2025-07-20 00:49:17 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 00:49:17 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:17 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 00:49:17 [DEBUG] [vanna.PromptLoader] load_prompts.py:37 - 成功加载提示词配置: C:\Projects\cursor_projects\Vanna-Chainlit-Chromadb\customllm\llm_prompts.yaml 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 00:49:17 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 00:49:17 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 00:49:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 00:49:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 00:49:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 00:49:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 00:49:19 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 00:49:20 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 00:49:55 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 00:49:55 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:55 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 00:49:56 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 00:49:56 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 00:49:58 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: 请问哪个服务区的档口数量最多？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析每个服务区关联的路线数量并找出覆盖路线最多的服务区 | similarity: 0.7464 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 哪些服务区只有单一方向的档口？ | similarity: 0.7459 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析各服务区关联的路段路线数量TOP10 | similarity: 0.7405 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 每个服务区的营业档口数量（曾经有交易的）？ | similarity: 0.7326 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 最近30天中车流量最高的服务区？ | similarity: 0.7325 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 各分公司管辖服务区的档口总数对比如何？ | similarity: 0.7275 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - SQL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.7464 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.7459 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.7405 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 4: similarity=0.7326 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 5: similarity=0.7325 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 6: similarity=0.7275 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属... | similarity: 0.649 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的... | similarity: 0.6368 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 cr... | similarity: 0.6357 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应... | similarity: 0.6313 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 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... | similarity: 0.626 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 存储路线段与服务区关联关系 -- 描述: 存储路线段与服务区关联关系，管理高速线路与... | similarity: 0.6199 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DDL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.649 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.6368 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.6357 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.6313 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.626 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 6: similarity=0.6199 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础... | similarity: 0.6543 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_serv... | similarity: 0.6345 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss... | similarity: 0.6339 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（路线与服务区关联表） bss_sect... | similarity: 0.6287 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表记录... | similarity: 0.627 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（记录高速公路路段路线与服务区的关联关系... | similarity: 0.6263 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DOC 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.6543 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.6345 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.6339 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.6287 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.627 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.6263 ✓ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: 请问哪个服务区的档口数量最多？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:654 - Error SQL Match: 查询所有部门信息 | similarity: 0.2713 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:392 - Error SQL 阈值过滤: 总数=1, 阈值=0.8 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] pgvector.py:410 - Error SQL 过滤结果: 所有 1 条结果都低于阈值 0.8，返回空列表 2025-07-20 00:49:58 [WARNING] [vanna.BaseLLMChat] pgvector.py:673 - 向量查询找到了 1 条错误SQL示例，但全部被阈值过滤掉. 2025-07-20 00:49:58 [WARNING] [vanna.BaseLLMChat] pgvector.py:674 - 问题: 请问哪个服务区的档口数量最多？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a PostgreSQL expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. ===Tables -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 create table bss_branch ( id varchar(32) not null, -- 主键ID version integer not null, -- 数据版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 branch_name varchar(255), -- 档口名称 branch_no varchar(255), -- 档口编码 service_area_id varchar(32), -- 服务区ID,外键(关联bss_service_area.id) company_id varchar(32), -- 公司ID,外键(关联bss_company.ID) classify varchar(256), -- 品类 product_brand varchar(256), -- 品牌 category varchar(256), -- 类别 section_route_id varchar(32), -- 所属路线ID,外键(关联bss_section_route.id) direction varchar(256), -- 所在方向 is_manual_entry integer default 0, -- 是否手工录入 co_company varchar(256) -- 合作公司名称 ) -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的归属关系。 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) ) -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 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) ) -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应关系 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) ) -- 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) ) -- 中文名: 存储路线段与服务区关联关系 -- 描述: 存储路线段与服务区关联关系，管理高速线路与服务区归属 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 -- 服务区编码，主键, primary key (section_route_id, service_area_id) ) ===Additional Context ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 字段列表： - id (varchar(32)) - 主键ID [示例: 00904903cae681aab7a494c3e88e5acd] - version (integer) - 数据版本号 [示例: 1] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-10-15 09:46:45.010] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-10-15 09:46:45.010] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - branch_name (varchar(255)) - 档口名称 [示例: 于都驿美餐饮南区] - branch_no (varchar(255)) - 档口编码（唯一业务标识）[示例: 003585] - service_area_id (varchar(32)) - 服务区ID（外键关联bss_service_area.id）[示例: c7e2f26df373e9cb75bd24ddba57f27f] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: ce5e6f553513dad393694e1fa663aaf4] - classify (varchar(256)) - 经营品类，枚举型：餐饮、小吃、便利店、整体租赁、其他 [示例: 餐饮] - product_brand (varchar(256)) - 品牌名称 [示例: 驿美餐饮] - category (varchar(256)) - 经营类别 [示例: 混沌] - section_route_id (varchar(32)) - 所属路线ID（外键关联bss_section_route.id）[示例: lvkcuu94d4487c42z7qltsvxcyz0iqu5] - direction (varchar(256)) - 所在方向（枚举：北区/南区/西区/东区/两区）[示例: 南区] - is_manual_entry (integer) - 是否手工录入（0=系统自动，1=手工录入）[示例: 0] - co_company (varchar(256)) - 合作公司名称 [示例: 江西驿美餐饮管理有限责任公司] 字段补充说明： - service_area_id 外键关联服务区基础信息表(bss_service_area) - company_id 外键关联服务区管理公司表(bss_company) - section_route_id 外键关联高速线路信息表(bss_section_route) - direction 表示档口在服务区内的物理位置分区,为枚举型：北区、南区、西区、东区、两区。 - is_manual_entry 标识数据来源（系统采集或人工录入） - classify 表示经营品类，为枚举型：餐饮、小吃、便利店、整体租赁、其他。 ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_service_area 表存储高速公路服务区基础信息及版本变更记录，支持服务区全生命周期管理。 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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)) - 运营状态 [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 ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss_service_area 表存储高速公路服务区基础信息（名称、编码）及操作记录，支撑BSS系统服务区全生命周期管理 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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 ## 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 ## bss_service_area（服务区基础信息表） bss_service_area 表记录高速公路服务区的基础属性，包括服务区编码、名称、方向、公司归属、地理位置、服务类型和状态，是业务分析与服务区定位的核心表。 字段列表： - id (varchar(32)) - 服务区唯一标识（主键，UUID) [示例: 0271d68ef93de9684b7ad8c7aae600b6] - version (integer) - 版本号 [示例: 3] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-05-21 13:26:40.589] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-07-10 15:41:28.795] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区] - service_area_no (varchar(255)) - 服务区编码（业务唯一标识）[示例: H0814] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: b1629f07c8d9ac81494fbc1de61f1ea5] - service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584] - service_area_type (varchar(50)) - 服务区类型（枚举：信息化服务区、智能化服务区）[示例: 信息化服务区] - service_state (varchar(50)) - 服务区状态（枚举：开放/关闭/上传数据）[示例: 开放] 字段补充说明： - id 为主键，使用 UUID 编码，唯一标识每个服务区。 - company_id 外键,关联服务区管理公司表(bss_company.id) - service_position 经纬度格式为"经度,纬度" - service_area_type 为枚举字段，包含两个取值：信息化服务区、智能化服务区。 - 是多个表(bss_branch, bss_car_day_count等)的核心关联实体 ## bss_section_route_area_link（记录高速公路路段路线与服务区的关联关系） bss_section_route_area_link 表记录高速公路路段路线与服务区的关联关系，支撑路线规划与服务区运营管理。 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 ===Response Guidelines **IMPORTANT**: All SQL queries MUST use Chinese aliases for ALL columns in SELECT clause. 1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. 2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql 3. If the provided context is insufficient, please explain why it can't be generated. 4. **Context Understanding**: If the question follows [CONTEXT]...[CURRENT] format, replace pronouns in [CURRENT] with specific entities from [CONTEXT]. - Example: If context mentions 'Nancheng Service Area has the most stalls', and current question is 'How many dining stalls does this service area have?', interpret it as 'How many dining stalls does Nancheng Service Area have?' 5. Please use the most relevant table(s). 6. If the question has been asked and answered before, please repeat the answer exactly as it was given before. 7. Ensure that the output SQL is PostgreSQL-compliant and executable, and free of syntax errors. 8. Always add NULLS LAST to ORDER BY clauses to handle NULL values properly (e.g., ORDER BY total DESC NULLS LAST). 9. **MANDATORY**: ALL columns in SELECT must have Chinese aliases. This is non-negotiable: - Every column MUST use AS with a Chinese alias - Raw column names without aliases are NOT acceptable - Examples: * CORRECT: SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入 * WRONG: SELECT service_name, SUM(pay_sum) AS total_revenue * WRONG: SELECT service_name AS service_area, SUM(pay_sum) AS 总收入 - Common aliases: COUNT(*) AS 数量, SUM(...) AS 总计, AVG(...) AS 平均值, MAX(...) AS 最大值, MIN(...) AS 最小值 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析每个服务区关联的路线数量并找出覆盖路线最多的服务区 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_area_id AS 服务区ID, COUNT(section_route_id) AS 关联路线数 FROM bss_section_route_area_link GROUP BY service_area_id ORDER BY 关联路线数 DESC LIMIT 1; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 哪些服务区只有单一方向的档口？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name, COUNT(DISTINCT b.direction) AS direction_count, STRING_AGG(DISTINCT b.direction, ', ') AS directions FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name HAVING COUNT(DISTINCT b.direction) = 1 ORDER BY sa.service_area_name; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析各服务区关联的路段路线数量TOP10 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name AS 服务区名称, COUNT(sr.id) AS 关联路段数 FROM bss_section_route_area_link link JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 关联路段数 DESC LIMIT 10; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 每个服务区的营业档口数量（曾经有交易的）？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name, COUNT(DISTINCT branch_no) AS branch_count FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 最近30天中车流量最高的服务区？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT s.service_area_name, SUM(c.customer_count) AS total_cars FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date >= CURRENT_DATE - INTERVAL '30 day' GROUP BY s.service_area_name ORDER BY total_cars DESC NULLS LAST LIMIT 10; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 各分公司管辖服务区的档口总数对比如何？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT c.company_name, COUNT(DISTINCT b.id) AS total_branches FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_branch b ON sa.id = b.service_area_id WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY c.company_name ORDER BY total_branches DESC NULLS LAST; 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 请问哪个服务区的档口数量最多？ 2025-07-20 00:49:58 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:70 - [Vanna] SQL Prompt: [{'role': 'system', 'content': 'You are a PostgreSQL expert. \nPlease help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the respon... 2025-07-20 00:49:58 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 2977.0 tokens (approx) 2025-07-20 00:49:58 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 00:49:58 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 00:50:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service... 2025-07-20 00:50:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-20 00:50:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-20 00:50:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:476 - 生成摘要 - 问题: 请问哪个服务区的档口数量最多？ 2025-07-20 00:50:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:477 - DataFrame 形状: (1, 2) 2025-07-20 00:50:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a professional data analysis assistant. The user asked: '请问哪个服务区的档口数量最多？' Here is the pandas DataFrame data from the query results:| | 服务区名称 | 档口数量 | |---:|:-------------|-----------:| | 0 | 南城服务区 | 39 | Please think and analyze in the context provided and respond accordingly. 2025-07-20 00:50:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: Based on the user's question, please briefly summarize this data. Requirements: 1. Provide only a brief summary without adding extra explanations 2. If there are numbers in the data, maintain appropriate precision Please respond in Chinese. 