2025-07-22 01:03:06 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 01:03:06 [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 2025-07-22 01:03:06 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 01:03:07 [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 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 01:03:07 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 01:03:08 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 01:03:08 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 01:03:08 [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 2025-07-22 01:03:08 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 01:03:08 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 01:03:09 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:33:44 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:33:44 [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 2025-07-22 11:33:44 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:33:44 [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 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:33:44 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:33:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:33:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:33:46 [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 2025-07-22 11:33:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:33:46 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:33:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:53:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:53:46 [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 2025-07-22 11:53:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:53:46 [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 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:53:46 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:53:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:53:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:53:47 [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 2025-07-22 11:53:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:53:47 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:53:49 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:56:58 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:56:58 [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 2025-07-22 11:56:58 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:56:59 [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 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:56:59 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:57:00 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 11:57:00 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 11:57:00 [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 2025-07-22 11:57:00 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 11:57:01 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 11:57:01 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 11:57:02 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 12:08:43 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 12:08:43 [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 2025-07-22 12:08:43 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 12:08:43 [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 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 12:08:43 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 12:08:43 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 12:08:45 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 12:08:45 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 12:08:45 [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 2025-07-22 12:08:45 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 12:08:45 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 12:08:45 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 12:08:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 12:26:17 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 12:26:17 [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 2025-07-22 12:26:17 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 12:26:17 [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 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 12:26:17 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 12:26:17 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 12:26:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 12:26:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 12:26:18 [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 2025-07-22 12:26:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 12:26:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 12:26:19 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 12:26:20 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 13:24:45 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 13:24:45 [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 2025-07-22 13:24:45 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 13:24:45 [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 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 13:24:45 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 