[02:46:53] Orchestration process started.

--- User Query ---
<query>
今年来 每周的销售额 和退货率

</query>
--- End of User Query ---

[02:46:53] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "data_query",
  "confidence": 0.96,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[02:46:54] Intent identified as 'data_query'. Proceeding with corresponding handler.
[02:46:54] Step 2: Handling data query.
[02:46:54] Step 2b: Generating SQL (Attempt 1 with RAG context).

--- Generated SQL (Attempt 1) ---
<sql>
WITH weekly_sales AS (
    SELECT
        DATE_TRUNC('week', order_date) AS week_start,
        SUM(quantity * price) AS sales_amount,
        SUM(quantity) AS sales_qty
    FROM orders
    WHERE order_date >= DATE '2025-01-01'
    GROUP BY 1
),
weekly_returns AS (
    SELECT
        DATE_TRUNC('week', r.return_date) AS week_start,
        SUM(o.quantity) AS return_qty
    FROM returns r
    JOIN orders o ON r.order_id = o.order_id
    WHERE r.return_date >= DATE '2025-01-01'
    GROUP BY 1
)
SELECT
    ws.week_start,
    ws.sales_amount,
    COALESCE(wr.return_qty, 0) AS return_qty,
    CASE
        WHEN ws.sales_qty > 0 THEN ROUND(COALESCE(wr.return_qty, 0)::numeric / ws.sales_qty, 4)
        ELSE 0
    END AS return_rate
FROM weekly_sales ws
LEFT JOIN weekly_returns wr ON ws.week_start = wr.week_start
ORDER BY ws.week_start
LIMIT 3000;
</sql>
--- End of Generated SQL (Attempt 1) ---


--- SQL Explanation (Attempt 1) ---
<explanation>
用户想要“今年来每周的销售额和退货率”。  
1. 销售额：用 orders 表，按周汇总 quantity * price。  
2. 退货率：需要知道每周的退货数量（或金额）与当周销售数量（或金额）的比值。returns 表只有 order_id、product_id、return_date，没有 quantity，因此需通过 order_id 关联 orders 表，用 orders.quantity 作为退货数量。  
3. 时间范围：今年（2025-01-01 至今）。  
4. 按周：用 DATE_TRUNC('week', order_date) 或 DATE_TRUNC('week', return_date) 分组。  
5. 先分别计算每周销售额和每周退货数量，再合并计算退货率。
</explanation>
--- End of SQL Explanation (Attempt 1) ---

[02:47:00] Step 2c: Validating and executing SQL.
[02:47:01] SQL executed successfully. Rows returned: 33
[02:47:01] Step 2d: Suggesting visualization.
[02:47:01] Suggested visualization: scatter
[02:47:01] Data query handling finished.

--- Final System Response ---
<final_response>
{
  "message_id": "47074b2f-6abc-47d5-b7a0-75a59d122d11",
  "author": "SYSTEM",
  "content": "用户想要“今年来每周的销售额和退货率”。  \n1. 销售额：用 orders 表，按周汇总 quantity * price。  \n2. 退货率：需要知道每周的退货数量（或金额）与当周销售数量（或金额）的比值。returns 表只有 order_id、product_id、return_date，没有 quantity，因此需通过 order_id 关联 orders 表，用 orders.quantity 作为退货数量。  \n3. 时间范围：今年（2025-01-01 至今）。  \n4. 按周：用 DATE_TRUNC('week', order_date) 或 DATE_TRUNC('week', return_date) 分组。  \n5. 先分别计算每周销售额和每周退货数量，再合并计算退货率。\n\n\n\nMore tips:\n是否需要查看按产品类别或地区的每周销售与退货情况？ \n\nHere are the results for your query:",
  "timestamp": "2025-08-30T02:47:01.911081",
  "explanation": "The query for '今年来 每周的销售额 和退货率\n' was executed successfully.",
  "sql_query": "WITH weekly_sales AS (\n    SELECT\n        DATE_TRUNC('week', order_date) AS week_start,\n        SUM(quantity * price) AS sales_amount,\n        SUM(quantity) AS sales_qty\n    FROM orders\n    WHERE order_date >= DATE '2025-01-01'\n    GROUP BY 1\n),\nweekly_returns AS (\n    SELECT\n        DATE_TRUNC('week', r.return_date) AS week_start,\n        SUM(o.quantity) AS return_qty\n    FROM returns r\n    JOIN orders o ON r.order_id = o.order_id\n    WHERE r.return_date >= DATE '2025-01-01'\n    GROUP BY 1\n)\nSELECT\n    ws.week_start,\n    ws.sales_amount,\n    COALESCE(wr.return_qty, 0) AS return_qty,\n    CASE\n        WHEN ws.sales_qty > 0 THEN ROUND(COALESCE(wr.return_qty, 0)::numeric / ws.sales_qty, 4)\n        ELSE 0\n    END AS return_rate\nFROM weekly_sales ws\nLEFT JOIN weekly_returns wr ON ws.week_start = wr.week_start\nORDER BY ws.week_start\nLIMIT 3000;",
  "data_result": {
    "columns": [
      "week_start",
      "sales_amount",
      "return_qty",
      "return_rate"
    ],
    "rows": [
      [
        "2024-12-29T16:00:00+00:00",
        52908.56,
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        0.0
      ],
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        63371.38,
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        61877.93,
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        62650.17,
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    ]
  },
  "visualization_config": {
    "type": "scatter",
    "title": "Scatter Plot",
    "data": [
      {
        "week_start": "2024-12-29T16:00:00+00:00",
        "sales_amount": 52908.56,
        "return_qty": 0,
        "return_rate": 0.0
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      },
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        "return_rate": 0.0
      },
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        "sales_amount": 55705.93,
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      },
      {
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        "sales_amount": 73560.31,
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      },
      {
        "week_start": "2025-07-27T16:00:00+00:00",
        "sales_amount": 62590.26,
        "return_qty": 502,
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      },
      {
        "week_start": "2025-08-03T16:00:00+00:00",
        "sales_amount": 58206.78,
        "return_qty": 516,
        "return_rate": 0.7257
      },
      {
        "week_start": "2025-08-10T16:00:00+00:00",
        "sales_amount": 23806.58,
        "return_qty": 150,
        "return_rate": 0.5639
      }
    ],
    "tooltip": {
      "fields": [
        "sales_amount",
        "return_qty"
      ]
    },
    "seriesField": null,
    "colorField": null,
    "xField": "sales_amount",
    "yField": "return_qty",
    "geometry": "point"
  },
  "intent_info": {
    "intent": "data_query",
    "confidence": 0.96,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": [],
  "sql_explanation": "用户想要“今年来每周的销售额和退货率”。  \n1. 销售额：用 orders 表，按周汇总 quantity * price。  \n2. 退货率：需要知道每周的退货数量（或金额）与当周销售数量（或金额）的比值。returns 表只有 order_id、product_id、return_date，没有 quantity，因此需通过 order_id 关联 orders 表，用 orders.quantity 作为退货数量。  \n3. 时间范围：今年（2025-01-01 至今）。  \n4. 按周：用 DATE_TRUNC('week', order_date) 或 DATE_TRUNC('week', return_date) 分组。  \n5. 先分别计算每周销售额和每周退货数量，再合并计算退货率。",
  "analysis_path": null,
  "share_id": null,
  "data_source_id": 9,
  "data_source_name": "sales_data_warehouse"
}
</final_response>
--- End of Final System Response ---

[02:47:01] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-01T01:12:19.767319 ---

[01:12:19] Orchestration process started.

