GPT TEXT TO DATA 接口文档

作用：通过自然语言查询数据, 集成GPT能力, 可多轮对话

接口信息：

PATH：/df/rest/table/list

METHOD： GET

获取系统中所有已导入表信息

Header 请求参数

json

Authorization: Bearer M2M5YzY2TE0ZmEwMDNZjY1MDRkMGNkNWIuYTUxYThkZjJkGQ3NGZlZmJlZmIMGM2YTA3MDhlNg=

请求参数

Name	Location	Type	Required	Description
name	query	string	no	表名查询参数
Authorization	header	string	yes	none

Response Examples

json

{
  "data": [
    {
      "columns": [
        {
          "colDisplayName": "产品类别",
          "colName": "产品类别",
          "dataType": "string",
          "id": "1872444643045654532"
        },
        {
          "colDisplayName": "区域",
          "colName": "区域",
          "dataType": "string",
          "id": "1872444643045654533"
        },
        {
          "colDisplayName": "快递公司",
          "colName": "快递公司",
          "dataType": "string",
          "id": "1872444643045654534"
        },
        {
          "colDisplayName": "目的城市",
          "colName": "目的城市",
          "dataType": "string",
          "id": "1872444643045654536"
        },
        {
          "colDisplayName": "订单单号",
          "colName": "订单单号",
          "dataType": "bigint",
          "id": "1872444643045654538"
        },
        {
          "colDisplayName": "顾客姓名",
          "colName": "顾客姓名",
          "dataType": "string",
          "id": "1872444643045654542"
        }
      ],
      "id": "1872444643041460225",
      "tblDisplayName": "test_flow_Datafocus流处理",
      "tblName": "test_flow_Datafocus流处理"
    }
    
  ],
  "errCode": 0,
  "exception": "",
  "msgParams": null,
  "promptMsg": null,
  "success": true
}

Responses Data Schema

HTTP Status Code 200

Name	Type	Required	Restrictions	Title	description
» success	boolean	true	none	结果	true：成功 false：失败
» exception	string	true	none	异常信息	异常信息
» errCode	integer	true	none	错误码	非零为出错
» promptMsg	object	false	none	异常提示	异常提示信息
» data	[数据表信息]	false	none	响应数据体	响应数据体

PATH：/df/rest/datasource/tables

METHOD： POST

获取数据源的所有表信息

Header 请求参数

json

Authorization: Bearer M2M5YzY2TE0ZmEwMDNZjY1MDRkMGNkNWIuYTUxYThkZjJkGQ3NGZlZmJlZmIMGM2YTA3MDhlNg=

Body Parameters

json

{
  "db": "test_1",
  "description": "",
  "host": "192.168.0.93",
  "jdbcSuffix": "",
  "name": "pg93Test1",
  "port": 5432,
  "schemaName": "public",
  "sourceType": "dataSource",
  "type": "postgresql",
  "user": "postgres",
  "password": "postgres"
}

Params

Name	Location	Type	Required	Description
Authorization	header	string	yes	none
body	body	any	no	none

Response Examples

json

{
  "data": [
    {
      "columns": [
        {
          "colDisplayName": "id",
          "colName": "id",
          "dataType": "bigint",
          "id": "0"
        },
        {
          "colDisplayName": "datasource_id",
          "colName": "datasource_id",
          "dataType": "bigint",
          "id": "0"
        },
        {
          "colDisplayName": "config_key",
          "colName": "config_key",
          "dataType": "string",
          "id": "0"
        },
        {
          "colDisplayName": "config_value",
          "colName": "config_value",
          "dataType": "string",
          "id": "0"
        }
      ],
      "id": "0",
      "tblDisplayName": "df_web_datasource_properties",
      "tblName": "df_web_datasource_properties"
    },
    {
      "columns": [
        {
          "colDisplayName": "a1",
          "colName": "a1",
          "dataType": "double",
          "id": "0"
        },
        {
          "colDisplayName": "a2",
          "colName": "a2",
          "dataType": "double",
          "id": "0"
        },
        {
          "colDisplayName": "a3",
          "colName": "a3",
          "dataType": "double",
          "id": "0"
        }
      ],
      "id": "0",
      "tblDisplayName": "test_v",
      "tblName": "test_v"
    },
    {
      "columns": [
        {
          "colDisplayName": "id",
          "colName": "id",
          "dataType": "bigint",
          "id": "0"
        },
        {
          "colDisplayName": "type",
          "colName": "type",
          "dataType": "string",
          "id": "0"
        }
      ],
      "id": "0",
      "tblDisplayName": "df_web_datasource",
      "tblName": "df_web_datasource"
    }
  ],
  "errCode": 0,
  "exception": "",
  "msgParams": null,
  "promptMsg": null,
  "success": true
}