2025-07-20 00:50:01 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 136.75 tokens (approx) 2025-07-20 00:50:01 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 00:50:01 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 00:50:02 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:502 - 隐藏thinking内容 - 原始长度: 19, 处理后长度: 19 2025-07-20 00:50:02 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:504 - 生成的摘要: 南城服务区的档口数量最多，共有39个。... 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: [CONTEXT] User: 请问哪个服务区的档口数量最多？ Assistant: 南城服务区的档口数量最多，共有39个。 [CURRENT] 请问这个服务区有几个餐饮档口？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 每个服务区的营业档口数量（曾经有交易的）？ | similarity: 0.722 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 各分公司管辖服务区的档口总数对比如何？ | similarity: 0.7118 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 哪些服务区只有单一方向的档口？ | similarity: 0.7021 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析各服务区关联的路段路线数量TOP10 | similarity: 0.6933 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 当前各运营状态下的服务区数量分布情况？ | similarity: 0.6761 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 各服务区不同类型车辆数量分布 | similarity: 0.6755 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - SQL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.722 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.7118 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.7021 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 4: similarity=0.6933 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 5: similarity=0.6761 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 6: similarity=0.6755 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属... | similarity: 0.6139 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的... | similarity: 0.5757 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 cr... | similarity: 0.5731 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应... | similarity: 0.5698 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 服务区基础信息表 -- 描述: 记录服务区的基础信息，如编码、名称、公司、经纬度、状... | similarity: 0.568 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 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... | similarity: 0.5665 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DDL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.6139 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.5757 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.5731 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.5698 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.568 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 6: similarity=0.5665 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础... | similarity: 0.6332 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_serv... | similarity: 0.5755 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss... | similarity: 0.5722 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表服务... | similarity: 0.5708 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表记录... | similarity: 0.5675 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基本信息（名称、编码等）） bss_ser... | similarity: 0.5625 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DOC 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.6332 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.5755 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.5722 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.5708 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.5675 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.5625 ✓ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: [CONTEXT] User: 请问哪个服务区的档口数量最多？ Assistant: 南城服务区的档口数量最多，共有39个。 [CURRENT] 请问这个服务区有几个餐饮档口？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:654 - Error SQL Match: 查询所有部门信息 | similarity: 0.2673 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:392 - Error SQL 阈值过滤: 总数=1, 阈值=0.8 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] pgvector.py:410 - Error SQL 过滤结果: 所有 1 条结果都低于阈值 0.8，返回空列表 2025-07-20 00:51:35 [WARNING] [vanna.BaseLLMChat] pgvector.py:673 - 向量查询找到了 1 条错误SQL示例，但全部被阈值过滤掉. 2025-07-20 00:51:35 [WARNING] [vanna.BaseLLMChat] pgvector.py:674 - 问题: [CONTEXT] User: 请问哪个服务区的档口数量最多？ Assistant: 南城服务区的档口数量最多，共有39个。 [CURRENT] 请问这个服务区有几个餐饮档口？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a PostgreSQL expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. ===Tables -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 create table bss_branch ( id varchar(32) not null, -- 主键ID version integer not null, -- 数据版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 branch_name varchar(255), -- 档口名称 branch_no varchar(255), -- 档口编码 service_area_id varchar(32), -- 服务区ID,外键(关联bss_service_area.id) company_id varchar(32), -- 公司ID,外键(关联bss_company.ID) classify varchar(256), -- 品类 product_brand varchar(256), -- 品牌 category varchar(256), -- 类别 section_route_id varchar(32), -- 所属路线ID,外键(关联bss_section_route.id) direction varchar(256), -- 所在方向 is_manual_entry integer default 0, -- 是否手工录入 co_company varchar(256) -- 合作公司名称 ) -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的归属关系。 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) ) -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 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) ) -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应关系 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) ) -- 中文名: 服务区基础信息表 -- 描述: 记录服务区的基础信息，如编码、名称、公司、经纬度、状态等，是业务活动的空间节点中心。 create table bss_service_area ( id varchar(32) not null, -- 主键ID version integer not null, -- 版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 service_area_name varchar(255), -- 服务区名称 service_area_no varchar(255), -- 服务区编码 company_id varchar(32), -- 公司ID,外键(关联bss_company.id) service_position varchar(255), -- 经纬度 service_area_type varchar(50), -- 服务区类型 service_state varchar(50), -- 服务区状态 primary key (id) ) -- 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) ) ===Additional Context ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 字段列表： - id (varchar(32)) - 主键ID [示例: 00904903cae681aab7a494c3e88e5acd] - version (integer) - 数据版本号 [示例: 1] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-10-15 09:46:45.010] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-10-15 09:46:45.010] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - branch_name (varchar(255)) - 档口名称 [示例: 于都驿美餐饮南区] - branch_no (varchar(255)) - 档口编码（唯一业务标识）[示例: 003585] - service_area_id (varchar(32)) - 服务区ID（外键关联bss_service_area.id）[示例: c7e2f26df373e9cb75bd24ddba57f27f] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: ce5e6f553513dad393694e1fa663aaf4] - classify (varchar(256)) - 经营品类，枚举型：餐饮、小吃、便利店、整体租赁、其他 [示例: 餐饮] - product_brand (varchar(256)) - 品牌名称 [示例: 驿美餐饮] - category (varchar(256)) - 经营类别 [示例: 混沌] - section_route_id (varchar(32)) - 所属路线ID（外键关联bss_section_route.id）[示例: lvkcuu94d4487c42z7qltsvxcyz0iqu5] - direction (varchar(256)) - 所在方向（枚举：北区/南区/西区/东区/两区）[示例: 南区] - is_manual_entry (integer) - 是否手工录入（0=系统自动，1=手工录入）[示例: 0] - co_company (varchar(256)) - 合作公司名称 [示例: 江西驿美餐饮管理有限责任公司] 字段补充说明： - service_area_id 外键关联服务区基础信息表(bss_service_area) - company_id 外键关联服务区管理公司表(bss_company) - section_route_id 外键关联高速线路信息表(bss_section_route) - direction 表示档口在服务区内的物理位置分区,为枚举型：北区、南区、西区、东区、两区。 - is_manual_entry 标识数据来源（系统采集或人工录入） - classify 表示经营品类，为枚举型：餐饮、小吃、便利店、整体租赁、其他。 ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_service_area 表存储高速公路服务区基础信息及版本变更记录，支持服务区全生命周期管理。 