--- User Query ---
<query>
你好
</query>
--- End of User Query ---

[01:12:21] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "clarification",
  "confidence": 0.85,
  "data_source_name": "None"
}
</intent>
--- End of Intent Recognition Result ---

[01:12:22] Intent identified as 'clarification'. Proceeding with corresponding handler.
[01:12:22] Step 2: Handling clarification.
[01:12:22] Step 2a: Fetching project info and RAG context.

--- RAG Context for Clarification ---
<rag_context>
[]
</rag_context>
--- End of RAG Context for Clarification ---

[01:12:23] Step 2b: Generating clarifying question with LLM.

--- Clarification Prompt ---
<prompt>

你是一名专属AI数据分析师，当需要更多信息来理解用户请求时，你的任务是提出一个清晰的澄清问题。

对话历史:
<conversation_history>

Conversation History:
user: 你好

</conversation_history>

用户最近的请求是：“你好”。

请根据上述对话历史和用户最近的请求，生成一个清晰、具体的问题，以帮助你更好地理解用户的意图。
请确保你提出的问题是开放式的，能够引导用户提供更多细节，例如：
- 具体的指标名称
- 明确的时间范围（例如：“上个月”、“2023年第一季度”）
- 相关的维度（例如：“按产品分类”、“按地区划分”）
- 业务背景或目标

请用用户提问的语言回复。

</prompt>
--- End of Clarification Prompt ---


--- LLM-generated Clarification ---
<llm_response>
您好！很高兴见到您。请问您今天想分析哪方面的数据？例如，您关注的是销售、用户行为、库存，还是其他业务场景？如果有具体的时间范围、指标名称或希望按哪些维度拆分，也请一并告诉我，这样我可以更精准地帮您分析。
</llm_response>
--- End of LLM-generated Clarification ---


--- Final System Response ---
<final_response>
{
  "message_id": "5d13da6b-2394-4a2d-83e6-6b4d7c9d7020",
  "author": "SYSTEM",
  "content": "您好！很高兴见到您。请问您今天想分析哪方面的数据？例如，您关注的是销售、用户行为、库存，还是其他业务场景？如果有具体的时间范围、指标名称或希望按哪些维度拆分，也请一并告诉我，这样我可以更精准地帮您分析。",
  "timestamp": "2025-09-01T01:12:24.212388",
  "explanation": "Clarification requested",
  "sql_query": null,
  "data_result": null,
  "visualization_config": null,
  "intent_info": {
    "intent": "clarification",
    "confidence": 0.85,
    "data_source_name": "None"
  },
  "rag_context": null,
  "sql_explanation": null,
  "analysis_path": null,
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": "你好"
}
</final_response>
--- End of Final System Response ---

[01:12:24] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-01T01:12:49.388491 ---

[01:12:49] Orchestration process started.

--- User Query ---
<query>
分析下 最近半年的利润 营收等情况，我们好优化
</query>
--- End of User Query ---

[01:12:49] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "analysis",
  "confidence": 0.93,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[01:12:50] Intent identified as 'analysis'. Proceeding with corresponding handler.
[01:12:50] Step 2: Handling analysis query.
[01:12:50] Analysis Service: Starting analysis.

--- Analysis Final Summary ---
<summary>
最近半年（2025-02 至 2025-07）整体经营概况

1. 月度营收与利润  
   • 过去 6 个月总营收约 1,338,900 元，但所有月份利润均为 0 元，显示成本与售价持平或核算口径导致利润为零。  
   • 各月营收：  
     – 2025-02：260,602 元  
     – 2025-03：249,746 元  
     – 2025-04：270,815 元  
     – 2025-05：267,599 元  
     – 2025-06：267,823 元  
     – 2025-07：112,315 元（仅为上半月数据，拉低整体）  

2. 趋势与异常  
   • 由于利润恒为 0，无法判断盈利趋势。  
   • 7 月营收骤降（环比-58%），需确认是否为数据不完整或业务真实下滑。  

3. 品类贡献  
   • 半年内各品类营收排名：  
     1) Sports：219,998 元  
     2) Food：218,764 元  
     3) Electronics：211,106 元  
     4) Toys：202,567 元  
     5) Home Goods：177,233 元  
     6) Apparel：154,283 元  
     7) Beauty：140,139 元  
     8) Books：104,811 元  
   • 所有品类利润同样为 0，需核查成本结构或定价策略。

优化建议  
• 立即核对 7 月数据完整性，确认营收骤降原因。  
• 深入分析各品类成本与毛利，找出利润为 0 的根因（采购价、折扣、运费等）。  
• 优先优化 Sports、Food、Electronics 三大高营收品类，通过调价或降本提升利润。
</summary>
--- End of Analysis Final Summary ---


