当聊天补全（chatCompletion）流中返回[DONE]时，您可调用此接口请求对话结果。

• 请求方式：GET
• 请求地址：/dataAgent/llm/openApi/v2/signed/agent/chatResult?sessionId={sessionId}&historyId={historyId}

Body参数如下。

返回结果的核心参数详细说明如下。

{
    "JSONIFY_PRETTYPRINT_REGULAR": false,
    "code": "llm/ok",
    "data": {
        "debugInfo": {
            "historyId": 2983696,
            "noDataReason": null,
            "requestId": "53882b70-5494-40e2-9723-e8d10bb46bd9.T2VuFNrMpS3JH5ZQZHAuZB.bi.35",
            "sessionId": 11469472
        },
        "disableBookmarkQuestion": false,
        "errorMsg": null,
        "execErrorMsg": "",
        "isEmpty": false,
        "isSuccess": true,
        "llmResult": {
            "code": "\n# 编写sql查询\nsql = \"\"\"select `scene_first_level_type`, sum(`uv`) as uv_sum from `风神大模型大盘数据集` where `date_range` = '2025-09-03' group by `scene_first_level_type`\"\"\"\n\n# 执行sql查询并获取结果\ndf = execute_sql(sql, max_rows=1000)\n\n# 输出结果\nanswer(df)\n\n\n\"\"\"\n实际执行sql：\nselect (`1700060096716_scene_first_level_type`) as `_1700060096716`,\n       (sum(`1700060096719_uv`)) as `uv_sum`\nfrom   `perfu.风神大模型大盘数据集` `风神大模型大盘数据集`\nwhere  ((`1700060096715_date_range`) = ('2025-09-03'))\n   and (todate(`1700060096713_date`) = '2025-09-03')\ngroup by `_1700060096716` limit 100000\n\"\"\"\n\n",
            "dataSetIdList": [
                3206432
            ],
            "execContext": "  select  (`1700060096716_scene_first_level_type`) as `_1700060096716`, (sum(`1700060096719_uv`)) as `uv_sum`  from `perfu.风神大模型大盘数据集` `风神大模型大盘数据集`     where ( (`1700060096715_date_range`) = ('2025-09-03') ) and (todate(`1700060096713_date`) = '2025-09-03')  group by `_1700060096716`    limit 100000   ",
            "execResult": "查询成功，查询结果的总行数为：13。查询结果的结构和完整数据明细如下：\n\n|一级场景类型|uv(求和)|\n|--|--|\n|数据集报错诊断|208|\n|图表配置优化|111|\n|sql查询助手|2578|\n|搜索|4288|\n|magibook|1628|\n|整体|8560|\n|数据解读|158|\n|表达式生成|680|\n|仪表盘制作|0|\n|notebook|9|\n|分析取数|3943|\n|代码补全|5258|\n|数据集元信息生成|126|",
            "executeSql": "SELECT (`scene_first_level_type`) AS `_1700060096716` ,\n    (SUM(`uv`)) AS `uv_sum` \n    FROM (select `date` as `date` , `product` as `product` , `date_type` as `date_type` , `date_range` as `date_range` , `scene_first_level_type` as `scene_first_level_type` , `scene_second_level_type` as `scene_second_level_type` , `env` as `env` , `uv` as `uv` , `penetration_rate` as `penetration_rate` , `coverage_rate` as `coverage_rate` , `adoption_rate` as `adoption_rate` , `badcase_rate` as `badcase_rate` , `retention_rate` as `retention_rate` , `last_uv` as `last_uv` , `last_penetration_rate` as `last_penetration_rate` , `last_coverage_rate` as `last_coverage_rate` , `last_adoption_rate` as `last_adoption_rate` , `last_badcase_rate` as `last_badcase_rate` , `last_retention_rate` as `last_retention_rate` , `uv_target` as `uv_target` , `penetration_rate_target` as `penetration_rate_target` , `coverage_rate_target` as `coverage_rate_target` , `adoption_rate_target` as `adoption_rate_target` , `badcase_rate_target` as `badcase_rate_target` , `retention_rate_target` as `retention_rate_target` , `apv_target` as `apv_target` , `apv` as `apv` , `last_apv` as `last_apv` , `date_range_lastday` as `date_range_lastday` , `active_5_day` as `active_5_day` , `active_2_day` as `active_2_day` , `active_3_day` as `active_3_day` , `active_4_day` as `active_4_day` from `llm_dw`.`app_sophon_df` where 1=1 and `product` = ('aeolus') and `product` = ('aeolus')) `\u98ce\u795e\u5927\u6a21\u578b\u5927\u76d8\u6570\u636e\u96c6` \n        WHERE ( (`date_range`) = ('2025-09-03') ) AND (toDate(`date`) = '2025-09-03') \n        GROUP BY `_1700060096716` \n        LIMIT 100000 \n            SET TINGS max_execution_time=180 \n                SET TINGS max_threads=16 , max_execution_time=100 , max_memory_usage=68719476736 , max_query_cpu_seconds=250 , max_bytes_to_read=2147483648000 , max_bytes_to_read_local=107374182400 FORMAT JSONCompact/*miss cache reason:no cache data , expire_duration: None , cache_time: None*/",