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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)) - 运营状态 [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 ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss_service_area 表存储高速公路服务区基础信息（名称、编码）及操作记录，支撑BSS系统服务区全生命周期管理 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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 ## bss_service_area（服务区基础信息表） bss_service_area 表服务区基础信息表，记录服务区名称、编码及操作审计信息 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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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 ## bss_service_area（服务区基础信息表） bss_service_area 表记录高速公路服务区的基础属性，包括服务区编码、名称、方向、公司归属、地理位置、服务类型和状态，是业务分析与服务区定位的核心表。 字段列表： - id (varchar(32)) - 服务区唯一标识（主键，UUID) [示例: 0271d68ef93de9684b7ad8c7aae600b6] - version (integer) - 版本号 [示例: 3] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-05-21 13:26:40.589] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-07-10 15:41:28.795] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区] - service_area_no (varchar(255)) - 服务区编码（业务唯一标识）[示例: H0814] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: b1629f07c8d9ac81494fbc1de61f1ea5] - service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584] - service_area_type (varchar(50)) - 服务区类型（枚举：信息化服务区、智能化服务区）[示例: 信息化服务区] - service_state (varchar(50)) - 服务区状态（枚举：开放/关闭/上传数据）[示例: 开放] 字段补充说明： - id 为主键，使用 UUID 编码，唯一标识每个服务区。 - company_id 外键,关联服务区管理公司表(bss_company.id) - service_position 经纬度格式为"经度,纬度" - service_area_type 为枚举字段，包含两个取值：信息化服务区、智能化服务区。 - 是多个表(bss_branch, bss_car_day_count等)的核心关联实体 ## 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 ===Response Guidelines **IMPORTANT**: All SQL queries MUST use Chinese aliases for ALL columns in SELECT clause. 1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. 2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql 3. If the provided context is insufficient, please explain why it can't be generated. 4. **Context Understanding**: If the question follows [CONTEXT]...[CURRENT] format, replace pronouns in [CURRENT] with specific entities from [CONTEXT]. - Example: If context mentions 'Nancheng Service Area has the most stalls', and current question is 'How many dining stalls does this service area have?', interpret it as 'How many dining stalls does Nancheng Service Area have?' 5. Please use the most relevant table(s). 6. If the question has been asked and answered before, please repeat the answer exactly as it was given before. 7. Ensure that the output SQL is PostgreSQL-compliant and executable, and free of syntax errors. 8. Always add NULLS LAST to ORDER BY clauses to handle NULL values properly (e.g., ORDER BY total DESC NULLS LAST). 9. **MANDATORY**: ALL columns in SELECT must have Chinese aliases. This is non-negotiable: - Every column MUST use AS with a Chinese alias - Raw column names without aliases are NOT acceptable - Examples: * CORRECT: SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入 * WRONG: SELECT service_name, SUM(pay_sum) AS total_revenue * WRONG: SELECT service_name AS service_area, SUM(pay_sum) AS 总收入 - Common aliases: COUNT(*) AS 数量, SUM(...) AS 总计, AVG(...) AS 平均值, MAX(...) AS 最大值, MIN(...) AS 最小值 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 每个服务区的营业档口数量（曾经有交易的）？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name, COUNT(DISTINCT branch_no) AS branch_count FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 各分公司管辖服务区的档口总数对比如何？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT c.company_name, COUNT(DISTINCT b.id) AS total_branches FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_branch b ON sa.id = b.service_area_id WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY c.company_name ORDER BY total_branches DESC NULLS LAST; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 哪些服务区只有单一方向的档口？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name, COUNT(DISTINCT b.direction) AS direction_count, STRING_AGG(DISTINCT b.direction, ', ') AS directions FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name HAVING COUNT(DISTINCT b.direction) = 1 ORDER BY sa.service_area_name; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析各服务区关联的路段路线数量TOP10 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name AS 服务区名称, COUNT(sr.id) AS 关联路段数 FROM bss_section_route_area_link link JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 关联路段数 DESC LIMIT 10; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 当前各运营状态下的服务区数量分布情况？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_state AS 运营状态, COUNT(*) AS 数量 FROM bss_service_area WHERE delete_ts IS NULL GROUP BY service_state ORDER BY 数量 DESC; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 各服务区不同类型车辆数量分布 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT b.service_area_name AS 服务区名称, a.car_type AS 车辆类型, SUM(a.customer_count) AS 车辆总数 FROM bss_car_day_count a JOIN bss_service_area b ON a.service_area_id = b.id AND b.delete_ts IS NULL GROUP BY b.service_area_name, a.car_type ORDER BY 服务区名称, 车辆总数 DESC; 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: [CONTEXT] User: 请问哪个服务区的档口数量最多？ Assistant: 南城服务区的档口数量最多，共有39个。 [CURRENT] 请问这个服务区有几个餐饮档口？ 2025-07-20 00:51:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:70 - [Vanna] SQL Prompt: [{'role': 'system', 'content': 'You are a PostgreSQL expert. \nPlease help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the respon... 2025-07-20 00:51:35 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 3469.25 tokens (approx) 2025-07-20 00:51:35 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 00:51:35 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 00:51:39 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch WHERE service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '南城服务区') AND classify = '餐饮' AND delete_ts IS NULL; 2025-07-20 00:51:39 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch WHERE service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '南城服务区') AND classify = '餐饮' AND delete_ts IS NULL; 2025-07-20 00:51:39 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT COUNT(*) AS 餐饮档口数量 FROM bss_branch WHERE service_area_id = (SELECT id FROM bss_service_area WHERE service_area_name = '南城服务区') AND classify = '餐饮' AND delete_ts IS NULL; 2025-07-20 00:51:39 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:476 - 生成摘要 - 问题: 请问这个服务区有几个餐饮档口？ 2025-07-20 00:51:39 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:477 - DataFrame 形状: (1, 1) 2025-07-20 00:51:39 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a professional data analysis assistant. The user asked: '请问这个服务区有几个餐饮档口？' Here is the pandas DataFrame data from the query results:| | 餐饮档口数量 | |---:|---------------:| | 0 | 6 | Please think and analyze in the context provided and respond accordingly. 2025-07-20 00:51:39 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: Based on the user's question, please briefly summarize this data. Requirements: 1. Provide only a brief summary without adding extra explanations 2. If there are numbers in the data, maintain appropriate precision Please respond in Chinese. 