Responses Data Schema

HTTP Status Code 200

Name	Type	Required	Restrictions	Title	description
» success	boolean	true	none	结果	true：成功 false：失败
» exception	string	true	none	异常信息	异常信息
» errCode	integer	true	none	错误码	非零为出错
» promptMsg	object	false	none	异常提示	异常提示信息
» data	[数据表信息]	false	none	响应数据体	响应数据体

Data Schema

数据源配置参数

json

{
  "type": "string",
  "name": "string",
  "description": "string",
  "schemaName": "string",
  "host": "string",
  "port": 0,
  "user": "string",
  "password": "string",
  "db": "string",
  "jdbcSuffix": "string"
}

Attribute

Name	Type	Required	Restrictions	Title	Description
type	string	true	none	数据源类型	none
name	string	true	none	数据源名称	none
description	string	false	none	数据源描述	none
schemaName	string	false	none	Schema信息	none
host	string	true	none	主机地址	none
port	integer	true	none	端口信息	none
user	string	true	none	用户名	none
password	string	true	none	用户密码	none
db	string	true	none	数据库名	none
jdbcSuffix	string	false	none	JDBC附加参数	none

数据源配置参数说明

数据源类型参数: type 支持下面的数据源

type	数据源
mysql	MySQL
postgresql	Posgresql
sqlserver	SQLServer
oracle	Oracle
ibmdb2	IBMDB2
gaussdb100	GaussDB100
sybase	Sybase
greenplum	Greenplum
clickhouse	Clickhouse
teradata	Teradata
doris	Doris
tidb	TIDB
hana	Hana
clickzetta	Clickzetta
impala	Impala
dameng	DM8
argodb	ArgoDB
kudu	Kudu
elasticsearch	Elasticsearch
mongodb	Mongodb
prometheus	Prometheus
hive	Hive
delta_lake	Delta-Lake
iceberg	Iceberg

每种数据源的配置方式多少有点不一样，支持JDBC的数据源配置基本是一样的，上面给的数据源的结构是按JDBC的数据源来的，下面是几个配置样例:

postgres:

json

{
    "db": "test_1",
    "description": "",
    "host": "192.168.0.93",
    "jdbcSuffix": "",
    "name": "pg93Test1",
    "port": 5432,
    "schemaName": "public",
    "sourceType": "dataSource",
    "type": "postgresql",
    "user": "postgres",
    "password": "postgres"
}

mysql:

json

{
        "db": "releaseci",
        "description": "",
        "host": "192.168.0.93",
        "jdbcSuffix": "serverTimezone=GMT-8",
        "name": "MySQL93",
        "port": 3316,
        "schemaName": "releaseci",
        "type": "mysql",
        "user": "root",
        "password": "root"
    }

sqlserver:

json

{
        "db": "releaseci",
        "description": "",
        "host": "192.168.0.93",
        "jdbcSuffix": "trustServerCertificate=true",
        "name": "SQLServer93",
        "port": 1433,
        "schemaName": "dbo",
        "sourceType": "dataSource",
        "type": "sqlserver",
        "user": "root",
        "username": "root"
    }

impala:

{
        "authType": "NONE",
        "db": "default",
        "description": "",
        "host": "192.168.0.57",
        "id": "1765671252440236033",
        "jdbcSuffix": "",
        "name": "impala",
        "port": 21050,
        "schemaName": "default",
        "sourceType": "dataSource",
        "type": "impala",
        "user": "",
        "password": ""
    }

数据表信息

json

{
  "id": "string",
  "tblName": "string",
  "tblDisplayName": "string",
  "columns": [
    {
      "id": "string",
      "colName": "string",
      "colDisplayName": "string",
      "dataType": "string"
    }
  ]
}