13:24:45 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 13:24:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 13:24:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 13:24:46 [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 2025-07-22 13:24:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 13:24:46 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 13:24:48 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 13:32:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 13:32:18 [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 2025-07-22 13:32:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 13:32:18 [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 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 13:32:18 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 13:32:19 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 13:32:19 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 13:32:19 [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 2025-07-22 13:32:19 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 13:32:20 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 13:32:20 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 13:32:21 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 17:38:36 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 17:38:36 [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 2025-07-22 17:38:36 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 17:38:36 [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 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 17:38:36 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 17:38:36 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 17:38:37 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 17:38:37 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 17:38:37 [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 2025-07-22 17:38:37 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 17:38:38 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 17:38:38 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 17:38:39 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 20:45:56 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例 2025-07-22 20:45:56 [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 2025-07-22 20:45:56 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:79 - 已配置使用API嵌入模型: text-embedding-v4 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下： 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - connection_string: postgresql://postgres:postgres@192.168.67.1:5432/highway_pgvector_db 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6 2025-07-22 20:45:56 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:48 - 错误SQL提示配置: ENABLE_ERROR_SQL_PROMPT = True 2025-07-22 20:45:56 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init 2025-07-22 20:45:57 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:86 - 已连接到业务数据库: 192.168.67.1:6432/highway_db 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: 请问系统中哪个服务区档口最多？ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 最近一周哪个服务区总车流量最高？取前5名。 | similarity: 0.6381 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 | similarity: 0.6209 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 找出2023年4月平均每日订单数最高的服务区TOP3？ | similarity: 0.6178 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 昨日车流量最低的服务区是哪一个？ | similarity: 0.6156 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 查询2023年4月订单数环比增长最快的服务区（相比3月）？ | similarity: 0.6115 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 查询2023年4月1日各服务区总收入排名前5的明细（包含订单总数）？ | similarity: 0.6092 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-22 20:45:57 [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) 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.6381 ✗ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.6209 ✗ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.6178 ✗ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记... | similarity: 0.5484 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段... | similarity: 0.5339 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与... | similarity: 0.5318 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编... | similarity: 0.5285 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速... | similarity: 0.5115 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日车辆流量统计表 -- 描述: 高速公路服务区每日车辆流量统计表，记... | similarity: 0.4766 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:341 - DDL 过滤结果: 保留 5 条, 过滤掉 1 条 (全部满足阈值) 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.5484 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.5339 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.5318 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.5285 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.5115 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area_mapper（服务区信息映射表） bss_service_a... | similarity: 0.5681 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（高速公路路线与服务区关联表） bss_... | similarity: 0.5468 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_business_day_data（高速公路服务区每日经营数据统计表） bss_bus... | similarity: 0.5467 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（高速公路服务区基础信息表） bss_service_area... | similarity: 