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Unverified
Commit
aab6ee51
authored
Jan 22, 2024
by
Archer
Committed by
GitHub
Jan 22, 2024
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V4.6.7-production (#759)
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1622 additions
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1958 deletions
+1622
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README.md
+6
-6
README_en.md
+46
-42
docSite/content/docs/development/openapi/chat.md
+2
-2
docSite/content/docs/development/openapi/dataset.md
+844
-1022
docSite/content/docs/development/upgrading/467.md
+5
-4
packages/global/common/error/utils.ts
+3
-1
packages/global/common/string/tools.ts
+6
-0
packages/service/common/file/utils.ts
+1
-1
packages/service/common/response/index.ts
+3
-2
packages/service/support/permission/controller.ts
+4
-0
packages/web/components/common/Icon/icons/support/account/loginoutLight.svg
+13
-2
projects/app/public/docs/versionIntro.md
+6
-5
projects/app/public/locales/zh/common.json
+3
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projects/app/src/components/ChatBox/MessageInput.tsx
+20
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projects/app/src/components/ChatBox/index.tsx
+413
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projects/app/src/components/Markdown/index.tsx
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projects/app/src/components/common/Textarea/TagTextarea.tsx
+2
-0
projects/app/src/components/core/module/AIChatSettingsModal.tsx
+9
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projects/app/src/components/support/user/team/TeamManageModal/InviteModal.tsx
+7
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projects/app/src/components/support/user/team/TeamManageModal/index.tsx
+2
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projects/app/src/pages/account/components/Info.tsx
+24
-20
projects/app/src/pages/api/core/dataset/collection/create/file.ts
+0
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projects/app/src/pages/api/core/dataset/collection/create/link.ts
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projects/app/src/pages/api/core/dataset/collection/create/text.ts
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projects/app/src/pages/api/core/dataset/create.ts
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projects/app/src/pages/api/core/dataset/delete.ts
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projects/app/src/pages/api/plusApi/[...path].ts
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projects/app/src/pages/api/proApi/[...path].ts
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projects/app/src/pages/dataset/detail/index.tsx
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projects/app/src/pages/dataset/list/index.tsx
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projects/app/src/pages/login/provider.tsx
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projects/app/src/web/common/hooks/useConfirm.tsx
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projects/app/src/web/core/dataset/api.ts
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projects/app/src/web/core/dataset/utils.ts
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projects/app/src/web/support/activity/promotion/api.ts
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projects/app/src/web/support/user/api.ts
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projects/app/src/web/support/user/inform/api.ts
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projects/app/src/web/support/user/team/api.ts
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projects/app/src/web/support/wallet/bill/api.ts
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projects/app/src/web/support/wallet/pay/api.ts
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projects/app/src/web/support/wallet/sub/api.ts
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README.md
View file @
aab6ee51
...
...
@@ -57,7 +57,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
-
[
x
]
源文件引用追踪
-
[
x
]
模块封装，实现多级复用
-
[
x
]
混合检索 & 重排
-
[
]
自查询规划
-
[
]
Tool 模块
-
[
]
嵌入
[
Laf
](
https://github.com/labring/laf
)
，实现在线编写 HTTP 模块
-
[
]
插件封装功能
...
...
@@ -67,10 +67,10 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
-
[
x
]
支持知识库单独设置向量模型
-
[
x
]
源文件存储
-
[
x
]
支持手动输入，直接分段，QA 拆分导入
-
[
x
]
支持 pdf、word、txt、md 等常用文件，支持 url 读取、CSV 批量导入
-
[
]
支持 HTML、csv、PPT、Excel 导入
-
[
x
]
支持 pdf，docx，txt，html，md，csv
-
[
x
]
支持 url 读取、CSV 批量导入
-
[
]
支持 PPT、Excel 导入
-
[
]
支持文件阅读器
-
[
]
支持差异性文件同步
-
[
]
更多的数据预处理方案
`3`
应用调试能力
...
...
@@ -81,8 +81,8 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
-
[
]
高级编排 DeBug 模式
`4`
OpenAPI 接口
-
[
x
]
completions 接口 (对齐 GPT 接口)
-
[
]
知识库 CRUD
-
[
x
]
completions 接口 (
chat 模式
对齐 GPT 接口)
-
[
x
]
知识库 CRUD
-
[
]
对话 CRUD
`5`
运营能力
...
...
README_en.md
View file @
aab6ee51
...
...
@@ -49,48 +49,52 @@ Cloud: [fastgpt.in](https://fastgpt.in/)
## 💡 Features
1.
Powerful visual workflows: Effortlessly craft AI applications
-
[
x
]
Simple mode on deck - no need for manual arrangement
-
[
x
]
User dialogue pre-guidance
-
[
x
]
Global variables
-
[
x
]
Knowledge base search
-
[
x
]
Dialogue via multiple LLM models
-
[
x
]
Text magic - convert to structured data
-
[
x
]
Extend with HTTP
-
[
]
Embed Laf for on-the-fly HTTP module crafting
-
[
x
]
Directions for the next dialogue steps
-
[
x
]
Tracking source file references
-
[
]
Custom file reader
-
[
]
Modules are packaged into plug-ins to achieve reuse
2.
Extensive knowledge base preprocessing
-
[
x
]
Reuse and mix multiple knowledge bases
-
[
x
]
Track chunk modifications and deletions
-
[
x
]
Supports manual entries, direct segmentation, and QA split imports
-
[
x
]
Supports URL fetching and batch CSV imports
-
[
x
]
Supports Set unique vector models for knowledge bases
-
[
x
]
Store original files
-
[
]
File learning Agent
3.
Multiple effect testing channels
-
[
x
]
Single-point knowledge base search test
-
[
x
]
Feedback references and ability to modify and delete during dialogue
-
[
x
]
Complete context presentation
-
[
]
Complete module intermediate value presentation
4.
OpenAPI
-
[
x
]
completions interface (aligned with GPT interface)
-
[
]
Knowledge base CRUD
5.
Operational functions
-
[
x
]
Login-free sharing window
-
[
x
]
One-click embedding with Iframe
-
[
]
Unified access to dialogue records
`1`
Application Orchestration Features
-
[
x
]
Offers a straightforward mode, eliminating the need for complex orchestration
-
[
x
]
Provides clear next-step instructions in dialogues
-
[
x
]
Facilitates workflow orchestration
-
[
x
]
Tracks references in source files
-
[
x
]
Encapsulates modules for enhanced reuse at multiple levels
-
[
x
]
Combines search and reordering functions
-
[
]
Includes a tool module
-
[
]
Integrates
[
Laf
](
https://github.com/labring/laf
)
for online HTTP module creation
-
[
]
Plugin encapsulation capabilities
`2`
Knowledge Base Features
-
[
x
]
Allows for the mixed use of multiple databases
-
[
x
]
Keeps track of modifications and deletions in data chunks
-
[
x
]
Enables specific vector models for each knowledge base
-
[
x
]
Stores original source files
-
[
x
]
Supports direct input and segment-based QA import
-
[
x
]
Compatible with a variety of file formats: pdf, docx, txt, html, md, csv
-
[
x
]
Facilitates URL reading and bulk CSV importing
-
[
]
Supports PPT and Excel file import
-
[
]
Features a file reader
-
[
]
Offers diverse data preprocessing options
`3`
Application Debugging Features
-
[
x
]
Enables targeted search testing within the knowledge base
-
[
x
]
Allows feedback, editing, and deletion during conversations
-
[
x
]
Presents the full context of interactions
-
[
x
]
Displays all intermediate values within modules
-
[
]
Advanced DeBug mode for orchestration
`4`
OpenAPI Interface
-
[
x
]
The completions interface (aligned with GPT's chat mode interface)
-
[
x
]
CRUD operations for the knowledge base
-
[
]
CRUD operations for conversations
`5`
Operational Features
-
[
x
]
Share without requiring login
-
[
x
]
Easy embedding with Iframe
-
[
x
]
Customizable chat window embedding with features like default open, drag-and-drop
-
[
x
]
Centralizes conversation records for review and annotation
<a
href=
"#readme"
>
<img
src=
"https://img.shields.io/badge/-Back_to_Top-7d09f1.svg"
alt=
"#"
align=
"right"
>
...
...
docSite/content/docs/development/openapi/chat.md
View file @
aab6ee51
...
...
@@ -48,7 +48,7 @@ curl --location --request POST 'https://api.fastgpt.in/api/v1/chat/completions'
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"
detail=true 响应
"
>
}}
{{
<
tab
tabName=
"
参数说明
"
>
}}
{{
<
markdownify
>
}}
{{% alert context="info" %}}
...
...
@@ -56,7 +56,7 @@ curl --location --request POST 'https://api.fastgpt.in/api/v1/chat/completions'
-
chatId: string | undefined 。
-
为
`undefined`
时（不传入），不使用 FastGpt 提供的上下文功能，完全通过传入的 messages 构建上下文。 不会将你的记录存储到数据库中，你也无法在记录汇总中查阅到。
-
为
`非空字符串`
时，意味着使用 chatId 进行对话，自动从 FastGpt 数据库取历史记录，并使用 messages 数组最后一个内容作为用户问题。请自行确保 chatId 唯一，长度小于250，通常可以是自己系统的对话框ID。
-
messages: 结构与
[
GPT接口
](
https://platform.openai.com/docs/api-reference/chat/object
)
完全
一致。
-
messages: 结构与
[
GPT接口
](
https://platform.openai.com/docs/api-reference/chat/object
)
chat模式
一致。
-
detail: 是否返回中间值（模块状态，响应的完整结果等），
`stream模式`
下会通过
`event`
进行区分，
`非stream模式`
结果保存在
`responseData`
中。
-
variables: 模块变量，一个对象，会替换模块中，输入框内容里的
`{{key}}`
{{% /alert %}}
...
