> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ai.relyt.cn/llms.txt
> Use this file to discover all available pages before exploring further.
> 获取指定会话中的任务记录。
# Get job history in session
**请求示例：**
```curl cURL theme={null}
curl --request GET \
--url https://app.ai.relyt.cn/app/api/v2/team/sessions/{id}/history?user_id=tmm-dafasdfasdfasdf \
--header 'x-pd-api-key: '
```
```python Python theme={null}
import requests
url = "https://app.ai.relyt.cn/app/api/v2/team/sessions/{id}/history?user_id=tmm-dafasdfasdfasdf"
headers = {"x-pd-api-key": ""}
response = requests.request("GET", url, headers=headers)
print(response.text)
```
**返回示例：**
````json 200 theme={null}
{
"code": 0,
"data": {
"total_items": 1,
"page_number": 1,
"page_size": 10,
"records": [
{
"job_id": "job-1dsfasddfasgddsaffds",
"question": {
"blocks": [
{
"type": "MESSAGE",
"content": "Check for negative values across all sales columns"
}
]
},
"answer": {
"blocks": [
{
"type": "MESSAGE",
"content": "- Check for negative values across all sales columns.\n- Filter the DataFrame to retain only rows with no negative sales values.",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(input_0: pd.DataFrame) -> pd.DataFrame:\n '''\n input_0: pd.DataFrame SalesByChannelByDay_Summary_Demo.Sheet1_0_table_1.csv\n '''\n # Identify columns that represent sales data (all except the first column)\n sales_columns = input_0.columns[1:]\n \n # Filter rows where any sales value is negative\n filtered_df = input_0[~(input_0[sales_columns] < 0).any(axis=1)]\n \n # Assign the result to the output variable\n output = filtered_df\n \n return output\n\n```",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/relytaitest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/baf7d6d1-fb81-4fdb-bcdd-32923d214c7b.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=599&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9bcb5af552793f162e35f41d62fb9306cf90888924bfbdce81ea687265fddf83",
"group_id": "ba582fc9-bb36-4c5d-a8e8-d35bda6389cd",
"group_name": "Identify the channels with negative sales values by examining each day's sales data. Filter out the rows where any sales value is negative.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Sum the sales across all days for each channel.\n- Create a new DataFrame with the channel names and their corresponding total sales.",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(negative_sales_channels: pd.DataFrame) -> pd.DataFrame:\n '''\n negative_sales_channels: pd.DataFrame negative_sales_channels.csv\n '''\n # Sum the sales across all days for each channel\n total_sales = negative_sales_channels.iloc[:, 1:].sum(axis=1)\n \n # Create a new DataFrame with the channel names and their corresponding total sales\n output = pd.DataFrame({\n 'Channel': negative_sales_channels.iloc[:, 0],\n 'Total Sales': total_sales\n })\n \n return output\n\n```",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/relytaitest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/10cffac2-8bf3-45f4-86e6-1ed8457329f2.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c6f5b522d2ddceea730304b86a45d5f5165f05f9fda3c1d275d11e9022c1e7ac",
"group_id": "47183fd1-307b-4408-9986-e9238d952ec1",
"group_name": "Calculate the overall sales trend for the identified channels with negative sales values. This involves summing up the sales across all days for each channel and analyzing the trend.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Replace any negative sales values with zero in the data.\n- Sum the sales across all days for each channel.\n- Create a new data structure with the summed sales values.",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(negative_sales_channels: pd.DataFrame) -> pd.DataFrame:\n '''\n negative_sales_channels: pd.DataFrame negative_sales_channels.csv\n '''\n # Replace negative values with zero\n negative_sales_channels.iloc[:, 1:] = negative_sales_channels.iloc[:, 1:].clip(lower=0)\n \n # Sum the sales across all days for each channel\n sales_sum = negative_sales_channels.iloc[:, 1:].sum(axis=1)\n \n # Create a new DataFrame with the summed sales values\n output = pd.DataFrame({\n 'Channel': negative_sales_channels.iloc[:, 0],\n 'Total Sales': sales_sum\n })\n \n return output\n\n```",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/relytaitest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/f4c99616-dd7c-48b1-8a35-d3141d732c36.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d21a8d939ab09547bc201754ba253ca6c0d1da5361752f2228237e3ff59be256",
