 服务区名称, 分公司名称 FROM (SELECT *, CASE WHEN 订单量 < 前一日订单量 AND 前一日订单量 < LAG(订单量,2) OVER (PARTITION BY 服务区名称 ORDER BY 日期) THEN 1 ELSE 0 END AS 连续下降 FROM order_trend) t WHERE 连续下降 = 1;"
-  },
-  {
-    "question": "查询现金支付比例超过10%的服务区明细",
-    "sql": "SELECT s.service_area_name AS 服务区名称, c.company_name AS 分公司名称, (SUM(b.rmb)/SUM(b.pay_sum))*100 AS 现金比例 FROM bss_business_day_data b JOIN bss_service_area s ON b.service_no = s.service_area_no ON s.company_id = c.id WHERE b.delete_ts IS NULL GROUP BY s.service_area_name, c.company_name HAVING (SUM(b.rmb)/SUM(b.pay_sum)) > 10;"
-  },
-  {
-    "question": "统计各分公司管辖服务区的月均车流TOP5",
-    "sql": "SELECT c.company_name AS 分公司名称, s.service_area_name AS 服务区名称, AVG(car.customer_count) AS 月均车流 FROM bss_car_day_count car JOIN bss_service_area s ON car.service_area_id = s.id JOIN bss_company c ON s.company_id = c.id WHERE car.delete_ts IS NULL GROUP BY c.company_name, s.service_area_name ORDER BY 月均车流 DESC LIMIT 5;"
-  },
-  {
-    "question": "统计各路线关联的总车流量及平均车流量（近30天），按总车流量排序",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(c.customer_count) AS 总车流量, AVG(c.customer_count) AS 平均车流量 FROM bss_section_route_area_link l JOIN bss_section_route r ON l.section_route_id = r.id JOIN bss_car_day_count c ON l.service_area_id = c.service_area_id WHERE c.count_date >= CURRENT_DATE - 30 AND c.delete_ts IS NULL GROUP BY r.route_name ORDER BY 总车流量 DESC;"
-  },
-  {
-    "question": "分析2023年4月各服务区车流量TOP10排名",
-    "sql": "SELECT s.service_area_name AS 服务区名称, SUM(c.customer_count) AS 月总车流量, RANK() OVER (ORDER BY SUM(c.customer_count) DESC) AS 排名 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date BETWEEN '2023-04-01' AND '2023-04-30' GROUP BY s.service_area_name LIMIT 10;"
-  },
-  {
-    "question": "计算每条路线关联服务区数量及最近服务区开通时间",
-    "sql": "SELECT r.route_name AS 路线名称, COUNT(l.service_area_id) AS 关联服务区数, MAX(s.create_ts) AS 最近开通时间 FROM bss_section_route r LEFT JOIN bss_section_route_area_link l ON r.id = l.section_route_id LEFT JOIN bss_service_area s ON l.service_area_id = s.id GROUP BY r.route_name ORDER BY 关联服务区数 DESC;"
-  },
-  {
-    "question": "对比不同公司管辖路段的车流量分布（按城际/过境车辆分类）",
-    "sql": "SELECT cmp.company_name AS 管辖公司, c.car_type AS 车辆类型, SUM(c.customer_count) AS 总车流量 FROM bss_car_day_count c JOIN bss_service_area sa ON c.service_area_id = sa.id JOIN bss_company cmp ON sa.company_id = cmp.id WHERE c.car_type IN ('城际','过境') GROUP BY cmp.company_name, c.car_type ORDER BY 管辖公司, 车辆类型;"
-  },
-  {
-    "question": "查找过去一周无车流记录的服务区清单",
-    "sql": "SELECT s.service_area_name AS 服务区名称 FROM bss_service_area s LEFT JOIN bss_car_day_count c ON s.id = c.service_area_id AND c.count_date >= CURRENT_DATE - 7 WHERE c.id IS NULL AND s.delete_ts IS NULL ORDER BY s.service_area_name;"
-  },
-  {
-    "question": "分析节假日与工作日车流量差异（以清明节假期为例）",
-    "sql": "SELECT CASE WHEN count_date IN ('2023-04-01','2023-04-02','2023-04-05') THEN '节假日' ELSE '工作日' END AS 日期类型, AVG(customer_count) AS 日均车流量 FROM bss_car_day_count WHERE count_date BETWEEN '2023-04-01' AND '2023-04-07' GROUP BY 日期类型;"
-  },
-  {
-    "question": "计算各路线车流密度（车流量/路段长度）并排序",
-    "sql": "SELECT r.route_name AS 路线名称, SUM(c.customer_count) / MAX(LENGTH(r.section_name))::numeric AS 车流密度 FROM bss_section_route r JOIN bss_section_route_area_link l ON r.id = l.section_route_id JOIN bss_car_day_count c ON l.service_area_id = c.service_area_id WHERE c.count_date = '2023-04-01' GROUP BY r.route_name ORDER BY 车流密度 DESC;"
-  },
-  {
-    "question": "查询昌九路线各服务区近30天日均车流量变化趋势",
-    "sql": "SELECT c.count_date AS 统计日期, s.service_area_name AS 服务区名称, AVG(c.customer_count) AS 日均车流量 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id JOIN bss_section_route_area_link l ON s.id = l.service_area_id JOIN bss_section_route r ON l.section_route_id = r.id WHERE r.route_name = '昌九' AND c.count_date >= CURRENT_DATE - 30 GROUP BY c.count_date, s.service_area_name ORDER BY 统计日期 DESC;"
-  },
-  {
-    "question": "统计各月份车流量同比增长率（对比2022与2023年）",
-    "sql": "WITH monthly_data AS (SELECT EXTRACT(MONTH FROM count_date) AS 月份, SUM(customer_count) AS 总车流量, EXTRACT(YEAR FROM count_date) AS 年份 FROM bss_car_day_count GROUP BY 月份, 年份) SELECT m1.月份, m1.总车流量 AS \"2023年流量\", m2.总车流量 AS \"2022年流量\", ROUND((m1.总车流量 - m2.总车流量)/m2.总车流量 * 100, 2) || '%' AS 增长率 FROM monthly_data m1 JOIN monthly_data m2 ON m1.月份 = m2.月份 WHERE m1.年份 = 2023 AND m2.年份 = 2022 ORDER BY 月份;"
-  },
-  {
-    "question": "查找未关联任何路线的服务区清单",
-    "sql": "SELECT service_area_name AS 服务区名称 FROM bss_service_area WHERE id NOT IN (SELECT service_area_id FROM bss_section_route_area_link) AND delete_ts IS NULL ORDER BY service_area_name;"
-  },
-  {
-    "question": "统计各来源系统（驿购/驿美等）映射的服务区数量及占比",
-    "sql": "SELECT source_system_type AS 数据来源系统, COUNT(*) AS 服务区数量, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM bss_service_area_mapper WHERE delete_ts IS NULL), 2) AS 占比百分比 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY source_system_type ORDER BY 服务区数量 DESC;"
-  },
-  {
-    "question": "对比不同来源系统的最新数据更新时间差异",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, MAX(b.update_ts) AS 最新经营数据更新时间, MAX(c.update_ts) AS 最新车流数据更新时间 FROM bss_service_area_mapper m LEFT JOIN bss_business_day_data b ON m.service_no = b.service_no LEFT JOIN bss_car_day_count c ON m.service_area_id = c.service_area_id WHERE m.delete_ts IS NULL GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "查询存在经营数据但无对应车流数据的服务区及来源系统",
-    "sql": "SELECT m.service_name AS 服务区名称, m.source_system_type AS 数据来源系统 FROM bss_service_area_mapper m LEFT JOIN bss_car_day_count c ON m.service_area_id = c.service_area_id WHERE m.delete_ts IS NULL AND c.id IS NULL AND EXISTS(SELECT 1 FROM bss_business_day_data d WHERE d.service_no = m.service_no);"
-  },
-  {
-    "question": "分析最近一周各来源系统的数据更新频率（按更新天数统计）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, COUNT(DISTINCT DATE(b.update_ts)) AS 更新天数 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.update_ts >= CURRENT_DATE - INTERVAL '7 days' GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "统计不同编码类型（数字/字母开头）的来源系统分布情况",
-    "sql": "SELECT CASE WHEN service_no ~ '^\\d' THEN '数字编码' ELSE '字母编码' END AS 编码类型, source_system_type AS 数据来源系统, COUNT(*) AS 数量 FROM bss_service_area_mapper WHERE delete_ts IS NULL GROUP BY 编码类型, source_system_type ORDER BY 编码类型, 数量 DESC;"
-  },
-  {
-    "question": "查询2023年4月1日新增且来源为手工录入的服务区明细",
-    "sql": "SELECT service_name AS 服务区名称, service_no AS 服务区编码, create_ts AS 创建时间 FROM bss_service_area_mapper WHERE DATE(create_ts) = '2023-04-01' AND source_system_type = '手工录入' AND delete_ts IS NULL;"
-  },
-  {
-    "question": "分析各公司下属服务区的来源系统覆盖完整性（按公司统计）",
-    "sql": "SELECT com.company_name AS 公司名称, COUNT(m.id) AS 已覆盖服务区数量, COUNT(s.id) AS 总服务区数量, ROUND(COUNT(m.id) * 100.0 / COUNT(s.id), 2) AS 覆盖率 FROM bss_company com LEFT JOIN bss_service_area s ON com.id = s.company_id LEFT JOIN bss_service_area_mapper m ON s.service_area_no = m.service_no AND m.delete_ts IS NULL WHERE com.delete_ts IS NULL GROUP BY com.company_name HAVING COUNT(s.id) > 0;"
-  },
-  {
-    "question": "计算各来源系统的经营数据更新延迟天数（与经营日期相比）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, AVG(EXTRACT(DAY FROM NOW() - b.oper_date)) AS 平均延迟天数 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.oper_date < CURRENT_DATE GROUP BY m.source_system_type;"
-  },
-  {