...
docSite/content/docs/development/openapi/dataset.md
View file @
aab6ee51
...
...
@@ -15,7 +15,9 @@ weight: 853
## 创建训练订单
**请求示例**
{{
<
tabs
tabTotal=
"2"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
POST
'https://api.fastgpt.in/api/support/wallet/bill/createTrainingBill'
\
...
...
@@ -26,7 +28,11 @@ curl --location --request POST 'https://api.fastgpt.in/api/support/wallet/bill/c
}'
```
**响应结果**
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
data 为 billId，可用于添加知识库数据时进行账单聚合。
...
...
@@ -39,35 +45,30 @@ data 为 billId，可用于添加知识库数据时进行账单聚合。
}
```
## 知识库添加数据
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
## 知识库
{{
<
tabs
tabTotal=
"4"
>
}}
### 创建一个知识库
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
POST
'http
s://api.fastgpt.in/api/core/dataset/data/pushData
'
\
--header
'Authorization: Bearer
apikey
'
\
curl
--location
--request
POST
'http
://localhost:3000/api/core/dataset/create
'
\
--header
'Authorization: Bearer
{{authorization}}
'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"collectionId": "64663f451ba1676dbdef0499",
"trainingMode": "chunk",
"prompt": "可选。qa 拆分引导词，chunk 模式下忽略",
"billId": "可选。如果有这个值，本次的数据会被聚合到一个订单中，这个值可以重复使用。可以参考 [创建训练订单] 获取该值。",
"data": [
{
"q": "你是谁？",
"a": "我是FastGPT助手"
},
{
"q": "你会什么？",
"a": "我什么都会",
"indexes": [{
"type":"custom",
"text":"你好"
}]
}
]
"parentId": null,
"type": "dataset",
"name":"测试",
"intro":"介绍",
"avatar": "",
"vectorModel": "text-embedding-ada-002",
"agentModel": "gpt-3.5-turbo-16k"
}'
```
...
...
@@ -77,101 +78,229 @@ curl --location --request POST 'https://api.fastgpt.in/api/core/dataset/data/pus
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
需要先了解 FastGPT 的多路索引概念：
{{% alert icon=" " context="success" %}}
-
parentId - 父级ID，用于构建目录结构。通常可以为 null 或者直接不传。
-
type -
`dataset`
或者
`folder`
，代表普通知识库和文件夹。不传则代表创建普通知识库。
-
name - 知识库名（必填）
-
intro - 介绍（可选）
-
avatar - 头像地址（可选）
-
vectorModel - 向量模型（建议传空，用系统默认的）
-
agentModel - 文本处理模型（建议传空，用系统默认的）
{{% /alert %}}
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
```
json
{
"code"
:
200
,
"statusText"
:
""
,
"message"
:
""
,
"data"
:
"65abc9bd9d1448617cba5e6c"
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
### 获取知识库列表
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
GET
'http://localhost:3000/api/core/dataset/list?parentId='
\
--header
'Authorization: Bearer {{authorization}}'
\
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
{{% alert icon=" " context="success" %}}
-
parentId - 父级ID，不传或为空，代表获取根目录下的知识库
{{% /alert %}}
在 FastGPT 中，你可以为一组数据创建多个索引，如果不指定索引，则系统会自动取对应的 chunk 作为索引。例如前面的请求示例中：
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
`q：你是谁？a:我是FastGPT助手`
它的
`indexes`
属性为空，意味着不自定义索引，而是使用默认的索引（你是谁？
\n
我是FastGPT助手）。
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
在第二组数据中
`q:你会什么？a:我什么都会`
指定了一个
`你好`
的索引，因此这组数据的索引为
`你好`
。
```json
{
"co
llectionId"
:
"文件集合的ID，参考上面的第二张图"
,
"
mode"
:
"chunk | qa "
,
//
chunk
模式
:
可自定义索引。qa
模型：无法自定义索引，会自动取
data
中的
q
作为数据，让模型自动生成问答对和索引。
"
prompt"
:
"QA 拆分提示词，需严格按照模板，建议不要传入。
"
,
"co
de": 200
,
"
statusText": "",
"
message": "
",
"data": [
{
"q"
:
"生成索引的内容，index 模式下最大 tokens 为3000，建议不超过 1000"
,
"a"
:
"预期回答/补充"
,
"indexes"
:
"自定义索引"
,
},
{
"q"
:
"xxx"
,
"a"
:
"xxxx"
"_id": "65abc9bd9d1448617cba5e6c",
"parentId": null,
"avatar": "",
"name": "测试",
"intro": "",
"type": "dataset",
"permission": "private",
"canWrite": true,
"isOwner": true,
"vectorModel": {
"model": "text-embedding-ada-002",
"name": "Embedding-2",
"inputPrice": 0,
"defaultToken": 512,
"maxToken": 8000,
"weight": 100
}
}
],
]
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
{{
<
tab
tabName=
"响应例子"
>
}}
### 获取知识库详情
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
GET
'http://localhost:3000/api/core/dataset/detail?id=6593e137231a2be9c5603ba7'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
{{% alert icon=" " context="success" %}}
-
id: 知识库的ID
{{% /alert %}}
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": {
"insertLen"
:
1
,
//
最终插入成功的数量
"overToken"
:
[],
//
超出
token
的
"repeat"
:
[],
//
重复的数量
"error"
:
[]
//
其他错误
"_id": "6593e137231a2be9c5603ba7",
"parentId": null,
"teamId": "65422be6aa44b7da77729ec8",
"tmbId": "65422be6aa44b7da77729ec9",
"type": "dataset",
"status": "active",
"avatar": "/icon/logo.svg",
"name": "FastGPT test",
"vectorModel": {
"model": "text-embedding-ada-002",
"name": "Embedding-2",
"inputPrice": 0,
"defaultToken": 512,
"maxToken": 8000,
"weight": 100
},
"agentModel": {
"model": "gpt-3.5-turbo-16k",
"name": "FastAI-16k",
"maxContext": 16000,
"maxResponse": 16000,
"inputPrice": 0,
"outputPrice": 0
},
"intro": "",
"permission": "private",
"updateTime": "2024-01-02T10:11:03.084Z",
"canWrite": true,
"isOwner": true
}
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
{{
<
tab
tabName=
"QA Prompt 模板"
>
}}
{{
<
markdownify
>
}}
### 删除一个知识库
{{theme}} 里的内容可以换成数据的主题。默认为：它们可能包含多个主题内容
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
DELETE
'http://localhost:3000/api/core/dataset/delete?id=65abc8729d1448617cba5df6'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
我会给你一段文本，{{theme}}，学习它们，并整理学习成果，要求为：
1. 提出最多 25 个问题。
2. 给出每个问题的答案。
3. 答案要详细完整，答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 markdown 元素。
4. 按格式返回多个问题和答案:
Q1: 问题。
A1: 答案。
Q2:
A2:
……
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
我的文本："""{{text}}"""
```
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
{{% alert icon=" " context="success" %}}
-
id: 知识库的ID
{{% /alert %}}
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
```json
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
## 搜索测试
## 集合
### 创建一个空的集合
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
POST
'http
s://api.fastgpt.in/api/core/dataset/searchTest
'
\
--header
'Authorization: Bearer
fastgpt-xxxxx
'
\
curl
--location
--request
POST
'http
://localhost:3000/api/core/dataset/collection/create
'
\
--header
'Authorization: Bearer
{{authorization}}
'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"datasetId": "知识库的ID",
"text": "导演是谁",
"limit": 5000,
"similarity": 0,
"searchMode": "embedding",
"usingReRank": false
"datasetId":"6593e137231a2be9c5603ba7",
"parentId": null,
"name":"测试",
"type":"virtual",
"metadata":{
"test":111
}
}'
```
...
...
@@ -181,12 +310,15 @@ curl --location --request POST 'https://api.fastgpt.in/api/core/dataset/searchTe
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
-
datasetId - 知识库ID
-
text - 需要测试的文本
-
limit - 最大 tokens 数量
-
similarity - 最低相关度（0~1，可选）
-
searchMode - 搜索模式：embedding | fullTextRecall | mixedRecall
-
usingReRank - 使用重排
{{% alert icon=" " context="success" %}}
-
datasetId: 知识库的ID(必填)
-
parentId： 父级ID，不填则默认为根目录
-
name: 集合名称（必填）
-
type:
-
folder：文件夹
-
virtual：虚拟集合(手动集合)
-
metadata： 元数据（暂时没啥用）
{{% /alert %}}
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
...
...
@@ -194,71 +326,76 @@ curl --location --request POST 'https://api.fastgpt.in/api/core/dataset/searchTe
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
返回 top k 结果， limit 为最大 Tokens 数量，最多 20000 tokens
。
data 为集合的 ID
。
```
bash
```
json
{
"code"
:
200
,
"statusText"
:
""
,
"data"
:
[
{
"id"
:
"65599c54a5c814fb803363cb"
,
"q"
:
"你是谁"
,
"a"
:
"我是FastGPT助手"
,
"datasetId"
:
"6554684f7f9ed18a39a4d15c"
,
"collectionId"
:
"6556cd795e4b663e770bb66d"
,
"sourceName"
:
"GBT 15104-2021 装饰单板贴面人造板.pdf"
,
"sourceId"
:
"6556cd775e4b663e770bb65c"
,
"score"
: 0.8050316572189331
}
,
......