0.5392 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route（路段路线信息表） bss_section_route 表路... | similarity: 0.5061 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day... | similarity: 0.5058 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:45:57 [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) 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.5681 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.5468 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.5467 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.5392 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.5061 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.5058 ✓ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: 请问系统中哪个服务区档口最多？ 2025-07-22 20:45:57 [WARNING] [vanna.BaseLLMChat] pgvector.py:666 - 向量查询未找到任何相关的错误SQL示例，问题: 请问系统中哪个服务区档口最多？ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-22 20:45:57 [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: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 create table public.bss_business_day_data ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, oper_date date -- 统计日期, service_no varchar(255) -- 服务区编码, service_name varchar(255) -- 服务区名称, branch_no varchar(255) -- 档口编码, branch_name varchar(255) -- 档口名称, wx numeric(19,4) -- 微信支付金额, wx_order integer -- 微信订单数量, zfb numeric(19,4) -- 支付宝支付金额, zf_order integer -- 支付宝订单数量, rmb numeric(19,4) -- 现金支付金额, rmb_order integer -- 现金订单数量, xs numeric(19,4) -- 行吧支付金额, xs_order integer -- 行吧支付订单数, jd numeric(19,4) -- 金豆支付金额, jd_order integer -- 金豆支付订单数, order_sum integer -- 订单总数, pay_sum numeric(19,4) -- 总支付金额, source_type integer -- 数据来源类型, primary key (id) ) -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 create table public.bss_section_route_area_link ( section_route_id varchar(32) not null -- 路段路线唯一标识，主键, service_area_id varchar(32) not null -- 服务区唯一标识，主键, primary key (section_route_id, service_area_id) ) -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 create table public.bss_service_area_mapper ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_name varchar(255) -- 服务区名称, service_no varchar(255) -- 服务区编码, service_area_id varchar(32) -- 服务区业务ID, source_system_type varchar(50) -- 数据来源系统, source_type integer -- 来源系统类型ID, primary key (id) ) -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 create table public.bss_service_area ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_area_name varchar(255) -- 服务区名称, service_area_no varchar(255) -- 服务区编码, company_id varchar(32) -- 所属公司ID, service_position varchar(255) -- 经纬度坐标, service_area_type varchar(50) -- 服务区类型, service_state varchar(50) -- 运营状态, primary key (id) ) -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 create table public.bss_section_route ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, section_name varchar(255) -- 路段名称, route_name varchar(255) -- 路线名称, code varchar(255) -- 路段编号, primary key (id) ) ===Additional Context ## bss_service_area_mapper（服务区信息映射表） bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区] - service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062] - service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f] - source_system_type (varchar(50)) - 数据来源系统 [示例: 驿美, 驿购] - source_type (integer) - 来源系统类型ID [示例: 3, 1] 字段补充说明： - id 为主键 - source_system_type 为枚举字段，包含取值：司乘管理、商业管理、驿购、驿美、手工录入 - source_type 为枚举字段，包含取值：5、0、1、3、4 ## bss_section_route_area_link（高速公路路线与服务区关联表） bss_section_route_area_link 表高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 字段列表： - section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp] - service_area_id (varchar(32)) - 服务区唯一标识 [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_business_day_data（高速公路服务区每日经营数据统计表） bss_business_day_data 表高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - created_by (varchar(50)) - 创建人 [示例: xingba] - update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - oper_date (date) - 统计日期 [示例: 2023-04-01] - service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501] - service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区] - branch_no (varchar(255)) - 档口编码 [示例: 1, H05016] - branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区] - wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000] - wx_order (integer) - 微信订单数量 [示例: 253, 133] - zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000] - zf_order (integer) - 支付宝订单数量 [示例: 15, 0] - rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000] - rmb_order (integer) - 现金订单数量 [示例: 56, 12] - xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000] - xs_order (integer) - 行吧支付订单数 [示例: 0, 1] - jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000] - jd_order (integer) - 金豆支付订单数 [示例: 0] - order_sum (integer) - 订单总数 [示例: 324, 146] - pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000] - source_type (integer) - 数据来源类型 [示例: 1, 0, 4] 字段补充说明： - id 为主键 - source_type 为枚举字段，包含取值：0、4、1、2、3 ## bss_service_area（高速公路服务区基础信息表） bss_service_area 表高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8] - version (integer) - 数据版本号 [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)) - 经纬度坐标 [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_section_route（路段路线信息表） bss_section_route 