-    "question": "查询最近30天未进行任何数据更新的服务区及对应来源系统",
-    "sql": "SELECT m.service_name AS 服务区名称, m.source_system_type AS 数据来源系统 FROM bss_service_area_mapper m LEFT JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL GROUP BY m.id HAVING MAX(b.update_ts) < CURRENT_DATE - INTERVAL '30 days' OR COUNT(b.id) = 0;"
-  },
-  {
-    "question": "分析不同来源系统的经营数据支付金额占比（按微信/支付宝/现金分类）",
-    "sql": "SELECT m.source_system_type AS 数据来源系统, SUM(b.wx) AS 微信支付总额, SUM(b.zfb) AS 支付宝支付总额, SUM(b.rmb) AS 现金支付总额 FROM bss_service_area_mapper m JOIN bss_business_day_data b ON m.service_no = b.service_no WHERE m.delete_ts IS NULL AND b.oper_date = CURRENT_DATE - INTERVAL '1 day' GROUP BY m.source_system_type;"
-  }
-]

data_pipeline/training_data/task_20250703_000820/sql_validation_result.json

@@ -1,19 +0,0 @@
-{
-  "original_sql_count": 50,
-  "valid_sql_count": 50,
-  "invalid_sql_count": 0,
-  "success_rate": 1.0,
-  "repair_stats": {
-    "attempted": 1,
-    "successful": 1,
-    "failed": 0
-  },
-  "file_modification_stats": {
-    "modified": 1,
-    "deleted": 0,
-    "failed_modifications": 0
-  },
-  "average_execution_time": 0.019466452598571778,
-  "total_retries": 0,
-  "duration": 64.52006721496582
-}

data_pipeline/training_data/task_20250703_000820/table_list.txt

@@ -1,11 +0,0 @@
-# 表清单文件
-# 生成时间: 2025-07-02 18:07:15
-# 表数量: 7
-
-bss_car_day_count
-bss_business_day_data
-bss_company
-bss_section_route
-bss_section_route_area_link
-bss_service_area
-bss_service_area_mapper

data_pipeline/training_data/task_20250703_000820/tables.txt

@@ -1,13 +0,0 @@
-# 示例表清单文件
-# 每行一个表名，支持 schema.table 格式
-# 以 # 开头的行为注释
-
-# 服务区相关表
-bss_car_day_count
-bss_business_day_data
-#bss_company
-#bss_section_route
-#bss_section_route_area_link
-#bss_service_area
-#bss_service_area_mapper
-

data_pipeline/training_data/task_20250703_000820/task_config.json

@@ -1,15 +0,0 @@
-{
-  "task_id": "task_20250703_000820",
-  "created_at": "2025-07-03T00:08:20.129529",
-  "parameters": {
-    "db_connection": "postgresql://postgres:postgres@192.168.67.1:6432/highway_db",
-    "table_list_file": "{task_directory}/table_list.txt",
-    "business_context": "高速公路服务区管理系统",
-    "file_upload_mode": true,
-    "enable_llm_repair": true,
-    "modify_original_file": true,
-    "enable_sql_validation": true,
-    "enable_training_data_load": true
-  },
-  "output_directory": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250703_000820"
-}

data_pipeline/training_data/task_20250703_000820/training_load_result.json

@@ -1,12 +0,0 @@
-{
-  "training_data_dir": "C:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\data_pipeline\\training_data\\task_20250703_000820",
-  "load_successful": true,
-  "total_records": 746,
-  "data_type_counts": {
-    "sql": 622,
-    "documentation": 66,
-    "ddl": 57,
-    "error_sql": 1
-  },
-  "duration": 78.09069848060608
-}

docs/react_agnet_设计方案v1.md

@@ -0,0 +1,217 @@
+## 设计方案
+
+### 我的需求
+
+请遍历知识库的github项目和*.ipynb文件，我想做一个测试： 
+
+1.我要使用langchain/langgraph v0.3.x版本开发一个Agent.
+2.这个Agent 拥有三个节点，入口的LLM节点，包含四个tools的节点，end节点。 
+a.) 作为入口的LLM， 
+b.) 包含四个tools函数： generate_sql()(产生sql)，valid_sql(验证sql)，run_sql(运行sql)，generate_summary()(总结执行结果)。 
+c.) 如果无法生成sql，也请LLM返回无法生成SQL的原因。 
+3.使用 create_react_agent()，通过langgraph把这三个节点连接在一起。 
+4.你可以参考“4. LangGraph 实现自治循环代理（ReAct）及事件流的应用.ipynb”。 
+5.如果入口节点判断数据库中的内容无法实现查询，比如是一个“荔枝几月上市”的问题，请用LLM常识来回答。 
+我上面的内容只是想做一个测试，请在项目的test目录下，使用 ipynb 来开发。
+
+### **项目结构**：
+
+这是一个 Vanna-Chainlit-Chromadb 项目，已经有完整的结构，包括：
+
+- `test/` 目录已存在，现在我要在test/目录下创建一个`vanna_agent_test.ipynb` 的代码用于测试场景的场景
+- 复用已有数据库查询和 LLM 集成的基础设施
+
+### 1. 核心理念
+
+- **所有决策都由 agent LLM 做出**，而不是工具
+- **工具只负责执行**，返回执行结果到 state
+- **agent LLM 根据 state 中的所有信息智能判断下一步**
+
+#### 架构图（修正版）
+
+```
+     ┌──────┐
+     │ START│
+     └───┬──┘
+         │
+    ┌────▼────┐
+    │  agent  │◄────┐
+    └──┬───┬──┘     │
+       │   │        │
+       │   └─→END   │
+       │            │
+    ┌──▼──────┐     │
+    │  tools  ├─────┘
+    └─────────┘
+```
+
+agent 节点决定：
+
+- 调用 tools（继续处理）
+- 或直接到 END（结束并返回结果）
+
+### 2. 改进的 State 设计
+
+```python
+class AgentState(TypedDict):
+    messages: Annotated[list[BaseMessage], operator.add]
+    # 执行状态记录
+    sql_generated: Optional[str]        # 生成的SQL
+    sql_validated: Optional[bool]       # 是否通过验证
+    sql_executed: Optional[bool]        # 是否执行成功
+    query_result: Optional[Any]         # 查询结果
+    summary_generated: Optional[str]    # 生成的总结
+    
+    # 错误信息记录
+    sql_generation_error: Optional[str]     # SQL生成错误
+    sql_validation_error: Optional[str]     # SQL验证错误
+    sql_execution_error: Optional[str]      # SQL执行错误
+    summary_generation_error: Optional[str] # 总结生成错误
+    
+    # 重试计数
+    sql_generation_attempts: int
+    sql_validation_attempts: int
+```
+
+### 3.  Agent 节点（完全由 LLM 决策）
+
+```python
+def agent_node(state: AgentState) -> dict:
+    """Agent节点：分析当前状态并决定下一步行动"""
+    
+    system_prompt = """
+    你是一个智能SQL查询助手。你有以下四个工具可以调用：
+    1. generate_sql: 生成SQL查询语句
+    2. valid_sql: 验证SQL语句的正确性
+    3. run_sql: 执行SQL查询
+    4. generate_summary: 总结查询结果
+    
+    工具必须按照上述顺序调用。
+    
+    重要规则：
+    - 首先判断用户问题是否需要查询数据库
+    - 如果generate_sql失败（返回空或错误），请判断是否为常识问题
+    - 如果是常识问题（如"荔枝几月上市"），直接用你的知识回答，不再调用工具
+    - 如果valid_sql失败且是语法错误，可以尝试修正后重新生成SQL
+    - 如果run_sql失败，判断是否需要重新生成SQL或直接返回错误
+    
+    当前执行状态：
+    - SQL生成: {sql_generated} (错误: {sql_generation_error})
+    - SQL验证: {sql_validated} (错误: {sql_validation_error})
+    - SQL执行: {sql_executed} (错误: {sql_execution_error})
+    - 查询结果: {有结果 if query_result else 无结果}
+    - 总结生成: {summary_generated}
+    
+    请根据当前状态，决定：
+    1. 调用哪个工具（或不调用工具）
+    2. 如果不调用工具，是直接回答还是返回错误信息
+    """
+    
+    # LLM 分析 state 并决定调用哪个工具或结束
+    # 返回的是 LLM 的决策，不是硬编码的逻辑
+```
+
+### 4. 工具设计（只返回结果）
+
+**复用项目现有功能**：
+
+```python
+# 使用项目中的 vn (Vanna实例)
+from core.vanna_llm_factory import get_vn
+vn = get_vn()
+
+# 复用现有的验证和总结方法
+# vn.validate_sql()
+# vn.generate_summary()
+```
+
+#### Tools 设计（作为一个统一的 tools 节点）
+
+```python
+# tools节点内部根据state.current_tool调用对应的工具
+def tools_node(state: AgentState):
+    current_tool = state.get("current_tool")
+    
+    if current_tool == "generate_sql":
+        return generate_sql(state)
+    elif current_tool == "valid_sql":
+        return valid_sql(state)
+    elif current_tool == "run_sql":
+        return run_sql(state)
+    elif current_tool == "generate_summary":
+        return generate_summary(state)
+```
+
+**创建四个工具函数**：
+
+- 每个工具返回更新后的 state
+- 包含详细的错误信息
+
+```python
+@tool
+def generate_sql(query: str) -> dict:
+    """生成SQL语句"""
+    try:
+        sql = vn.generate_sql(query)
+        return {
+            "sql": sql,
+            "success": bool(sql),
+            "error": "无法生成SQL" if not sql else None
+        }
+    except Exception as e:
+        return {
+            "sql": None,
+            "success": False,
+            "error": str(e)
+        }
+
+@tool
+def valid_sql(sql: str) -> dict:
+    """验证SQL语句"""
+    try:
+        is_valid = vn.validate_sql(sql)
+        return {
+            "valid": is_valid,
+            "success": True,
+            "error": "SQL语法错误" if not is_valid else None
+        }
+    except Exception as e:
+        return {
+            "valid": False,
+            "success": False,
+            "error": str(e)
+        }
+
+# 其他工具类似...