--- Final System Response ---
<final_response>
{
  "message_id": "c80167a8-a65a-4773-88f8-9964931208e6",
  "author": "SYSTEM",
  "content": "Analysis complete: 最近半年（2025-02 至 2025-07）整体经营概况\n\n1. 月度营收与利润  \n   • 过去 6 个月总营收约 1,338,900 元，但所有月份利润均为 0 元，显示成本与售价持平或核算口径导致利润为零。  \n   • 各月营收：  \n     – 2025-02：260,602 元  \n     – 2025-03：249,746 元  \n     – 2025-04：270,815 元  \n     – 2025-05：267,599 元  \n     – 2025-06：267,823 元  \n     – 2025-07：112,315 元（仅为上半月数据，拉低整体）  \n\n2. 趋势与异常  \n   • 由于利润恒为 0，无法判断盈利趋势。  \n   • 7 月营收骤降（环比-58%），需确认是否为数据不完整或业务真实下滑。  \n\n3. 品类贡献  \n   • 半年内各品类营收排名：  \n     1) Sports：219,998 元  \n     2) Food：218,764 元  \n     3) Electronics：211,106 元  \n     4) Toys：202,567 元  \n     5) Home Goods：177,233 元  \n     6) Apparel：154,283 元  \n     7) Beauty：140,139 元  \n     8) Books：104,811 元  \n   • 所有品类利润同样为 0，需核查成本结构或定价策略。\n\n优化建议  \n• 立即核对 7 月数据完整性，确认营收骤降原因。  \n• 深入分析各品类成本与毛利，找出利润为 0 的根因（采购价、折扣、运费等）。  \n• 优先优化 Sports、Food、Electronics 三大高营收品类，通过调价或降本提升利润。",
  "timestamp": "2025-09-01T01:13:03.727974",
  "explanation": "",
  "sql_query": null,
  "data_result": {},
  "visualization_config": null,
  "intent_info": {
    "intent": "analysis",
    "confidence": 0.93,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": null,
  "sql_explanation": null,
  "analysis_path": [],
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": null
}
</final_response>
--- End of Final System Response ---

[01:13:03] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-01T01:14:03.033815 ---

[01:14:03] Orchestration process started.

--- User Query ---
<query>
最近半年 每周的销售额
</query>
--- End of User Query ---

[01:14:03] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "data_query",
  "confidence": 0.96,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[01:14:04] Intent identified as 'data_query'. Proceeding with corresponding handler.
[01:14:04] Step 2: Handling data query.
[01:14:04] Step 2b: Generating SQL (Attempt 1 with RAG context).

--- Generated SQL (Attempt 1) ---
<sql>
SELECT
    DATE_TRUNC('week', order_date) AS week_start,
    SUM(quantity * price)          AS weekly_sales
FROM
    orders
WHERE
    order_date >= DATE '2025-03-01'
    AND order_date <  DATE '2025-09-01'
GROUP BY
    week_start
ORDER BY
    week_start
LIMIT 3000;
</sql>
--- End of Generated SQL (Attempt 1) ---


--- SQL Explanation (Attempt 1) ---
<explanation>
用户希望查看“最近半年”按周汇总的销售额。  
1. 计算最近半年：从当前日期 2025-09-01 往前推 6 个月，即 2025-03-01 至 2025-09-01。  
2. 按周汇总：使用 DATE_TRUNC('week', order_date) 将订单日期对齐到每周的起始日（周一），再求和销售额（quantity * price）。  
3. 排序：按周升序排列，方便观察趋势。  
4. 限制结果：默认返回最多 3000 行。
</explanation>
--- End of SQL Explanation (Attempt 1) ---

[01:14:08] Step 2c: Validating and executing SQL.
[01:14:08] SQL executed successfully. Rows returned: 25
[01:14:08] Step 2d: Suggesting visualization.
[01:14:09] Suggested visualization: bar
[01:14:09] Data query handling finished.