]
"message"
:
""
,
"data"
:
"65abcd009d1448617cba5ee1"
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
#
更多接口
#
## 创建一个纯文本集合(商业版)
目前未整理，简陋导出：
传入一段文字，创建一个集合，会根据传入的文字进行分割。
## POST 知识库搜索测试
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
POST /core/dataset/searchTest
```
bash
curl
--location
--request
POST
'http://localhost:3000/api/proApi/core/dataset/collection/create/text'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"text":"xxxxxxxx",
"datasetId":"6593e137231a2be9c5603ba7",
"parentId": null,
"name":"测试训练",
> Body Parameters
"trainingType": "qa",
"chunkSize":8000,
"chunkSplitter":"",
"qaPrompt":"11",
```
json
{
"datasetId"
:
"656c2ccff7f114064daa72f6"
,
"text"
:
"导演是谁"
,
"limit"
:
1500
,
"searchMode"
:
"embedding"
,
"usingReRank"
:
true
,
"similarity"
:
0.5
}
"metadata":{}
}'
```
### Params
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
{{% alert icon=" " context="success" %}}
-
text: 原文本
-
datasetId: 知识库的ID(必填)
-
parentId： 父级ID，不填则默认为根目录
-
name: 集合名称（必填）
-
metadata： 元数据（暂时没啥用）
-
trainingType:（必填）
-
chunk: 按文本长度进行分割
-
qa: QA拆分
-
chunkSize: 每个 chunk 的长度（可选）. chunk模式:100~3000; qa模式: 4000~模型最大token（16k模型通常建议不超过10000）
-
chunkSplitter: 自定义最高优先分割符号（可选）
-
qaPrompt: qa拆分自定义提示词（可选）
{{% /alert %}}
|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|Authorization|header|string| no |none|
|body|body|object| no |none|
|» datasetId|body|string| yes |none|
|» text|body|string| yes |none|
|» limit|body|integer| no |none|
|» searchMode|body|
[
search mode
](
#schemasearch%20mode
)
| yes |none|
|» usingReRank|body|boolean| no |none|
|» similarity|body|
[
similary
](
#schemasimilary
)
| no |none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
> Response Examples
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> 成功
data 为集合的 ID。
```
json
{
...
...
@@ -266,1110 +403,795 @@ POST /core/dataset/searchTest
"statusText"
:
""
,
"message"
:
""
,
"data"
:
{
"list"
:
[
{
"id"
:
"65962b23f5fac58e46330dfd"
,
"q"
:
"# 快速了解 FastGPT
\n
FastGPT 的能力与优势
\n\n
FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开箱即用的数据处理、模型调用等能力。同时可以通过 Flow 可视化进行工作流编排，从而实现复杂的问答场景！
\n\n
🤖
\n\n
FastGPT 在线使用：[https://fastgpt.in](https://fastgpt.in)
\n\n
| | |
\n
| --- | --- |
\n
|  |  |
\n
|  |  |
\n\n
"
,
"a"
:
""
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65962b2089642fd209da3b03"
,
"sourceName"
:
"https://doc.fastgpt.in/docs/intro/"
,
"sourceId"
:
"https://doc.fastgpt.in/docs/intro/"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8036568760871887
,
"index"
:
20
},
{
"type"
:
"fullText"
,
"value"
:
1.168349443855932
,
"index"
:
2
},
{
"type"
:
"reRank"
,
"value"
:
0.9870296135626316
,
"index"
:
0
},
{
"type"
:
"rrf"
,
"value"
:
0.04366449476962486
,
"index"
:
0
}
]
},
{
"id"
:
"65962b24f5fac58e46330dff"
,
"q"
:
"# 快速了解 FastGPT
\n
## FastGPT 能力
\n
### 2. 简单易用的可视化界面
\n
FastGPT 采用直观的可视化界面设计，为各种应用场景提供了丰富实用的功能。通过简洁易懂的操作步骤，可以轻松完成 AI 客服的创建和训练流程。
\n\n
\n\n
"
,
"a"
:
""
,
"chunkIndex"
:
2
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65962b2089642fd209da3b03"
,
"sourceName"
:
"https://doc.fastgpt.in/docs/intro/"
,
"sourceId"
:
"https://doc.fastgpt.in/docs/intro/"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8152669668197632
,
"index"
:
3
},
{
"type"
:
"fullText"
,
"value"
:
1.0511363636363635
,
"index"
:
8
},
{
"type"
:
"reRank"
,
"value"
:
0.9287972729281414
,
"index"
:
14
},
{
"type"
:
"rrf"
,
"value"
:
0.04265696347031964
,
"index"
:
1
}
]
},
{
"id"
:
"65962b25f5fac58e46330e00"
,
"q"
:
"# 快速了解 FastGPT
\n
## FastGPT 能力
\n
### 3. 自动数据预处理
\n
提供手动输入、直接分段、LLM 自动处理和 CSV 等多种数据导入途径，其中“直接分段”支持通过 PDF、WORD、Markdown 和 CSV 文档内容作为上下文。FastGPT 会自动对文本数据进行预处理、向量化和 QA 分割，节省手动训练时间，提升效能。
\n\n
\n\n
"
,
"a"
:
""
,
"chunkIndex"
:
3
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65962b2089642fd209da3b03"
,
"sourceName"
:
"https://doc.fastgpt.in/docs/intro/"
,
"sourceId"
:
"https://doc.fastgpt.in/docs/intro/"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8158369064331055
,
"index"
:
2
},
{
"type"
:
"fullText"
,
"value"
:
1.014030612244898
,
"index"
:
20
},
{
"type"
:
"reRank"
,
"value"
:
0.9064876908461501
,
"index"
:
17
},
{
"type"
:
"rrf"
,
"value"
:
0.04045823457588163
,
"index"
:
2
}
]
},
{
"id"
:
"65a7e1e8fc13bdf20fd46d41"
,
"q"
:
"# 快速了解 FastGPT
\n
## FastGPT 能力
\n
### 5. 强大的 API 集成
\n
FastGPT 对外的 API 接口对齐了 OpenAI 官方接口，可以直接接入现有的 GPT 应用，也可以轻松集成到企业微信、公众号、飞书等平台。
\n\n
"
,
"a"
:
""
,
"chunkIndex"
:
66
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7e1d4fc13bdf20fd46abe"
,
"sourceName"
:
"dataset - 2024-01-04T151625.388.csv"
,
"sourceId"
:
"65a7e1d2fc13bdf20fd46abc"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.803692102432251
,
"index"
:
18
},
{
"type"
:
"fullText"
,
"value"
:
1.0511363636363635
,
"index"
:
7
},
{
"type"
:
"reRank"
,
"value"
:
0.9177460552422909
,
"index"
:
15
},
{
"type"
:
"rrf"
,
"value"
:
0.03970501147383226
,
"index"
:
3
}
]
},
{
"id"
:
"65a7be319d96e21823f69c9b"
,
"q"
:
"FastGPT Flow 的工作流设计方案提供了哪些操作？"
,
"a"
:
"FastGPT Flow 的工作流设计方案提供了数据预处理、各类 AI 应用设置、调试测试及结果反馈等操作。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8283981680870056
,
"index"
:
0
},
{
"type"
:
"reRank"
,
"value"
:
0.9620363047907355
,
"index"
:
4
},
{
"type"
:
"rrf"
,
"value"
:
0.03177805800756621
,
"index"
:
4
}
]
},
{
"id"
:
"65a7be389d96e21823f69d58"
,
"q"
:
"FastGPT Flow 的实验室预约示例中使用了哪些参数？"
,
"a"
:
"FastGPT Flow 的实验室预约示例中使用了姓名、时间和实验室名称等参数。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8143455386161804
,
"index"
:
9
},
{
"type"
:
"reRank"
,
"value"
:
0.9806919138043485
,
"index"
:
1
},
{
"type"
:
"rrf"
,
"value"
:
0.0304147465437788
,
"index"
:
5
}
]
},
{
"id"
:
"65a7be309d96e21823f69c78"
,
"q"
:
"FastGPT Flow 是什么？"
,
"a"
:
"FastGPT Flow 是一款基于大型语言模型的知识库问答系统，通过引入 Flow 可视化工作流编排技术，提供了一个即插即用的解决方案。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8115077018737793
,
"index"
:
11
},
{
"type"
:
"reRank"
,
"value"
:
0.9686195704870232
,
"index"
:
3
},
{
"type"
:
"rrf"
,
"value"
:
0.029513888888888888
,
"index"
:
6
}
]
},
{
"id"
:
"65a7be389d96e21823f69d5e"
,
"q"
:
"FastGPT Flow 的实验室预约示例中的代码实现了哪些功能？"
,
"a"
:
"FastGPT Flow 的实验室预约示例中的代码实现了预约实验室、修改预约、查询预约和取消预约等功能。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8166953921318054
,
"index"
:
1
},
{
"type"
:
"reRank"
,
"value"
:
0.8350804533361768
,
"index"
:
20
},
{
"type"
:
"rrf"
,
"value"
:
0.028474711270410194
,
"index"
:
8
}
]
},
{
"id"
:
"65a7be389d96e21823f69d4f"
,
"q"
:
"FastGPT Flow 的联网搜索示例中使用了哪些参数？"
,
"a"
:
"FastGPT Flow 的联网搜索示例中使用了搜索关键词、Google 搜索的 API 密钥和自定义搜索引擎 ID。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8025297522544861
,
"index"
:
21
},
{
"type"
:
"reRank"
,
"value"
:
0.9730876959261983
,
"index"
:
2
},
{
"type"
:
"rrf"
,
"value"
:
0.028068137824235385
,
"index"
:
10
}
]
},
{
"id"
:
"65a7e1e8fc13bdf20fd46d55"
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7e1d4fc13bdf20fd46abe"
,
"sourceName"
:
"dataset - 2024-01-04T151625.388.csv"
,
"sourceId"
:
"65a7e1d2fc13bdf20fd46abc"
,
"q"
:
"# 快速了解 FastGPT
\n
## FastGPT 特点
\n
1. **项目开源**
\n
\n
FastGPT 遵循附加条件 Apache License 2.0 开源协议，你可以 [Fork](https://github.com/labring/FastGPT/fork) 之后进行二次开发和发布。FastGPT 社区版将保留核心功能，商业版仅在社区版基础上使用 API 的形式进行扩展，不影响学习使用。
\n
\n
2. **独特的 QA 结构**
\n
\n
针对客服问答场景设计的 QA 结构，提高在大量数据场景中的问答准确性。
\n