表路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7] - version (integer) - 数据版本号 [非空] [示例: 1, 0] - create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁] - route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶] - code (varchar(255)) - 路段编号 [示例: SR0001, SR0002] 字段补充说明： - id 为主键 - created_by 为枚举字段，包含取值：admin ## bss_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day_count 表高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - created_by (varchar(50)) - 创建人 - update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - customer_count (bigint) - 车辆数量 [示例: 1114, 295] - car_type (varchar(100)) - 车辆类别 [示例: 其他] - count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02] - service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef] 字段补充说明： - id 为主键 - car_type 为枚举字段，包含取值：其他、危化品、城际、过境 ===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 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 最近一周哪个服务区总车流量最高？取前5名。 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT s.service_area_name AS 服务区名称, SUM(c.customer_count) AS 总车流量 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date >= CURRENT_DATE - 7 AND c.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.service_area_name ORDER BY 总车流量 DESC LIMIT 5; 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sr.route_name AS 路线名称, COUNT(DISTINCT link.service_area_id) AS 服务区数量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id WHERE sr.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 服务区数量 DESC; 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 找出2023年4月平均每日订单数最高的服务区TOP3？ 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT service_name AS 服务区名称, AVG(order_sum) AS 日均订单数 FROM bss_business_day_data WHERE oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY service_name ORDER BY 日均订单数 DESC LIMIT 3; 2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 请问系统中哪个服务区档口最多？ 2025-07-22 20:45:57 [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... 2025-07-22 20:45:57 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 2957.0 tokens (approx) 2025-07-22 20:45:57 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-22 20:45:57 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-22 20:46:00 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT service_name AS 服务区名称, COUNT(DISTINCT branch_no) AS 档口数量 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-22 20:46:00 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT service_name AS 服务区名称, COUNT(DISTINCT branch_no) AS 档口数量 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-22 20:46:00 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT service_name AS 服务区名称, COUNT(DISTINCT branch_no) AS 档口数量 FROM bss_business_day_data WHERE delete_ts IS NULL GROUP BY service_name ORDER BY 档口数量 DESC LIMIT 1; 2025-07-22 20:54:14 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:16 [DEBUG] [vanna.EmbeddingFunction] embedding_function.py:169 - 成功生成embedding向量，维度: 1024 2025-07-22 20:54:19 [DEBUG] [vanna.EmbeddingFunction] embedding_function.py:169 - 成功生成embedding向量，维度: 1024 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 | similarity: 0.6985 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析庐山服务区2023年4月各档口收入占比（仅显示前3名）？ | similarity: 0.6528 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 最近一周哪个服务区总车流量最高？取前5名。 | similarity: 0.6383 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 查询2023年4月1日各服务区总收入排名前5的明细（包含订单总数）？ | similarity: 0.636 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 找出2023年4月平均每日订单数最高的服务区TOP3？ | similarity: 0.6116 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 计算每个服务区的“状态影响指数”=日均营收 × 平均车流量，并按此指数排序TOP 10？ | similarity: 0.6106 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:348 - SQL 过滤结果: 保留 3 条, 过滤掉 3 条 (满足阈值: 2, 强制保留: 1) 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.6985 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.6528 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.6383 ✗ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记... | similarity: 0.6253 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段... | similarity: 0.5987 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编... | similarity: 0.5917 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与... | similarity: 0.574 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速... | similarity: 0.5615 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日车辆流量统计表 -- 描述: 高速公路服务区每日车辆流量统计表，记... | similarity: 0.5517 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:54:19 [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) 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.6253 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.5987 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.5917 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.574 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.5615 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 6: similarity=0.5517 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_business_day_data（高速公路服务区每日经营数据统计表） bss_bus... | similarity: 0.6161 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area_mapper（服务区信息映射表） bss_service_a... | similarity: 0.6125 