2025-07-20 00:51:39 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 130.5 tokens (approx) 2025-07-20 00:51:39 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 00:51:39 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 00:51:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:502 - 隐藏thinking内容 - 原始长度: 14, 处理后长度: 14 2025-07-20 00:51:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:504 - 生成的摘要: 这个服务区共有6个餐饮档口。... 2025-07-20 00:53:21 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: 你是Citu智能数据问答平台的AI助手，为用户提供全面的帮助和支持。 你的职责包括： 1. 回答关于平台功能和使用方法的问题 2. 解释数据分析相关的概念和术语 3. 提供操作指导和建议 4. 进行友好的日常对话 5. 回答用户的各类知识问题和常识咨询 回答原则： - 保持友好、专业、热情的语调 - 提供准确、有用的信息 - 对于通用知识问题，尽量给出准确回答 - 如果不确定某个问题，诚实地表达不确定性 - 适时鼓励用户尝试数据查询功能 - 回答要简洁明了，避免过于冗长 - 保持中文回答，语言自然流畅 - 以帮助用户为目标，不要过度限制回答范围 2025-07-20 00:53:21 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: [CONTEXT] User: 请问哪个服务区的档口数量最多？ Assistant: 南城服务区的档口数量最多，共有39个。 User: 请问这个服务区有几个餐饮档口？ Assistant: 这个服务区共有6个餐饮档口。 [CURRENT] 请问中国的CBA联赛赛季在哪几个月？ 2025-07-20 00:53:21 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 105.0 tokens (approx) 2025-07-20 00:53:21 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 00:53:21 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 00:53:25 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:399 - chat_with_llm隐藏thinking内容 - 原始长度: 90, 处理后长度: 90 2025-07-20 01:12:13 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: [CONTEXT] User: 请问这个服务区有几个餐饮档口？ Assistant: 这个服务区共有6个餐饮档口。 User: 请问中国的CBA联赛赛季在哪几个月？ Assistant: CBA（中国男子篮球职业联赛）通常从每年的10月开始，持续到次年的4月或5月。常规赛一般在10月至次年1月进行，随后是季后赛，可能延续到4月或5月，具体时间会根据赛季安排略有调整。 [CURRENT] 请问荔枝通常是几月份上市 2025-07-20 01:12:14 [DEBUG] [vanna.EmbeddingFunction] embedding_function.py:169 - 成功生成embedding向量，维度: 1024 2025-07-20 01:12:15 [DEBUG] [vanna.EmbeddingFunction] embedding_function.py:169 - 成功生成embedding向量，维度: 1024 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 每个服务区的营业档口数量（曾经有交易的）？ | similarity: 0.5485 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 计算各服务区行吧支付方式的月均交易次数 | similarity: 0.5436 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 哪些服务区受季节性影响最大？ | similarity: 0.5435 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 统计2023年春节期间各服务区节假日营收占Q1季度总营收比例 | similarity: 0.5363 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 查询2023年6月1日庐山服务区各档口订单数排名 | similarity: 0.5361 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析服务区关联路段的创建时间分布情况 | similarity: 0.5287 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:348 - SQL 过滤结果: 保留 3 条, 过滤掉 3 条 (满足阈值: 0, 强制保留: 3) 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.5485 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.5436 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.5435 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属... | similarity: 0.4857 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 档口日营业数据表 -- 描述: 记录每天每个档口的营业情况，包含微信、支付宝、现金、... | similarity: 0.472 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 服务区基础信息表 -- 描述: 记录服务区的基础信息，如编码、名称、公司、经纬度、状... | similarity: 0.4642 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 cr... | similarity: 0.4573 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的... | similarity: 0.4566 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 记录各服务区每日营业统计数据 -- 描述: 记录各服务区每日营业统计数据，支持运营分... | similarity: 0.4547 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:348 - DDL 过滤结果: 保留 3 条, 过滤掉 3 条 (满足阈值: 0, 强制保留: 3) 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.4857 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.472 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.4642 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础... | similarity: 0.506 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表服务... | similarity: 0.4792 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_serv... | similarity: 0.479 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表记录... | similarity: 0.4752 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss... | similarity: 0.4732 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基本信息（名称、编码等）） bss_ser... | similarity: 0.4689 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:348 - DOC 过滤结果: 保留 3 条, 过滤掉 3 条 (满足阈值: 1, 强制保留: 2) 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.506 ✓ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.4792 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.479 ✗ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: [CONTEXT] User: 请问这个服务区有几个餐饮档口？ Assistant: 这个服务区共有6个餐饮档口。 User: 请问中国的CBA联赛赛季在哪几个月？ Assistant: CBA（中国男子篮球职业联赛）通常从每年的10月开始，持续到次年的4月或5月。常规赛一般在10月至次年1月进行，随后是季后赛，可能延续到4月或5月，具体时间会根据赛季安排略有调整。 [CURRENT] 请问荔枝通常是几月份上市 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:654 - Error SQL Match: 查询所有部门信息 | similarity: 0.2301 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:392 - Error SQL 阈值过滤: 总数=1, 阈值=0.8 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] pgvector.py:410 - Error SQL 过滤结果: 所有 1 条结果都低于阈值 0.8，返回空列表 2025-07-20 01:12:15 [WARNING] [vanna.BaseLLMChat] pgvector.py:673 - 向量查询找到了 1 条错误SQL示例，但全部被阈值过滤掉. 2025-07-20 01:12:15 [WARNING] [vanna.BaseLLMChat] pgvector.py:674 - 问题: [CONTEXT] User: 请问这个服务区有几个餐饮档口？ Assistant: 这个服务区共有6个餐饮档口。 User: 请问中国的CBA联赛赛季在哪几个月？ Assistant: CBA（中国男子篮球职业联赛）通常从每年的10月开始，持续到次年的4月或5月。常规赛一般在10月至次年1月进行，随后是季后赛，可能延续到4月或5月，具体时间会根据赛季安排略有调整。 [CURRENT] 请问荔枝通常是几月份上市 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a PostgreSQL expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. ===Tables -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 create table bss_branch ( id varchar(32) not null, -- 主键ID version integer not null, -- 数据版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 branch_name varchar(255), -- 档口名称 branch_no varchar(255), -- 档口编码 service_area_id varchar(32), -- 服务区ID,外键(关联bss_service_area.id) company_id varchar(32), -- 公司ID,外键(关联bss_company.ID) classify varchar(256), -- 品类 product_brand varchar(256), -- 品牌 category varchar(256), -- 类别 section_route_id varchar(32), -- 所属路线ID,外键(关联bss_section_route.id) direction varchar(256), -- 所在方向 is_manual_entry integer default 0, -- 是否手工录入 co_company varchar(256) -- 合作公司名称 ) -- 中文名: 档口日营业数据表 -- 描述: 记录每天每个档口的营业情况，包含微信、支付宝、现金、金豆等支付方式的金额与订单数，是核心交易数据表。 create table bss_business_day_data ( id varchar(32) not null, -- 主键ID version integer not null, -- 数据版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 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, -- 微信支付订单数量 zfb numeric(19,4), -- 支付宝支付金额 zf_order integer, -- 支付宝支付订单数量 rmb numeric(19,4), -- 现金支付金额 rmb_order integer, -- 现金支付订单数量 xs numeric(19,4), -- 行吧支付金额 xs_order integer, -- 行吧支付订单数量 jd numeric(19,4), -- 金豆支付金额 jd_order integer, -- 金豆支付订单数量 order_sum integer, -- 订单总数 pay_sum numeric(19,4), -- 支付总金额 source_type integer, -- 数据来源类型ID primary key (id) ) -- 中文名: 服务区基础信息表 -- 描述: 记录服务区的基础信息，如编码、名称、公司、经纬度、状态等，是业务活动的空间节点中心。 