\n
3. **可视化工作流**
\n
\n
通过 Flow 模块展示了从问题输入到模型输出的完整流程，便于调试和设计复杂流程。
\n
\n
4. **无限扩展**
\n
\n
基于 API 进行扩展，无需修改 FastGPT 源码，也可快速接入现有的程序中。
\n
\n
5. **便于调试**
\n
\n
提供搜索测试、引用修改、完整对话预览等多种调试途径。
\n
\n
6. **支持多种模型**
\n
\n
支持 GPT、Claude、文心一言等多种 LLM 模型，未来也将支持自定义的向量模型。"
,
"a"
:
""
,
"chunkIndex"
:
67
,
"score"
:
[
{
"type"
:
"fullText"
,
"value"
:
1.0340073529411764
,
"index"
:
12
},
{
"type"
:
"reRank"
,
"value"
:
0.9542227274192233
,
"index"
:
9
},
{
"type"
:
"rrf"
,
"value"
:
0.027272727272727275
,
"index"
:
11
}
]
},
{
"id"
:
"65a7be319d96e21823f69c8f"
,
"q"
:
"FastGPT Flow 的工作流设计中，模块之间如何进行组合和组装？"
,
"a"
:
"FastGPT Flow 允许用户在核心工作流模块中进行自由组合和组装，从而衍生出一个新的模块。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8098832368850708
,
"index"
:
13
},
{
"type"
:
"reRank"
,
"value"
:
0.9478657435317039
,
"index"
:
12
},
{
"type"
:
"rrf"
,
"value"
:
0.027212143650499815
,
"index"
:
12
}
]
},
{
"id"
:
"65a7be359d96e21823f69ce0"
,
"q"
:
"FastGPT Flow 的模块的输入和输出如何连接？"
,
"a"
:
"FastGPT Flow 的模块的输入和输出通过连接点进行连接，连接点的颜色代表了不同的数据类型。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.8060981035232544
,
"index"
:
16
},
{
"type"
:
"reRank"
,
"value"
:
0.9530133603823691
,
"index"
:
10
},
{
"type"
:
"rrf"
,
"value"
:
0.027071520029266508
,
"index"
:
13
}
]
},
{
"id"
:
"65a7be319d96e21823f69c98"
,
"q"
:
"FastGPT Flow 的工作流设计方案能够满足哪些问答场景？"
,
"a"
:
"FastGPT Flow 的工作流设计方案能够满足基本的 AI 知识库问答需求，并适应各种复杂的问答场景，例如联网搜索、数据库操作、数据实时更新、消息通知等。"
,
"chunkIndex"
:
0
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7be059d96e21823f69af5"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7be059d96e21823f69ae8"
,
"score"
:
[
{
"type"
:
"embedding"
,
"value"
:
0.814436137676239
,
"index"
:
8
},
{
"type"
:
"reRank"
,
"value"
:
0.8814109034236719
,
"index"
:
19
},
{
"type"
:
"rrf"
,
"value"
:
0.026992753623188405
,
"index"
:
16
}
]
},
{
"id"
:
"65a7e058fc13bdf20fd46577"
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"collectionId"
:
"65a7e01efc13bdf20fd45815"
,
"sourceName"
:
"FastGPT软著.pdf"
,
"sourceId"
:
"65a7e01dfc13bdf20fd457f3"
,
"q"
:
"FastGPT Flow 工作流设计112312 3123213123 232321312 21312 23一、介绍FastGPT 作为一款基于大型语言模型（LLM）的知识库问答系统，旨在为用户提供一个即插即用的解决方案。它集成了数据处理、模型调用等多项功能，通过引入 Flow 可视化工作流编排技术，进一步增强了对复杂问答场景的支持能力。本文将重点介绍 FastGPT Flow工作流的设计方案和应用优势。
\n
FastGPT Flow 工 作 流 采 用 了 React Flow 框 架 作 为 UI 底 座 ， 结 合 自 研 的 FlowController 实现工作流的运行。FastGPT 使用 Flow 模块为用户呈现了一个直观、可视化的界面，从而简化了 AI 应用工作流程的设计和管理方式。React Flow 的应用使得用户能够以图形化的方式组织和编排工作流，这不仅使得工作流的创建过程更为直观，同时也为用户提供了强大且灵活的工作流编辑器。在 FastGPT Flow 工作流设计中，核心工作流模块包括用户引导、问题输入、知识库检索、AI 文本生成、问题分类、结构化内容提取、指定回复、应用调用和 HTTP 扩展，并允许用户在这类模块中进行自由组合和组装，从而衍生出一个新的模块。"
,
"a"
:
""
,
"chunkIndex"
:
0
,
"score"
:
[
{
"type"
:
"fullText"
,
"value"
:
1.0229779411764706
,
"index"
:
15
},
{
"type"
:
"reRank"
,
"value"
:
0.9577545043363116
,
"index"
:
8
},
{
"type"
:
"rrf"
,
"value"
:
0.026992753623188405
,
"index"
:
17
}
]
"collectionId"
:
"65abcfab9d1448617cba5f0d"
,
"results"
:
{
"insertLen"
:
5
,
//
分割成多少段
"overToken"
:
[],
"repeat"
:
[],
"error"
:
[]
}
],
"duration"
:
"2.978s"
,
"searchMode"
:
"mixedRecall"
,
"limit"
:
1500
,
"similarity"
:
0.1
,
"usingReRank"
:
true
,
"usingSimilarityFilter"
:
true
}
}
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
### 创建一个链接集合(商业版)
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
传入一个网络链接，创建一个集合，会先去对应网页抓取内容，再抓取的文字进行分割。
### Responses Data Schema
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
HTTP Status Code
**200**
```
bash
curl
--location
--request
POST
'http://localhost:3000/api/proApi/core/dataset/collection/create/link'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"link":"https://doc.fastgpt.in/docs/course/quick-start/",
"datasetId":"6593e137231a2be9c5603ba7",
"parentId": null,
|Name|Type|Required|Restrictions|Title|description|
|---|---|---|---|---|---|
|» code|integer|true|none||none|
|» statusText|string|true|none||none|
|» message|string|true|none||none|
|» data|object|true|none||none|
|»» list|
[
object
]
|true|none||none|
|»»» id|string|true|none||none|
|»»» q|string|true|none||none|
|»»» a|string|true|none||none|
|»»» chunkIndex|integer|true|none||none|
|»»» datasetId|string|true|none||none|
|»»» collectionId|string|true|none||none|
|»»» sourceName|string|true|none||none|
|»»» sourceId|string|true|none||none|
|»»» score|
[
object
]
|true|none||none|
|»»»» type|string|true|none||none|
|»»»» value|number|true|none||none|
|»»»» index|integer|true|none||none|
|»» duration|string|true|none||none|
|»» searchMode|string|true|none||none|
|»» limit|integer|true|none||none|
|»» similarity|number|true|none||none|
|»» usingReRank|boolean|true|none||none|
|»» usingSimilarityFilter|boolean|true|none||none|
"trainingType": "chunk",
"chunkSize":512,
"chunkSplitter":"",
"qaPrompt":"",
# openapi/知识库/知识库crud
"metadata":{
"webPageSelector":".docs-content"
}
}'
```
## GET 获取知识库列表
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
GET /core/dataset/list
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Params
{{% alert icon=" " context="success" %}}
-
link: 网络链接
-
datasetId: 知识库的ID(必填)
-
parentId： 父级ID，不填则默认为根目录
-
metadata.webPageSelector: 网页选择器，用于指定网页中的哪个元素作为文本(可选)
-
trainingType:（必填）
-
chunk: 按文本长度进行分割
-
qa: QA拆分
-
chunkSize: 每个 chunk 的长度（可选）. chunk模式:100~3000; qa模式: 4000~模型最大token（16k模型通常建议不超过10000）
-
chunkSplitter: 自定义最高优先分割符号（可选）
-
qaPrompt: qa拆分自定义提示词（可选）
{{% /alert %}}
|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|parentId|query|string| no |父级的ID|
|Authorization|header|string| no |none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
> Response Examples
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> 200 Response
data 为集合的 ID。
```
json
{}
{
"code"
:
200
,
"statusText"
:
""
,
"message"
:
""
,
"data"
:
{
"collectionId"
:
"65abd0ad9d1448617cba6031"
}
}
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### 创建一个文件集合(商业版)
### Responses Data Schema
传入一个文件，创建一个集合，会读取文件内容进行分割。目前支持：pdf, docx, md, txt, html, csv。
## GET 获取知识库详情
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
GET /core/dataset/detail
```
bash
curl
--location
--request
POST
'http://localhost:3000/api/proApi/core/dataset/collection/create/file'
\
--header
'Authorization: Bearer {{authorization}}'
\
--form
'file=@"C:\\Users\\user\\Desktop\\fastgpt测试文件\\index.html"'
\
--form
'data="{\"datasetId\":\"6593e137231a2be9c5603ba7\",\"parentId\":null,\"trainingType\":\"chunk\",\"chunkSize\":512,\"chunkSplitter\":\"\",\"qaPrompt\":\"\",\"metadata\":{}}"'
```
### Params
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|id|query|string| no |知识库id|
|Authorization|header|string| no |none|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
> Response Examples
需要使用 POST form-data 的格式上传。包含 file 和 data 两个字段。
{{% alert icon=" " context="success" %}}
-
file: 文件
-
data: 知识库相关信息（json序列化后传入）
-
datasetId: 知识库的ID(必填)
-
parentId： 父级ID，不填则默认为根目录
-
trainingType:（必填）
-
chunk: 按文本长度进行分割
-
qa: QA拆分
-
chunkSize: 每个 chunk 的长度（可选）. chunk模式:100~3000; qa模式: 4000~模型最大token（16k模型通常建议不超过10000）
-
chunkSplitter: 自定义最高优先分割符号（可选）
-
qaPrompt: qa拆分自定义提示词（可选）
{{% /alert %}}
> 200 Response
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
```
json
{}
```
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
# openapi/知识库/集合crud
## POST 获取知识库集合列表
POST /core/dataset/collection/list