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（高速公路服务区基础信息表） bss_service_area... | similarity: 0.6007 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（高速公路路线与服务区关联表） bss_... | similarity: 0.5907 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day... | similarity: 0.5816 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route（路段路线信息表） bss_section_route 表路... | similarity: 0.5589 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:54:19 [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) 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.6161 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.6125 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.6007 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.5907 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.5816 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.5589 ✓ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:19 [WARNING] [vanna.BaseLLMChat] pgvector.py:666 - 向量查询未找到任何相关的错误SQL示例，问题: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-22 20:54:19 [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: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 create table public.bss_business_day_data ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, oper_date date -- 统计日期, service_no varchar(255) -- 服务区编码, service_name varchar(255) -- 服务区名称, branch_no varchar(255) -- 档口编码, branch_name varchar(255) -- 档口名称, wx numeric(19,4) -- 微信支付金额, wx_order integer -- 微信订单数量, zfb numeric(19,4) -- 支付宝支付金额, zf_order integer -- 支付宝订单数量, rmb numeric(19,4) -- 现金支付金额, rmb_order integer -- 现金订单数量, xs numeric(19,4) -- 行吧支付金额, xs_order integer -- 行吧支付订单数, jd numeric(19,4) -- 金豆支付金额, jd_order integer -- 金豆支付订单数, order_sum integer -- 订单总数, pay_sum numeric(19,4) -- 总支付金额, source_type integer -- 数据来源类型, primary key (id) ) -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 create table public.bss_section_route_area_link ( section_route_id varchar(32) not null -- 路段路线唯一标识，主键, service_area_id varchar(32) not null -- 服务区唯一标识，主键, primary key (section_route_id, service_area_id) ) -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 create table public.bss_service_area ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_area_name varchar(255) -- 服务区名称, service_area_no varchar(255) -- 服务区编码, company_id varchar(32) -- 所属公司ID, service_position varchar(255) -- 经纬度坐标, service_area_type varchar(50) -- 服务区类型, service_state varchar(50) -- 运营状态, primary key (id) ) -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 create table public.bss_service_area_mapper ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_name varchar(255) -- 服务区名称, service_no varchar(255) -- 服务区编码, service_area_id varchar(32) -- 服务区业务ID, source_system_type varchar(50) -- 数据来源系统, source_type integer -- 来源系统类型ID, primary key (id) ) -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 create table public.bss_section_route ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, section_name varchar(255) -- 路段名称, route_name varchar(255) -- 路线名称, code varchar(255) -- 路段编号, primary key (id) ) -- 中文名: 高速公路服务区每日车辆流量统计表 -- 描述: 高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。 create table public.bss_car_day_count ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, customer_count bigint -- 车辆数量, car_type varchar(100) -- 车辆类别, count_date date -- 统计日期, service_area_id varchar(32) -- 服务区ID, primary key (id) ) ===Additional Context ## bss_business_day_data（高速公路服务区每日经营数据统计表） bss_business_day_data 表高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - created_by (varchar(50)) - 创建人 [示例: xingba] - update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - oper_date (date) - 统计日期 [示例: 2023-04-01] - service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501] - service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区] - branch_no (varchar(255)) - 档口编码 [示例: 1, H05016] - branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区] - wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000] - wx_order (integer) - 微信订单数量 [示例: 253, 133] - zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000] - zf_order (integer) - 支付宝订单数量 [示例: 15, 0] - rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000] - rmb_order (integer) - 现金订单数量 [示例: 56, 12] - xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000] - xs_order (integer) - 行吧支付订单数 [示例: 0, 1] - jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000] - jd_order (integer) - 金豆支付订单数 [示例: 0] - order_sum (integer) - 订单总数 [示例: 324, 146] - pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000] - source_type (integer) - 数据来源类型 [示例: 1, 0, 4] 字段补充说明： - id 为主键 - source_type 为枚举字段，包含取值：0、4、1、2、3 ## bss_service_area_mapper（服务区信息映射表） bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区] - service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062] - service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f] - source_system_type (varchar(50)) - 数据来源系统 [示例: 驿美, 驿购] - source_type (integer) - 来源系统类型ID [示例: 3, 1] 字段补充说明： - id 为主键 - source_system_type 为枚举字段，包含取值：司乘管理、商业管理、驿购、驿美、手工录入 - source_type 为枚举字段，包含取值：5、0、1、3、4 ## bss_service_area（高速公路服务区基础信息表） bss_service_area 表高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8] - version (integer) - 数据版本号 [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)) - 经纬度坐标 [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_section_route_area_link（高速公路路线与服务区关联表） bss_section_route_area_link 表高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 字段列表： - section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp] - service_area_id (varchar(32)) - 服务区唯一标识 [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_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day_count 表高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - created_by (varchar(50)) - 创建人 - update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - customer_count (bigint) - 车辆数量 [示例: 1114, 295] - car_type (varchar(100)) - 车辆类别 [示例: 其他] - count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02] - service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef] 字段补充说明： - id 为主键 - car_type 为枚举字段，包含取值：其他、危化品、城际、过境 ## bss_section_route（路段路线信息表） bss_section_route 表路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7] - version (integer) - 数据版本号 [非空] [示例: 1, 0] - create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁] - route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶] - code (varchar(255)) - 路段编号 [示例: SR0001, SR0002] 字段补充说明： - id 为主键 - created_by 为枚举字段，包含取值：admin ===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 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sr.route_name AS 路线名称, COUNT(DISTINCT link.service_area_id) AS 服务区数量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id WHERE sr.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 服务区数量 DESC; 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析庐山服务区2023年4月各档口收入占比（仅显示前3名）？ 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT branch_name AS 档口名称, ROUND(SUM(pay_sum)::numeric, 2) AS 收入总额 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY branch_name ORDER BY 收入总额 DESC LIMIT 3; 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 最近一周哪个服务区总车流量最高？取前5名。 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT s.service_area_name AS 服务区名称, SUM(c.customer_count) AS 总车流量 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date >= CURRENT_DATE - 7 AND c.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.service_area_name ORDER BY 总车流量 DESC LIMIT 5; 2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:19 [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... 2025-07-22 20:54:19 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 3195.75 tokens (approx) 2025-07-22 20:54:19 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-22 20:54:19 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-22 20:54:23 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY serv... 2025-07-22 20:54:23 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY service_name; 2025-07-22 20:54:23 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY service_name; 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:270 - 尝试为问题生成SQL: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 | similarity: 0.6985 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 分析庐山服务区2023年4月各档口收入占比（仅显示前3名）？ | similarity: 0.6528 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 最近一周哪个服务区总车流量最高？取前5名。 | similarity: 0.6383 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 查询2023年4月1日各服务区总收入排名前5的明细（包含订单总数）？ | similarity: 0.636 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 找出2023年4月平均每日订单数最高的服务区TOP3？ | similarity: 0.6116 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:153 - SQL Match: 计算每个服务区的“状态影响指数”=日均营收 × 平均车流量，并按此指数排序TOP 10？ | similarity: 0.6106 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - SQL 阈值过滤: 总数=6, 阈值=0.65, 最少保留=3 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:348 - SQL 过滤结果: 保留 3 条, 过滤掉 3 条 (满足阈值: 2, 强制保留: 1) 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 1: similarity=0.6985 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 2: similarity=0.6528 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - SQL 保留 3: similarity=0.6383 ✗ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记... | similarity: 0.6253 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段... | similarity: 0.5987 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编... | similarity: 0.5917 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与... | similarity: 0.574 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速... | similarity: 0.5615 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:210 - DDL Match: -- 中文名: 高速公路服务区每日车辆流量统计表 -- 描述: 高速公路服务区每日车辆流量统计表，记... | similarity: 0.5517 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DDL 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:54:40 [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) 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 1: similarity=0.6253 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 2: similarity=0.5987 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 3: similarity=0.5917 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 4: similarity=0.574 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 5: similarity=0.5615 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DDL 保留 6: similarity=0.5517 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_business_day_data（高速公路服务区每日经营数据统计表） bss_bus... | similarity: 