Attribute

Name	Type	Required	Restrictions	Title	Description
id	string	true	none	表id	none
tblName	string	true	none	原始表名	none
tblDisplayName	string	true	none	显示表名	none
columns	[object]	true	none	列列表	none
» id	string	true	none	列id	none
» colName	string	true	none	原始列名	none
» colDisplayName	string	true	none	显示列名	none
» dataType	string	true	none	数据类型	none

基本响应对象

json

{
  "success": true,
  "exception": "string",
  "errCode": 0,
  "promptMsg": {},
  "data": {}
}

Attribute

Name	Type	Required	Restrictions	Title	Description
success	boolean	true	none	结果	true：成功 false：失败
exception	string	true	none	异常信息	异常信息
errCode	integer	true	none	错误码	非零为出错
promptMsg	object	false	none	异常提示	异常提示信息
data	object	false	none	响应数据体	响应数据体

PATH：/df/rest/gpt/init

METHOD： POST

初始化上下文[注册表、选表]

Header 请求参数

json

Authorization: Bearer M2M5YzY2TE0ZmEwMDNZjY1MDRkMGNkNWIuYTUxYThkZjJkGQ3NGZlZmJlZmIMGM2YTA3MDhlNg=

Body 请求参数

json

{
  "language": "string",
  "dataSource": {
    "type": "string",
    "name": "string",
    "description": "string",
    "schemaName": "string",
    "host": "string",
    "port": 0,
    "user": "string",
    "password": "string",
    "db": "string",
    "jdbcSuffix": "string"
  },
  "names": [
    "string"
  ]
}

请求参数

名称	位置	类型	必选	说明
Authorization	header	string	是	bearer token
body	body	object	否	none
» language	body	string	是	语言环境
» dataSource	body	object	否	数据源信息
»» type	body	string	是	数据源类型
»» name	body	string	是	数据源名称
»» description	body	string	否	数据源描述
»» schemaName	body	string	否	Schema信息
»» host	body	string	是	主机地址
»» port	body	integer	是	端口信息
»» user	body	string	是	用户名
»» password	body	string	是	用户密码
»» db	body	string	是	数据库名
»» jdbcSuffix	body	string	否	JDBC附加参数
» names	body	[string]	是	表显示名列表

说明

当 dataSource 为空时，会从系统中选择 names 列表中的表使用。当 dataSource 不为空时，会注册dataSource中 names 列表中的表到系统中使用[不会重复注册]。

枚举值

language：语言环境

值	含义
chinese	中文
english	英文

dataSource.type：数据源类型参数

值	含义
mysql	MySQL
postgresql	Posgresql
sqlserver	SQLServer
oracle	Oracle
ibmdb2	IBMDB2
gaussdb100	GaussDB100
sybase	Sybase
greenplum	Greenplum
clickhouse	Clickhouse
teradata	Teradata
doris	Doris
tidb	TIDB
hana	Hana
clickzetta	Clickzetta
impala	Impala
dameng	DM8
argodb	ArgoDB
kudu	Kudu
elasticsearch	Elasticsearch
mongodb	Mongodb
prometheus	Prometheus
hive	Hive
delta_lake	Delta-Lake
iceberg	Iceberg

数据源参数示例: 每种数据源的配置方式多少有点不一样，支持JDBC的数据源配置基本是一样的，上面给的数据源的结构是按JDBC的数据源来的，下面是几个配置样例:

postgres:

json

{
    "db": "test_1",
    "description": "",
    "host": "192.168.0.93",
    "jdbcSuffix": "",
    "name": "pg93Test1",
    "port": 5432,
    "schemaName": "public",
    "sourceType": "dataSource",
    "type": "postgresql",
    "user": "postgres",
    "password": "postgres"
}

mysql:

json

{
        "db": "releaseci",
        "description": "",
        "host": "192.168.0.93",
        "jdbcSuffix": "serverTimezone=GMT-8",
        "name": "MySQL93",
        "port": 3316,
        "schemaName": "releaseci",
        "type": "mysql",
        "user": "root",
        "password": "root"
    }

sqlserver:

json

{
        "db": "releaseci",
        "description": "",
        "host": "192.168.0.93",
        "jdbcSuffix": "trustServerCertificate=true",
        "name": "SQLServer93",
        "port": 1433,
        "schemaName": "dbo",
        "sourceType": "dataSource",
        "type": "sqlserver",
        "user": "root",
        "username": "root"
    }

impala:

{
        "authType": "NONE",
        "db": "default",
        "description": "",
        "host": "192.168.0.57",
        "id": "1765671252440236033",
        "jdbcSuffix": "",
        "name": "impala",
        "port": 21050,
        "schemaName": "default",
        "sourceType": "dataSource",
        "type": "impala",
        "user": "",
        "password": ""
    }

返回示例

200 Response

json

{
  "errCode": 0,
  "exception": "string",
  "data": "string"
}

返回数据结构

状态码 200

名称	类型	必选	约束	说明
» errCode	integer	true	none	none
» exception	string	true	none	none
» data	string	true	none	chat id:后续对话使用

PATH：/df/rest/gpt/data

METHOD： POST

对话查询

Header 请求参数

json

Authorization: Bearer M2M5YzY2TE0ZmEwMDNZjY1MDRkMGNkNWIuYTUxYThkZjJkGQ3NGZlZmJlZmIMGM2YTA3MDhlNg=

Body 请求参数

json

{
  "input": "string",
  "chatId": "string"
}

请求参数

名称	位置	类型	必选	说明
Authorization	header	string	是	bearer token
body	body	object	否	none
» input	body	string	是	用户输入的问题
» chatId	body	string	是	对话id

返回示例

200 Response

json

{
  "errCode": 0,
  "exception": "string",
  "data": {
    "code": 0,
    "info": "string",
    "type": "data",
    "content": {
      "duration": "string",
      "headers": [
        {
          "suf": "string",
          "display": "string",
          "name": "string"
        }
      ],
      "columns": [
        [
          "string"
        ]
      ],
      "count": 0,
      "title": "string",
      "sql": {
        "where_clause": "string",
        "order_by_clause": "string",
        "select_clause": "string",
        "from_clause": "string",
        "group_by_clause": "string",
        "having_clause": "string"
      }
    },
    "question": "string"
  },
  "msgParams": null,
  "promptMsg": null,
  "success": true
}

返回数据结构

状态码 200

名称	类型	必选	约束	说明
» errCode	integer	true	none	none
» exception	string	true	none	none
» data	object	true	none	none
»» code	integer	true	none	错误码, 非0表示错误
»» info	string	true	none	提示信息或者错误信息
»» type	string	true	none	类型
»» content	object	true	none	none
»»» duration	string	true	none	查询耗时
»»» headers	[object]	true	none	列头信息
»»»» suf	string	true	none	列头操作
»»»» display	string	true	none	列头名字+列头操作
»»»» name	string	true	none	列头名字
»»» columns	[array]	true	none	查询结果,二维数组
»»» count	integer	true	none	查询的记录数
»»» title	string	true	none	标题
»»» sql	object	true	none	实际执行的sql
»»»» where_clause	string	true	none	none
»»»» order_by_clause	string	true	none	none
»»»» select_clause	string	true	none	none
»»»» from_clause	string	true	none	none
»»»» group_by_clause	string	true	none	none
»»»» having_clause	string	true	none	none
»» question	string	true	none	解析后的问题
» msgParams	null	true	none	none
» promptMsg	null	true	none	none
» success	boolean	true	none	none

枚举值

type：返回结果类型

值	含义
data	数据结果
assistant	gpt交互信息

Bearer token 获取方式如下图:

curl 样例：

json


获取系统内表列表
curl -X GET "https://test.datafocus.ai/df/rest/table/list" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU="