+```
+
+### 5. 条件边逻辑（should_continue）
+
+```python
+def should_continue(state: AgentState) -> str:
+    """由最后一条消息决定继续还是结束"""
+    last_message = state["messages"][-1]
+    
+    # 如果 LLM 决定调用工具，继续到 tools
+    if last_message.tool_calls:
+        return "tools"
+    
+    # 否则结束
+    return "end"
+```
+
+### 6. 关键改进点
+
+1. **完全由 LLM 智能决策**：
+   - 不在 state 中硬编码 next_step
+   - LLM 根据所有状态信息自主判断
+2. **工具只负责执行**：
+   - 工具返回执行结果
+   - 不包含任何决策逻辑
+3. **更灵活的错误处理**：
+   - LLM 可以根据具体错误类型智能处理
+   - 支持更复杂的重试逻辑
+4. **常识问题判断**：
+   - 在提示词中明确：SQL生成失败时要再次判断是否为常识问题
+   - LLM 可以直接给出答案
+

test/vanna_agent_test.ipynb

@@ -0,0 +1,4263 @@
+{
+  "cells": [
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "# Vanna Agent Test - 基于 create_react_agent 的实现\n",
+        "\n",
+        "## 目标\n",
+        "使用 LangGraph 的 `create_react_agent()` 创建一个包含四个工具的智能Agent：\n",
+        "1. generate_sql - 生成SQL\n",
+        "2. valid_sql - 验证SQL\n",
+        "3. run_sql - 执行SQL\n",
+        "4. generate_summary - 生成摘要\n",
+        "\n",
+        "## 架构\n",
+        "- 三节点结构：Agent节点 → Tools节点 → END节点\n",
+        "- Agent自主决策是否需要查询数据库\n",
+        "- 对于常识问题直接用LLM回答"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 1. 环境准备和导入"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "项目根目录: c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\n",
+            "common目录存在: True\n",
+            "Python路径已更新: True\n",
+            "✅ 导入完成\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 添加项目根目录到Python路径\n",
+        "import sys\n",
+        "import os\n",
+        "\n",
+        "# 方法1: 基于当前notebook文件位置计算项目根目录\n",
+        "current_dir = os.path.dirname(os.path.abspath(__file__)) if '__file__' in globals() else os.getcwd()\n",
+        "project_root = os.path.dirname(current_dir)  # test/ 的上一级就是项目根目录\n",
+        "\n",
+        "# 方法2: 备用方案，如果方法1失败\n",
+        "if not os.path.exists(os.path.join(project_root, 'common')):\n",
+        "    # 尝试当前工作目录的上一级\n",
+        "    project_root = os.path.dirname(os.getcwd())\n",
+        "\n",
+        "# 添加到Python路径\n",
+        "if project_root not in sys.path:\n",
+        "    sys.path.insert(0, project_root)\n",
+        "\n",
+        "print(f\"项目根目录: {project_root}\")\n",
+        "print(f\"common目录存在: {os.path.exists(os.path.join(project_root, 'common'))}\")\n",
+        "print(f\"Python路径已更新: {project_root in sys.path}\")\n",
+        "\n",
+        "# 基础导入\n",
+        "from typing import Dict, Any, List, Optional\n",
+        "import pandas as pd\n",
+        "import re\n",
+        "import json\n",
+        "from datetime import datetime\n",
+        "\n",
+        "# LangChain/LangGraph 导入\n",
+        "from langchain.tools import tool\n",
+        "from langchain_core.messages import HumanMessage, AIMessage, SystemMessage\n",
+        "from langgraph.prebuilt import create_react_agent\n",
+        "\n",
+        "# 项目导入\n",
+        "from common.vanna_instance import get_vanna_instance\n",
+        "from common.utils import get_current_llm_config\n",
+        "\n",
+        "print(\"✅ 导入完成\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ 配置参数已设置\n",
+            "最大工具调用次数: 10\n",
+            "最大返回行数: 200\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ========== 可配置参数 ==========\n",
+        "\n",
+        "# 最大工具调用次数（防止无限循环）\n",
+        "MAX_TOOL_CALLS = 10\n",
+        "\n",
+        "# 最大返回行数\n",
+        "MAX_RETURN_ROWS = 200\n",
+        "\n",
+        "# 是否启用详细日志\n",
+        "VERBOSE = True\n",
+        "\n",
+        "# 数据库业务范围描述（请根据实际情况修改）\n",
+        "DATABASE_SCOPE = \"\"\"\n",
+        "=== 数据库业务范围 ===\n",
+        "本系统是高速公路服务区商业管理系统，包含以下业务数据：\n",
+        "\n",
+        "核心业务实体：\n",
+        "- 服务区(bss_service_area)：服务区基础信息、位置、状态\n",
+        "- 档口/商铺(bss_business_day_data)：档口信息、品类、营业数据\n",
+        "- 车流量(bss_car_day_count)：按车型统计的日流量数据\n",
+        "- 公司信息(bss_company)：服务区管理公司\n",
+        "\n",
+        "关键业务指标：\n",
+        "- 支付方式：微信支付、支付宝支付、现金支付等\n",
+        "- 营业数据：支付金额、订单数量、营业额、收入统计\n",
+        "- 车流统计：按车型的流量分析\n",
+        "- 经营分析：餐饮、小吃、便利店等品类收入\n",
+        "\n",
+        "时间范围：\n",
+        "- 数据更新到最近的营业日\n",
+        "- 历史数据可追溯到系统上线时间\n",
+        "\"\"\"\n",
+        "\n",
+        "print(\"✅ 配置参数已设置\")\n",
+        "print(f\"最大工具调用次数: {MAX_TOOL_CALLS}\")\n",
+        "print(f\"最大返回行数: {MAX_RETURN_ROWS}\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 3. 获取LLM实例\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔧 检测到模型: qwen-plus\n",
+            "🔧 为模型 qwen-plus 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n"
+          ]
+        }
+      ],
+      "source": [
+        "def get_llm():\n",
+        "    \"\"\"获取兼容的LLM实例\"\"\"\n",
+        "    try:\n",
+        "        # 尝试使用OpenAI兼容的API\n",
+        "        from langchain_openai import ChatOpenAI\n",
+        "        from common.utils import get_current_llm_config\n",
+        "        \n",
+        "        llm_config = get_current_llm_config()\n",
+        "        \n",
+        "        if llm_config.get(\"base_url\") and llm_config.get(\"api_key\"):\n",
+        "            # 构建参数，确保thinking功能正确设置\n",
+        "            model_name = llm_config.get(\"model\", \"\").lower()\n",
+        "            print(f\"🔧 检测到模型: {model_name}\")\n",
+        "            \n",
+        "            # 方法1：尝试使用model_kwargs传递参数\n",
+        "            model_kwargs = {}\n",
+        "            if \"deepseek\" in model_name or \"qianwen\" in model_name or \"qwen\" in model_name:\n",
+        "                model_kwargs[\"enable_thinking\"] = False\n",
+        "                print(f\"🔧 为模型 {model_name} 设置 enable_thinking=False\")\n",
+        "            \n",
+        "            llm = ChatOpenAI(\n",
+        "                base_url=llm_config.get(\"base_url\"),\n",
+        "                api_key=llm_config.get(\"api_key\"),\n",
+        "                model=llm_config.get(\"model\"),\n",
+        "                temperature=llm_config.get(\"temperature\", 0.7),\n",
+        "                model_kwargs=model_kwargs\n",
+        "            )\n",
+        "            print(\"✅ 使用OpenAI兼容API（方法1：model_kwargs）\")\n",
+        "            return llm\n",
+        "    except Exception as e:\n",
+        "        print(f\"⚠️ OpenAI API方法1失败: {e}\")\n",
+        "        \n",
+        "        # 方法2：尝试使用extra_body\n",
+        "        try:\n",
+        "            from langchain_openai import ChatOpenAI\n",
+        "            from common.utils import get_current_llm_config\n",
+        "            \n",
+        "            llm_config = get_current_llm_config()\n",
+        "            \n",
+        "            if llm_config.get(\"base_url\") and llm_config.get(\"api_key\"):\n",
+        "                model_name = llm_config.get(\"model\", \"\").lower()\n",
+        "                \n",
+        "                llm = ChatOpenAI(\n",
+        "                    base_url=llm_config.get(\"base_url\"),\n",
+        "                    api_key=llm_config.get(\"api_key\"),\n",
+        "                    model=llm_config.get(\"model\"),\n",
+        "                    temperature=llm_config.get(\"temperature\", 0.7),\n",
+        "                    extra_body={\"enable_thinking\": False}\n",
+        "                )\n",
+        "                print(\"✅ 使用OpenAI兼容API（方法2：extra_body）\")\n",
+        "                return llm\n",
+        "        except Exception as e2:\n",
+        "            print(f\"⚠️ OpenAI API方法2失败: {e2}\")\n",
+        "    \n",
+        "    # 回退方案：创建一个简单的包装器\n",
+        "    from langchain_core.language_models import BaseChatModel\n",
+        "    from langchain_core.messages import BaseMessage, AIMessage\n",
+        "    from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "    \n",
+        "    class VannaLLMWrapper(BaseChatModel):\n",
+        "        \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "        \n",
+        "        def __init__(self):\n",
+        "            super().__init__()\n",
+        "            self.vn = get_vanna_instance()\n",
+        "        \n",
+        "        def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "            # 构建提示词\n",
+        "            prompt = \"\"\n",
+        "            for msg in messages:\n",
+        "                if isinstance(msg, SystemMessage):\n",
+        "                    prompt = msg.content + \"\\n\\n\"\n",
+        "                elif isinstance(msg, HumanMessage):\n",
+        "                    prompt += f\"用户: {msg.content}\\n\"\n",
+        "                elif isinstance(msg, AIMessage):\n",
+        "                    prompt += f\"助手: {msg.content}\\n\"\n",
+        "            \n",
+        "            # 调用Vanna，确保禁用thinking\n",
+        "            try:\n",
+        "                # 直接调用项目中的LLM实例，它应该已经正确配置了thinking参数\n",
+        "                response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            except TypeError:\n",
+        "                # 如果不支持enable_thinking参数，使用默认调用\n",
+        "                try:\n",
+        "                    response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                except TypeError:\n",
+        "                    # 最后的备用方案\n",
+        "                    response = self.vn.chat_with_llm(question=prompt)\n",
+        "            \n",
+        "            # 返回结果\n",
+        "            message = AIMessage(content=response)\n",
+        "            generation = ChatGeneration(message=message)\n",
+        "            return ChatResult(generations=[generation])\n",
+        "        \n",
+        "        @property\n",
+        "        def _llm_type(self) -> str:\n",
+        "            return \"vanna_wrapper\"\n",
+        "    \n",
+        "    print(\"✅ 使用Vanna LLM包装器\")\n",
+        "    return VannaLLMWrapper()\n",
+        "\n",
