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+2025-07-22 01:03:06 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+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
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000022E4EDBB830>
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 01:03:07 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 01:03:08 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000022E4F300350>
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 01:03:08 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 11:33:44 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+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
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x000001F8D8D51D90>
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 11:33:44 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 11:33:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x000001F8D97D3230>
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 11:33:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 11:53:46 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+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
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000024333E01DC0>
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 11:53:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 11:53:47 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
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+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
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x0000024335C330B0>
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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+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
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+2025-07-22 11:53:47 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
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+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
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+2025-07-22 11:56:58 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
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+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
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+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
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+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
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:29 - 传入的 config 参数如下：
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - api_key: sk-db68e37f00974031935395315bfe07f0
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - n_results: 6
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - language: Chinese
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+2025-07-22 11:56:59 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
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+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: <core.embedding_function.EmbeddingFunction object at 0x00000281E9462A20>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x00000281E9B23080>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x000002198FF31D60>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000021991D13170>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000025BB4681D60>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000025BB5F38050>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x000001C7F90D1D30>
|
|
|
+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
|
|
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - model: qwen-plus-latest
|
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|
+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - allow_llm_to_see_data: True
|
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - temperature: 0.6
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+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
|
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|
+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - stream: False
|
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - enable_thinking: False
|
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|
+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
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x000001C7FAAF3080>
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:37 - temperature is changed to: 0.6
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|
+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
|
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+2025-07-22 13:24:46 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:11 - QianWenChat init
|
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|
+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
|
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|
+2025-07-22 13:32:18 [INFO] [vanna.VannaFactory] vanna_llm_factory.py:55 - 创建QIANWEN+PGVECTOR实例
|
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|
+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
|
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+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
|
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|
+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
|
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|
+2025-07-22 13:32:18 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:31 - embedding_function: <core.embedding_function.EmbeddingFunction object at 0x000001438F619A00>
|
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|
+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：
|
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|
+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
|
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|
+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
|
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|
+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: <core.embedding_function.EmbeddingFunction object at 0x000001438FDA3080>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000023E05F45FA0>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000023E07CF30B0>
|
|
|
+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: <core.embedding_function.EmbeddingFunction object at 0x0000023E09531AF0>
|
|
|
+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？
|
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|
+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
|
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|
+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
|
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|
+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
|
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|
+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
|
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|
+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
|
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|
+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
|
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|
+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)
|
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|
+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
|
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|
+-- 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 ✓
|
|
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+2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] pgvector.py:354 - DOC 保留 6: similarity=0.5058 ✓