--- Final System Response ---
<final_response>
{
  "message_id": "48bb6a79-9d12-4879-8d36-831faeeed310",
  "author": "SYSTEM",
  "content": "用户希望查看“最近半年”按周汇总的销售额。  \n1. 计算最近半年：从当前日期 2025-09-01 往前推 6 个月，即 2025-03-01 至 2025-09-01。  \n2. 按周汇总：使用 DATE_TRUNC('week', order_date) 将订单日期对齐到每周的起始日（周一），再求和销售额（quantity * price）。  \n3. 排序：按周升序排列，方便观察趋势。  \n4. 限制结果：默认返回最多 3000 行。\n\n\n\nMore tips:\n需要进一步查看每周销售额按品类或门店的分布吗？ \n\nHere are the results for your query:",
  "timestamp": "2025-09-01T01:14:09.023542",
  "explanation": "The query for '最近半年 每周的销售额' was executed successfully.",
  "sql_query": "SELECT\n    DATE_TRUNC('week', order_date) AS week_start,\n    SUM(quantity * price)          AS weekly_sales\nFROM\n    orders\nWHERE\n    order_date >= DATE '2025-03-01'\n    AND order_date <  DATE '2025-09-01'\nGROUP BY\n    week_start\nORDER BY\n    week_start\nLIMIT 3000;",
  "data_result": {
    "columns": [
      "week_start",
      "weekly_sales"
    ],
    "rows": [
      [
        "2025-02-23T16:00:00+00:00",
        20684.9
      ],
      [
        "2025-03-02T16:00:00+00:00",
        58823.86
      ],
      [
        "2025-03-09T16:00:00+00:00",
        54268.81
      ],
      [
        "2025-03-16T16:00:00+00:00",
        60872.11
      ],
      [
        "2025-03-23T16:00:00+00:00",
        57601.79
      ],
      [
        "2025-03-30T16:00:00+00:00",
        69944.49
      ],
      [
        "2025-04-06T16:00:00+00:00",
        60172.96
      ],
      [
        "2025-04-13T16:00:00+00:00",
        58077.23
      ],
      [
        "2025-04-20T16:00:00+00:00",
        52741.39
      ],
      [
        "2025-04-27T16:00:00+00:00",
        45446.87
      ],
      [
        "2025-05-04T16:00:00+00:00",
        58035.28
      ],
      [
        "2025-05-11T16:00:00+00:00",
        74916.07
      ],
      [
        "2025-05-18T16:00:00+00:00",
        58500.07
      ],
      [
        "2025-05-25T16:00:00+00:00",
        58157.47
      ],
      [
        "2025-06-01T16:00:00+00:00",
        66142.14
      ],
      [
        "2025-06-08T16:00:00+00:00",
        62762.35
      ],
      [
        "2025-06-15T16:00:00+00:00",
        57232.0
      ],
      [
        "2025-06-22T16:00:00+00:00",
        65319.59
      ],
      [
        "2025-06-29T16:00:00+00:00",
        54569.54
      ],
      [
        "2025-07-06T16:00:00+00:00",
        60761.8
      ],
      [
        "2025-07-13T16:00:00+00:00",
        55705.93
      ],
      [
        "2025-07-20T16:00:00+00:00",
        73560.31
      ],
      [
        "2025-07-27T16:00:00+00:00",
        62590.26
      ],
      [
        "2025-08-03T16:00:00+00:00",
        58206.78
      ],
      [
        "2025-08-10T16:00:00+00:00",
        23806.58
      ]
    ]
  },
  "visualization_config": {
    "type": "bar",
    "title": "Bar Chart: week_start vs weekly_sales",
    "data": [
      {
        "week_start": "2025-02-23T16:00:00+00:00",
        "weekly_sales": 20684.9
      },
      {
        "week_start": "2025-03-02T16:00:00+00:00",
        "weekly_sales": 58823.86
      },
      {
        "week_start": "2025-03-09T16:00:00+00:00",
        "weekly_sales": 54268.81
      },
      {
        "week_start": "2025-03-16T16:00:00+00:00",
        "weekly_sales": 60872.11
      },
      {
        "week_start": "2025-03-23T16:00:00+00:00",
        "weekly_sales": 57601.79
      },
      {
        "week_start": "2025-03-30T16:00:00+00:00",
        "weekly_sales": 69944.49
      },
      {
        "week_start": "2025-04-06T16:00:00+00:00",
        "weekly_sales": 60172.96
      },
      {
        "week_start": "2025-04-13T16:00:00+00:00",
        "weekly_sales": 58077.23
      },
      {
        "week_start": "2025-04-20T16:00:00+00:00",
        "weekly_sales": 52741.39
      },
      {
        "week_start": "2025-04-27T16:00:00+00:00",
        "weekly_sales": 45446.87
      },
      {
        "week_start": "2025-05-04T16:00:00+00:00",
        "weekly_sales": 58035.28
      },
      {
        "week_start": "2025-05-11T16:00:00+00:00",
        "weekly_sales": 74916.07
      },
      {
        "week_start": "2025-05-18T16:00:00+00:00",
        "weekly_sales": 58500.07
      },
      {
        "week_start": "2025-05-25T16:00:00+00:00",
        "weekly_sales": 58157.47
      },
      {
        "week_start": "2025-06-01T16:00:00+00:00",
        "weekly_sales": 66142.14
      },
      {
        "week_start": "2025-06-08T16:00:00+00:00",
        "weekly_sales": 62762.35
      },
      {
        "week_start": "2025-06-15T16:00:00+00:00",
        "weekly_sales": 57232.0
      },
      {
        "week_start": "2025-06-22T16:00:00+00:00",
        "weekly_sales": 65319.59
      },
      {
        "week_start": "2025-06-29T16:00:00+00:00",
        "weekly_sales": 54569.54
      },
      {
        "week_start": "2025-07-06T16:00:00+00:00",
        "weekly_sales": 60761.8
      },
      {
        "week_start": "2025-07-13T16:00:00+00:00",
        "weekly_sales": 55705.93
      },
      {
        "week_start": "2025-07-20T16:00:00+00:00",
        "weekly_sales": 73560.31
      },
      {
        "week_start": "2025-07-27T16:00:00+00:00",
        "weekly_sales": 62590.26
      },
      {
        "week_start": "2025-08-03T16:00:00+00:00",
        "weekly_sales": 58206.78
      },
      {
        "week_start": "2025-08-10T16:00:00+00:00",
        "weekly_sales": 23806.58
      }
    ],
    "tooltip": {
      "fields": [
        "week_start",
        "weekly_sales"
      ]
    },
    "seriesField": null,
    "colorField": null,
    "xField": "week_start",
    "yField": "weekly_sales",
    "geometry": "interval"
  },
  "intent_info": {
    "intent": "data_query",
    "confidence": 0.96,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": [],
  "sql_explanation": "用户希望查看“最近半年”按周汇总的销售额。  \n1. 计算最近半年：从当前日期 2025-09-01 往前推 6 个月，即 2025-03-01 至 2025-09-01。  \n2. 按周汇总：使用 DATE_TRUNC('week', order_date) 将订单日期对齐到每周的起始日（周一），再求和销售额（quantity * price）。  \n3. 排序：按周升序排列，方便观察趋势。  \n4. 限制结果：默认返回最多 3000 行。",
  "analysis_path": null,
  "share_id": null,
  "data_source_id": 9,
  "data_source_name": "sales_data_warehouse",
  "conversation_title": null
}
</final_response>
--- End of Final System Response ---

[01:14:09] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-01T01:14:27.236524 ---

[01:14:27] Orchestration process started.

--- User Query ---
<query>
最近半年 每周的销售额
</query>
--- End of User Query ---

[01:14:28] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "data_query",
  "confidence": 0.96,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[01:14:29] Intent identified as 'data_query'. Proceeding with corresponding handler.
[01:14:29] Step 2: Handling data query.
[01:14:29] Step 2b: Generating SQL (Attempt 1 with RAG context).

--- Generated SQL (Attempt 1) ---
<sql>
SELECT
    DATE_TRUNC('week', order_date) AS week_start,
    SUM(quantity * price) AS weekly_sales
FROM
    orders
WHERE
    order_date >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY
    week_start
ORDER BY
    week_start DESC
LIMIT 3000;
</sql>
--- End of Generated SQL (Attempt 1) ---


--- SQL Explanation (Attempt 1) ---
<explanation>
用户想要“最近半年 每周的销售额”。我需要从 orders 表中按周汇总销售额。首先，计算最近半年的起始日期（从今天往前推 6 个月），然后按周分组并求和销售额。使用 DATE_TRUNC('week', order_date) 来按周分组，确保每周从周一开始。
</explanation>
--- End of SQL Explanation (Attempt 1) ---

[01:14:33] Step 2c: Validating and executing SQL.
[01:14:33] SQL executed successfully. Rows returned: 25
[01:14:33] Step 2d: Suggesting visualization.
[01:14:33] Suggested visualization: bar
[01:14:33] Data query handling finished.