"group_id": "6b93c2b1-8908-4c2b-afb2-2a81f2d24739",
"group_name": "Calculate the overall sales trend for the same channels but excluding the negative sales values. This involves setting negative values to zero or removing them and then summing up the sales across all days for each channel.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "- Merge the two datasets on the 'Channel' column to align sales data for comparison.\n- Calculate the difference in 'Total Sales' between the datasets for each channel.\n- Store the results, including channel name and calculated difference, in a new dataset.",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "CODE",
"content": "```python\n\nimport pandas as pd\n\ndef invoke(sales_trend_with_negatives: pd.DataFrame, sales_trend_without_negatives: pd.DataFrame) -> pd.DataFrame:\n # Merge the two DataFrames on the 'Channel' column\n merged_df = pd.merge(sales_trend_with_negatives, sales_trend_without_negatives, on='Channel', suffixes=('_with_negatives', '_without_negatives'))\n \n # Calculate the difference in 'Total Sales' between the two DataFrames\n merged_df['Sales Difference'] = merged_df['Total Sales_without_negatives'] - merged_df['Total Sales_with_negatives']\n \n # Create a new DataFrame to store the results\n output = merged_df[['Channel', 'Sales Difference']]\n \n return output\n\n```",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/relytaitest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52",
"group_id": "3488f538-f7fc-4c0e-a265-b66e3a38d41e",
"group_name": "Compare the sales trends with and without negative sales values to determine the impact of negative sales on the overall sales trend for the affected channels.",
"stage": "Analyze"
},
{
"type": "MESSAGE",
"content": "\n\n`Analyzing Conclusions` \n\n### The impact of negative sales values on overall sales trends\n\n#### Sales variance analysis\n\n",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "TABLE",
"content": "https://s3.amazonaws.com/relytaitest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "MESSAGE",
"content": "\n\n- **Sales variance**：In all channels (including EC, JD, Tmall, WeChat, retail, corporate stores, outlets, and total), the sales difference is 0.0. This indicates that regardless of the presence of negative sales values, the sales trend has not changed.\n\n#### Conclusion and Insights\n- **The impact of negative sales values**：Based on the provided data, negative sales values have no impact on the overall sales trend of the affected channels, as the sales variance for all channels is 0.0.\n- **Data consistency**：The sales discrepancies across all channels are consistent, indicating that there are no anomalies or deviations caused by negative sales values during data processing or analysis.",
"group_id": "fd1a62e6-48cf-4ac1-8bac-025665444710",
"group_name": "Conclusions",
"stage": "Respond"
},
{
"type": "SOURCES",
"content": [
{
"source": "SalesByChannelByDay_Summary_Demo.xlsx",
"datasource_id": "cm2ej4wmo000001fcdkwbdrml",
"dataset_id": "cm2ej4vx900hp01l1o378zr9o",
"file_type": "xlsx",
"external_id": ""
}
],
"group_id": "",
"group_name": "",
"stage": "Respond"
},
{
"type": "QUESTIONS",
"content": [
"Analyze the specific channels with negative sales values on different dates and discuss whether the sales strategies of these channels might lead to negative values.",
"Study the long-term impact of negative sales values on overall sales trends and assess whether adjustments to data analysis methods are needed to more accurately reflect the actual situation.",
"Investigate the source of negative sales values, whether they are related to returns, discounts, or other factors, and propose possible solutions to reduce the occurrence of negative values."