create table bss_service_area ( id varchar(32) not null, -- 主键ID version integer not null, -- 版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 service_area_name varchar(255), -- 服务区名称 service_area_no varchar(255), -- 服务区编码 company_id varchar(32), -- 公司ID,外键(关联bss_company.id) service_position varchar(255), -- 经纬度 service_area_type varchar(50), -- 服务区类型 service_state varchar(50), -- 服务区状态 primary key (id) ) ===Additional Context ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 字段列表： - id (varchar(32)) - 主键ID [示例: 00904903cae681aab7a494c3e88e5acd] - version (integer) - 数据版本号 [示例: 1] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-10-15 09:46:45.010] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-10-15 09:46:45.010] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - branch_name (varchar(255)) - 档口名称 [示例: 于都驿美餐饮南区] - branch_no (varchar(255)) - 档口编码（唯一业务标识）[示例: 003585] - service_area_id (varchar(32)) - 服务区ID（外键关联bss_service_area.id）[示例: c7e2f26df373e9cb75bd24ddba57f27f] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: ce5e6f553513dad393694e1fa663aaf4] - classify (varchar(256)) - 经营品类，枚举型：餐饮、小吃、便利店、整体租赁、其他 [示例: 餐饮] - product_brand (varchar(256)) - 品牌名称 [示例: 驿美餐饮] - category (varchar(256)) - 经营类别 [示例: 混沌] - section_route_id (varchar(32)) - 所属路线ID（外键关联bss_section_route.id）[示例: lvkcuu94d4487c42z7qltsvxcyz0iqu5] - direction (varchar(256)) - 所在方向（枚举：北区/南区/西区/东区/两区）[示例: 南区] - is_manual_entry (integer) - 是否手工录入（0=系统自动，1=手工录入）[示例: 0] - co_company (varchar(256)) - 合作公司名称 [示例: 江西驿美餐饮管理有限责任公司] 字段补充说明： - service_area_id 外键关联服务区基础信息表(bss_service_area) - company_id 外键关联服务区管理公司表(bss_company) - section_route_id 外键关联高速线路信息表(bss_section_route) - direction 表示档口在服务区内的物理位置分区,为枚举型：北区、南区、西区、东区、两区。 - is_manual_entry 标识数据来源（系统采集或人工录入） - classify 表示经营品类，为枚举型：餐饮、小吃、便利店、整体租赁、其他。 ## bss_service_area（服务区基础信息表） bss_service_area 表服务区基础信息表，记录服务区名称、编码及操作审计信息 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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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 ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_service_area 表存储高速公路服务区基础信息及版本变更记录，支持服务区全生命周期管理。 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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)) - 运营状态 [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 ===Response Guidelines **IMPORTANT**: All SQL queries MUST use Chinese aliases for ALL columns in SELECT clause. 1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. 2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql 3. If the provided context is insufficient, please explain why it can't be generated. 4. **Context Understanding**: If the question follows [CONTEXT]...[CURRENT] format, replace pronouns in [CURRENT] with specific entities from [CONTEXT]. - Example: If context mentions 'Nancheng Service Area has the most stalls', and current question is 'How many dining stalls does this service area have?', interpret it as 'How many dining stalls does Nancheng Service Area have?' 5. Please use the most relevant table(s). 6. If the question has been asked and answered before, please repeat the answer exactly as it was given before. 7. Ensure that the output SQL is PostgreSQL-compliant and executable, and free of syntax errors. 8. Always add NULLS LAST to ORDER BY clauses to handle NULL values properly (e.g., ORDER BY total DESC NULLS LAST). 9. **MANDATORY**: ALL columns in SELECT must have Chinese aliases. This is non-negotiable: - Every column MUST use AS with a Chinese alias - Raw column names without aliases are NOT acceptable - Examples: * CORRECT: SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入 * WRONG: SELECT service_name, SUM(pay_sum) AS total_revenue * WRONG: SELECT service_name AS service_area, SUM(pay_sum) AS 总收入 - Common aliases: COUNT(*) AS 数量, SUM(...) AS 总计, AVG(...) AS 平均值, MAX(...) AS 最大值, MIN(...) AS 最小值 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 每个服务区的营业档口数量（曾经有交易的）？ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name, COUNT(DISTINCT branch_no) AS branch_count FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name; 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 计算各服务区行吧支付方式的月均交易次数 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name AS 服务区名称, EXTRACT(MONTH FROM oper_date) AS 月份, AVG(xs_order) AS 月均交易次数 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY 服务区名称, 月份 ORDER BY 服务区名称, 月份; 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 哪些服务区受季节性影响最大？ 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: WITH monthly_service_revenue AS (SELECT service_name, EXTRACT(MONTH FROM oper_date) AS month, SUM(pay_sum) AS monthly_revenue FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name, EXTRACT(MONTH FROM oper_date)), service_seasonality AS (SELECT service_name, MAX(monthly_revenue) AS max_monthly, MIN(monthly_revenue) AS min_monthly, ROUND((MAX(monthly_revenue) - MIN(monthly_revenue)) * 100.0 / MIN(monthly_revenue), 2) AS seasonality_index FROM monthly_service_revenue GROUP BY service_name HAVING MIN(monthly_revenue) > 0) SELECT service_name, max_monthly, min_monthly, seasonality_index FROM service_seasonality ORDER BY seasonality_index DESC NULLS LAST LIMIT 10; 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: [CONTEXT] User: 请问这个服务区有几个餐饮档口？ Assistant: 这个服务区共有6个餐饮档口。 User: 请问中国的CBA联赛赛季在哪几个月？ Assistant: CBA（中国男子篮球职业联赛）通常从每年的10月开始，持续到次年的4月或5月。常规赛一般在10月至次年1月进行，随后是季后赛，可能延续到4月或5月，具体时间会根据赛季安排略有调整。 [CURRENT] 请问荔枝通常是几月份上市 2025-07-20 01:12:15 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:70 - [Vanna] SQL Prompt: [{'role': 'system', 'content': "You are a PostgreSQL expert. \nPlease help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the respon... 2025-07-20 01:12:15 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 2561.75 tokens (approx) 2025-07-20 01:12:15 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 01:12:15 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 01:12:17 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: 荔枝通常在每年的5月至7月期间上市，具体时间取决于产地和当年的气候条件。例如，广东、广西等主要产区的荔枝一般在6月达到上市高峰。 2025-07-20 01:12:17 [WARNING] [vanna.BaseLLMChat] base_llm_chat.py:311 - 返回内容不像有效SQL: 荔枝通常在每年的5月至7月期间上市，具体时间取决于产地和当年的气候条件。例如，广东、广西等主要产区的荔枝一般在6月达到上市高峰。 2025-07-20 01:12:17 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:316 - 隐藏thinking内容 - SQL生成非有效SQL内容 2025-07-20 01:57:09 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 01:57:09 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:09 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 01:57:09 [DEBUG] [vanna.PromptLoader] load_prompts.py:37 - 成功加载提示词配置: C:\Projects\cursor_projects\Vanna-Chainlit-Chromadb\customllm\llm_prompts.yaml 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 01:57:09 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 01:57:09 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 01:57:10 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 01:57:10 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 01:57:10 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:10 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 01:57:10 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 01:57:10 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 01:57:12 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 01:57:35 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-20 01:57:35 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:74 - 已配置使用PgVector，连接字符串: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:35 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-20 01:57:35 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-20 01:57:35 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-20 01:57:37 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: 请问哪个服务区的档口数量最多？