0.6161 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area_mapper（服务区信息映射表） bss_service_a... | similarity: 0.6125 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_service_area（高速公路服务区基础信息表） bss_service_area... | similarity: 0.6007 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route_area_link（高速公路路线与服务区关联表） bss_... | similarity: 0.5907 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day... | similarity: 0.5816 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:269 - Doc Match: ## bss_section_route（路段路线信息表） bss_section_route 表路... | similarity: 0.5589 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:328 - DOC 阈值过滤: 总数=6, 阈值=0.5, 最少保留=3 2025-07-22 20:54:40 [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) 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 1: similarity=0.6161 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 2: similarity=0.6125 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 3: similarity=0.6007 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 4: similarity=0.5907 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 5: similarity=0.5816 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.5589 ✓ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:104 - 开始生成SQL提示词，问题: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:40 [WARNING] [vanna.BaseLLMChat] pgvector.py:666 - 向量查询未找到任何相关的错误SQL示例，问题: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:159 - 未找到相关的错误SQL示例 2025-07-22 20:54:40 [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: 高速公路服务区每日经营数据统计表 -- 描述: 高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 create table public.bss_business_day_data ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, oper_date date -- 统计日期, service_no varchar(255) -- 服务区编码, service_name varchar(255) -- 服务区名称, branch_no varchar(255) -- 档口编码, branch_name varchar(255) -- 档口名称, wx numeric(19,4) -- 微信支付金额, wx_order integer -- 微信订单数量, zfb numeric(19,4) -- 支付宝支付金额, zf_order integer -- 支付宝订单数量, rmb numeric(19,4) -- 现金支付金额, rmb_order integer -- 现金订单数量, xs numeric(19,4) -- 行吧支付金额, xs_order integer -- 行吧支付订单数, jd numeric(19,4) -- 金豆支付金额, jd_order integer -- 金豆支付订单数, order_sum integer -- 订单总数, pay_sum numeric(19,4) -- 总支付金额, source_type integer -- 数据来源类型, primary key (id) ) -- 中文名: 高速公路路线与服务区关联表 -- 描述: 高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 create table public.bss_section_route_area_link ( section_route_id varchar(32) not null -- 路段路线唯一标识，主键, service_area_id varchar(32) not null -- 服务区唯一标识，主键, primary key (section_route_id, service_area_id) ) -- 中文名: 高速公路服务区基础信息表 -- 描述: 高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 create table public.bss_service_area ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_area_name varchar(255) -- 服务区名称, service_area_no varchar(255) -- 服务区编码, company_id varchar(32) -- 所属公司ID, service_position varchar(255) -- 经纬度坐标, service_area_type varchar(50) -- 服务区类型, service_state varchar(50) -- 运营状态, primary key (id) ) -- 中文名: 服务区信息映射表 -- 描述: 服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 create table public.bss_service_area_mapper ( id varchar(32) not null -- 唯一标识符，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, service_name varchar(255) -- 服务区名称, service_no varchar(255) -- 服务区编码, service_area_id varchar(32) -- 服务区业务ID, source_system_type varchar(50) -- 数据来源系统, source_type integer -- 来源系统类型ID, primary key (id) ) -- 中文名: 路段路线信息表 -- 描述: 路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 create table public.bss_section_route ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, section_name varchar(255) -- 路段名称, route_name varchar(255) -- 路线名称, code varchar(255) -- 路段编号, primary key (id) ) -- 中文名: 高速公路服务区每日车辆流量统计表 -- 描述: 高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。 create table public.bss_car_day_count ( id varchar(32) not null -- 主键ID，主键, version integer not null -- 数据版本号, create_ts timestamp -- 创建时间, created_by varchar(50) -- 创建人, update_ts timestamp -- 更新时间, updated_by varchar(50) -- 更新人, delete_ts timestamp -- 删除时间, deleted_by varchar(50) -- 删除人, customer_count bigint -- 车辆数量, car_type varchar(100) -- 车辆类别, count_date date -- 统计日期, service_area_id varchar(32) -- 服务区ID, primary key (id) ) ===Additional Context ## bss_business_day_data（高速公路服务区每日经营数据统计表） bss_business_day_data 表高速公路服务区每日经营数据统计表，记录各服务区按日维度的业务指标及操作信息。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - created_by (varchar(50)) - 创建人 [示例: xingba] - update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - oper_date (date) - 统计日期 [示例: 2023-04-01] - service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501] - service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区] - branch_no (varchar(255)) - 档口编码 [示例: 1, H05016] - branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区] - wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000] - wx_order (integer) - 微信订单数量 [示例: 253, 133] - zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000] - zf_order (integer) - 支付宝订单数量 [示例: 15, 0] - rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000] - rmb_order (integer) - 现金订单数量 [示例: 56, 12] - xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000] - xs_order (integer) - 行吧支付订单数 [示例: 0, 1] - jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000] - jd_order (integer) - 金豆支付订单数 [示例: 0] - order_sum (integer) - 订单总数 [示例: 324, 146] - pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000] - source_type (integer) - 数据来源类型 [示例: 1, 0, 4] 字段补充说明： - id 为主键 - source_type 为枚举字段，包含取值：0、4、1、2、3 ## bss_service_area_mapper（服务区信息映射表） bss_service_area_mapper 表服务区信息映射表，用于管理高速公路上各服务区的编码与名称对应关系。