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
data 为集合的 ID。
```
json
{
"pageNum"
:
1
,
"pageSize"
:
10
,
"datasetId"
:
"6597ca43e26f2a90a1501414"
,
"parentId"
:
null
,
"searchText"
:
""
,
"simple"
:
true
"code"
:
200
,
"statusText"
:
""
,
"message"
:
""
,
"data"
:
{
"collectionId"
:
"65abc044e4704bac793fbd81"
,
"results"
:
{
"insertLen"
:
1
,
"overToken"
:
[],
"repeat"
:
[],
"error"
:
[]
}
}
}
```
### Params
|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|Authorization|header|string| no |none|
|body|body|object| no |none|
|» pageNum|body|integer| no |none|
|» pageSize|body|integer| no |none|
|» datasetId|body|string| yes |none|
|» parentId|body|null| no |none|
|» searchText|body|string| no |none|
|» simple|body|boolean| no |none|
> Response Examples
> 200 Response
```
json
{}
```
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
## GET 获取集合详情
GET /core/dataset/collection/detail
### Params
|Name|Location|Type|Required|Description|
|---|---|---|---|---|
|id|query|string| no |知识库id|
|Authorization|header|string| no |none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
> Response Examples
### 获取集合列表
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
POST
'http://localhost:3000/api/core/dataset/collection/list'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"pageNum":1,
"pageSize": 10,
"datasetId":"6593e137231a2be9c5603ba7",
"parentId": null,
"searchText":""
}'
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
pageNum: 页码（选填）
-
pageSize: 每页数量，最大30（选填）
-
datasetId: 知识库的ID(必填)
-
parentId: 父级Id（选填）
-
searchText: 模糊搜索文本（选填）
{{% /alert %}}
## PUT 更新集合
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
PUT /core/dataset/collection/update
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"id"
:
"6597ce094e10ee661f0891c8"
,
"parentId"
:
null
,
"name"
:
"222"
"code": 200,
"statusText": "",
"message": "",
"data": {
"pageNum": 1,
"pageSize": 10,
"data": [
{
"_id": "6593e137231a2be9c5603ba9",
"parentId": null,
"tmbId": "65422be6aa44b7da77729ec9",
"type": "virtual",
"name": "手动录入",
"updateTime": "2099-01-01T00:00:00.000Z",
"dataAmount": 3,
"trainingAmount": 0,
"canWrite": true
},
{
"_id": "65abd0ad9d1448617cba6031",
"parentId": null,
"tmbId": "65422be6aa44b7da77729ec9",
"type": "link",
"name": "快速上手 | FastGPT",
"rawLink": "https://doc.fastgpt.in/docs/course/quick-start/",
"updateTime": "2024-01-20T13:54:53.031Z",
"dataAmount": 3,
"trainingAmount": 0,
"canWrite": true
}
],
"total": 93
}
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» id|body|string| yes ||none|
|» parentId|body|null| no | 父级的id|none|
|» name|body|string| no | 名称|none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
> Response Examples
### 获取集合详情
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
GET
'http://localhost:3000/api/core/dataset/collection/detail?id=65abcfab9d1448617cba5f0d'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
id: 集合的ID
{{% /alert %}}
## POST 创建空集合（文件夹或者一个空集合）
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
POST /core/dataset/collection/create
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"datasetId"
:
"6597ca43e26f2a90a1501414"
,
"parentId"
:
null
,
"name"
:
"集合名"
,
"type"
:
"folder"
,
"metadata"
:
{}
"code": 200,
"statusText": "",
"message": "",
"data": {
"_id": "65abcfab9d1448617cba5f0d",
"parentId": null,
"teamId": "65422be6aa44b7da77729ec8",
"tmbId": "65422be6aa44b7da77729ec9",
"datasetId": {
"_id": "6593e137231a2be9c5603ba7",
"parentId": null,
"teamId": "65422be6aa44b7da77729ec8",
"tmbId": "65422be6aa44b7da77729ec9",
"type": "dataset",
"status": "active",
"avatar": "/icon/logo.svg",
"name": "FastGPT test",
"vectorModel": "text-embedding-ada-002",
"agentModel": "gpt-3.5-turbo-16k",
"intro": "",
"permission": "private",
"updateTime": "2024-01-02T10:11:03.084Z"
},
"type": "virtual",
"name": "测试训练",
"trainingType": "qa",
"chunkSize": 8000,
"chunkSplitter": "",
"qaPrompt": "11",
"rawTextLength": 40466,
"hashRawText": "47270840614c0cc122b29daaddc09c2a48f0ec6e77093611ab12b69cba7fee12",
"createTime": "2024-01-20T13:50:35.838Z",
"updateTime": "2024-01-20T13:50:35.838Z",
"canWrite": true,
"sourceName": "测试训练"
}
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| yes ||none|
|» parentId|body|null| no ||none|
|» name|body|string| yes ||none|
|» type|body|
[
collection type
](
#schemacollection%20type
)
| yes ||none|
|» metadata|body|object| no ||none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
> Response Examples
### 修改集合信息
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
PUT
'http://localhost:3000/api/core/dataset/collection/update'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"id":"65abcfab9d1448617cba5f0d",
"parentId":null,
"name":"测2222试"
}'
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
id: 集合的ID
-
parentId: 修改父级ID（可选）
-
name: 修改集合名称（可选）
{{% /alert %}}
## POST 创建文本集合
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
POST /core/dataset/collection/create/text
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"text"
:
"xxxxxxxxxxxxxx"
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"parentId"
:
null
,
"name"
:
"测试"
,
"trainingType"
:
"qa"
,
"chunkSize"
:
8000
,
"chunkSplitter"
:
""
,
"qaPrompt"
:
""
,
"metadata"
:
{}
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| no ||none|
|» parentId|body|null| no ||none|
|» name|body|string| yes ||none|
|» text|body|string| yes | 原文本|none|
|» trainingType|body|
[
training type
](
#schematraining%20type
)
| yes ||none|
|» chunkSize|body|integer| no | 分块大小|none|
|» chunkSplitter|body|string| no | 自定义最高优先级的分段符号|none|
|» qaPrompt|body|string| no ||none|
|» metadata|body|object| no ||none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
> Response Examples
### 删除一个集合
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
DELETE
'http://localhost:3000/api/core/dataset/collection/delete?id=65aa2a64e6cb9b8ccdc00de8'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
id: 集合的ID
{{% /alert %}}
## POST 创建网络链接集合
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
POST /core/dataset/collection/create/link
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"link"
:
"https://doc.fastgpt.in/docs/course/quick-start/"
,
"datasetId"
:
"6593e137231a2be9c5603ba7"
,
"parentId"
:
null
,
"trainingType"
:
"chunk"
,
"chunkSize"
:
512
,
"chunkSplitter"
:
""
,
"qaPrompt"
:
""
,
"metadata"
:
{
"webPageSelector"
:
".docs-content"
}
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» datasetId|body|string| yes ||none|
|» parentId|body|null| no ||none|
|» link|body|string| yes ||none|
|» trainingType|body|
[
training type
](
#schematraining%20type
)
| yes ||none|
|» chunkSize|body|integer| no ||none|
|» chunkSplitter|body|string| no ||none|
|» qaPrompt|body|string| no ||none|
|» metadata|body|object| no ||none|
|»» webPageSelector|body|string| no | web选择器|none|
> Response Examples
> 200 Response
```
json
{}
```
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
## DELETE 删除一个集合
DELETE /core/dataset/collection/delete
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
##
# Params
##
数据
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| no ||知识库id|
|Authorization|header|string| no ||none|