|
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+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？
|
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|
+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.
|
|
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+
|
|
|
+===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]
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+- service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
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+- service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
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+- service_state (varchar(50)) - 运营状态 [示例: 开放, 关闭]
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+字段补充说明：
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|
+- id 为主键
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+- service_area_type 为枚举字段，包含取值：信息化服务区、智能化服务区
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+- service_state 为枚举字段，包含取值：开放、关闭、上传数据
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+
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+## bss_section_route（路段路线信息表）
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+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。
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+字段列表：
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+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 04ri3j67a806uw2c6o6dwdtz4knexczh, 0g5mnefxxtukql2cq6acul7phgskowy7]
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+- version (integer) - 数据版本号 [非空] [示例: 1, 0]
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+- create_ts (timestamp) - 创建时间 [示例: 2021-10-29 19:43:50, 2022-03-04 16:07:16]
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+- created_by (varchar(50)) - 创建人 [示例: admin]
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+- update_ts (timestamp) - 更新时间
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+- updated_by (varchar(50)) - 更新人
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+- delete_ts (timestamp) - 删除时间
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+- deleted_by (varchar(50)) - 删除人
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+- section_name (varchar(255)) - 路段名称 [示例: 昌栗, 昌宁]
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+- route_name (varchar(255)) - 路线名称 [示例: 昌栗, 昌韶]
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+- code (varchar(255)) - 路段编号 [示例: SR0001, SR0002]
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|
+字段补充说明：
|
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|
+- id 为主键
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|
+- created_by 为枚举字段，包含取值：admin
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+
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+## bss_car_day_count（高速公路服务区每日车辆流量统计表）
|
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|
+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。
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+字段列表：
|
|
|
+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00022c1c99ff11ec86d4fa163ec0f8fc, 00022caa99ff11ec86d4fa163ec0f8fc]
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|
+- version (integer) - 数据版本号 [非空] [示例: 1]
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|
+- create_ts (timestamp) - 创建时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
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+- created_by (varchar(50)) - 创建人
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|
+- update_ts (timestamp) - 更新时间 [示例: 2022-03-02 16:01:43, 2022-02-02 14:18:55]
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+- updated_by (varchar(50)) - 更新人
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|
+- delete_ts (timestamp) - 删除时间
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|
+- deleted_by (varchar(50)) - 删除人
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|
+- customer_count (bigint) - 车辆数量 [示例: 1114, 295]
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|
+- car_type (varchar(100)) - 车辆类别 [示例: 其他]
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+- count_date (date) - 统计日期 [示例: 2022-03-02, 2022-02-02]
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|
+- service_area_id (varchar(32)) - 服务区ID [示例: 17461166e7fa3ecda03534a5795ce985, 81f4eb731fb0728aef17ae61f1f1daef]
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|
+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
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+
|
|
|
+===Response Guidelines
|
|
|
+**IMPORTANT**: All SQL queries MUST use Chinese aliases for ALL columns in SELECT clause.
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+
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+1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question.
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|
+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
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+3. If the provided context is insufficient, please explain why it can't be generated.
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+4. **Context Understanding**: If the question follows [CONTEXT]...[CURRENT] format, replace pronouns in [CURRENT] with specific entities from [CONTEXT].
|
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|
+ - Example: If context mentions 'Nancheng Service Area has the most stalls', and current question is 'How many dining stalls does this service area have?',
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|
+ interpret it as 'How many dining stalls does Nancheng Service Area have?'
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+5. Please use the most relevant table(s).
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|
+6. If the question has been asked and answered before, please repeat the answer exactly as it was given before.
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|
+7. Ensure that the output SQL is PostgreSQL-compliant and executable, and free of syntax errors.
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|
+8. Always add NULLS LAST to ORDER BY clauses to handle NULL values properly (e.g., ORDER BY total DESC NULLS LAST).
|
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|
+9. **MANDATORY**: ALL columns in SELECT must have Chinese aliases. This is non-negotiable:
|
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|
+ - Every column MUST use AS with a Chinese alias
|
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|
+ - 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
|
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|
+ - 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
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+
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+2025-07-22 20:45:57 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 -
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|
|
+user_content: 最近一周哪个服务区总车流量最高？取前5名。
|
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|
+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;
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|
|
+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
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+2025-07-22 20:54:19 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:87 - system_content: You are a PostgreSQL expert.
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+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.
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+