            "originSql": "select `scene_first_level_type` ,\n    sum(`uv`) as uv_sum \n    from `风神大模型大盘数据集` \n        where `date_range` = '2025-09-03' \n        group by `scene_first_level_type`",
            "recallKnowledge": "\n\n## 表`3206432`的相关信息\n表信息如下:\n表id: `3206432`\n表名: `风神大模型大盘数据集`\n\n字段列表，字段名均以``包围：\n\n|字段名|数据类型|相关信息|抽样示例值(非全量)|\n| ---- | ---- | ---- | ---- |\n|`scene_first_level_type`|string|||\n|`last_badcase_rate`|float|||\n|`last_retention_rate`|float|||\n|`active_3_day`|int|||\n|`retention_rate_target`|float|||\n|`last_uv`|int|||\n|`coverage_rate_target`|float|||\n|`badcase_rate_target`|float|||\n|`env`|string|||\n|`badcase_rate`|float|||\n|`last_apv`|float|||\n|`coverage_rate`|float|||\n|`adoption_rate_target`|float|||\n|`scene_second_level_type`|string|||\n|`last_coverage_rate`|float|||\n|`active_2_day`|int|||\n|`active_5_day`|int|||\n|`last_adoption_rate`|float|||\n|`adoption_rate`|float|||\n|`apv_target`|float|||\n|`date_range`|string|||\n|`penetration_rate_target`|float|||\n|`penetration_rate`|float|||\n|`uv_target`|int|||\n|`last_penetration_rate`|float|||\n|`uv`|int|||\n|`date_type`|string|||\n|`apv`|float|||\n|`retention_rate`|float|||\n|`active_4_day`|int|||\n\n\n\n",
            "rewriteSql": "select (`1700060096716_scene_first_level_type`) as `_1700060096716` ,\n    (sum(`1700060096719_uv`)) as `uv_sum` \n    from `perfu.风神大模型大盘数据集` `风神大模型大盘数据集` \n        where ( (`1700060096715_date_range`) = ('2025-09-03') ) and (todate(`1700060096713_date`) = '2025-09-03') \n        group by `_1700060096716` \n        limit 100000",
            "sqlList": [
                "select `scene_first_level_type`, sum(`uv`) as uv_sum from `风神大模型大盘数据集` where `date_range` = '2025-09-03' group by `scene_first_level_type`"
            ],
            "thought": "用户需要分析昨天（2025-09-03）使用风神大模型按一级场景分组的各场景uv，从探查结果可知，`风神大模型大盘数据集`包含所需的`scene_first_level_type`和`uv`字段，且有日期字段`date_range`。因此，选择该表进行查询，通过where子句筛选出日期为2025-09-03的数据，再按`scene_first_level_type`分组并对`uv`求和得到各场景的uv。",
            "total": [
                {
                    "key": "thought",
                    "value": "用户需要分析昨天（2025-09-03）使用风神大模型按一级场景分组的各场景uv，从探查结果可知，`风神大模型大盘数据集`包含所需的`scene_first_level_type`和`uv`字段，且有日期字段`date_range`。因此，选择该表进行查询，通过where子句筛选出日期为2025-09-03的数据，再按`scene_first_level_type`分组并对`uv`求和得到各场景的uv。"
                },
                {
                    "key": "exec_context",
                    "value": "-- 模型生成的sql \n select `scene_first_level_type` ,\n    sum(`uv`) as uv_sum \n    from `风神大模型大盘数据集` \n        where `date_range` = '2025-09-03' \n        group by `scene_first_level_type`\n\n-- 实际执行的sql \nselect (`1700060096716_scene_first_level_type`) as `_1700060096716` ,\n    (sum(`1700060096719_uv`)) as `uv_sum` \n    from `perfu.风神大模型大盘数据集` `风神大模型大盘数据集` \n        where ( (`1700060096715_date_range`) = ('2025-09-03') ) and (todate(`1700060096713_date`) = '2025-09-03') \n        group by `_1700060096716` \n        limit 100000"
                },
                {