],
"group_id": "-1",
"stage": "Respond"
}
]
}
}
]
}
}
````
## OpenAPI
````yaml get /v2/team/sessions/{id}/history
openapi: 3.0.1
info:
title: Team Open API Version 2 中文版本
description: ''
version: 1.0.0
servers:
- url: https://app.ai.relyt.cn/app/api
description: 体验环境
security:
- apikey-header-x-pd-api-key: []
tags:
- name: Session
- name: Dataset
- name: Data source
- name: File
- name: Job
paths:
/v2/team/sessions/{id}/history:
get:
tags:
- Session
summary: Get job history in session
description: 获取指定会话中的任务记录。
parameters:
- name: id
in: path
description: >-
目标会话的 ID。
如需查看项目中存在的会话，请调用 [GET
/v2/team/sessions](/api-reference/list-sessions) 接口。
required: true
schema:
type: string
- name: page_number
in: query
description: 分页返回的起始页码。如不指定，则使用默认值 `1`。
required: false
schema:
type: integer
- name: page_size
in: query
description: 每页返回的记录数量。如不指定，则使用默认值 `10`。
required: false
schema:
type: integer
- name: user_id
in: query
description: 用户 ID，即您在组织中的唯一身份标识。
required: true
schema:
type: string
- name: x-pd-external-trace-id
in: header
description: >-
您本地系统中设置的 Trace ID，至多支持 128 个字符。当请求发生错误时，可以将此 ID 提供给 Relyt AI
团队，协助进行故障排查。
required: false
example: ''
schema:
type: string
responses:
'200':
description: ''
content:
application/json:
schema:
type: object
properties:
code:
type: integer
description: >-
状态码。**0** 表示操作成功。其它值则表示操作失败。如需进行错误排查，请参阅
[错误码](/introduction/error-codes)。
data:
type: object
properties:
total_items:
type: integer
description: 返回的任务记录总数量。
page_number:
type: integer
description: 当前页面的页码。
page_size:
type: integer
description: 每页返回的记录数量。
records:
type: array
items:
description: 任务对象。
type: object
properties:
question:
description: 您的提问（即提示词）。
type: object
properties:
blocks:
type: array
items:
type: object
properties:
type:
type: string
description: >-
The content type of the question block.
Possible values are:
- `MESSAGE`: The content is a piece of
text.
- `CODE`: The content a code snippet in
Markdown format.
enum:
- MESSAGE
- CODE
content:
type: string
description: >-
The block content. It is a piece of text
when the `type` is `MESSAGE`, and a code
snippet when the `type` is `CODE`.
description: 每个问题块的内容。
required:
- type
- content
description: 构成整个问题的内容块列表。
required:
- blocks
answer:
description: Relyt AI 的回答。
type: object
properties:
blocks:
type: array
items:
$ref: '#/components/schemas/BlockDTO'
description: 构成整个答案的内容块列表。
description: Relyt AI 的答案。
required:
- blocks
job_id:
type: string
description: 任务 ID，即任务在当前会话中的唯一标识。
required:
- question
- answer
- job_id
description: 当前页面返回的任务记录列表。
required:
- total_items
- page_number
- page_size
- records
description: 返回的任务记录的分页列表。
required:
- data
- code
examples:
'1':
summary: 成功示例
value:
code: 0
data:
total_items: 1
page_number: 1
page_size: 10
records:
- job_id: job-1dsfasddfasgddsaffds
question:
blocks:
- type: MESSAGE
content: >-
Check for negative values across all sales
columns
answer:
blocks:
- type: MESSAGE
content: >-
- Check for negative values across all sales
columns.
- Filter the DataFrame to retain only rows
with no negative sales values.
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales
values by examining each day's sales data.
Filter out the rows where any sales value is
negative.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(input_0: pd.DataFrame) ->
pd.DataFrame:
'''
input_0: pd.DataFrame SalesByChannelByDay_Summary_Demo.Sheet1_0_table_1.csv
'''
# Identify columns that represent sales data (all except the first column)
sales_columns = input_0.columns[1:]
# Filter rows where any sales value is negative
filtered_df = input_0[~(input_0[sales_columns] < 0).any(axis=1)]
# Assign the result to the output variable
output = filtered_df
return output
```
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales
values by examining each day's sales data.