--- Final System Response ---
<final_response>
{
  "message_id": "4b993b76-c7eb-4bb4-8bed-fa2b98e62fad",
  "author": "SYSTEM",
  "content": "用户想要“最近半年 每周的销售额”。我需要从 orders 表中按周汇总销售额。首先，计算最近半年的起始日期（从今天往前推 6 个月），然后按周分组并求和销售额。使用 DATE_TRUNC('week', order_date) 来按周分组，确保每周从周一开始。\n\n\n\nMore tips:\n您想进一步查看最近半年每周各产品品类的销售额分布吗？ \n\nHere are the results for your query:",
  "timestamp": "2025-09-01T01:14:33.865288",
  "explanation": "The query for '最近半年 每周的销售额' was executed successfully.",
  "sql_query": "SELECT\n    DATE_TRUNC('week', order_date) AS week_start,\n    SUM(quantity * price) AS weekly_sales\nFROM\n    orders\nWHERE\n    order_date >= CURRENT_DATE - INTERVAL '6 months'\nGROUP BY\n    week_start\nORDER BY\n    week_start DESC\nLIMIT 3000;",
  "data_result": {
    "columns": [
      "week_start",
      "weekly_sales"
    ],
    "rows": [
      [
        "2025-08-10T16:00:00+00:00",
        23806.58
      ],
      [
        "2025-08-03T16:00:00+00:00",
        58206.78
      ],
      [
        "2025-07-27T16:00:00+00:00",
        62590.26
      ],
      [
        "2025-07-20T16:00:00+00:00",
        73560.31
      ],
      [
        "2025-07-13T16:00:00+00:00",
        55705.93
      ],
      [
        "2025-07-06T16:00:00+00:00",
        60761.8
      ],
      [
        "2025-06-29T16:00:00+00:00",
        54569.54
      ],
      [
        "2025-06-22T16:00:00+00:00",
        65319.59
      ],
      [
        "2025-06-15T16:00:00+00:00",
        57232.0
      ],
      [
        "2025-06-08T16:00:00+00:00",
        62762.35
      ],
      [
        "2025-06-01T16:00:00+00:00",
        66142.14
      ],
      [
        "2025-05-25T16:00:00+00:00",
        58157.47
      ],
      [
        "2025-05-18T16:00:00+00:00",
        58500.07
      ],
      [
        "2025-05-11T16:00:00+00:00",
        74916.07
      ],
      [
        "2025-05-04T16:00:00+00:00",
        58035.28
      ],
      [
        "2025-04-27T16:00:00+00:00",
        45446.87
      ],
      [
        "2025-04-20T16:00:00+00:00",
        52741.39
      ],
      [
        "2025-04-13T16:00:00+00:00",
        58077.23
      ],
      [
        "2025-04-06T16:00:00+00:00",
        60172.96
      ],
      [
        "2025-03-30T16:00:00+00:00",
        69944.49
      ],
      [
        "2025-03-23T16:00:00+00:00",
        57601.79
      ],
      [
        "2025-03-16T16:00:00+00:00",
        60872.11
      ],
      [
        "2025-03-09T16:00:00+00:00",
        54268.81
      ],
      [
        "2025-03-02T16:00:00+00:00",
        58823.86
      ],
      [
        "2025-02-23T16:00:00+00:00",
        20684.9
      ]
    ]
  },
  "visualization_config": {
    "type": "bar",
    "title": "Bar Chart: week_start vs weekly_sales",
    "data": [
      {
        "week_start": "2025-08-10T16:00:00+00:00",
        "weekly_sales": 23806.58
      },
      {
        "week_start": "2025-08-03T16:00:00+00:00",
        "weekly_sales": 58206.78
      },
      {
        "week_start": "2025-07-27T16:00:00+00:00",
        "weekly_sales": 62590.26
      },
      {
        "week_start": "2025-07-20T16:00:00+00:00",
        "weekly_sales": 73560.31
      },
      {
        "week_start": "2025-07-13T16:00:00+00:00",
        "weekly_sales": 55705.93
      },
      {
        "week_start": "2025-07-06T16:00:00+00:00",
        "weekly_sales": 60761.8
      },
      {
        "week_start": "2025-06-29T16:00:00+00:00",
        "weekly_sales": 54569.54
      },
      {
        "week_start": "2025-06-22T16:00:00+00:00",
        "weekly_sales": 65319.59
      },
      {
        "week_start": "2025-06-15T16:00:00+00:00",
        "weekly_sales": 57232.0
      },
      {
        "week_start": "2025-06-08T16:00:00+00:00",
        "weekly_sales": 62762.35
      },
      {
        "week_start": "2025-06-01T16:00:00+00:00",
        "weekly_sales": 66142.14
      },
      {
        "week_start": "2025-05-25T16:00:00+00:00",
        "weekly_sales": 58157.47
      },
      {
        "week_start": "2025-05-18T16:00:00+00:00",
        "weekly_sales": 58500.07
      },
      {
        "week_start": "2025-05-11T16:00:00+00:00",
        "weekly_sales": 74916.07
      },
      {
        "week_start": "2025-05-04T16:00:00+00:00",
        "weekly_sales": 58035.28
      },
      {
        "week_start": "2025-04-27T16:00:00+00:00",
        "weekly_sales": 45446.87
      },
      {
        "week_start": "2025-04-20T16:00:00+00:00",
        "weekly_sales": 52741.39
      },
      {
        "week_start": "2025-04-13T16:00:00+00:00",
        "weekly_sales": 58077.23
      },
      {
        "week_start": "2025-04-06T16:00:00+00:00",
        "weekly_sales": 60172.96
      },
      {
        "week_start": "2025-03-30T16:00:00+00:00",
        "weekly_sales": 69944.49
      },
      {
        "week_start": "2025-03-23T16:00:00+00:00",
        "weekly_sales": 57601.79
      },
      {
        "week_start": "2025-03-16T16:00:00+00:00",
        "weekly_sales": 60872.11
      },
      {
        "week_start": "2025-03-09T16:00:00+00:00",
        "weekly_sales": 54268.81
      },
      {
        "week_start": "2025-03-02T16:00:00+00:00",
        "weekly_sales": 58823.86
      },
      {
        "week_start": "2025-02-23T16:00:00+00:00",
        "weekly_sales": 20684.9
      }
    ],
    "tooltip": {
      "fields": [
        "week_start",
        "weekly_sales"
      ]
    },
    "seriesField": null,
    "colorField": null,
    "xField": "week_start",
    "yField": "weekly_sales",
    "geometry": "interval"
  },
  "intent_info": {
    "intent": "data_query",
    "confidence": 0.96,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": [],
  "sql_explanation": "用户想要“最近半年 每周的销售额”。我需要从 orders 表中按周汇总销售额。首先，计算最近半年的起始日期（从今天往前推 6 个月），然后按周分组并求和销售额。使用 DATE_TRUNC('week', order_date) 来按周分组，确保每周从周一开始。",
  "analysis_path": null,
  "share_id": null,
  "data_source_id": 9,
  "data_source_name": "sales_data_warehouse",
  "conversation_title": "半年周销售额趋势"
}
</final_response>
--- End of Final System Response ---

[01:14:33] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-01T01:15:41.029828 ---

[01:15:41] Orchestration process started.

--- User Query ---
<query>
分析下 最近半年的 利润和经营情况
</query>
--- End of User Query ---

[01:15:42] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "analysis",
  "confidence": 0.93,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[01:15:43] Intent identified as 'analysis'. Proceeding with corresponding handler.
[01:15:43] Step 2: Handling analysis query.
[01:15:43] Analysis Service: Starting analysis.