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+===Tables
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+-- 中文名: 高速公路服务区每日经营数据统计表
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+-- 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。
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+create table public.bss_business_day_data (
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+ id varchar(32) not null -- 主键ID，主键,
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+ version integer not null -- 数据版本号,
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+ create_ts timestamp -- 创建时间,
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+ created_by varchar(50) -- 创建人,
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+ update_ts timestamp -- 更新时间,
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+ updated_by varchar(50) -- 更新人,
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+ delete_ts timestamp -- 删除时间,
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+ deleted_by varchar(50) -- 删除人,
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+ oper_date date -- 统计日期,
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+ service_no varchar(255) -- 服务区编码,
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+ service_name varchar(255) -- 服务区名称,
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+ branch_no varchar(255) -- 档口编码,
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+ branch_name varchar(255) -- 档口名称,
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+ wx numeric(19,4) -- 微信支付金额,
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+ wx_order integer -- 微信订单数量,
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+ zfb numeric(19,4) -- 支付宝支付金额,
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+ zf_order integer -- 支付宝订单数量,
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+ rmb numeric(19,4) -- 现金支付金额,
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+ rmb_order integer -- 现金订单数量,
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+ xs numeric(19,4) -- 行吧支付金额,
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+ xs_order integer -- 行吧支付订单数,
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+ jd numeric(19,4) -- 金豆支付金额,
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+ jd_order integer -- 金豆支付订单数,
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+ order_sum integer -- 订单总数,
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+ pay_sum numeric(19,4) -- 总支付金额,
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+ source_type integer -- 数据来源类型,
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+ primary key (id)
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+)
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+
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+-- 中文名: 高速公路路线与服务区关联表
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+-- 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。
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+create table public.bss_section_route_area_link (
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+ section_route_id varchar(32) not null -- 路段路线唯一标识，主键,
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+ service_area_id varchar(32) not null -- 服务区唯一标识，主键,
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+ primary key (section_route_id, service_area_id)
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+)
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+
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+-- 中文名: 高速公路服务区基础信息表
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+-- 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。
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+create table public.bss_service_area (
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+ id varchar(32) not null -- 唯一标识符，主键,
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+ version integer not null -- 数据版本号,
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+ create_ts timestamp -- 创建时间,
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+ created_by varchar(50) -- 创建人,
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+ update_ts timestamp -- 更新时间,
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+ updated_by varchar(50) -- 更新人,
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+ delete_ts timestamp -- 删除时间,
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+ deleted_by varchar(50) -- 删除人,
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+ service_area_name varchar(255) -- 服务区名称,
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+ service_area_no varchar(255) -- 服务区编码,
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+ company_id varchar(32) -- 所属公司ID,
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+ service_position varchar(255) -- 经纬度坐标,
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+ service_area_type varchar(50) -- 服务区类型,
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+ service_state varchar(50) -- 运营状态,
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+ primary key (id)
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+)
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+
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+-- 中文名: 服务区信息映射表
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+-- 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。
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+create table public.bss_service_area_mapper (
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+ id varchar(32) not null -- 唯一标识符，主键,
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+ version integer not null -- 数据版本号,
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+ create_ts timestamp -- 创建时间,
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+ created_by varchar(50) -- 创建人,
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+ update_ts timestamp -- 更新时间,
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+ updated_by varchar(50) -- 更新人,
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+ delete_ts timestamp -- 删除时间,
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+ deleted_by varchar(50) -- 删除人,
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+ service_name varchar(255) -- 服务区名称,
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+ service_no varchar(255) -- 服务区编码,
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+ service_area_id varchar(32) -- 服务区业务ID,
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+ source_system_type varchar(50) -- 数据来源系统,
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+ source_type integer -- 来源系统类型ID,
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+ primary key (id)
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+)
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+
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+-- 中文名: 路段路线信息表
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+-- 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。
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+create table public.bss_section_route (
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+ id varchar(32) not null -- 主键ID，主键,
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+ version integer not null -- 数据版本号,
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+ create_ts timestamp -- 创建时间,
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+ created_by varchar(50) -- 创建人,
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+ update_ts timestamp -- 更新时间,