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析每个服务区关联的路线数量并找出覆盖路线最多的服务区 | similarity: 0.7464 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 哪些服务区只有单一方向的档口？ | similarity: 0.7459 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析各服务区关联的路段路线数量TOP10 | similarity: 0.7405 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 每个服务区的营业档口数量（曾经有交易的）？ | similarity: 0.7326 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 最近30天中车流量最高的服务区？ | similarity: 0.7325 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 各分公司管辖服务区的档口总数对比如何？ | similarity: 0.7275 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - SQL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.7464 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.7459 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.7405 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 4: similarity=0.7326 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 5: similarity=0.7325 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 6: similarity=0.7275 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属... | similarity: 0.649 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的... | similarity: 0.6368 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 cr... | similarity: 0.6357 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应... | similarity: 0.6313 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 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... | similarity: 0.626 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 存储路线段与服务区关联关系 -- 描述: 存储路线段与服务区关联关系，管理高速线路与... | similarity: 0.6199 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DDL 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.649 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.6368 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.6357 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.6313 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.626 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 6: similarity=0.6199 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础... | similarity: 0.6543 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_serv... | similarity: 0.6345 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss... | similarity: 0.6339 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（路线与服务区关联表） bss_sect... | similarity: 0.6287 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（服务区基础信息表） bss_service_area 表记录... | similarity: 0.627 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（记录高速公路路段路线与服务区的关联关系... | similarity: 0.6263 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DOC 过滤结果: 保留 6 条, 过滤掉 0 条 (全部满足阈值) 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.6543 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.6345 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.6339 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.6287 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.627 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.6263 ✓ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: 请问哪个服务区的档口数量最多？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:654 - Error SQL Match: 查询所有部门信息 | similarity: 0.2713 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:392 - Error SQL 阈值过滤: 总数=1, 阈值=0.8 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] pgvector.py:410 - Error SQL 过滤结果: 所有 1 条结果都低于阈值 0.8，返回空列表 2025-07-20 01:57:37 [WARNING] [vanna.BaseLLMChat] pgvector.py:673 - 向量查询找到了 1 条错误SQL示例，但全部被阈值过滤掉. 2025-07-20 01:57:37 [WARNING] [vanna.BaseLLMChat] pgvector.py:674 - 问题: 请问哪个服务区的档口数量最多？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a PostgreSQL expert. Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. ===Tables -- 中文名: 档口基础信息表 -- 描述: 存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 create table bss_branch ( id varchar(32) not null, -- 主键ID version integer not null, -- 数据版本号 create_ts timestamp(3), -- 创建时间 created_by varchar(50), -- 创建人 update_ts timestamp(3), -- 更新时间 updated_by varchar(50), -- 更新人 delete_ts timestamp(3), -- 删除时间 deleted_by varchar(50), -- 删除人 branch_name varchar(255), -- 档口名称 branch_no varchar(255), -- 档口编码 service_area_id varchar(32), -- 服务区ID,外键(关联bss_service_area.id) company_id varchar(32), -- 公司ID,外键(关联bss_company.ID) classify varchar(256), -- 品类 product_brand varchar(256), -- 品牌 category varchar(256), -- 类别 section_route_id varchar(32), -- 所属路线ID,外键(关联bss_section_route.id) direction varchar(256), -- 所在方向 is_manual_entry integer default 0, -- 是否手工录入 co_company varchar(256) -- 合作公司名称 ) -- 中文名: 路段路线与服务区关联表 -- 描述: 路段路线与服务区关联表，维护路线与服务区之间的归属关系。 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) ) -- 中文名: 路线与服务区关联表 -- 描述: 路线与服务区关联表，记录路线经过的服务区信息 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) ) -- 中文名: 路线分段与服务区关联表 -- 描述: 路线分段与服务区关联表，记录路线与服务区的对应关系 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) ) -- 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) ) -- 中文名: 存储路线段与服务区关联关系 -- 描述: 存储路线段与服务区关联关系，管理高速线路与服务区归属 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 -- 服务区编码，主键, primary key (section_route_id, service_area_id) ) ===Additional Context ## bss_branch（档口基础信息表） bss_branch 表存储服务区内的档口（商铺）基础信息，如名称、编码、所属服务区、所属公司、品类、品牌等，是商业数据分析的基础实体表。 字段列表： - id (varchar(32)) - 主键ID [示例: 00904903cae681aab7a494c3e88e5acd] - version (integer) - 数据版本号 [示例: 1] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-10-15 09:46:45.010] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-10-15 09:46:45.010] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - branch_name (varchar(255)) - 档口名称 [示例: 于都驿美餐饮南区] - branch_no (varchar(255)) - 档口编码（唯一业务标识）[示例: 003585] - service_area_id (varchar(32)) - 服务区ID（外键关联bss_service_area.id）[示例: c7e2f26df373e9cb75bd24ddba57f27f] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: ce5e6f553513dad393694e1fa663aaf4] - classify (varchar(256)) - 经营品类，枚举型：餐饮、小吃、便利店、整体租赁、其他 [示例: 餐饮] - product_brand (varchar(256)) - 品牌名称 [示例: 驿美餐饮] - category (varchar(256)) - 经营类别 [示例: 混沌] - section_route_id (varchar(32)) - 所属路线ID（外键关联bss_section_route.id）[示例: lvkcuu94d4487c42z7qltsvxcyz0iqu5] - direction (varchar(256)) - 所在方向（枚举：北区/南区/西区/东区/两区）[示例: 南区] - is_manual_entry (integer) - 是否手工录入（0=系统自动，1=手工录入）[示例: 0] - co_company (varchar(256)) - 合作公司名称 [示例: 江西驿美餐饮管理有限责任公司] 字段补充说明： - service_area_id 外键关联服务区基础信息表(bss_service_area) - company_id 外键关联服务区管理公司表(bss_company) - section_route_id 外键关联高速线路信息表(bss_section_route) - direction 表示档口在服务区内的物理位置分区,为枚举型：北区、南区、西区、东区、两区。 - is_manual_entry 标识数据来源（系统采集或人工录入） - classify 表示经营品类，为枚举型：餐饮、小吃、便利店、整体租赁、其他。 ## bss_service_area（存储高速公路服务区基础信息及版本变更记录） bss_service_area 表存储高速公路服务区基础信息及版本变更记录，支持服务区全生命周期管理。 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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)) - 运营状态 [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 ## bss_service_area（存储高速公路服务区基础信息（名称、编码）及操作记录） bss_service_area 表存储高速公路服务区基础信息（名称、编码）及操作记录，支撑BSS系统服务区全生命周期管理 字段列表： - id (varchar(32)) - 主键标识符 [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) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 [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 ## 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 ## bss_service_area（服务区基础信息表） bss_service_area 表记录高速公路服务区的基础属性，包括服务区编码、名称、方向、公司归属、地理位置、服务类型和状态，是业务分析与服务区定位的核心表。 