--- Analysis Final Summary ---
<summary>
最近半年（2025-02 至 2025-07）利润与经营情况概要

1. 收入与订单规模  
• 月度收入：2 月 26.1 万 → 3 月 25.0 万（-4.2%）→ 4 月 27.1 万（+8.4%）→ 5 月 26.8 万（-1.2%）→ 6 月 26.8 万（+0.1%）→ 7 月 11.2 万（-58%）。  
• 订单量：2 月 594 单 → 6 月 641 单保持增长，但 7 月骤降至 247 单。  
• 活跃客户：2 月 327 人 → 7 月 195 人，呈逐月减少趋势。  

2. 品类贡献  
• 收入占比最高的品类：Electronics、Food、Sports、Toys 每月均居前 4。  
• 7 月所有品类收入均大幅下滑，其中 Electronics 从 6 月的 3.4 万降至 1.4 万，Food 从 3.6 万降至 1.8 万。  

3. 退货影响  
• 最近 3 个月产生退货：5 月 8.2 万、6 月 14.9 万、7 月 7.2 万。  
• 6 月退货额占当月收入 55.6%，显著侵蚀利润。  

4. 经营健康度判断  
• 5-6 月收入虽稳定在 26-27 万，但 6 月高额退货导致实际可确认利润大幅缩水。  
• 7 月收入、订单、客户数均断崖式下跌，显示经营出现明显恶化。  

结论：近半年整体呈“前稳后降”态势，6 月受高额退货拖累，7 月销售端全面收缩，需重点关注退货控制与 7 月销售骤降的根本原因。
</summary>
--- End of Analysis Final Summary ---


--- Final System Response ---
<final_response>
{
  "message_id": "ab644ef8-1981-4af8-af9c-a05fc35e0da8",
  "author": "SYSTEM",
  "content": "Analysis complete: 最近半年（2025-02 至 2025-07）利润与经营情况概要\n\n1. 收入与订单规模  \n• 月度收入：2 月 26.1 万 → 3 月 25.0 万（-4.2%）→ 4 月 27.1 万（+8.4%）→ 5 月 26.8 万（-1.2%）→ 6 月 26.8 万（+0.1%）→ 7 月 11.2 万（-58%）。  \n• 订单量：2 月 594 单 → 6 月 641 单保持增长，但 7 月骤降至 247 单。  \n• 活跃客户：2 月 327 人 → 7 月 195 人，呈逐月减少趋势。  \n\n2. 品类贡献  \n• 收入占比最高的品类：Electronics、Food、Sports、Toys 每月均居前 4。  \n• 7 月所有品类收入均大幅下滑，其中 Electronics 从 6 月的 3.4 万降至 1.4 万，Food 从 3.6 万降至 1.8 万。  \n\n3. 退货影响  \n• 最近 3 个月产生退货：5 月 8.2 万、6 月 14.9 万、7 月 7.2 万。  \n• 6 月退货额占当月收入 55.6%，显著侵蚀利润。  \n\n4. 经营健康度判断  \n• 5-6 月收入虽稳定在 26-27 万，但 6 月高额退货导致实际可确认利润大幅缩水。  \n• 7 月收入、订单、客户数均断崖式下跌，显示经营出现明显恶化。  \n\n结论：近半年整体呈“前稳后降”态势，6 月受高额退货拖累，7 月销售端全面收缩，需重点关注退货控制与 7 月销售骤降的根本原因。",
  "timestamp": "2025-09-01T01:15:56.823954",
  "explanation": "",
  "sql_query": null,
  "data_result": {},
  "visualization_config": null,
  "intent_info": {
    "intent": "analysis",
    "confidence": 0.93,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": null,
  "sql_explanation": null,
  "analysis_path": [],
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": "半年利润与经营分析"
}
</final_response>
--- End of Final System Response ---

[01:15:56] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-03T06:09:28.479276 ---

[06:09:28] Orchestration process started.

--- User Query ---
<query>
最近半年 每天的 利润

AI 2025-09-01 02:32:51
</query>
--- End of User Query ---

[06:09:30] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "data_query",
  "confidence": 0.98,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[06:09:31] Intent identified as 'data_query'. Proceeding with corresponding handler.
[06:09:31] Step 2: Handling data query.
[06:09:31] Step 2b: Generating SQL (Attempt 1 with RAG context).

--- Generated SQL (Attempt 1) ---
<sql>
SELECT
    order_date,
    SUM(quantity * price) AS daily_sales
FROM
    orders
WHERE
    order_date >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY
    order_date
ORDER BY
    order_date DESC
LIMIT 3000;
</sql>
--- End of Generated SQL (Attempt 1) ---


--- SQL Explanation (Attempt 1) ---
<explanation>
用户想要“最近半年每天的利润”。然而，在提供的 schema 中，只有 `orders` 表包含销售额（`sales_amount` 或 `quantity * price`），但没有成本或利润相关的字段。因此，无法直接计算利润。我只能计算每天的销售额。
</explanation>
--- End of SQL Explanation (Attempt 1) ---

[06:09:35] Step 2c: Validating and executing SQL.
[06:09:36] SQL executed successfully. Rows returned: 7
[06:09:36] Step 2d: Suggesting visualization.
[06:09:36] Suggested visualization: pie
[06:09:36] Data query handling finished.