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+ updated_by varchar(50) -- 更新人,
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+ delete_ts timestamp -- 删除时间,
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+ deleted_by varchar(50) -- 删除人,
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+ section_name varchar(255) -- 路段名称,
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+ route_name varchar(255) -- 路线名称,
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+ code varchar(255) -- 路段编号,
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+ primary key (id)
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+)
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+
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+-- 中文名: 高速公路服务区每日车辆流量统计表
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+-- 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。
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+create table public.bss_car_day_count (
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+ id varchar(32) not null -- 主键ID，主键,
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+ version integer not null -- 数据版本号,
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+ create_ts timestamp -- 创建时间,
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+ created_by varchar(50) -- 创建人,
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+ update_ts timestamp -- 更新时间,
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+ updated_by varchar(50) -- 更新人,
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+ delete_ts timestamp -- 删除时间,
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+ deleted_by varchar(50) -- 删除人,
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+ customer_count bigint -- 车辆数量,
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+ car_type varchar(100) -- 车辆类别,
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+ count_date date -- 统计日期,
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+ service_area_id varchar(32) -- 服务区ID,
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+ primary key (id)
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+)
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+
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+
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+===Additional Context
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+
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+## bss_business_day_data（高速公路服务区每日经营数据统计表）
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+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。
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+字段列表：
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+- id (varchar(32)) - 主键ID [主键, 非空] [示例: 00827DFF993D415488EA1F07CAE6C440, 00e799048b8cbb8ee758eac9c8b4b820]
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+- version (integer) - 数据版本号 [非空] [示例: 1]
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+- create_ts (timestamp) - 创建时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
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+- created_by (varchar(50)) - 创建人 [示例: xingba]
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+- update_ts (timestamp) - 更新时间 [示例: 2023-04-02 08:31:51, 2023-04-02 02:30:08]
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+- updated_by (varchar(50)) - 更新人
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+- delete_ts (timestamp) - 删除时间
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+- deleted_by (varchar(50)) - 删除人
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+- oper_date (date) - 统计日期 [示例: 2023-04-01]
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+- service_no (varchar(255)) - 服务区编码 [示例: 1028, H0501]
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+- service_name (varchar(255)) - 服务区名称 [示例: 宜春服务区, 庐山服务区]
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+- branch_no (varchar(255)) - 档口编码 [示例: 1, H05016]
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+- branch_name (varchar(255)) - 档口名称 [示例: 宜春南区, 庐山鲜徕客东区]
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+- wx (numeric(19,4)) - 微信支付金额 [示例: 4790.0000, 2523.0000]
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+- wx_order (integer) - 微信订单数量 [示例: 253, 133]
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+- zfb (numeric(19,4)) - 支付宝支付金额 [示例: 229.0000, 0.0000]
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+- zf_order (integer) - 支付宝订单数量 [示例: 15, 0]
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+- rmb (numeric(19,4)) - 现金支付金额 [示例: 1058.5000, 124.0000]
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+- rmb_order (integer) - 现金订单数量 [示例: 56, 12]
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+- xs (numeric(19,4)) - 行吧支付金额 [示例: 0.0000, 40.0000]
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+- xs_order (integer) - 行吧支付订单数 [示例: 0, 1]
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+- jd (numeric(19,4)) - 金豆支付金额 [示例: 0.0000]
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+- jd_order (integer) - 金豆支付订单数 [示例: 0]
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+- order_sum (integer) - 订单总数 [示例: 324, 146]
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+- pay_sum (numeric(19,4)) - 总支付金额 [示例: 6077.5000, 2687.0000]
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|
+- source_type (integer) - 数据来源类型 [示例: 1, 0, 4]
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+字段补充说明：
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+- id 为主键
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+- source_type 为枚举字段，包含取值：0、4、1、2、3
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+
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+## bss_service_area_mapper（服务区信息映射表）
|
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|
+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。
|
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|
+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]
|
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|
+- create_ts (timestamp) - 创建时间 [示例: 2023-01-10 10:54:03, 2023-01-17 12:47:29]
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+- created_by (varchar(50)) - 创建人 [示例: admin]
|
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+- update_ts (timestamp) - 更新时间 [示例: 2023-01-10 10:54:07, 2023-01-17 12:47:32]
|
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+- updated_by (varchar(50)) - 更新人
|
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|
+- delete_ts (timestamp) - 删除时间
|
|
|
+- deleted_by (varchar(50)) - 删除人
|
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|
+- service_name (varchar(255)) - 服务区名称 [示例: 信丰西服务区, 南康北服务区]
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+- service_no (varchar(255)) - 服务区编码 [示例: 1067, 1062]
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+- service_area_id (varchar(32)) - 服务区业务ID [示例: 97cd6cd516a551409a4d453a58f9e170, fdbdd042962011ed8ee6fa163eaf653f]
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+- source_system_type (varchar(50)) - 数据来源系统 [示例: 驿美, 驿购]
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+- source_type (integer) - 来源系统类型ID [示例: 3, 1]
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+字段补充说明：
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+- id 为主键
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+- 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
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+- source_type 为枚举字段，包含取值：5、0、1、3、4
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+