Filter out the rows where any sales value is
negative.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/baf7d6d1-fb81-4fdb-bcdd-32923d214c7b.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=599&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9bcb5af552793f162e35f41d62fb9306cf90888924bfbdce81ea687265fddf83
group_id: ba582fc9-bb36-4c5d-a8e8-d35bda6389cd
group_name: >-
Identify the channels with negative sales
values by examining each day's sales data.
Filter out the rows where any sales value is
negative.
stage: Analyze
- type: MESSAGE
content: >-
- Sum the sales across all days for each
channel.
- Create a new DataFrame with the channel
names and their corresponding total sales.
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales
values. This involves summing up the sales
across all days for each channel and analyzing
the trend.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(negative_sales_channels:
pd.DataFrame) -> pd.DataFrame:
'''
negative_sales_channels: pd.DataFrame negative_sales_channels.csv
'''
# Sum the sales across all days for each channel
total_sales = negative_sales_channels.iloc[:, 1:].sum(axis=1)
# Create a new DataFrame with the channel names and their corresponding total sales
output = pd.DataFrame({
'Channel': negative_sales_channels.iloc[:, 0],
'Total Sales': total_sales
})
return output
```
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales
values. This involves summing up the sales
across all days for each channel and analyzing
the trend.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/10cffac2-8bf3-45f4-86e6-1ed8457329f2.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c6f5b522d2ddceea730304b86a45d5f5165f05f9fda3c1d275d11e9022c1e7ac
group_id: 47183fd1-307b-4408-9986-e9238d952ec1
group_name: >-
Calculate the overall sales trend for the
identified channels with negative sales
values. This involves summing up the sales
across all days for each channel and analyzing
the trend.
stage: Analyze
- type: MESSAGE
content: >-
- Replace any negative sales values with zero
in the data.
- Sum the sales across all days for each
channel.
- Create a new data structure with the summed
sales values.
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales
values. This involves setting negative values
to zero or removing them and then summing up
the sales across all days for each channel.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(negative_sales_channels:
pd.DataFrame) -> pd.DataFrame:
'''
negative_sales_channels: pd.DataFrame negative_sales_channels.csv
'''
# Replace negative values with zero
negative_sales_channels.iloc[:, 1:] = negative_sales_channels.iloc[:, 1:].clip(lower=0)
# Sum the sales across all days for each channel
sales_sum = negative_sales_channels.iloc[:, 1:].sum(axis=1)
# Create a new DataFrame with the summed sales values
output = pd.DataFrame({
'Channel': negative_sales_channels.iloc[:, 0],
'Total Sales': sales_sum
})
return output
```
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales
values. This involves setting negative values
to zero or removing them and then summing up
the sales across all days for each channel.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/f4c99616-dd7c-48b1-8a35-d3141d732c36.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d21a8d939ab09547bc201754ba253ca6c0d1da5361752f2228237e3ff59be256
group_id: 6b93c2b1-8908-4c2b-afb2-2a81f2d24739
group_name: >-
Calculate the overall sales trend for the same
channels but excluding the negative sales
values. This involves setting negative values
to zero or removing them and then summing up
the sales across all days for each channel.
stage: Analyze
- type: MESSAGE
content: >-
- Merge the two datasets on the 'Channel'
column to align sales data for comparison.
- Calculate the difference in 'Total Sales'
between the datasets for each channel.
- Store the results, including channel name
and calculated difference, in a new dataset.
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without
negative sales values to determine the impact
of negative sales on the overall sales trend
for the affected channels.
stage: Analyze
- type: CODE
content: >-
```python
import pandas as pd
def invoke(sales_trend_with_negatives:
pd.DataFrame, sales_trend_without_negatives:
pd.DataFrame) -> pd.DataFrame:
# Merge the two DataFrames on the 'Channel' column
merged_df = pd.merge(sales_trend_with_negatives, sales_trend_without_negatives, on='Channel', suffixes=('_with_negatives', '_without_negatives'))
# Calculate the difference in 'Total Sales' between the two DataFrames
merged_df['Sales Difference'] = merged_df['Total Sales_without_negatives'] - merged_df['Total Sales_with_negatives']
# Create a new DataFrame to store the results
output = merged_df[['Channel', 'Sales Difference']]
return output
```
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without
negative sales values to determine the impact
of negative sales on the overall sales trend
for the affected channels.