                    "key": "exec_result",
                    "status": "success",
                    "value": "查询成功，查询结果的总行数为：13"
                }
            ]
        },
        "renderResult": {
            "datasets": [
                {
                    "1756965117283": "数据集报错诊断",
                    "1756965117284": "208"
                },
                {
                    "1756965117283": "图表配置优化",
                    "1756965117284": "111"
                },
                {
                    "1756965117283": "sql查询助手",
                    "1756965117284": "2578"
                },
                {
                    "1756965117283": "搜索",
                    "1756965117284": "4288"
                },
                {
                    "1756965117283": "magibook",
                    "1756965117284": "1628"
                },
                {
                    "1756965117283": "整体",
                    "1756965117284": "8560"
                },
                {
                    "1756965117283": "数据解读",
                    "1756965117284": "158"
                },
                {
                    "1756965117283": "表达式生成",
                    "1756965117284": "680"
                },
                {
                    "1756965117283": "仪表盘制作",
                    "1756965117284": "0"
                },
                {
                    "1756965117283": "notebook",
                    "1756965117284": "9"
                },
                {
                    "1756965117283": "分析取数",
                    "1756965117284": "3943"
                },
                {
                    "1756965117283": "代码补全",
                    "1756965117284": "5258"
                },
                {
                    "1756965117283": "数据集元信息生成",
                    "1756965117284": "126"
                }
            ],
            "dimensions": [
                "一级场景类型"
            ],
            "metrics": [
                "uv(\u6c42\u548c)"
            ],
            "vizSchema": {
                "fields": [
                    {
                        "alias": "\u4e00\u7ea7\u573a\u666f\u7c7b\u578b",
                        "expr": "`1700060096716_scene_first_level_type`",
                        "id": "1756965117283",
                        "location": "dimension",
                        "role": "dimension",
                        "type": "string",
                        "visible": true
                    },
                    {
                        "alias": "uv(\u6c42\u548c)",
                        "dataFormat": null,
                        "enableFormat": true,
                        "expr": "SUM(`1700060096719_uv`)",
                        "format": {
                            "auto": false,
                            "kSep": true,
                            "precision": 2,
                            "precisionType": "significantDecimal",
                            "prefix": "",
                            "suffix": "",
                            "type": "digit",
                            "unit": "auto"
                        },
                        "id": "1756965117284",
                        "location": "measure",
                        "minValue": 9,
                        "role": "measure",
                        "type": "int",
                        "visible": true
                    }
                ]
            }
        }
    },
    "msg": {}
}