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+## bss_service_area（高速公路服务区基础信息表）
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+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。
|
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|
+字段列表：
|
|
|
+- id (varchar(32)) - 唯一标识符 [主键, 非空] [示例: 0271d68ef93de9684b7ad8c7aae600b6, 08e01d7402abd1d6a4d9fdd5df855ef8]
|
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+- version (integer) - 数据版本号 [非空] [示例: 3, 6]
|
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|
+- create_ts (timestamp) - 创建时间 [示例: 2021-05-21 13:26:40.589000, 2021-05-20 19:51:46.314000]
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+- created_by (varchar(50)) - 创建人 [示例: admin]
|
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+- update_ts (timestamp) - 更新时间 [示例: 2021-07-10 15:41:28.795000, 2021-07-11 09:33:08.455000]
|
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+- updated_by (varchar(50)) - 更新人 [示例: admin]
|
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+- delete_ts (timestamp) - 删除时间
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+- deleted_by (varchar(50)) - 删除人 [示例: ]
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+- service_area_name (varchar(255)) - 服务区名称 [示例: 白鹭湖停车区, 南昌南服务区]
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+- service_area_no (varchar(255)) - 服务区编码 [示例: H0814, H0105]
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+- company_id (varchar(32)) - 所属公司ID [示例: b1629f07c8d9ac81494fbc1de61f1ea5, ee9bf1180a2b45003f96e597a4b7f15a]
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+- service_position (varchar(255)) - 经纬度坐标 [示例: 114.574721,26.825584, 115.910549,28.396355]
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+- service_area_type (varchar(50)) - 服务区类型 [示例: 信息化服务区]
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+- service_state (varchar(50)) - 运营状态 [示例: 开放, 关闭]
|
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+字段补充说明：
|
|
|
+- id 为主键
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|
+- service_area_type 为枚举字段，包含取值：信息化服务区、智能化服务区
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+- service_state 为枚举字段，包含取值：开放、关闭、上传数据
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+
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+## bss_section_route_area_link（高速公路路线与服务区关联表）
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+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：
|
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|
+- 复合主键：section_route_id, service_area_id
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+
|
|
|
+## 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
|
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|
+
|
|
|
+## 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
|
|
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+
|
|
|
+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 -
|
|
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+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）？
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+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;
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+2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 -
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+user_content: 最近一周哪个服务区总车流量最高？取前5名。
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+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;
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+2025-07-22 20:54:40 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:92 -
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+user_content: Previous conversation context:
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+human: 请问系统中哪个服务区档口最多？
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+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**。
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+
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+Current user question:
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+human: 请问这个服务区有几个餐饮档口？
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+
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+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.
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+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...
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+2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:115 -
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+Using model qwen-plus-latest for 3195.75 tokens (approx)
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+2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:116 - Enable thinking: False, Stream mode: False
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+2025-07-22 20:54:40 [INFO] [vanna.BaseLLMChat] qianwen_chat.py:158 - 使用非流式处理模式
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+2025-07-22 20:54:48 [DEBUG] [vanna.BaseLLMChat] base_llm_chat.py:77 - [Vanna] LLM Response: SELECT
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+ service_name AS 服务区名称,
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+ COUNT(*) AS 餐饮档口数量
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+FROM
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+ bss_business_day_data
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+WHERE
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+ service_name = '南城服务区'
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+ AND branch_name LIKE '%餐饮%'
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+ AND delete_ts IS NULL
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+GROUP BY
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+ serv...
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+2025-07-22 20:54:48 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:80 - [Vanna] Extracted SQL: SELECT
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+ service_name AS 服务区名称,
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+ COUNT(*) AS 餐饮档口数量
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+FROM
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+ bss_business_day_data
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+WHERE
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+ service_name = '南城服务区'
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+ AND branch_name LIKE '%餐饮%'
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+ AND delete_ts IS NULL
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+GROUP BY
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+ service_name;
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+2025-07-22 20:54:48 [INFO] [vanna.BaseLLMChat] base_llm_chat.py:320 - 成功生成SQL:
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+ SELECT
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+ service_name AS 服务区名称,
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+ COUNT(*) AS 餐饮档口数量
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+FROM
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+ bss_business_day_data
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+WHERE
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+ service_name = '南城服务区'
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+ AND branch_name LIKE '%餐饮%'
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+ AND delete_ts IS NULL
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+GROUP BY
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+ service_name;
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wangxq