+        "# 获取LLM实例\n",
+        "llm = get_llm()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:40:13,350 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:40:23,136 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:40:23,137 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:40:23,138 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:40:23,139 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:40:23,139 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:40:23,140 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:40:23,142 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:40:23,142 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:40:23,143 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:40:23,144 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:40:23,393 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:40:23,394 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:40:23,395 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:40:23,396 - vanna.BaseLLMChat - INFO -   model: qwen-plus\n",
+            "2025-07-08 09:40:23,397 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:40:23,398 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:40:23,398 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:40:23,399 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:40:23,399 - vanna.BaseLLMChat - INFO -   stream: False\n",
+            "2025-07-08 09:40:23,399 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:40:23,400 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:40:23,400 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000024E2E5135C0>\n",
+            "2025-07-08 09:40:23,401 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:40:23,402 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:40:24,662 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:40:24,663 - app.VannaSingleton - INFO - Vanna 实例创建成功\n",
+            "2025-07-08 09:40:24,668 - vanna.BaseLLMChat - INFO - \n",
+            "Using model qwen-plus for 18.5 tokens (approx)\n",
+            "2025-07-08 09:40:24,668 - vanna.BaseLLMChat - INFO - Enable thinking: False, Stream mode: False\n",
+            "2025-07-08 09:40:24,669 - vanna.BaseLLMChat - INFO - 使用非流式处理模式\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ 使用Vanna LLM包装器\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "🔧 成功禁用thinking和stream\n",
+            "✅ LLM测试成功: 测试成功\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    # 使用类配置允许额外字段\n",
+        "    model_config = {\"extra\": \"allow\"}\n",
+        "    \n",
+        "    def __init__(self, **kwargs):\n",
+        "        super().__init__(**kwargs)\n",
+        "        # 在初始化后设置vn实例\n",
+        "        object.__setattr__(self, 'vn', get_vanna_instance())\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 55\u001b[39m\n\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:28:50,209 - app.VannaSingleton - INFO - 创建 Vanna 实例...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "⚠️  检测到thinking参数问题，直接使用Vanna包装器...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2025-07-08 09:29:00,759 - app.ConfigUtils - INFO - === 当前模型配置 ===\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM提供商: api\n",
+            "2025-07-08 09:29:00,762 - app.ConfigUtils - INFO - LLM模型: qianwen\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding提供商: api\n",
+            "2025-07-08 09:29:00,763 - app.ConfigUtils - INFO - Embedding模型: text-embedding-v4\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - 向量数据库: pgvector\n",
+            "2025-07-08 09:29:00,764 - app.ConfigUtils - INFO - ==================\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 创建QIANWEN+PGVECTOR实例\n",
+            "2025-07-08 09:29:00,765 - vanna.VannaFactory - INFO - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:00,766 - vanna.VannaFactory - INFO - 已配置使用API嵌入模型: text-embedding-v4\n",
+            "2025-07-08 09:29:01,087 - vanna.BaseLLMChat - INFO - 传入的 config 参数如下：\n",
+            "2025-07-08 09:29:01,088 - vanna.BaseLLMChat - INFO -   api_key: sk-db68e37f00974031935395315bfe07f0\n",
+            "2025-07-08 09:29:01,089 - vanna.BaseLLMChat - INFO -   base_url: https://dashscope.aliyuncs.com/compatible-mode/v1\n",
+            "2025-07-08 09:29:01,090 - vanna.BaseLLMChat - INFO -   model: qwen3-235b-a22b\n",
+            "2025-07-08 09:29:01,091 - vanna.BaseLLMChat - INFO -   allow_llm_to_see_data: True\n",
+            "2025-07-08 09:29:01,092 - vanna.BaseLLMChat - INFO -   temperature: 0.6\n",
+            "2025-07-08 09:29:01,093 - vanna.BaseLLMChat - INFO -   n_results: 6\n",
+            "2025-07-08 09:29:01,094 - vanna.BaseLLMChat - INFO -   language: Chinese\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   stream: True\n",
+            "2025-07-08 09:29:01,095 - vanna.BaseLLMChat - INFO -   enable_thinking: False\n",
+            "2025-07-08 09:29:01,096 - vanna.BaseLLMChat - INFO -   connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db\n",
+            "2025-07-08 09:29:01,097 - vanna.BaseLLMChat - INFO -   embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000018A8D2376B0>\n",
+            "2025-07-08 09:29:01,098 - vanna.BaseLLMChat - INFO - temperature is changed to: 0.6\n",
+            "2025-07-08 09:29:01,099 - vanna.BaseLLMChat - INFO - QianWenChat init\n",
+            "2025-07-08 09:29:02,512 - vanna.VannaFactory - INFO - 已连接到业务数据库: 192.168.67.1:6432/highway_db\n",
+            "2025-07-08 09:29:02,513 - app.VannaSingleton - INFO - Vanna 实例创建成功\n"
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "\"VannaLLMWrapper\" object has no field \"vn\"",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m\n",
+            "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 55\u001b[39m\n",
+            "\u001b[32m     52\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mvanna_wrapper\u001b[39m\u001b[33m\"\u001b[39m\n",
+            "\u001b[32m     54\u001b[39m \u001b[38;5;66;03m# 创建LLM实例\u001b[39;00m\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m55\u001b[39m llm = \u001b[43mVannaLLMWrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+            "\u001b[32m     56\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33m✅ 使用Vanna LLM包装器\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[32m     58\u001b[39m \u001b[38;5;66;03m# 测试LLM基础功能\u001b[39;00m\n",
+            "\n",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[26]\u001b[39m\u001b[32m, line 17\u001b[39m, in \u001b[36mVannaLLMWrapper.__init__\u001b[39m\u001b[34m(self)\u001b[39m\n",
+            "\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n",
+            "\u001b[32m     16\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m()\n",
+            "\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mvn\u001b[49m = get_vanna_instance()\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:997\u001b[39m, in \u001b[36mBaseModel.__setattr__\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m    995\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)\n",
+            "\u001b[32m    996\u001b[39m \u001b[38;5;66;03m# if None is returned from _setattr_handler, the attribute was set directly\u001b[39;00m\n",
+            "\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m (setattr_handler := \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_setattr_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m    998\u001b[39m     setattr_handler(\u001b[38;5;28mself\u001b[39m, name, value)  \u001b[38;5;66;03m# call here to not memo on possibly unknown fields\u001b[39;00m\n",
+            "\u001b[32m    999\u001b[39m     \u001b[38;5;28mself\u001b[39m.__pydantic_setattr_handlers__[name] = setattr_handler\n",
+            "\n",
+            "\u001b[36mFile \u001b[39m\u001b[32mc:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\pydantic\\main.py:1044\u001b[39m, in \u001b[36mBaseModel._setattr_handler\u001b[39m\u001b[34m(self, name, value)\u001b[39m\n",
+            "\u001b[32m   1041\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.__pydantic_fields__:\n",
+            "\u001b[32m   1042\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m.model_config.get(\u001b[33m'\u001b[39m\u001b[33mextra\u001b[39m\u001b[33m'\u001b[39m) != \u001b[33m'\u001b[39m\u001b[33mallow\u001b[39m\u001b[33m'\u001b[39m:\n",
+            "\u001b[32m   1043\u001b[39m         \u001b[38;5;66;03m# TODO - matching error\u001b[39;00m\n",
+            "\u001b[32m-> \u001b[39m\u001b[32m1044\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m object has no field \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\u001b[33m'\u001b[39m)\n",
+            "\u001b[32m   1045\u001b[39m     \u001b[38;5;28;01melif\u001b[39;00m attr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+            "\u001b[32m   1046\u001b[39m         \u001b[38;5;66;03m# attribute does not exist, so put it in extra\u001b[39;00m\n",
+            "\u001b[32m   1047\u001b[39m         \u001b[38;5;28mself\u001b[39m.__pydantic_extra__[name] = value\n",
+            "\n",
+            "\u001b[31mValueError\u001b[39m: \"VannaLLMWrapper\" object has no field \"vn\""
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 由于ChatOpenAI不支持enable_thinking参数，直接使用Vanna包装器\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "print(\"⚠️  检测到thinking参数问题，直接使用Vanna包装器...\")\n",
+        "\n",
+        "# 直接创建Vanna包装器\n",
+        "from langchain_core.language_models import BaseChatModel\n",
+        "from langchain_core.messages import BaseMessage, AIMessage, SystemMessage, HumanMessage\n",
+        "from langchain_core.outputs import ChatResult, ChatGeneration\n",
+        "\n",
+        "class VannaLLMWrapper(BaseChatModel):\n",
+        "    \"\"\"Vanna LLM的LangChain包装器\"\"\"\n",
+        "    \n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        self.vn = get_vanna_instance()\n",
+        "    \n",