curl 返回结果:
{
    "data": [
        {
            "columns": [
                {
                    "colDisplayName": "a",
                    "colName": "a",
                    "dataType": "string",
                    "id": "1776861878589542401"
                }
            ],
            "id": "1776861878581153793",
            "tblDisplayName": "test-test",
            "tblName": "test-test"
        },
        {
            "columns": [
                {
                    "colDisplayName": "Id",
                    "colName": "Id",
                    "dataType": "int",
                    "id": "1674954668239998978"
                },
                {
                    "colDisplayName": "Reputation",
                    "colName": "Reputation",
                    "dataType": "int",
                    "id": "1674954668239998979"
                },
                {
                    "colDisplayName": "公式名",
                    "colName": "公式名",
                    "dataType": "string",
                    "id": "1674954668239998980"
                }
            ],
            "id": "1674954668235804674",
            "tblDisplayName": "test-test-flow",
            "tblName": "test-test-flow"
        },
        {
            "columns": [
                {
                    "colDisplayName": "name",
                    "colName": "name",
                    "dataType": "string",
                    "id": "1706850708101890050"
                },
                {
                    "colDisplayName": "age",
                    "colName": "age",
                    "dataType": "int",
                    "id": "1706850708114472962"
                },
                {
                    "colDisplayName": "aaa",
                    "colName": "aaa",
                    "dataType": "double",
                    "id": "1706850807854481409"
                }
            ],
            "id": "1706850708055752706",
            "tblDisplayName": "test-merge",
            "tblName": "test-merge"
        }
    ],
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

获取数据源表列表
curl -X POST "https://test.datafocus.ai/df/rest/datasource/tables" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"db":"test_1","description":"","host":"192.168.0.93","jdbcSuffix":"","name":"releaseci","port":5432,"schemaName":"public","type":"postgresql","user":"postgres","password":"**********"}'

curl 返回结果:
{
    "data": [
        {
            "columns": [
                {
                    "colDisplayName": "id",
                    "colName": "id",
                    "dataType": "bigint",
                    "id": "0"
                },
                {
                    "colDisplayName": "datasource_id",
                    "colName": "datasource_id",
                    "dataType": "bigint",
                    "id": "0"
                },
                {
                    "colDisplayName": "config_key",
                    "colName": "config_key",
                    "dataType": "string",
                    "id": "0"
                },
                {
                    "colDisplayName": "config_value",
                    "colName": "config_value",
                    "dataType": "string",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "df_web_datasource_properties",
            "tblName": "df_web_datasource_properties"
        },
        {
            "columns": [
                {
                    "colDisplayName": "a1",
                    "colName": "a1",
                    "dataType": "double",
                    "id": "0"
                },
                {
                    "colDisplayName": "a2",
                    "colName": "a2",
                    "dataType": "double",
                    "id": "0"
                },
                {
                    "colDisplayName": "a3",
                    "colName": "a3",
                    "dataType": "double",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "test_v",
            "tblName": "test_v"
        },
        {
            "columns": [
                {
                    "colDisplayName": "id",
                    "colName": "id",
                    "dataType": "bigint",
                    "id": "0"
                },
                {
                    "colDisplayName": "type",
                    "colName": "type",
                    "dataType": "string",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "df_web_datasource",
            "tblName": "df_web_datasource"
        },
        {
            "columns": [
                {
                    "colDisplayName": "id",
                    "colName": "id",
                    "dataType": "int",
                    "id": "0"
                },
                {
                    "colDisplayName": "name",
                    "colName": "name",
                    "dataType": "string",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "test_2",
            "tblName": "test_2"
        },
        {
            "columns": [
                {
                    "colDisplayName": "id",
                    "colName": "id",
                    "dataType": "int",
                    "id": "0"
                },
                {
                    "colDisplayName": "name",
                    "colName": "name",
                    "dataType": "string",
                    "id": "0"
                },
                {
                    "colDisplayName": "sex",
                    "colName": "sex",
                    "dataType": "string",
                    "id": "0"
                },
                {
                    "colDisplayName": "u_id",
                    "colName": "u_id",
                    "dataType": "int",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "test_1",
            "tblName": "test_1"
        },
        {
            "columns": [
                {
                    "colDisplayName": "a1",
                    "colName": "a1",
                    "dataType": "double",
                    "id": "0"
                },
                {
                    "colDisplayName": "a2",
                    "colName": "a2",
                    "dataType": "double",
                    "id": "0"
                },
                {
                    "colDisplayName": "a3",
                    "colName": "a3",
                    "dataType": "double",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "test_num",
            "tblName": "test_num"
        },
        {
            "columns": [
                {
                    "colDisplayName": "id",
                    "colName": "id",
                    "dataType": "bigint",
                    "id": "0"
                },
                {
                    "colDisplayName": "datasource_id",
                    "colName": "datasource_id",
                    "dataType": "bigint",
                    "id": "0"
                },
                {
                    "colDisplayName": "config_key",
                    "colName": "config_key",
                    "dataType": "string",
                    "id": "0"
                },
                {
                    "colDisplayName": "config_value",
                    "colName": "config_value",
                    "dataType": "string",
                    "id": "0"
                }
            ],
            "id": "0",
            "tblDisplayName": "datasource_properties",
            "tblName": "datasource_properties"
        }
    ],
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