### 为集合批量添加添加数据
> Response Examples
注意，每次最多推送 200 组数据。
> 200 Response
{{
<
tabs
tabTotal=
"4"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
POST
'https://api.fastgpt.in/api/core/dataset/data/pushData'
\
--header
'Authorization: Bearer apikey'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"collectionId": "64663f451ba1676dbdef0499",
"trainingMode": "chunk",
"prompt": "可选。qa 拆分引导词，chunk 模式下忽略",
"billId": "可选。如果有这个值，本次的数据会被聚合到一个订单中，这个值可以重复使用。可以参考 [创建训练订单] 获取该值。",
"data": [
{
"q": "你是谁？",
"a": "我是FastGPT助手"
},
{
"q": "你会什么？",
"a": "我什么都会",
"indexes": [{
"defaultIndex": false,
"type":"custom",
"text":"自定义索引，不使用默认索引"
}]
}
]
}'
```
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
# openapi/知识库/数据crud
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
## POST 获取数据列表
{{% alert icon=" " context="success" %}}
-
collectionId: 集合ID（必填）
-
trainingType:（必填）
-
chunk: 按文本长度进行分割
-
qa: QA拆分
-
prompt: 自定义 QA 拆分提示词，需严格按照模板，建议不要传入。（选填）
-
data：（具体数据）
-
q: 主要数据（必填）
-
a: 辅助数据（选填）
-
indexes: 自定义索引（选填），不传入则默认使用q和a构建索引。也可以传入
{{% /alert %}}
POST /core/dataset/data/list
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
> Body Parameters
{{
<
tab
tabName=
"响应例子"
>
}}
{{
<
markdownify
>
}}
```
json
{
"pageNum"
:
1
,
"pageSize"
:
10
,
"collectionId"
:
"65a8d2700d70d3de0bf09186"
,
"searchText"
:
""
"code"
:
200
,
"statusText"
:
""
,
"data"
:
{
"insertLen"
:
1
,
//
最终插入成功的数量
"overToken"
:
[],
//
超出
token
的
"repeat"
:
[],
//
重复的数量
"error"
:
[]
//
其他错误
}
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» pageNum|body|integer| yes ||none|
|» pageSize|body|integer| yes ||none|
|» searchText|body|string| yes ||none|
|» collectionId|body|string| yes ||none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
> Response Examples
{{
<
tab
tabName=
"QA Prompt 模板"
>
}}
{{
<
markdownify
>
}}
> 200 Response
{{theme}} 里的内容可以换成数据的主题。默认为：它们可能包含多个主题内容
```
json
{}
```
我会给你一段文本，{{theme}}，学习它们，并整理学习成果，要求为：
1. 提出最多 25 个问题。
2. 给出每个问题的答案。
3. 答案要详细完整，答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 markdown 元素。
4. 按格式返回多个问题和答案:
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
## GET 获取数据详情
GET /core/dataset/data/detail
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| yes ||none|
|Authorization|header|string| no ||none|
> Response Examples
> 200 Response
Q1: 问题。
A1: 答案。
Q2:
A2:
……
```
json
{}
我的文本："""{{text}}"""
```
### Responses
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
### Responses Data Schema
## DELETE 删除一条数据
DELETE /core/dataset/data/delete
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
### Params
{{
<
/
tabs
>
}}
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|id|query|string| no ||none|
|Authorization|header|string| no ||none|
> Response Examples
### 获取集合的数据列表
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
POST
'http://localhost:3000/api/core/dataset/data/list'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"pageNum":1,
"pageSize": 10,
"collectionId":"65abd4ac9d1448617cba6171",
"searchText":""
}'
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
pageNum: 页码（选填）
-
pageSize: 每页数量，最大30（选填）
-
collectionId: 集合的ID（必填）
-
searchText: 模糊搜索词（选填）
{{% /alert %}}
## PUT 更新数据
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
PUT /core/dataset/data/update
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"id"
:
"6597ce094e10ee661f0891c8"
,
"parentId"
:
null
,
"name"
:
"222"
"code": 200,
"statusText": "",
"message": "",
"data": {
"pageNum": 1,
"pageSize": 10,
"data": [
{
"_id": "65abd4b29d1448617cba61db",
"datasetId": "65abc9bd9d1448617cba5e6c",
"collectionId": "65abd4ac9d1448617cba6171",
"q": "N o . 2 0 2 2 1 2中 国 信 息 通 信 研 究 院京东探索研究院2022年 9月人工智能生成内容（AIGC）白皮书(2022 年)版权声明本白皮书版权属于中国信息通信研究院和京东探索研究院，并受法律保护。转载、摘编或利用其它方式使用本白皮书文字或者观点的，应注明“来源：中国信息通信研究院和京东探索研究院”。违反上述声明者，编者将追究其相关法律责任。前 言习近平总书记曾指出，“数字技术正以新理念、新业态、新模式全面融入人类经济、政治、文化、社会、生态文明建设各领域和全过程”。在当前数字世界和物理世界加速融合的大背景下，人工智能生成内容（Artificial Intelligence Generated Content，简称 AIGC）正在悄然引导着一场深刻的变革，重塑甚至颠覆数字内容的生产方式和消费模式，将极大地丰富人们的数字生活，是未来全面迈向数字文明新时代不可或缺的支撑力量。",
"a": "",
"chunkIndex": 0
},
{
"_id": "65abd4b39d1448617cba624d",
"datasetId": "65abc9bd9d1448617cba5e6c",
"collectionId": "65abd4ac9d1448617cba6171",
"q": "本白皮书重点从 AIGC 技术、应用和治理等维度进行了阐述。在技术层面，梳理提出了 AIGC 技术体系，既涵盖了对现实世界各种内容的数字化呈现和增强，也包括了基于人工智能的自主内容创作。在应用层面，重点分析了 AIGC 在传媒、电商、影视等行业和场景的应用情况，探讨了以虚拟数字人、写作机器人等为代表的新业态和新应用。在治理层面，从政策监管、技术能力、企业应用等视角，分析了AIGC 所暴露出的版权纠纷、虚假信息传播等各种问题。最后，从政府、行业、企业、社会等层面，给出了 AIGC 发展和治理建议。由于人工智能仍处于飞速发展阶段，我们对 AIGC 的认识还有待进一步深化，白皮书中存在不足之处，敬请大家批评指正。目 录一、 人工智能生成内容的发展历程与概念.............................................................. 1（一）AIGC 历史沿革 .......................................................................................... 1（二）AIGC 的概念与内涵 .................................................................................. 4二、人工智能生成内容的技术体系及其演进方向.................................................... 7（一）AIGC 技术升级步入深化阶段 .................................................................. 7（二）AIGC 大模型架构潜力凸显 .................................................................... 10（三）AIGC 技术演化出三大前沿能力 ............................................................ 18三、人工智能生成内容的应用场景.......................................................................... 26（一）AIGC+传媒：人机协同生产，",
"a": "",
"chunkIndex": 1
}
],
"total": 63
}
}
```
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» id|body|string| yes ||none|
|» q|body|string| yes ||none|
|» a|body|string| no ||none|
|» indexes|body|
[
[数据自定义向量
](
#schema%e6%95%b0%e6%8d%ae%e8%87%aa%e5%ae%9a%e4%b9%89%e5%90%91%e9%87%8f
)
]| no ||none|
> Response Examples
### 获取单条数据详情
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
GET
'http://localhost:3000/api/core/dataset/data/detail?id=65abd4b29d1448617cba61db'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
id: 数据的id
{{% /alert %}}
## POST 知识库插入记录（批量插入）
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
POST /core/dataset/data/pushData
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
> Body Parameters
```json
{
"collectionId"
:
"string"
,
"data"
:
[
{
"a"
:
"string"
,
"q"
:
"string"
,
"chunkIndex"
:
1
"code": 200,
"statusText": "",
"message": "",
"data": {
"id": "65abd4b29d1448617cba61db",
"q": "N o . 2 0 2 2 1 2中 国 信 息 通 信 研 究 院京东探索研究院2022年 9月人工智能生成内容（AIGC）白皮书(2022 年)版权声明本白皮书版权属于中国信息通信研究院和京东探索研究院，并受法律保护。转载、摘编或利用其它方式使用本白皮书文字或者观点的，应注明“来源：中国信息通信研究院和京东探索研究院”。违反上述声明者，编者将追究其相关法律责任。前 言习近平总书记曾指出，“数字技术正以新理念、新业态、新模式全面融入人类经济、政治、文化、社会、生态文明建设各领域和全过程”。在当前数字世界和物理世界加速融合的大背景下，人工智能生成内容（Artificial Intelligence Generated Content，简称 AIGC）正在悄然引导着一场深刻的变革，重塑甚至颠覆数字内容的生产方式和消费模式，将极大地丰富人们的数字生活，是未来全面迈向数字文明新时代不可或缺的支撑力量。",
"a": "",
"chunkIndex": 0,
"indexes": [
{
"defaultIndex": true,
"type": "chunk",
"dataId": "3720083",
"text": "N o . 2 0 2 2 1 2中 国 信 息 通 信 研 究 院京东探索研究院2022年 9月人工智能生成内容（AIGC）白皮书(2022 年)版权声明本白皮书版权属于中国信息通信研究院和京东探索研究院，并受法律保护。转载、摘编或利用其它方式使用本白皮书文字或者观点的，应注明“来源：中国信息通信研究院和京东探索研究院”。违反上述声明者，编者将追究其相关法律责任。前 言习近平总书记曾指出，“数字技术正以新理念、新业态、新模式全面融入人类经济、政治、文化、社会、生态文明建设各领域和全过程”。在当前数字世界和物理世界加速融合的大背景下，人工智能生成内容（Artificial Intelligence Generated Content，简称 AIGC）正在悄然引导着一场深刻的变革，重塑甚至颠覆数字内容的生产方式和消费模式，将极大地丰富人们的数字生活，是未来全面迈向数字文明新时代不可或缺的支撑力量。",
"_id": "65abd4b29d1448617cba61dc"
}
],
"datasetId": "65abc9bd9d1448617cba5e6c",
"collectionId": "65abd4ac9d1448617cba6171",