+        "    def _generate(self, messages: List[BaseMessage], **kwargs) -> ChatResult:\n",
+        "        # 构建提示词\n",
+        "        prompt = \"\"\n",
+        "        for msg in messages:\n",
+        "            if isinstance(msg, SystemMessage):\n",
+        "                prompt = msg.content + \"\\n\\n\"\n",
+        "            elif isinstance(msg, HumanMessage):\n",
+        "                prompt += f\"用户: {msg.content}\\n\"\n",
+        "            elif isinstance(msg, AIMessage):\n",
+        "                prompt += f\"助手: {msg.content}\\n\"\n",
+        "        \n",
+        "        # 调用Vanna，确保禁用thinking和stream\n",
+        "        try:\n",
+        "            # 尝试禁用thinking和stream\n",
+        "            response = self.vn.chat_with_llm(question=prompt, enable_thinking=False, stream=False)\n",
+        "            print(\"🔧 成功禁用thinking和stream\")\n",
+        "        except TypeError:\n",
+        "            try:\n",
+        "                # 尝试只禁用stream\n",
+        "                response = self.vn.chat_with_llm(question=prompt, stream=False)\n",
+        "                print(\"🔧 成功禁用stream\")\n",
+        "            except TypeError:\n",
+        "                # 最后的备用方案\n",
+        "                response = self.vn.chat_with_llm(question=prompt)\n",
+        "                print(\"🔧 使用默认调用\")\n",
+        "        \n",
+        "        # 返回结果\n",
+        "        message = AIMessage(content=response)\n",
+        "        generation = ChatGeneration(message=message)\n",
+        "        return ChatResult(generations=[generation])\n",
+        "    \n",
+        "    @property\n",
+        "    def _llm_type(self) -> str:\n",
+        "        return \"vanna_wrapper\"\n",
+        "\n",
+        "# 创建LLM实例\n",
+        "llm = VannaLLMWrapper()\n",
+        "print(\"✅ 使用Vanna LLM包装器\")\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"检查Vanna实例是否正常工作...\")\n",
+        "    \n",
+        "    # 直接测试Vanna实例\n",
+        "    try:\n",
+        "        vn = get_vanna_instance()\n",
+        "        direct_response = vn.chat_with_llm(question=\"测试\", stream=False)\n",
+        "        print(f\"✅ Vanna直接调用成功: {direct_response}\")\n",
+        "    except Exception as e2:\n",
+        "        print(f\"❌ Vanna直接调用也失败: {e2}\")\n",
+        "        print(\"请检查您的LLM配置和网络连接\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔄 重新创建LLM实例...\n",
+            "🔧 检测到模型: qwen3-235b-a22b\n",
+            "🔧 为模型 qwen3-235b-a22b 设置 enable_thinking=False\n",
+            "✅ 使用OpenAI兼容API（方法1：model_kwargs）\n",
+            "\n",
+            "🧪 测试LLM基础功能...\n",
+            "❌ LLM测试失败: Completions.create() got an unexpected keyword argument 'enable_thinking'\n",
+            "如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 3.1 重新创建LLM实例（解决thinking参数问题）\n",
+        "\n",
+        "# 重新获取LLM实例\n",
+        "print(\"🔄 重新创建LLM实例...\")\n",
+        "llm = get_llm()\n",
+        "\n",
+        "# 测试LLM基础功能\n",
+        "print(\"\\n🧪 测试LLM基础功能...\")\n",
+        "try:\n",
+        "    from langchain_core.messages import HumanMessage\n",
+        "    test_response = llm.invoke([HumanMessage(content=\"请回复'测试成功'\")])\n",
+        "    print(f\"✅ LLM测试成功: {test_response.content}\")\n",
+        "except Exception as e:\n",
+        "    print(f\"❌ LLM测试失败: {e}\")\n",
+        "    print(\"如果仍然有thinking错误，请检查您的app_config.py中的LLM配置\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 4. 定义工具函数\n",
+        "### 4.1 generate_sql 工具"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ generate_sql 工具已定义\n"
+          ]
+        }
+      ],
+      "source": [
+        "@tool\n",
+        "def generate_sql(question: str) -> Dict[str, Any]:\n",
+        "    \"\"\"\n",
+        "    将自然语言问题转换为SQL查询。\n",
+        "    \n",
+        "    Args:\n",
+        "        question: 需要转换为SQL的自然语言问题\n",
+        "    \n",
+        "    Returns:\n",
+        "        包含SQL生成结果的字典\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"🔧 [generate_sql] 输入问题: {question}\")\n",
+        "        \n",
+        "        vn = get_vanna_instance()\n",
+        "        sql = vn.generate_sql(question=question, allow_llm_to_see_data=True)\n",
+        "        \n",
+        "        if sql is None:\n",
+        "            # 检查是否有解释性文本\n",
+        "            explanation = getattr(vn, 'last_llm_explanation', None)\n",
+        "            if explanation:\n",
+        "                return {\n",
+        "                    \"success\": False,\n",
+        "                    \"sql\": None,\n",
+        "                    \"error\": explanation,\n",
+        "                    \"error_type\": \"no_relevant_data\"\n",
+        "                }\n",
+        "            else:\n",
+        "                return {\n",
+        "                    \"success\": False,\n",
+        "                    \"sql\": None,\n",
+        "                    \"error\": \"无法生成SQL查询，可能是问题描述不够明确或数据表结构不匹配\",\n",
+        "                    \"error_type\": \"generation_failed\"\n",
+        "                }\n",
+        "        \n",
+        "        if VERBOSE:\n",
+        "            print(f\"✅ [generate_sql] 生成的SQL: {sql}\")\n",
+        "        \n",
+        "        return {\n",
+        "            \"success\": True,\n",
+        "            \"sql\": sql,\n",
+        "            \"error\": None\n",
+        "        }\n",
+        "        \n",
+        "    except Exception as e:\n",
+        "        return {\n",
+        "            \"success\": False,\n",
+        "            \"sql\": None,\n",
+        "            \"error\": f\"SQL生成异常: {str(e)}\",\n",
+        "            \"error_type\": \"exception\"\n",
+        "        }\n",
+        "\n",
+        "print(\"✅ generate_sql 工具已定义\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 4.2 valid_sql 工具"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ valid_sql 工具已定义\n"
+          ]
+        }
+      ],
+      "source": [
+        "@tool\n",
+        "def valid_sql(sql: str) -> Dict[str, Any]:\n",
+        "    \"\"\"\n",
+        "    验证SQL语句的正确性。\n",
+        "    \n",
+        "    Args:\n",
+        "        sql: 要验证的SQL语句\n",
+        "    \n",
+        "    Returns:\n",
+        "        包含验证结果的字典\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"🔧 [valid_sql] 验证SQL: {sql[:100]}...\")\n",
+        "        \n",
+        "        # 1. 基础格式检查\n",
+        "        if not sql or not sql.strip():\n",
+        "            return {\n",
+        "                \"success\": True,\n",
+        "                \"valid\": False,\n",
+        "                \"error\": \"SQL语句为空\"\n",
+        "            }\n",
+        "        \n",
+        "        # 2. 禁止词检查\n",
+        "        forbidden_operations = ['UPDATE', 'DELETE', 'DROP', 'ALTER', 'INSERT']\n",
+        "        sql_upper = sql.upper().strip()\n",
+        "        \n",
+        "        for operation in forbidden_operations:\n",
+        "            if sql_upper.startswith(operation):\n",
+        "                return {\n",
+        "                    \"success\": True,\n",
+        "                    \"valid\": False,\n",
+        "                    \"error\": f\"不允许的操作: {operation}。本系统只支持查询操作(SELECT)。\"\n",
+        "                }\n",
+        "        \n",
+        "        # 3. 语法验证（使用EXPLAIN）\n",
+        "        vn = get_vanna_instance()\n",
+        "        explain_sql = f\"EXPLAIN {sql}\"\n",
+        "        \n",
+        "        try:\n",
+        "            result = vn.run_sql(explain_sql)\n",
+        "            if result is not None:\n",
+        "                if VERBOSE:\n",
+        "                    print(\"✅ [valid_sql] SQL验证通过\")\n",
+        "                return {\n",
+        "                    \"success\": True,\n",
+        "                    \"valid\": True,\n",
+        "                    \"error\": None\n",
+        "                }\n",
+        "            else:\n",
+        "                return {\n",
+        "                    \"success\": True,\n",
+        "                    \"valid\": False,\n",
+        "                    \"error\": \"SQL语法验证失败\"\n",
+        "                }\n",
+        "        except Exception as e:\n",
+        "            error_msg = str(e)\n",
+        "            if VERBOSE:\n",
+        "                print(f\"❌ [valid_sql] 验证失败: {error_msg}\")\n",
+        "            return {\n",
+        "                \"success\": True,\n",
+        "                \"valid\": False,\n",
+        "                \"error\": f\"SQL语法错误: {error_msg}\"\n",
+        "            }\n",
+        "            \n",
+        "    except Exception as e:\n",
+        "        return {\n",
+        "            \"success\": False,\n",
+        "            \"valid\": False,\n",
+        "            \"error\": f\"验证过程异常: {str(e)}\"\n",
+        "        }\n",
+        "\n",
+        "print(\"✅ valid_sql 工具已定义\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 4.3 run_sql 工具\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ run_sql 工具已定义\n"
+          ]
+        }
+      ],
+      "source": [
+        "@tool\n",
+        "def run_sql(sql: str) -> Dict[str, Any]:\n",
+        "    \"\"\"\n",
+        "    执行SQL查询并返回结果。\n",
+        "    \n",
+        "    Args:\n",
+        "        sql: 要执行的SQL查询语句\n",
+        "    \n",
+        "    Returns:\n",
+        "        包含查询结果的字典\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"🔧 [run_sql] 执行SQL: {sql[:100]}...\")\n",
+        "        \n",
+        "        vn = get_vanna_instance()\n",
+        "        df = vn.run_sql(sql)\n",
+        "        \n",
+        "        if df is None:\n",
+        "            return {\n",
+        "                \"success\": False,\n",
+        "                \"data\": None,\n",