stage: Analyze
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52
group_id: 3488f538-f7fc-4c0e-a265-b66e3a38d41e
group_name: >-
Compare the sales trends with and without
negative sales values to determine the impact
of negative sales on the overall sales trend
for the affected channels.
stage: Analyze
- type: MESSAGE
content: >+
`Analyzing Conclusions`
### The impact of negative sales values on
overall sales trends
#### Sales variance analysis
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: TABLE
content: >-
https://s3.amazonaws.com/powerdrilltest/tmp_datasource_cache/code_result/clvl4cad2001q01l1m522hxlu/aaf4f2f7-e2db-4f2e-98ae-0bdd18f42333.csv?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241018T104617Z&X-Amz-SignedHeaders=host&X-Amz-Expires=600&X-Amz-Credential=AKIARLSQLXURHEIDN4OZ%2F20241018%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19fcfe97c70bf40292b920ecbf0299c101e1de11e49b54fb8f62934b6e874f52
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: MESSAGE
content: >-
- **Sales variance**：In all channels
(including EC, JD, Tmall, WeChat, retail,
corporate stores, outlets, and total), the
sales difference is 0.0. This indicates that
regardless of the presence of negative sales
values, the sales trend has not changed.
#### Conclusion and Insights
- **The impact of negative sales
values**：Based on the provided data, negative
sales values have no impact on the overall
sales trend of the affected channels, as the
sales variance for all channels is 0.0.
- **Data consistency**：The sales discrepancies
across all channels are consistent, indicating
that there are no anomalies or deviations
caused by negative sales values during data
processing or analysis.
group_id: fd1a62e6-48cf-4ac1-8bac-025665444710
group_name: Conclusions
stage: Respond
- type: SOURCES
content:
- source: SalesByChannelByDay_Summary_Demo.xlsx
datasource_id: cm2ej4wmo000001fcdkwbdrml
dataset_id: cm2ej4vx900hp01l1o378zr9o
file_type: xlsx
external_id: ''
group_id: ''
group_name: ''
stage: Respond
- type: QUESTIONS
content:
- >-
Analyze the specific channels with negative
sales values on different dates and discuss
whether the sales strategies of these
channels might lead to negative values.
- >-
Study the long-term impact of negative sales
values on overall sales trends and assess
whether adjustments to data analysis methods
are needed to more accurately reflect the
actual situation.
- >-
Investigate the source of negative sales
values, whether they are related to returns,
discounts, or other factors, and propose
possible solutions to reduce the occurrence
of negative values.
group_id: '-1'
stage: Respond
headers:
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example: ''
required: true
description: Relyt AI 返回的 Trace ID。当请求发生错误时，可以将此 ID 提供给 Relyt AI 团队，协助进行故障排查。
schema:
type: string
deprecated: false
security:
- apikey-header-x-pd-api-key: []
components:
schemas:
BlockDTO:
type: object
properties:
type:
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答案块（Block）的内容类型。可能值为：
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- `QUESTIONS`：即 Relyt AI 生成的建议问题，帮助引导您后续的数据探索和分析。
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- 当 `type` 为 `QUESTIONS` 时，内容为 Relyt AI 生成的建议问题，帮助您引导后续的数据探索与分析。
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Relyt AI 生成答案的过程分为两个阶段：`Analyze` 和 `Respond`。
- `Analyze` 阶段的答案块并非最终答案的构成部分，为分析过程中的输出，旨在帮助您理解答案的生成方式。
- `Respond` 阶段的答案块是 Relyt AI 基于您的问题生成的最终答复。
required:
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- content
- group_id
- group_name
- stage
securitySchemes:
apikey-header-x-pd-api-key:
type: apiKey
in: header
name: x-pd-api-key
````