初始化对话
curl -X POST "https://test.datafocus.ai//df/rest/gpt/init" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"names":["电商销售数据"]}'

curl 返回结果:
{
    "data": "439f3ec2b4a54303981f11ad4dba53c3",
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

gpt对话
curl -X POST "https://test.datafocus.ai/df/rest/gpt/data" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"chatId":"439f3ec2b4a54303981f11ad4dba53c3", "input":"怎么分析数据"}'

curl 返回结果:
{
    "data": {
        "content": {
            "code": 0,
            "info": "您可以从以下几个方面进行数据分析：
1. **时间序列分析**：分析订单日期与销售数量、销售额的趋势和季节性变化。
2. **产品分析**：比较不同产品名称的销售表现，找出畅销产品。
3. **地域分析**：分析不同区域、省份、城市的销售情况，找出销售热点。
4. **客户分析**：分析不同顾客的购买行为，找出高价值客户。
5. **快递公司分析**：分析不同快递公司的服务表现，找出效率高的快递公司。
6. **收款方式分析**：分析不同收款方式的使用情况，找出常用的收款方式。

您可以根据具体的业务需求选择合适的分析方法。如果需要具体的查询或计算，请告诉我。"
        },
        "question": null,
        "type": "assistant"
    },
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

gpt查询
curl -X POST "https://test.datafocus.ai/df/rest/gpt/data" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"chatId":"439f3ec2b4a54303981f11ad4dba53c3", "input":"去年的销售数量"}'

curl 返回结果:
{
    "data": {
        "content": {
            "duration": "0.217257",
            "headers": [
                {
                    "suf": "总和",
                    "display": "销售数量(总和)",
                    "name": "销售数量"
                }
            ],
            "columns": [
                [
                    null
                ]
            ],
            "count": 1,
            "title": "去年   销售数量",
            "sql": {
                "where_clause": "",
                "order_by_clause": "",
                "select_clause": "select sum("电商销售数据"."销售数量") as col_1 from "电商销售数据" where ((("电商销售数据"."订单日期" >= try_cast('2024-01-01 00:00:00.000' as timestamp)) and ("电商销售数据"."订单日期" < try_cast('2025-01-01 00:00:00.000' as timestamp))) = true) order by sum("电商销售数据"."销售数量")",
                "from_clause": "",
                "group_by_clause": "",
                "having_clause": ""
            }
        },
        "question": "去年   销售数量",
        "type": "data"
    },
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

gpt追问
curl -X POST "https://test.datafocus.ai/df/rest/gpt/data" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"chatId":"439f3ec2b4a54303981f11ad4dba53c3", "input":"那销售金额呢"}'

curl 返回结果:
{
    "data": {
        "content": {
            "duration": "0.206304",
            "headers": [
                {
                    "suf": "总和",
                    "display": "销售金额(总和)",
                    "name": "销售金额"
                }
            ],
            "columns": [
                [
                    1159624.4
                ]
            ],
            "count": 1,
            "title": "去年   销售金额",
            "sql": {
                "where_clause": "",
                "order_by_clause": "",
                "select_clause": "select sum("电商销售数据"."销售金额") as col_1 from "电商销售数据" where ((("电商销售数据"."订单日期" >= try_cast('2024-01-01 00:00:00.000' as timestamp)) and ("电商销售数据"."订单日期" < try_cast('2025-01-01 00:00:00.000' as timestamp))) = true) order by sum("电商销售数据"."销售金额")",
                "from_clause": "",
                "group_by_clause": "",
                "having_clause": ""
            }
        },
        "question": "去年   销售金额",
        "type": "data"
    },
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