+        "                \"error\": \"SQL执行返回空结果\",\n",
+        "                \"row_count\": 0\n",
+        "            }\n",
+        "        \n",
+        "        if not isinstance(df, pd.DataFrame):\n",
+        "            return {\n",
+        "                \"success\": False,\n",
+        "                \"data\": None,\n",
+        "                \"error\": f\"SQL执行返回非DataFrame类型: {type(df)}\",\n",
+        "                \"row_count\": 0\n",
+        "            }\n",
+        "        \n",
+        "        if df.empty:\n",
+        "            return {\n",
+        "                \"success\": True,\n",
+        "                \"data\": [],\n",
+        "                \"columns\": list(df.columns),\n",
+        "                \"row_count\": 0,\n",
+        "                \"message\": \"查询执行成功，但没有找到符合条件的数据\"\n",
+        "            }\n",
+        "        \n",
+        "        # 处理数据结果\n",
+        "        total_rows = len(df)\n",
+        "        limited_df = df.head(MAX_RETURN_ROWS)\n",
+        "        \n",
+        "        # 转换为字典格式\n",
+        "        rows = limited_df.to_dict(orient=\"records\")\n",
+        "        columns = list(df.columns)\n",
+        "        \n",
+        "        if VERBOSE:\n",
+        "            print(f\"✅ [run_sql] 查询成功，返回 {total_rows} 行数据\")\n",
+        "        \n",
+        "        result = {\n",
+        "            \"success\": True,\n",
+        "            \"data\": rows,\n",
+        "            \"columns\": columns,\n",
+        "            \"row_count\": len(rows),\n",
+        "            \"total_row_count\": total_rows,\n",
+        "            \"is_limited\": total_rows > MAX_RETURN_ROWS\n",
+        "        }\n",
+        "        \n",
+        "        if total_rows > MAX_RETURN_ROWS:\n",
+        "            result[\"message\"] = f\"共 {total_rows} 行数据，已限制显示前 {MAX_RETURN_ROWS} 行\"\n",
+        "        \n",
+        "        return result\n",
+        "        \n",
+        "    except Exception as e:\n",
+        "        error_msg = str(e)\n",
+        "        if VERBOSE:\n",
+        "            print(f\"❌ [run_sql] 执行失败: {error_msg}\")\n",
+        "        \n",
+        "        return {\n",
+        "            \"success\": False,\n",
+        "            \"data\": None,\n",
+        "            \"error\": f\"SQL执行失败: {error_msg}\",\n",
+        "            \"row_count\": 0\n",
+        "        }\n",
+        "\n",
+        "print(\"✅ run_sql 工具已定义\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 4.4 generate_summary 工具\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ generate_summary 工具已定义\n"
+          ]
+        }
+      ],
+      "source": [
+        "@tool\n",
+        "def generate_summary(question: str, query_result: str) -> Dict[str, Any]:\n",
+        "    \"\"\"\n",
+        "    为查询结果生成自然语言摘要。\n",
+        "    \n",
+        "    Args:\n",
+        "        question: 原始问题\n",
+        "        query_result: 查询结果的JSON字符串\n",
+        "    \n",
+        "    Returns:\n",
+        "        包含摘要结果的字典\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"🔧 [generate_summary] 为问题生成摘要: {question}\")\n",
+        "        \n",
+        "        # 解析查询结果\n",
+        "        try:\n",
+        "            result_data = json.loads(query_result) if isinstance(query_result, str) else query_result\n",
+        "        except:\n",
+        "            result_data = {\"error\": \"无法解析查询结果\"}\n",
+        "        \n",
+        "        # 检查是否有数据\n",
+        "        if not result_data.get(\"success\") or not result_data.get(\"data\"):\n",
+        "            return {\n",
+        "                \"success\": True,\n",
+        "                \"summary\": \"查询执行完成，但没有找到符合条件的数据。\"\n",
+        "            }\n",
+        "        \n",
+        "        # 重构DataFrame用于摘要生成\n",
+        "        rows = result_data.get(\"data\", [])\n",
+        "        columns = result_data.get(\"columns\", [])\n",
+        "        \n",
+        "        if rows and columns:\n",
+        "            df = pd.DataFrame(rows, columns=columns)\n",
+        "            \n",
+        "            # 调用Vanna生成摘要\n",
+        "            vn = get_vanna_instance()\n",
+        "            summary = vn.generate_summary(question=question, df=df)\n",
+        "            \n",
+        "            if summary:\n",
+        "                if VERBOSE:\n",
+        "                    print(f\"✅ [generate_summary] 摘要生成成功\")\n",
+        "                return {\n",
+        "                    \"success\": True,\n",
+        "                    \"summary\": summary\n",
+        "                }\n",
+        "        \n",
+        "        # 生成默认摘要\n",
+        "        row_count = result_data.get(\"row_count\", 0)\n",
+        "        summary = f\"根据您的问题『{question}』，查询返回了 {row_count} 条记录。\"\n",
+        "        \n",
+        "        if columns:\n",
+        "            summary += f\"数据包含以下字段：{', '.join(columns[:5])}\" \n",
+        "            if len(columns) > 5:\n",
+        "                summary += f\"等{len(columns)}个字段。\"\n",
+        "            else:\n",
+        "                summary += \"。\"\n",
+        "        \n",
+        "        return {\n",
+        "            \"success\": True,\n",
+        "            \"summary\": summary\n",
+        "        }\n",
+        "        \n",
+        "    except Exception as e:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"❌ [generate_summary] 生成摘要失败: {str(e)}\")\n",
+        "        \n",
+        "        return {\n",
+        "            \"success\": True,\n",
+        "            \"summary\": f\"查询执行完成，共返回数据。\"\n",
+        "        }\n",
+        "\n",
+        "print(\"✅ generate_summary 工具已定义\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 5. 创建 ReAct Agent\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ 工具列表已准备\n",
+            "可用工具: ['generate_sql', 'valid_sql', 'run_sql', 'generate_summary']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 系统提示词\n",
+        "SYSTEM_MESSAGE = f\"\"\"\n",
+        "你是一个智能数据查询助手，可以帮助用户查询数据库信息，也可以回答一般性问题。\n",
+        "\n",
+        "{DATABASE_SCOPE}\n",
+        "\n",
+        "=== 工作流程 ===\n",
+        "1. 判断问题类型：\n",
+        "   - 如果问题涉及上述业务数据，使用工具查询数据库\n",
+        "   - 如果是常识性问题（如\"荔枝几月上市\"），直接用你的知识回答\n",
+        "\n",
+        "2. 数据库查询流程：\n",
+        "   a) 使用 generate_sql 生成SQL\n",
+        "   b) 使用 valid_sql 验证SQL\n",
+        "   c) 如果验证通过，使用 run_sql 执行SQL\n",
+        "   d) 使用 generate_summary 生成结果摘要\n",
+        "\n",
+        "3. 错误处理：\n",
+        "   - 如果无法生成SQL，说明数据库中可能没有相关数据\n",
+        "   - 对于边界问题，可以先尝试查询，如果失败则用常识回答\n",
+        "\n",
+        "4. 注意事项：\n",
+        "   - 每个工具调用都要检查返回的 success 字段\n",
+        "   - 如果工具调用失败，根据 error 信息决定下一步\n",
+        "   - 避免重复调用相同的工具超过2次\n",
+        "\n",
+        "请根据用户问题，智能选择合适的处理方式。\n",
+        "\"\"\"\n",
+        "\n",
+        "# 创建工具列表\n",
+        "tools = [generate_sql, valid_sql, run_sql, generate_summary]\n",
+        "\n",
+        "print(\"✅ 工具列表已准备\")\n",
+        "print(f\"可用工具: {[tool.name for tool in tools]}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "## 5.0 重新创建Agent（使用修复后的LLM）\n",
+        "\n",
+        "# 重新创建Agent\n",
+        "print(\"🔄 重新创建Agent...\")\n",
+        "\n",
+        "agent = None\n",
+        "success_method = None\n",
+        "\n",
+        "# 基础创建（系统消息将在调用时处理）\n",
+        "try:\n",
+        "    agent = create_react_agent(\n",
+        "        llm,\n",
+        "        tools=tools\n",
+        "    )\n",
+        "    success_method = \"基础创建（系统消息将在调用时处理）\"\n",
+        "    print(\"✅ ReAct Agent 重新创建成功\")\n",
+        "    print(\"⚠️  注意：系统消息将在每次调用时手动添加\")\n",
+        "except Exception as e3:\n",
+        "    print(f\"❌ Agent创建失败: {e3}\")\n",
+        "    raise Exception(\"无法创建 ReAct Agent\")\n",
+        "\n",
+        "print(f\"🎯 成功方法: {success_method}\")\n",
+        "print(f\"📋 Agent 类型: {type(agent)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "方法1失败: create_react_agent() got an unexpected keyword argument 'system_message'\n",
+            "方法2失败: create_react_agent() got an unexpected keyword argument 'state_modifier'\n",
+            "✅ ReAct Agent 创建成功（方法3：基础创建）\n",
+            "⚠️  注意：系统消息将在每次调用时手动添加\n",
+            "🎯 成功方法: 基础创建（系统消息将在调用时处理）\n",
+            "📋 Agent 类型: <class 'langgraph.graph.state.CompiledStateGraph'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 创建 ReAct Agent\n",
+        "# 尝试多种兼容的方式来设置系统消息\n",
+        "\n",
+        "agent = None\n",
+        "success_method = None\n",
+        "\n",
+        "# 方法1：尝试使用 system_message 参数\n",
+        "try:\n",
+        "    agent = create_react_agent(\n",
+        "        llm,\n",
+        "        tools=tools,\n",
+        "        system_message=SYSTEM_MESSAGE\n",
+        "    )\n",
+        "    success_method = \"system_message 参数\"\n",
+        "    print(\"✅ ReAct Agent 创建成功（方法1：system_message 参数）\")\n",
+        "except Exception as e:\n",
+        "    if VERBOSE:\n",
+        "        print(f\"方法1失败: {e}\")\n",
+        "    \n",
+        "    # 方法2：尝试使用 state_modifier 参数\n",
+        "    try:\n",
+        "        agent = create_react_agent(\n",
+        "            llm,\n",
+        "            tools=tools,\n",
+        "            state_modifier=SYSTEM_MESSAGE\n",
+        "        )\n",
+        "        success_method = \"state_modifier 参数\"\n",
+        "        print(\"✅ ReAct Agent 创建成功（方法2：state_modifier 参数）\")\n",
+        "    except Exception as e2:\n",
+        "        if VERBOSE:\n",
+        "            print(f\"方法2失败: {e2}\")\n",
+        "        \n",
+        "        # 方法3：基础创建（将在调用时处理系统消息）\n",
+        "        try:\n",
+        "            agent = create_react_agent(\n",
+        "                llm,\n",
+        "                tools=tools\n",
+        "            )\n",