--- Final System Response ---
<final_response>
{
  "message_id": "2b95dbd9-9a9b-490e-813a-18b4f159f09e",
  "author": "SYSTEM",
  "content": "用户想要“最近半年每天的利润”。然而，在提供的 schema 中，只有 `orders` 表包含销售额（`sales_amount` 或 `quantity * price`），但没有成本或利润相关的字段。因此，无法直接计算利润。我只能计算每天的销售额。\n\n\n\nMore tips:\n由于当前数据库中没有成本数据，无法计算利润。您是否想查看最近半年每天的销售额，或按产品类别、地区等维度进一步分析？ \n\nHere are the results for your query:",
  "timestamp": "2025-09-03T06:09:36.119979",
  "explanation": "The query for '最近半年 每天的 利润\n\nAI 2025-09-01 02:32:51' was executed successfully.",
  "sql_query": "SELECT\n    order_date,\n    SUM(quantity * price) AS daily_sales\nFROM\n    orders\nWHERE\n    order_date >= CURRENT_DATE - INTERVAL '6 months'\nGROUP BY\n    order_date\nORDER BY\n    order_date DESC\nLIMIT 3000;",
  "data_result": {
    "columns": [
      "order_date",
      "daily_sales"
    ],
    "rows": [
      [
        "2025-07-26",
        10.0
      ],
      [
        "2025-07-25",
        100.0
      ],
      [
        "2025-07-24",
        50.0
      ],
      [
        "2025-07-23",
        10.0
      ],
      [
        "2025-07-22",
        15.0
      ],
      [
        "2025-07-21",
        25.0
      ],
      [
        "2025-07-20",
        20.0
      ]
    ]
  },
  "visualization_config": {
    "type": "pie",
    "title": "Pie Chart: order_date Distribution",
    "data": [
      {
        "order_date": "2025-07-20",
        "value": 20.0
      },
      {
        "order_date": "2025-07-21",
        "value": 25.0
      },
      {
        "order_date": "2025-07-22",
        "value": 15.0
      },
      {
        "order_date": "2025-07-23",
        "value": 10.0
      },
      {
        "order_date": "2025-07-24",
        "value": 50.0
      },
      {
        "order_date": "2025-07-25",
        "value": 100.0
      },
      {
        "order_date": "2025-07-26",
        "value": 10.0
      }
    ],
    "tooltip": {
      "fields": [
        "order_date",
        "value"
      ]
    },
    "seriesField": null,
    "colorField": "order_date",
    "xField": "order_date",
    "yField": "value",
    "angleField": "value"
  },
  "intent_info": {
    "intent": "data_query",
    "confidence": 0.98,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": [],
  "sql_explanation": "用户想要“最近半年每天的利润”。然而，在提供的 schema 中，只有 `orders` 表包含销售额（`sales_amount` 或 `quantity * price`），但没有成本或利润相关的字段。因此，无法直接计算利润。我只能计算每天的销售额。",
  "analysis_path": null,
  "share_id": null,
  "data_source_id": 9,
  "data_source_name": "sales_data_warehouse",
  "conversation_title": "半年日利润概览"
}
</final_response>
--- End of Final System Response ---

[06:09:36] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-03T06:10:07.272753 ---

[06:10:07] Orchestration process started.

--- User Query ---
<query>
深度调研 BI 商业智能
</query>
--- End of User Query ---

[06:10:08] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "deep_report",
  "confidence": 0.98,
  "data_source_name": "sales_data_warehouse"
}
</intent>
--- End of Intent Recognition Result ---

[06:10:10] Intent identified as 'deep_report'. Proceeding with corresponding handler.
[06:10:10] Step 2: Handling deep report query.
[06:10:10] Step 2a: Initial request for deep report. Generating clarifying questions.

--- RAG Context for Deep Report Clarification ---
<rag_context>
[
  {
    "type": "data_source_schema",
    "content": "==============\n\nTable: products\nDescription: Stores information about products, including their categories.\n  Column: product_id (in table products, Type: INTEGER, Description: Unique identifier for each product.)\n  Column: product_name (in table products, Type: TEXT, Description: Name of the product.)\n  Column: category (in table products, Type: TEXT, Description: Category of the product (e.g., Electronics, Apparel).)\n==============\n\nTable: customers\nDescription: Stores customer demographic information.\n  Column: customer_id (in table customers, Type: INTEGER, Description: Unique identifier for each customer.)\n  Column: customer_name (in table customers, Type: TEXT, Description: Full name of the customer.)\n  Column: email (in table customers, Type: TEXT, Description: Email address of the customer.)\n  Column: city (in table customers, Type: TEXT, Description: City where the customer resides.)\n  Column: country (in table customers, Type: TEXT, Description: Country where the customer resides.)\n==============\n\nTable: stores\nDescription: Information about retail stores.\n  Column: store_id (in table stores, Type: INTEGER, Description: Unique identifier for each store.)\n  Column: store_name (in table stores, Type: TEXT, Description: Name of the store.)\n  Column: city (in table stores, Type: TEXT, Description: City where the store is located.)\n  Column: region (in table stores, Type: TEXT, Description: Geographical region of the store.)\n==============\n\nTable: employees\nDescription: Information about employees, linked to stores.\n  Column: employee_id (in table employees, Type: INTEGER, Description: Unique identifier for each employee.)\n  Column: employee_name (in table employees, Type: TEXT, Description: Full name of the employee.)\n  Column: hire_date (in table employees, Type: DATE, Description: Date when the employee was hired.)\n  Column: store_id (in table employees, Type: INTEGER, Description: Identifier of the store where the employee works.)\n==============\n\nTable: shipments\nDescription: Details about product shipments for orders.\n  Column: shipment_id (in table shipments, Type: INTEGER, Description: Unique identifier for each shipment.)\n  Column: order_id (in table shipments, Type: INTEGER, Description: Identifier of the order associated with the shipment.)\n  Column: shipment_date (in table shipments, Type: DATE, Description: Date when the shipment was made.)\n  Column: delivery_status (in table shipments, Type: TEXT, Description: Current delivery status (e.g., Shipped, Delivered, In Transit).)\n==============\n\nTable: returns\nDescription: Records of returned products.\n  Column: return_id (in table returns, Type: INTEGER, Description: Unique identifier for each return.)\n  Column: order_id (in table returns, Type: INTEGER, Description: Identifier of the original order for the returned product.)\n  Column: product_id (in table returns, Type: INTEGER, Description: Identifier of the product that was returned.)\n  Column: return_date (in table returns, Type: DATE, Description: Date when the product was returned.)\n  Column: reason (in table returns, Type: TEXT, Description: Reason for the return (e.g., Defective, Wrong Size, Unwanted).)\n==============\n\nTable: orders\nDescription: Stores customer order information, including sales amount and order date.234\n  Column: order_id (in table orders, Type: INTEGER, Description: Unique identifier for each order.)\n  Column: customer_id (in table orders, Type: INTEGER, Description: Identifier for the customer who placed the order.)\n  Column: order_date (in table orders, Type: DATE, Description: The date when the order was placed.)\n  Column: product_id (in table orders, Type: INTEGER, Description: Identifier for the product purchased.)\n  Column: quantity (in table orders, Type: INTEGER, Description: Number of units of the product sold in the order.)\n  Column: price (in table orders, Type: REAL, Description: Unit price of the product at the time of order.)\n  Column: sales_amount (in table orders, Type: REAL, Description: Total sales amount for the order item (quantity * price).)\nMetric: test\nDefinition: SELECT\n    SUM(o.quantity * o.price) AS total_sales\nFROM\n    orders o\nJOIN\n    products p ON o.product_id = p.product_id\nWHERE\n    p.category = 'Electronics'\nLIMIT 3000;\nBusiness Meaning: The sum of all sales amounts across all orders.\nMetric: Total Sales\nDefinition: SUM(sales_amount)\nBusiness Meaning: The sum of all sales amounts across all orders.\nMetric: Average Order Value\nDefinition: AVG(sales_amount)\nBusiness Meaning: The average sales amount per order.\n"
  }
]
</rag_context>
--- End of RAG Context for Deep Report Clarification ---