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 00e1e893909211ed8ee6fa163eaf653f, 013867f5962211ed8ee6fa163eaf653f] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区] - service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062] - service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f] - source_system_type (varchar(50)) - 数据来源系统 [示例: 驿美, 驿购] - source_type (integer) - 来源系统类型ID [示例: 3, 1] 字段补充说明： - id 为主键 - source_system_type 为枚举字段，包含取值：司乘管理、商业管理、驿购、驿美、手工录入 - source_type 为枚举字段，包含取值：5、0、1、3、4 ## bss_service_area（高速公路服务区基础信息表） bss_service_area 表高速公路服务区基础信息表，存储服务区名称、编码及全生命周期管理数据。 字段列表： - id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8] - version (integer) - 数据版本号 [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)) - 经纬度坐标 [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_section_route_area_link（高速公路路线与服务区关联表） bss_section_route_area_link 表高速公路路线与服务区关联表，用于管理各路段所属的服务区信息。 字段列表： - section_route_id (varchar(32)) - 路段路线唯一标识 [主键, 非空] [示例: v8elrsfs5f7lt7jl8a6p87smfzesn3rz, hxzi2iim238e3s1eajjt1enmh9o4h3wp] - service_area_id (varchar(32)) - 服务区唯一标识 [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_car_day_count（高速公路服务区每日车辆流量统计表） bss_car_day_count 表高速公路服务区每日车辆流量统计表，记录各类型车辆数量及变更历史。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc] - version (integer) - 数据版本号 [非空] [示例: 1] - create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - created_by (varchar(50)) - 创建人 - update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55] - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - customer_count (bigint) - 车辆数量 [示例: 1114, 295] - car_type (varchar(100)) - 车辆类别 [示例: 其他] - count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02] - service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef] 字段补充说明： - id 为主键 - car_type 为枚举字段，包含取值：其他、危化品、城际、过境 ## bss_section_route（路段路线信息表） bss_section_route 表路段路线信息表，记录服务区所属路段及路线名称，支撑高速路网运营管理。 字段列表： - id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7] - version (integer) - 数据版本号 [非空] [示例: 1, 0] - create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16] - created_by (varchar(50)) - 创建人 [示例: admin] - update_ts (timestamp) - 更新时间 - updated_by (varchar(50)) - 更新人 - delete_ts (timestamp) - 删除时间 - deleted_by (varchar(50)) - 删除人 - section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁] - route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶] - code (varchar(255)) - 路段编号 [示例: SR0001, SR0002] 字段补充说明： - id 为主键 - created_by 为枚举字段，包含取值：admin ===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 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 统计每个路线名称下服务区的数量，并按服务区数量降序排列。 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT sr.route_name AS 路线名称, COUNT(DISTINCT link.service_area_id) AS 服务区数量 FROM bss_section_route sr JOIN bss_section_route_area_link link ON sr.id = link.section_route_id WHERE sr.delete_ts IS NULL GROUP BY sr.route_name ORDER BY 服务区数量 DESC; 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 分析庐山服务区2023年4月各档口收入占比（仅显示前3名）？ 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT branch_name AS 档口名称, ROUND(SUM(pay_sum)::numeric, 2) AS 收入总额 FROM bss_business_day_data WHERE service_name = '庐山服务区' AND oper_date BETWEEN '2023-04-01' AND '2023-04-30' AND delete_ts IS NULL GROUP BY branch_name ORDER BY 收入总额 DESC LIMIT 3; 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: 最近一周哪个服务区总车流量最高？取前5名。 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:97 - assistant_content: SELECT s.service_area_name AS 服务区名称, SUM(c.customer_count) AS 总车流量 FROM bss_car_day_count c JOIN bss_service_area s ON c.service_area_id = s.id WHERE c.count_date >= CURRENT_DATE - 7 AND c.delete_ts IS NULL AND s.delete_ts IS NULL GROUP BY s.service_area_name ORDER BY 总车流量 DESC LIMIT 5; 2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 - user_content: Previous conversation context: human: 请问系统中哪个服务区档口最多？ ai: 根据查询结果，系统中档口数量最多的服务区是**南城服务区**，共有**38个档口**。 Current user question: human: 请问这个服务区有几个餐饮档口？ Please analyze the conversation history to understand any references (like "this service area", "that branch", etc.) in the current question, and generate the appropriate SQL query. 2025-07-22 20:54:40 [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... 2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 - Using model qwen-plus-latest for 3195.75 tokens (approx) 2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False 2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式 2025-07-22 20:54:48 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY serv... 2025-07-22 20:54:48 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY service_name; 2025-07-22 20:54:48 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL: SELECT service_name AS 服务区名称, COUNT(*) AS 餐饮档口数量 FROM bss_business_day_data WHERE service_name = '南城服务区' AND branch_name LIKE '%餐饮%' AND delete_ts IS NULL GROUP BY service_name;