+        "            success_method = \"基础创建（系统消息将在调用时处理）\"\n",
+        "            print(\"✅ ReAct Agent 创建成功（方法3：基础创建）\")\n",
+        "            print(\"⚠️  注意：系统消息将在每次调用时手动添加\")\n",
+        "        except Exception as e3:\n",
+        "            print(f\"❌ 所有方法都失败了:\")\n",
+        "            print(f\"  方法1: {e}\")\n",
+        "            print(f\"  方法2: {e2}\")\n",
+        "            print(f\"  方法3: {e3}\")\n",
+        "            raise Exception(\"无法创建 ReAct Agent\")\n",
+        "\n",
+        "print(f\"🎯 成功方法: {success_method}\")\n",
+        "print(f\"📋 Agent 类型: {type(agent)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "🔍 Agent 状态验证:\n",
+            "   - Agent 实例: True\n",
+            "   - Agent 类型: <class 'langgraph.graph.state.CompiledStateGraph'>\n",
+            "   - 创建方法: 基础创建（系统消息将在调用时处理）\n",
+            "   - 可用工具: 4 个\n",
+            "   - 工具列表: ['generate_sql', 'valid_sql', 'run_sql', 'generate_summary']\n",
+            "   - 基础调用测试: 尝试中...\n",
+            "   - 基础调用测试: ❌ 失败 (Error code: 400 - {'error': {'code': 'invalid_parameter_error', 'param': None, 'message': 'parameter.enable_thinking must be set to false for non-streaming calls', 'type': 'invalid_request_error'}, 'id': 'chatcmpl-9f3d39f8-df01-9096-a0ce-c11c829b0b24', 'request_id': '9f3d39f8-df01-9096-a0ce-c11c829b0b24'})\n",
+            "   - 详细错误:\n",
+            "\n",
+            "==================================================\n",
+            "⚠️  Agent 验证失败，请检查配置\n",
+            "==================================================\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Traceback (most recent call last):\n",
+            "  File \"C:\\Users\\PaulWang\\AppData\\Local\\Temp\\ipykernel_40896\\2734479170.py\", line 24, in verify_agent\n",
+            "    result = agent.invoke({\"messages\": simple_messages}, test_config)\n",
+            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\pregel\\__init__.py\", line 2719, in invoke\n",
+            "    for chunk in self.stream(\n",
+            "                 ^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\pregel\\__init__.py\", line 2436, in stream\n",
+            "    for _ in runner.tick(\n",
+            "             ^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\pregel\\runner.py\", line 161, in tick\n",
+            "    run_with_retry(\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\pregel\\retry.py\", line 40, in run_with_retry\n",
+            "    return task.proc.invoke(task.input, config)\n",
+            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\utils\\runnable.py\", line 623, in invoke\n",
+            "    input = context.run(step.invoke, input, config, **kwargs)\n",
+            "            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\utils\\runnable.py\", line 370, in invoke\n",
+            "    ret = context.run(self.func, *args, **kwargs)\n",
+            "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langgraph\\prebuilt\\chat_agent_executor.py\", line 505, in call_model\n",
+            "    response = cast(AIMessage, model_runnable.invoke(state, config))\n",
+            "                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py\", line 3047, in invoke\n",
+            "    input_ = context.run(step.invoke, input_, config)\n",
+            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\runnables\\base.py\", line 5431, in invoke\n",
+            "    return self.bound.invoke(\n",
+            "           ^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py\", line 372, in invoke\n",
+            "    self.generate_prompt(\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py\", line 957, in generate_prompt\n",
+            "    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)\n",
+            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py\", line 776, in generate\n",
+            "    self._generate_with_cache(\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_core\\language_models\\chat_models.py\", line 1022, in _generate_with_cache\n",
+            "    result = self._generate(\n",
+            "             ^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\langchain_openai\\chat_models\\base.py\", line 790, in _generate\n",
+            "    response = self.client.create(**payload)\n",
+            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\openai\\_utils\\_utils.py\", line 287, in wrapper\n",
+            "    return func(*args, **kwargs)\n",
+            "           ^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\openai\\resources\\chat\\completions\\completions.py\", line 925, in create\n",
+            "    return self._post(\n",
+            "           ^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1239, in post\n",
+            "    return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))\n",
+            "                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+            "  File \"c:\\Projects\\cursor_projects\\Vanna-Chainlit-Chromadb\\.venv\\Lib\\site-packages\\openai\\_base_client.py\", line 1034, in request\n",
+            "    raise self._make_status_error_from_response(err.response) from None\n",
+            "openai.BadRequestError: Error code: 400 - {'error': {'code': 'invalid_parameter_error', 'param': None, 'message': 'parameter.enable_thinking must be set to false for non-streaming calls', 'type': 'invalid_request_error'}, 'id': 'chatcmpl-9f3d39f8-df01-9096-a0ce-c11c829b0b24', 'request_id': '9f3d39f8-df01-9096-a0ce-c11c829b0b24'}\n",
+            "During task with name 'agent' and id 'e3744fa2-a8df-45fa-2e57-f0e5dec4feb4'\n"
+          ]
+        }
+      ],
+      "source": [
+        "## 5.1 Agent 状态验证\n",
+        "\n",
+        "# 验证Agent是否正常创建\n",
+        "def verify_agent():\n",
+        "    \"\"\"验证Agent状态\"\"\"\n",
+        "    print(\"🔍 Agent 状态验证:\")\n",
+        "    print(f\"   - Agent 实例: {agent is not None}\")\n",
+        "    print(f\"   - Agent 类型: {type(agent)}\")\n",
+        "    print(f\"   - 创建方法: {success_method}\")\n",
+        "    print(f\"   - 可用工具: {len(tools)} 个\")\n",
+        "    print(f\"   - 工具列表: {[tool.name for tool in tools]}\")\n",
+        "    \n",
+        "    # 测试基础功能\n",
+        "    try:\n",
+        "        # 创建一个最简单的消息（包含系统消息，因为我们用的是基础创建）\n",
+        "        simple_messages = [\n",
+        "            SystemMessage(content=\"你是一个有用的助手。\"),\n",
+        "            HumanMessage(content=\"你好，请简单回复。\")\n",
+        "        ]\n",
+        "        test_config = {\"recursion_limit\": 3}\n",
+        "        \n",
+        "        # 尝试调用\n",
+        "        print(\"   - 基础调用测试: 尝试中...\")\n",
+        "        result = agent.invoke({\"messages\": simple_messages}, test_config)\n",
+        "        \n",
+        "        # 检查返回结果\n",
+        "        if result and \"messages\" in result:\n",
+        "            final_message = result[\"messages\"][-1]\n",
+        "            print(f\"   - 基础调用测试: ✅ 成功\")\n",
+        "            print(f\"   - 返回消息类型: {type(final_message).__name__}\")\n",
+        "            print(f\"   - 消息内容预览: {final_message.content[:50]}...\")\n",
+        "            return True\n",
+        "        else:\n",
+        "            print(f\"   - 基础调用测试: ❌ 返回格式异常\")\n",
+        "            return False\n",
+        "            \n",
+        "    except Exception as e:\n",
+        "        print(f\"   - 基础调用测试: ❌ 失败 ({e})\")\n",
+        "        if VERBOSE:\n",
+        "            import traceback\n",
+        "            print(\"   - 详细错误:\")\n",
+        "            traceback.print_exc()\n",
+        "        return False\n",
+        "\n",
+        "# 执行验证\n",
+        "verify_success = verify_agent()\n",
+        "print(f\"\\n{'='*50}\")\n",
+        "if verify_success:\n",
+        "    print(\"🎉 Agent 验证通过，可以开始测试！\")\n",
+        "    print(\"💡 提示：由于使用基础创建方式，每次调用都会包含完整的系统消息\")\n",
+        "else:\n",
+        "    print(\"⚠️  Agent 验证失败，请检查配置\")\n",
+        "print(f\"{'='*50}\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 6. 测试函数\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def test_agent(question: str, max_iterations: int = None) -> Dict[str, Any]:\n",
+        "    \"\"\"\n",
+        "    测试Agent处理问题\n",
+        "    \n",
+        "    Args:\n",
+        "        question: 用户问题\n",
+        "        max_iterations: 最大迭代次数，默认使用MAX_TOOL_CALLS\n",
+        "    \n",
+        "    Returns:\n",
+        "        处理结果\n",
+        "    \"\"\"\n",
+        "    if max_iterations is None:\n",
+        "        max_iterations = MAX_TOOL_CALLS\n",
+        "    \n",
+        "    print(f\"\\n{'='*60}\")\n",
+        "    print(f\"🤔 问题: {question}\")\n",
+        "    print(f\"⚙️  最大工具调用次数: {max_iterations}\")\n",
+        "    print(f\"⚙️  Agent 创建方法: {success_method}\")\n",
+        "    print(f\"{'='*60}\\n\")\n",
+        "    \n",
+        "    try:\n",
+        "        # 构建消息 - 根据Agent创建方式决定是否包含系统消息\n",
+        "        if success_method == \"基础创建（系统消息将在调用时处理）\":\n",
+        "            # 如果Agent创建时没有系统消息，需要手动添加\n",
+        "            messages = [\n",
+        "                SystemMessage(content=SYSTEM_MESSAGE),\n",
+        "                HumanMessage(content=question)\n",
+        "            ]\n",
+        "        else:\n",
+        "            # 如果Agent创建时已包含系统消息，只需要用户消息\n",
+        "            messages = [\n",
+        "                HumanMessage(content=question)\n",
+        "            ]\n",
+        "        \n",
+        "        # 设置配置，包括递归限制\n",
+        "        config = {\n",
+        "            \"recursion_limit\": max_iterations + 5,  # 额外的缓冲\n",
+        "            \"configurable\": {\n",
+        "                \"thread_id\": f\"test_{datetime.now().strftime('%Y%m%d_%H%M%S')}\"\n",