"sourceName": "中文-AIGC白皮书2022.pdf",
"sourceId": "65abd4ac9d1448617cba6166",
"isOwner": true,
"canWrite": true
}
],
"trainingMode"
:
"string"
,
"promot"
:
"string"
,
"billId"
:
""
}
```
### Params
|Name|Location|Type|Required|Title|Description|
|---|---|---|---|---|---|
|Authorization|header|string| no ||none|
|body|body|object| no ||none|
|» collectionId|body|string| yes ||none|
|» data|body|
[
object
]
| yes ||none|
|»» a|body|string| no ||none|
|»» q|body|string| no ||none|
|»» chunkIndex|body|integer| no ||none|
|» trainingMode|body|
[
training type
](
#schematraining%20type
)
| no ||none|
|» promot|body|string| no ||none|
|» billId|body|string| no ||none|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
> Response Examples
### 修改单条数据
> 200 Response
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
json
{}
```
bash
curl
--location
--request
PUT
'http://localhost:3000/api/core/dataset/data/update'
\
--header
'Authorization: Bearer {{authorization}}'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"id":"65abd4b29d1448617cba61db",
"q":"测试111",
"a":"sss",
"indexes":[]
}'
```
### Responses
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|HTTP Status Code |Meaning|Description|Data schema|
|---|---|---|---|
|200|
[
OK
](
https://tools.ietf.org/html/rfc7231#section-6.3.1
)
|成功|Inline|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
### Responses Data Schema
{{% alert icon=" " context="success" %}}
-
id: 数据的id
-
q: 主要数据（选填）
-
a: 辅助数据（选填）
-
indexes: 自定义索引（选填），类型参考
`为集合批量添加添加数据`
，建议直接不传。更新q,a后，如果有默认索引，则会直接更新默认索引。
{{% /alert %}}
# Data Schema
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
<h2
id=
"tocS_similary"
>
similary
</h2>
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
<a
id=
"schemasimilary"
></a>
<a
id=
"schema_similary"
></a>
<a
id=
"tocSsimilary"
></a>
<a
id=
"tocssimilary"
></a>
```json
1
{
"code": 200,
"statusText": "",
"message": "",
"data": null
}
```
### Attribute
|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|
*anonymous*
|integer|false|none||none|
<h2
id=
"tocS_search mode"
>
search mode
</h2>
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
<a
id=
"schemasearch mode"
></a>
<a
id=
"schema_search mode"
></a>
<a
id=
"tocSsearch mode"
></a>
<a
id=
"tocssearch mode"
></a>
### 删除单条数据
```
json
"embedding"
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
DELETE
'http://localhost:3000/api/core/dataset/data/delete?id=65abd4b39d1448617cba624d'
\
--header
'Authorization: Bearer {{authorization}}'
\
```
### Attribute
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|
*anonymous*
|string|false|none||none|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
#### Enum
{{% alert icon=" " context="success" %}}
-
id: 数据的id
{{% /alert %}}
|Name|Value|
|---|---|
|
*anonymous*
|embedding|
|
*anonymous*
|fullTextRecall|
|
*anonymous*
|mixedRecall|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
<h2
id=
"tocS_training type"
>
training type
</h2>
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
<a
id=
"schematraining type"
></a>
<a
id=
"schema_training type"
></a>
<a
id=
"tocStraining type"
></a>
<a
id=
"tocstraining type"
></a>
```json
"chunk"
{
"code": 200,
"statusText": "",
"message": "",
"data": "success"
}
```
### Attribute
|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|
*anonymous*
|string|false|none||none|
#### Enum
|Name|Value|
|---|---|
|
*anonymous*
|chunk|
|
*anonymous*
|qa|
<h2
id=
"tocS_collection type"
>
collection type
</h2>
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
<a
id=
"schemacollection type"
></a>
<a
id=
"schema_collection type"
></a>
<a
id=
"tocScollection type"
></a>
<a
id=
"tocscollection type"
></a>
## 搜索测试
```
json
"folder"
{{
<
tabs
tabTotal=
"3"
>
}}
{{
<
tab
tabName=
"请求示例"
>
}}
{{
<
markdownify
>
}}
```
bash
curl
--location
--request
POST
'https://api.fastgpt.in/api/core/dataset/searchTest'
\
--header
'Authorization: Bearer fastgpt-xxxxx'
\
--header
'Content-Type: application/json'
\
--data-raw
'{
"datasetId": "知识库的ID",
"text": "导演是谁",
"limit": 5000,
"similarity": 0,
"searchMode": "embedding",
"usingReRank": false
}'
```
### Attribute
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|
*anonymous*
|string|false|none||none|
{{
<
tab
tabName=
"参数说明"
>
}}
{{
<
markdownify
>
}}
#### Enum
{{% alert icon=" " context="success" %}}
-
datasetId - 知识库ID
-
text - 需要测试的文本
-
limit - 最大 tokens 数量
-
similarity - 最低相关度（0~1，可选）
-
searchMode - 搜索模式：embedding | fullTextRecall | mixedRecall
-
usingReRank - 使用重排
{{% /alert %}}
|Name|Value|
|---|---|
|
*anonymous*
|folder|
|
*anonymous*
|virtual|
|
*anonymous*
|link|
|
*anonymous*
|file|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
<h2
id=
"tocS_数据自定义向量"
>
数据自定义向量
</h2>
{{
<
tab
tabName=
"响应示例"
>
}}
{{
<
markdownify
>
}}
<a
id=
"schema数据自定义向量"
></a>
<a
id=
"schema_数据自定义向量"
></a>
<a
id=
"tocS数据自定义向量"
></a>
<a
id=
"tocs数据自定义向量"
></a>
返回 top k 结果， limit 为最大 Tokens 数量，最多 20000 tokens。
```
json
{
"defaultIndex"
:
true
,
"type"
:
"string"
,
"text"
:
"string"
"code"
:
200
,
"statusText"
:
""
,
"data"
:
[
{
"id"
:
"65599c54a5c814fb803363cb"
,
"q"
:
"你是谁"
,
"a"
:
"我是FastGPT助手"
,
"datasetId"
:
"6554684f7f9ed18a39a4d15c"
,
"collectionId"
:
"6556cd795e4b663e770bb66d"
,
"sourceName"
:
"GBT 15104-2021 装饰单板贴面人造板.pdf"
,
"sourceId"
:
"6556cd775e4b663e770bb65c"
,
"score"
:
0.8050316572189331
},
......
]
}
```
### Attribute
|Name|Type|Required|Restrictions|Title|Description|
|---|---|---|---|---|---|
|defaultIndex|boolean|false|none||是否为默认|
|type|string|true|none||none|
|text|string|true|none||索引文本|
{{
<
/
markdownify
>
}}
{{
<
/
tab
>
}}
{{
<
/
tabs
>
}}
docSite/content/docs/development/upgrading/467.md
View file @
aab6ee51
...
...
@@ -27,7 +27,8 @@ curl --location --request POST 'https://{{host}}/api/admin/initv467' \
1.
修改了知识库UI及新的导入交互方式。
2.
优化知识库和对话的数据索引。
3.
知识库 openAPI，支持通过
API 操作知识库。（文档待补充）
3.
知识库 openAPI，支持通过
[
API 操作知识库
](
/docs/development/openapi/dataset
)
。
4.
新增 - 输入框变量提示。输入 { 号后将会获得可用变量提示。根据社区针对高级编排的反馈，我们计划于 2 月份的版本中，优化变量内容，支持模块的局部变量以及更多全局变量写入。
5.
修复 - API 对话时，chatId 冲突问题。
6.
修复 - Iframe 嵌入网页可能导致的 window.onLoad 冲突。
\ No newline at end of file
5.
优化 - 切换团队后会保存记录，下次登录时优先登录该团队。
6.
修复 - API 对话时，chatId 冲突问题。
7.
修复 - Iframe 嵌入网页可能导致的 window.onLoad 冲突。
\ No newline at end of file
packages/global/common/error/utils.ts
View file @
aab6ee51
import
{
replaceSensitiveLink
}
from
'../string/tools'
;
export
const
getErrText
=
(
err
:
any
,
def
=
''
)
=>
{
const
msg
:
string
=
typeof
err
===
'string'
?
err
:
err
?.
message
||
def
||
''
;
msg
&&
console
.
log
(
'error =>'
,
msg
);
return
msg
;
return
replaceSensitiveLink
(
msg
)
;
};
packages/global/common/string/tools.ts
View file @
aab6ee51
...
...
@@ -38,6 +38,12 @@ export function replaceVariable(text: string, obj: Record<string, string | numbe
return
text
||
''
;
}
/* replace sensitive link */
export
const
replaceSensitiveLink
=
(
text
:
string
)
=>
{
const
urlRegex
=
/
(?<
=https
?
:
\/\/)[^\s]
+/g
;
return
text
.
replace
(
urlRegex
,
'xxx'
);
};
export
const
getNanoid
=
(
size
=
12
)
=>
{
return
customAlphabet
(
'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890'
,
size
)();
};
packages/service/common/file/utils.ts
View file @
aab6ee51
...