字段列表： - id (varchar(32)) - 服务区唯一标识（主键，UUID) [示例: 0271d68ef93de9684b7ad8c7aae600b6] - version (integer) - 版本号 [示例: 3] - create_ts (timestamp(3)) - 创建时间 [示例: 2021-05-21 13:26:40.589] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp(3)) - 更新时间 [示例: 2021-07-10 15:41:28.795] - updated_by (varchar(50)) - 更新人 [示例: admin] - delete_ts (timestamp(3)) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区] - service_area_no (varchar(255)) - 服务区编码（业务唯一标识）[示例: H0814] - company_id (varchar(32)) - 公司ID（外键关联bss_company.id）[示例: b1629f07c8d9ac81494fbc1de61f1ea5] - service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584] - service_area_type (varchar(50)) - 服务区类型（枚举：信息化服务区、智能化服务区）[示例: 信息化服务区] - service_state (varchar(50)) - 服务区状态（枚举：开放/关闭/上传数据）[示例: 开放] 字段补充说明： - id 为主键，使用 UUID 编码，唯一标识每个服务区。 - company_id 外键,关联服务区管理公司表(bss_company.id) - service_position 经纬度格式为"经度,纬度" - service_area_type 为枚举字段，包含两个取值：信息化服务区、智能化服务区。 - 是多个表(bss_branch, bss_car_day_count等)的核心关联实体 ## bss_section_route_area_link（记录高速公路路段路线与服务区的关联关系） bss_section_route_area_link 表记录高速公路路段路线与服务区的关联关系，支撑路线规划与服务区运营管理。 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 ===Response Guidelines **IMPORTANT**: All SQL queries MUST use Chinese aliases for ALL columns in SELECT clause. 1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. 2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql 3. If the provided context is insufficient, please explain why it can't be generated. 4. **Context Understanding**: If the question follows [CONTEXT]...[CURRENT] format, replace pronouns in [CURRENT] with specific entities from [CONTEXT]. - Example: If context mentions 'Nancheng Service Area has the most stalls', and current question is 'How many dining stalls does this service area have?', interpret it as 'How many dining stalls does Nancheng Service Area have?' 5. Please use the most relevant table(s). 6. If the question has been asked and answered before, please repeat the answer exactly as it was given before. 7. Ensure that the output SQL is PostgreSQL-compliant and executable, and free of syntax errors. 8. Always add NULLS LAST to ORDER BY clauses to handle NULL values properly (e.g., ORDER BY total DESC NULLS LAST). 9. **MANDATORY**: ALL columns in SELECT must have Chinese aliases. This is non-negotiable: - Every column MUST use AS with a Chinese alias - Raw column names without aliases are NOT acceptable - Examples: * CORRECT: SELECT service_name AS 服务区名称, SUM(pay_sum) AS 总收入 * WRONG: SELECT service_name, SUM(pay_sum) AS total_revenue * WRONG: SELECT service_name AS service_area, SUM(pay_sum) AS 总收入 - Common aliases: COUNT(*) AS 数量, SUM(...) AS 总计, AVG(...) AS 平均值, MAX(...) AS 最大值, MIN(...) AS 最小值 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析每个服务区关联的路线数量并找出覆盖路线最多的服务区 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_area_id AS 服务区ID, COUNT(section_route_id) AS 关联路线数 FROM bss_section_route_area_link GROUP BY service_area_id ORDER BY 关联路线数 DESC LIMIT 1; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 哪些服务区只有单一方向的档口？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name, COUNT(DISTINCT b.direction) AS direction_count, STRING_AGG(DISTINCT b.direction, ', ') AS directions FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name HAVING COUNT(DISTINCT b.direction) = 1 ORDER BY sa.service_area_name; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析各服务区关联的路段路线数量TOP10 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sa.service_area_name AS 服务区名称, COUNT(sr.id) AS 关联路段数 FROM bss_section_route_area_link link JOIN bss_service_area sa ON link.service_area_id = sa.id JOIN bss_section_route sr ON link.section_route_id = sr.id WHERE sa.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 关联路段数 DESC LIMIT 10; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 每个服务区的营业档口数量（曾经有交易的）？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name, COUNT(DISTINCT branch_no) AS branch_count FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 最近30天中车流量最高的服务区？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT s.service_area_name, SUM(c.customer_count) AS total_cars FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date >= CURRENT_DATE - INTERVAL '30 day' GROUP BY s.service_area_name ORDER BY total_cars DESC NULLS LAST LIMIT 10; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 各分公司管辖服务区的档口总数对比如何？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT c.company_name, COUNT(DISTINCT b.id) AS total_branches FROM bss_company c JOIN bss_service_area sa ON c.id = sa.company_id JOIN bss_branch b ON sa.id = b.service_area_id WHERE c.delete_ts IS NULL AND sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY c.company_name ORDER BY total_branches DESC NULLS LAST; 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 请问哪个服务区的档口数量最多？ 2025-07-20 01:57:37 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:70 - [Vanna] SQL Prompt: [{'role': 'system', 'content': 'You are a PostgreSQL expert. \nPlease help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the respon... 2025-07-20 01:57:37 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 2977.0 tokens (approx) 2025-07-20 01:57:37 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 01:57:37 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 01:57:41 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service... 2025-07-20 01:57:41 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-20 01:57:41 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT sa.service_area_name AS 服务区名称, COUNT(b.id) AS 档口数量 FROM bss_service_area sa JOIN bss_branch b ON sa.id = b.service_area_id WHERE sa.delete_ts IS NULL AND b.delete_ts IS NULL GROUP BY sa.service_area_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-20 01:57:41 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:476 - 生成摘要 - 问题: 请问哪个服务区的档口数量最多？ 2025-07-20 01:57:41 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:477 - DataFrame 形状: (1, 2) 2025-07-20 01:57:41 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a professional data analysis assistant. The user asked: '请问哪个服务区的档口数量最多？' Here is the pandas DataFrame data from the query results:| | 服务区名称 | 档口数量 | |---:|:-------------|-----------:| | 0 | 南城服务区 | 39 | Please think and analyze in the context provided and respond accordingly. 2025-07-20 01:57:41 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: Based on the user's question, please briefly summarize this data. Requirements: 1. Provide only a brief summary without adding extra explanations 2. If there are numbers in the data, maintain appropriate precision Please respond in Chinese. 2025-07-20 01:57:41 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 136.75 tokens (approx) 2025-07-20 01:57:41 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-20 01:57:41 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-20 01:57:42 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:502 - 隐藏thinking内容 - 原始长度: 19, 处理后长度: 19 2025-07-20 01:57:42 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:504 - 生成的摘要: 南城服务区的档口数量最多，共有39个。... 2025-07-20 01:58:41 [DEBUG] [vanna.test_vanna] :15 - 测试vanna模块日志 - 时间滚动配置