--- Final System Response ---
<final_response>
{
  "message_id": "5599c2d5-3cfd-4f6d-a62f-652b093d1161",
  "author": "SYSTEM",
  "content": "To provide you with the best possible report, please answer the following questions:\n\n1. 您希望聚焦 BI 的哪一类应用场景？（例如：零售、制造、金融、医疗、政府等）\n2. 报告深度与篇幅预期如何？（概念综述、行业全景、技术细节、案例剖析、战略落地指南等）\n3. 目标读者是谁？（CXO 决策层、IT/数据团队、投资人、咨询顾问、学术研究者）\n4. 您更关注 BI 的哪些维度？（市场格局、技术栈、厂商对比、ROI 评估、数据治理、AI 融合、实施路径、合规风险）\n5. 交付形式偏好？（PPT 摘要、万字深度报告、数据包 + 图表、可落地的实施路线图、双语版本等）",
  "timestamp": "2025-09-03T06:10:12.530150",
  "explanation": "Deep report clarification in progress",
  "sql_query": null,
  "data_result": null,
  "visualization_config": null,
  "intent_info": {
    "intent": "deep_report",
    "confidence": 0.98,
    "data_source_name": "sales_data_warehouse"
  },
  "rag_context": [
    {
      "original_question": "深度调研 BI 商业智能",
      "clarification_round": 1,
      "all_questions_asked": [
        "您希望聚焦 BI 的哪一类应用场景？（例如：零售、制造、金融、医疗、政府等）",
        "报告深度与篇幅预期如何？（概念综述、行业全景、技术细节、案例剖析、战略落地指南等）",
        "目标读者是谁？（CXO 决策层、IT/数据团队、投资人、咨询顾问、学术研究者）",
        "您更关注 BI 的哪些维度？（市场格局、技术栈、厂商对比、ROI 评估、数据治理、AI 融合、实施路径、合规风险）",
        "交付形式偏好？（PPT 摘要、万字深度报告、数据包 + 图表、可落地的实施路线图、双语版本等）"
      ]
    }
  ],
  "sql_explanation": null,
  "analysis_path": null,
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": "深度BI调研"
}
</final_response>
--- End of Final System Response ---

[06:10:12] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-03T06:10:52.751152 ---

[06:10:52] Orchestration process started.

--- User Query ---
<query>
零售、制造，行业全景、技术细节，IT/数据团队、投资人、咨询顾问，市场格局、技术栈、厂商对比、万字深度报告、
</query>
--- End of User Query ---

[06:10:52] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "deep_report",
  "confidence": 0.98,
  "data_source_name": "None"
}
</intent>
--- End of Intent Recognition Result ---

[06:10:54] Intent identified as 'deep_report'. Proceeding with corresponding handler.
[06:10:54] Step 2: Handling deep report query.
[06:10:54] Step 2a: Continuing clarification, round 1.
[06:10:56] Step 2b: More clarification needed. Asking follow-up questions.

--- Final System Response ---
<final_response>
{
  "message_id": "4ab99744-8288-4797-a908-694c8b45e221",
  "author": "SYSTEM",
  "content": "To provide you with the best possible report, please answer these follow-up questions:\n\n1. 在零售与制造两大行业中，您希望报告以哪一类为主、哪一类为辅，还是两者并重？\n2. 报告是否需要包含中国本土市场与国际市场的对比，还是仅聚焦某一区域？",
  "timestamp": "2025-09-03T06:10:56.584752",
  "explanation": "Deep report clarification in progress",
  "sql_query": null,
  "data_result": null,
  "visualization_config": null,
  "intent_info": {
    "intent": "deep_report",
    "confidence": 0.98,
    "data_source_name": "None"
  },
  "rag_context": [
    {
      "original_question": "深度调研 BI 商业智能",
      "clarification_round": 2,
      "all_questions_asked": [
        "您希望聚焦 BI 的哪一类应用场景？（例如：零售、制造、金融、医疗、政府等）",
        "报告深度与篇幅预期如何？（概念综述、行业全景、技术细节、案例剖析、战略落地指南等）",
        "目标读者是谁？（CXO 决策层、IT/数据团队、投资人、咨询顾问、学术研究者）",
        "您更关注 BI 的哪些维度？（市场格局、技术栈、厂商对比、ROI 评估、数据治理、AI 融合、实施路径、合规风险）",
        "交付形式偏好？（PPT 摘要、万字深度报告、数据包 + 图表、可落地的实施路线图、双语版本等）",
        "在零售与制造两大行业中，您希望报告以哪一类为主、哪一类为辅，还是两者并重？",
        "报告是否需要包含中国本土市场与国际市场的对比，还是仅聚焦某一区域？"
      ]
    }
  ],
  "sql_explanation": null,
  "analysis_path": null,
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": null
}
</final_response>
--- End of Final System Response ---

[06:10:56] Orchestration process finished successfully.

=======================

--- New session started at 2025-09-03T06:11:13.197220 ---

[06:11:13] Orchestration process started.

--- User Query ---
<query>
两者并重 ，中国本土市场与国际市场
</query>
--- End of User Query ---

[06:11:13] Step 1: Recognizing intent.

--- Intent Recognition Result ---
<intent>
{
  "intent": "deep_report",
  "confidence": 0.98,
  "data_source_name": "None"
}
</intent>
--- End of Intent Recognition Result ---

[06:11:15] Intent identified as 'deep_report'. Proceeding with corresponding handler.
[06:11:15] Step 2: Handling deep report query.
[06:11:15] Step 2a: Continuing clarification, round 2.
[06:11:17] Step 2b: Clarification complete. Starting deep report generation.

--- Final System Response ---
<final_response>
{
  "message_id": "9670e22a-3781-4acf-bf69-a2bfe97059b7",
  "author": "SYSTEM",
  "content": "Thank you for the details. I am now starting the deep research process. This may take several minutes. Please refreash this page later..\n感谢您提供详细信息。我现在正在开始深入研究。这可能需要几分钟。请稍后刷新本页面。",
  "timestamp": "2025-09-03T06:11:17.608768",
  "explanation": "Deep report generation started.",
  "sql_query": null,
  "data_result": null,
  "visualization_config": null,
  "intent_info": {
    "intent": "deep_report",
    "confidence": 0.98,
    "data_source_name": "None"
  },
  "rag_context": null,
  "sql_explanation": null,
  "analysis_path": null,
  "share_id": null,
  "data_source_id": null,
  "data_source_name": null,
  "conversation_title": null
}
</final_response>
--- End of Final System Response ---

[06:11:17] Orchestration process finished successfully.