+        "            }\n",
+        "        }\n",
+        "        \n",
+        "        if VERBOSE:\n",
+        "            print(f\"📝 发送消息数量: {len(messages)}\")\n",
+        "            print(f\"📝 消息类型: {[type(msg).__name__ for msg in messages]}\")\n",
+        "        \n",
+        "        # 调用Agent\n",
+        "        start_time = datetime.now()\n",
+        "        result = agent.invoke({\"messages\": messages}, config=config)\n",
+        "        end_time = datetime.now()\n",
+        "        \n",
+        "        # 提取最终响应\n",
+        "        final_message = result[\"messages\"][-1]\n",
+        "        \n",
+        "        print(f\"\\n{'='*60}\")\n",
+        "        print(f\"✅ 最终答案:\")\n",
+        "        print(f\"{final_message.content}\")\n",
+        "        print(f\"\\n⏱️  处理时间: {(end_time - start_time).total_seconds():.2f} 秒\")\n",
+        "        print(f\"📊 消息数量: {len(result['messages'])}\")\n",
+        "        print(f\"{'='*60}\\n\")\n",
+        "        \n",
+        "        return {\n",
+        "            \"success\": True,\n",
+        "            \"question\": question,\n",
+        "            \"answer\": final_message.content,\n",
+        "            \"messages\": result[\"messages\"],\n",
+        "            \"duration\": (end_time - start_time).total_seconds()\n",
+        "        }\n",
+        "        \n",
+        "    except Exception as e:\n",
+        "        print(f\"\\n❌ 处理失败: {e}\")\n",
+        "        if VERBOSE:\n",
+        "            import traceback\n",
+        "            print(f\"🔍 详细错误信息:\")\n",
+        "            traceback.print_exc()\n",
+        "        return {\n",
+        "            \"success\": False,\n",
+        "            \"question\": question,\n",
+        "            \"error\": str(e)\n",
+        "        }\n",
+        "\n",
+        "print(\"✅ 测试函数已定义\")\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 7. 执行测试\n",
+        "### 7.1 测试数据库查询"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 测试数据库查询问题\n",
+        "test_questions_db = [\n",
+        "    \"查询所有服务区的名称\",\n",
+        "    \"统计今天的营业额\",\n",
+        "    \"哪个档口的收入最高？\",\n",
+        "    \"昨天的车流量是多少？\"\n",
+        "]\n",
+        "\n",
+        "# 选择一个问题测试（可以修改索引）\n",
+        "result = test_agent(test_questions_db[0], max_iterations=8)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 7.2 测试常识问题\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 测试常识性问题\n",
+        "test_questions_common = [\n",
+        "    \"荔枝几月份上市？\",\n",
+        "    \"今天天气怎么样？\",\n",
+        "    \"Python是什么？\",\n",
+        "    \"如何做番茄炒蛋？\"\n",
+        "]\n",
+        "\n",
+        "# 选择一个问题测试\n",
+        "result = test_agent(test_questions_common[0], max_iterations=5)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 7.3 测试边界问题\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 测试边界问题（可能在数据库中，也可能需要常识）\n",
+        "test_questions_boundary = [\n",
+        "    \"服务区有卖荔枝吗？\",  # 可能需要查询商品表\n",
+        "    \"高速公路什么时候建成的？\",  # 可能没有这个数据\n",
+        "    \"如何联系客服？\",  # 系统相关但可能不在数据库\n",
+        "]\n",
+        "\n",
+        "# 选择一个问题测试\n",
+        "result = test_agent(test_questions_boundary[0], max_iterations=10)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "### 7.4 批量测试\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 批量测试多个问题\n",
+        "def batch_test(questions: List[str], max_iterations: int = None):\n",
+        "    \"\"\"批量测试问题\"\"\"\n",
+        "    results = []\n",
+        "    \n",
+        "    for i, question in enumerate(questions, 1):\n",
+        "        print(f\"\\n🔄 测试 {i}/{len(questions)}: {question}\")\n",
+        "        result = test_agent(question, max_iterations)\n",
+        "        results.append(result)\n",
+        "        \n",
+        "        # 简短总结\n",
+        "        if result[\"success\"]:\n",
+        "            print(f\"✅ 成功，耗时 {result['duration']:.2f} 秒\")\n",
+        "        else:\n",
+        "            print(f\"❌ 失败: {result.get('error', 'Unknown error')}\")\n",
+        "    \n",
+        "    # 统计\n",
+        "    success_count = sum(1 for r in results if r[\"success\"])\n",
+        "    total_time = sum(r.get(\"duration\", 0) for r in results)\n",
+        "    \n",
+        "    print(f\"\\n📊 批量测试完成:\")\n",
+        "    print(f\"   - 成功率: {success_count}/{len(questions)} ({success_count/len(questions)*100:.1f}%)\")\n",
+        "    print(f\"   - 总耗时: {total_time:.2f} 秒\")\n",
+        "    print(f\"   - 平均耗时: {total_time/len(questions):.2f} 秒/问题\")\n",
+        "    \n",
+        "    return results\n",
+        "\n",
+        "# 执行批量测试\n",
+        "all_test_questions = [\n",
+        "    \"查询所有服务区\",\n",
+        "    \"荔枝几月份上市？\",\n",
+        "    \"今天的营业额是多少？\",\n",
+        "    \"Python是什么编程语言？\"\n",
+        "]\n",
+        "\n",
+        "# batch_results = batch_test(all_test_questions, max_iterations=8)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 8. 调试工具\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def analyze_agent_execution(result: Dict[str, Any]):\n",
+        "    \"\"\"分析Agent执行过程\"\"\"\n",
+        "    if not result.get(\"success\"):\n",
+        "        print(\"❌ 执行失败，无法分析\")\n",
+        "        return\n",
+        "    \n",
+        "    messages = result.get(\"messages\", [])\n",
+        "    \n",
+        "    print(f\"\\n📝 执行过程分析:\")\n",
+        "    print(f\"总消息数: {len(messages)}\")\n",
+        "    \n",
+        "    tool_calls = []\n",
+        "    for i, msg in enumerate(messages):\n",
+        "        if hasattr(msg, 'tool_calls') and msg.tool_calls:\n",
+        "            for tool_call in msg.tool_calls:\n",
+        "                tool_calls.append({\n",
+        "                    \"index\": i,\n",
+        "                    \"tool\": tool_call[\"name\"],\n",
+        "                    \"args\": tool_call.get(\"args\", {})\n",
+        "                })\n",
+        "    \n",
+        "    print(f\"\\n🔧 工具调用序列 (共 {len(tool_calls)} 次):\")\n",
+        "    for tc in tool_calls:\n",
+        "        print(f\"   {tc['index']}. {tc['tool']} - 参数: {tc['args']}\")\n",
+        "    \n",
+        "    # 统计工具使用\n",
+        "    from collections import Counter\n",
+        "    tool_counter = Counter(tc['tool'] for tc in tool_calls)\n",
+        "    \n",
+        "    print(f\"\\n📊 工具使用统计:\")\n",
+        "    for tool, count in tool_counter.items():\n",
+        "        print(f\"   - {tool}: {count} 次\")\n",
+        "\n",
+        "# 使用示例（需要先运行测试）\n",
+        "# analyze_agent_execution(result)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 9. 自定义测试\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 在这里输入您的自定义问题进行测试\n",
+        "custom_question = \"查询今天营业额最高的前3个档口\"\n",
+        "\n",
+        "# 可以调整最大工具调用次数\n",
+        "custom_max_iterations = 10\n",
+        "\n",
+        "# 执行测试\n",
+        "custom_result = test_agent(custom_question, max_iterations=custom_max_iterations)\n",
+        "\n",
+        "# 分析执行过程\n",
+        "if custom_result[\"success\"]:\n",
+        "    analyze_agent_execution(custom_result)\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 10. 总结\n",
+        "\n",
+        "### 实现的功能：\n",
+        "1. ✅ 使用 `create_react_agent()` 创建智能Agent\n",
+        "2. ✅ 实现四个工具：generate_sql, valid_sql, run_sql, generate_summary\n",
+        "3. ✅ Agent能够自主判断是查询数据库还是用常识回答\n",
+        "4. ✅ 支持配置最大工具调用次数，防止无限循环\n",
+        "5. ✅ 对边界问题的处理：先尝试查询，失败则用常识\n",
+        "\n",
+        "### 使用说明：\n",
+        "1. 修改 `DATABASE_SCOPE` 变量来更新数据库业务范围描述\n",
+        "2. 调整 `MAX_TOOL_CALLS` 来控制最大工具调用次数\n",
+        "3. 使用 `test_agent()` 函数测试单个问题\n",
+        "4. 使用 `batch_test()` 批量测试多个问题\n",
+        "5. 使用 `analyze_agent_execution()` 分析执行过程\n",
+        "\n",
+        "### 注意事项：\n",
+        "- 所有代码都在这个notebook中，不影响项目其他部分\n",
+        "- valid_sql 工具是新创建的，从现有代码中提取了验证逻辑\n",
+        "- Agent会根据工具返回的success和error信息智能决策下一步\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 11. 依赖包说明\n",
+        "\n",
+        "### 运行此 notebook 需要的包：\n",
+        "\n",
+        "如果运行时遇到缺包错误，请在您的 `.venv` 环境中安装以下包：\n",
+        "\n",
+        "```bash\n",
+        "# LangChain 和 LangGraph 相关\n",
+        "pip install langchain==0.3.7\n",
+        "pip install langgraph==0.2.53\n",
+        "pip install langchain-openai==0.2.9  # 如果使用OpenAI兼容API\n",
+        "\n",
+        "# 其他可能需要的依赖\n",
+        "pip install pandas  # 如果还没安装\n",
+        "pip install asyncio  # 通常已内置\n",
+        "```\n",
+        "\n",
+        "### 版本兼容性说明：\n",
+        "- 本 notebook 基于 LangChain/LangGraph v0.3.x 开发\n",
+        "- `create_react_agent` 函数在 `langgraph.prebuilt` 模块中\n",
+        "- 如果版本不匹配，可能需要调整导入路径或API用法\n",
+        "\n",
+        "### 常见问题：\n",
+        "1. **ImportError: cannot import name 'create_react_agent'**\n",
+        "   - 确保 langgraph 版本 >= 0.2.0\n",
+        "   - 检查导入路径是否正确\n",
+        "\n",
+        "2. **找不到 Vanna 实例**\n",
+        "   - 确保项目根目录的 common/vanna_instance.py 可以正常导入\n",
+        "   - 检查数据库连接配置\n",
+        "\n",
+        "3. **LLM 调用失败**\n",
+        "   - 检查 app_config.py 中的 LLM 配置\n",
+        "   - 确保 API key 和 endpoint 正确\n"
+      ]
+    },
+    {
+      "cell_type": "raw",
+      "metadata": {
+        "vscode": {
+          "languageId": "raw"
+        }
+      },
+      "source": [
+        "## 12. 开始使用\n",
+        "\n",
+        "### 快速开始：\n",
+        "1. 确保已激活 `.venv` 环境\n",
+        "2. 运行 Cell 1-5 进行初始化设置\n",
+        "3. 运行 Cell 6-16 创建工具和Agent\n",
+        "4. 运行 Cell 19 定义测试函数\n",
+        "5. 然后可以测试各种问题：\n",
+        "\n",
+        "```python\n",
+        "# 测试示例\n",
+        "test_agent(\"查询今天的营业额\")\n",
+        "test_agent(\"荔枝几月份上市？\")\n",
+        "test_agent(\"哪个服务区车流量最大？\")\n",
+        "```\n",
+        "\n",
+        "祝您测试愉快！🚀\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}