-------------------------

gpt查询
curl -X POST "https://test.datafocus.ai/df/rest/gpt/data" -H "Authorization: Bearer ZJmYzg3ZTQ3MzViNGE3OGE0ZDZlMjdiYjAwYTI4ZmMuMzA0OTg4YjI4YzM3NDJkOWEzMjUwMRksNTk0ZDQxZDU=" -H "Content-Type:application/json" -d '{"chatId":"439f3ec2b4a54303981f11ad4dba53c3", "input":"销售额的年增长率"}''

curl 返回结果:
{
    "data": {
        "content": {
            "duration": "0.414440",
            "headers": [
                {
                    "suf": "每年",
                    "display": "订单日期(每年)",
                    "name": "订单日期"
                },
                {
                    "suf": "环比的总和增长量率",
                    "display": "销售金额(环比的总和增长量率)",
                    "name": "销售金额"
                },
                {
                    "suf": "总和",
                    "display": "销售金额(前)(总和)",
                    "name": "销售金额(前)"
                },
                {
                    "suf": "总和",
                    "display": "销售金额(后)(总和)",
                    "name": "销售金额(后)"
                }
            ],
            "columns": [
                [
                    "2014-01-01 00:00:00.000",
                    null,
                    null,
                    1098535.542
                ],
                [
                    "2015-01-01 00:00:00.000",
                    -0.0094446074827131,
                    1098535.542,
                    1088160.305
                ],
                [
                    "2016-01-01 00:00:00.000",
                    -0.172273145913,
                    1088160.305,
                    900699.506
                ],
                [
                    "2017-01-01 00:00:00.000",
                    0.039591651557984,
                    900699.506,
                    936359.687
                ]
            ],
            "count": 4,
            "title": "销售金额的年增长率",
            "sql": {
                "where_clause": "",
                "order_by_clause": "",
                "select_clause": "select col_0 as col_9,case (abs(col_3)) when 0 then 0 else (try_cast((col_1 - col_3) as double))/(abs(col_3)) end as col_6,col_3 as col_7,col_1 as col_8 from (select date_trunc('year', "电商销售数据"."订单日期") as col_0,sum("电商销售数据"."销售金额") as col_1 from "电商销售数据" group by date_trunc('year', "电商销售数据"."订单日期")) tbl_1 LEFT JOIN (select sum("电商销售数据"."销售金额") as col_3,date_add('year', try_cast(1 as bigint), date_trunc('year', "电商销售数据"."订单日期")) as col_5 from "电商销售数据" group by date_add('year', try_cast(1 as bigint), date_trunc('year', "电商销售数据"."订单日期"))) tbl_3 on col_0=col_5 order by col_0",
                "from_clause": "",
                "group_by_clause": "",
                "having_clause": ""
            }
        },
        "question": "销售金额的年增长率",
        "type": "data"
    },
    "errCode": 0,
    "exception": "",
    "msgParams": null,
    "promptMsg": null,
    "success": true
}

GPT TEXT TO DATA 接口文档 ​

接口信息： ​

PATH：/df/rest/table/list ​

METHOD： GET ​

获取系统中所有已导入表信息 ​

请求参数 ​

Responses Data Schema ​

PATH：/df/rest/datasource/tables ​

METHOD： POST ​

获取数据源的所有表信息 ​

Params ​

Responses Data Schema ​

Data Schema ​

数据源配置参数

Attribute ​

数据表信息

Attribute ​

基本响应对象

Attribute ​

PATH：/df/rest/gpt/init ​

METHOD： POST ​

初始化上下文[注册表、选表] ​

请求参数 ​

说明 ​

枚举值 ​

返回数据结构 ​

PATH：/df/rest/gpt/data ​

METHOD： POST ​

对话查询 ​

请求参数 ​

返回数据结构 ​

枚举值 ​

Bearer token 获取方式如下图: ​

curl 样例： ​

GPT TEXT TO DATA 接口文档

接口信息：

PATH：/df/rest/table/list

METHOD： GET

获取系统中所有已导入表信息

请求参数

Responses Data Schema

PATH：/df/rest/datasource/tables

METHOD： POST

获取数据源的所有表信息

Params

Responses Data Schema

Data Schema

Attribute

Attribute

Attribute

PATH：/df/rest/gpt/init

METHOD： POST

初始化上下文[注册表、选表]

请求参数

说明

枚举值

返回数据结构

PATH：/df/rest/gpt/data

METHOD： POST

对话查询

请求参数

返回数据结构

枚举值

Bearer token 获取方式如下图:

curl 样例：