...
@@ -4,7 +4,7 @@ export const removeFilesByPaths = (paths: string[]) => {
paths
.
forEach
((
path
)
=>
{
fs
.
unlink
(
path
,
(
err
)
=>
{
if
(
err
)
{
console
.
error
(
err
);
//
console.error(err);
}
});
});
...
...
packages/service/common/response/index.ts
View file @
aab6ee51
...
...
@@ -3,6 +3,7 @@ import { sseResponseEventEnum } from './constant';
import
{
proxyError
,
ERROR_RESPONSE
,
ERROR_ENUM
}
from
'@fastgpt/global/common/error/errorCode'
;
import
{
addLog
}
from
'../system/log'
;
import
{
clearCookie
}
from
'../../support/permission/controller'
;
import
{
replaceSensitiveLink
}
from
'@fastgpt/global/common/string/tools'
;
export
interface
ResponseType
<
T
=
any
>
{
code
:
number
;
...
...
@@ -52,7 +53,7 @@ export const jsonRes = <T = any>(
res
.
status
(
code
).
json
({
code
,
statusText
:
''
,
message
:
message
||
msg
,
message
:
replaceSensitiveLink
(
message
||
msg
)
,
data
:
data
!==
undefined
?
data
:
null
});
};
...
...
@@ -90,7 +91,7 @@ export const sseErrRes = (res: NextApiResponse, error: any) => {
responseWrite({
res,
event: sseResponseEventEnum.error,
data: JSON.stringify({ message:
msg
})
data: JSON.stringify({ message:
replaceSensitiveLink(msg)
})
});
};
...
...
packages/service/support/permission/controller.ts
View file @
aab6ee51
...
...
@@ -168,6 +168,10 @@ export async function parseHeaderCert({
return
Promise
.
reject
(
ERROR_ENUM
.
unAuthorization
);
})();
if
(
!
authRoot
&&
(
!
teamId
||
!
tmbId
))
{
return
Promise
.
reject
(
ERROR_ENUM
.
unAuthorization
);
}
return
{
userId
:
String
(
uid
),
teamId
:
String
(
teamId
),
...
...
packages/web/components/common/Icon/icons/support/account/loginoutLight.svg
View file @
aab6ee51
<?xml version="1.0" standalone="no"?><!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg
t=
"1689855121257"
class=
"icon"
viewBox=
"0 0 1024 1024"
version=
"1.1"
xmlns=
"http://www.w3.org/2000/svg"
p-id=
"3135"
xmlns:xlink=
"http://www.w3.org/1999/xlink"
width=
"64"
height=
"64"
><path
d=
"M952.7 492.1c-1.4-1.8-3.1-3.4-4.8-4.9l-179-178.9c-12.5-12.5-32.9-12.5-45.4 0s-12.5 32.9 0 45.4l126 126H421.3h-0.1c-18.2 0-32.9 14.8-32.9 33s14.7 33 32.9 33c0.3 0.1 0.5 0 0.7 0h427.8l-126 126c-12.3 12.3-12.3 32.4 0 44.7l0.7 0.7c12.3 12.3 32.4 12.3 44.7 0l182-182c11.7-11.7 12.3-30.6 1.6-43z"
fill=
"#515151"
p-id=
"3136"
></path><path
d=
"M562.3 799c-18 0-32.7 14.7-32.7 32.7v63.8H129.2V128.7h400.4v63.1c0 18 14.7 32.7 32.7 32.7s32.7-14.7 32.7-32.7V96.3c0-3.5-0.6-6.8-1.6-10-4.2-13.3-16.6-23-31.2-23H96.6c-18 0-32.7 14.7-32.7 32.7v831.9c0 14.2 9.2 26.3 21.8 30.8 3.6 1.4 7.5 2.1 11.5 2.1h463.2c0.6 0 1.3 0.1 1.9 0.1 18 0 32.7-14.7 32.7-32.7v-96.5c0-18-14.7-32.7-32.7-32.7z"
fill=
"#515151"
p-id=
"3137"
></path><path
d=
"M256.8 512.7a32.9 33 0 1 0 65.8 0 32.9 33 0 1 0-65.8 0Z"
fill=
"#515151"
p-id=
"3138"
></path></svg>
\ No newline at end of file
<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg
t=
"1689855121257"
class=
"icon"
viewBox=
"0 0 1024 1024"
version=
"1.1"
xmlns=
"http://www.w3.org/2000/svg"
p-id=
"3135"
xmlns:xlink=
"http://www.w3.org/1999/xlink"
width=
"64"
height=
"64"
>
<path
d=
"M952.7 492.1c-1.4-1.8-3.1-3.4-4.8-4.9l-179-178.9c-12.5-12.5-32.9-12.5-45.4 0s-12.5 32.9 0 45.4l126 126H421.3h-0.1c-18.2 0-32.9 14.8-32.9 33s14.7 33 32.9 33c0.3 0.1 0.5 0 0.7 0h427.8l-126 126c-12.3 12.3-12.3 32.4 0 44.7l0.7 0.7c12.3 12.3 32.4 12.3 44.7 0l182-182c11.7-11.7 12.3-30.6 1.6-43z"
p-id=
"3136"
></path>
<path
d=
"M562.3 799c-18 0-32.7 14.7-32.7 32.7v63.8H129.2V128.7h400.4v63.1c0 18 14.7 32.7 32.7 32.7s32.7-14.7 32.7-32.7V96.3c0-3.5-0.6-6.8-1.6-10-4.2-13.3-16.6-23-31.2-23H96.6c-18 0-32.7 14.7-32.7 32.7v831.9c0 14.2 9.2 26.3 21.8 30.8 3.6 1.4 7.5 2.1 11.5 2.1h463.2c0.6 0 1.3 0.1 1.9 0.1 18 0 32.7-14.7 32.7-32.7v-96.5c0-18-14.7-32.7-32.7-32.7z"
p-id=
"3137"
></path>
<path
d=
"M256.8 512.7a32.9 33 0 1 0 65.8 0 32.9 33 0 1 0-65.8 0Z"
p-id=
"3138"
></path>
</svg>
\ No newline at end of file
projects/app/public/docs/versionIntro.md
View file @
aab6ee51
...
...
@@ -4,8 +4,9 @@
2.
优化知识库和对话的数据索引，加快数据操作。
3.
知识库 openAPI，支持通过 API 操作知识库。
4.
新增 - 输入框变量提示。输入 { 号后将会获得可用变量提示。根据社区针对高级编排的反馈，我们计划于 2 月份的版本中，优化变量内容，支持模块的局部变量以及更多全局变量写入。
5.
修复 - API 对话时，chatId 冲突问题。
6.
修复 - Iframe 嵌入网页可能导致的 window.onLoad 冲突。
7.
[
使用文档
](
https://doc.fastgpt.in/docs/intro/
)
8.
[
点击查看高级编排介绍文档
](
https://doc.fastgpt.in/docs/workflow
)
9.
[
点击查看商业版
](
https://doc.fastgpt.in/docs/commercial/
)
5.
优化 - 切换团队后会保存记录，下次登录时优先登录该团队。
6.
修复 - API 对话时，chatId 冲突问题。
7.
修复 - Iframe 嵌入网页可能导致的 window.onLoad 冲突。
8.
[
使用文档
](
https://doc.fastgpt.in/docs/intro/
)
9.
[
点击查看高级编排介绍文档
](
https://doc.fastgpt.in/docs/workflow
)
10.
[
点击查看商业版
](
https://doc.fastgpt.in/docs/commercial/
)
projects/app/public/locales/zh/common.json
View file @
aab6ee51
...
...
@@ -95,7 +95,7 @@
"Last Step"
:
"上一步"
,
"Last use time"
:
"最后使用时间"
,
"Load Failed"
:
"加载失败"
,
"Loading"
:
"加载中"
,
"Loading"
:
"加载中
...
"
,
"Max credit"
:
"最大金额"
,
"Max credit tips"
:
"该链接最大可消耗多少金额，超出后链接将被禁止使用。-1 代表无限制。"
,
"More settings"
:
"更多设置"
,
...
...
@@ -541,7 +541,8 @@
"success"
:
"开始同步"
}
},
"training"
:
{}
"training"
:
{
}
},
"data"
:
{
"Auxiliary Data"
:
"辅助数据"
,
...
...
projects/app/src/components/ChatBox/MessageInput.tsx
View file @
aab6ee51
import
{
useSpeech
}
from
'@/web/common/hooks/useSpeech'
;
import
{
useSystemStore
}
from
'@/web/common/system/useSystemStore'
;
import
{
Box
,
Flex
,
Image
,
Spinner
,
Textarea
}
from
'@chakra-ui/react'
;
import
React
,
{
useRef
,
useEffect
,
useCallback
,
useState
}
from
'react'
;
import
React
,
{
useRef
,
useEffect
,
useCallback
,
useState
,
useTransition
}
from
'react'
;
import
{
useTranslation
}
from
'next-i18next'
;
import
MyTooltip
from
'../MyTooltip'
;
import
MyIcon
from
'@fastgpt/web/components/common/Icon'
;
...
...
@@ -37,7 +37,7 @@ const MessageInput = ({
showFileSelector
=
false
,
resetInputVal
}:
{
onChange
:
(
e
:
string
)
=>
void
;
onChange
?
:
(
e
:
string
)
=>
void
;
onSendMessage
:
(
e
:
string
)
=>
void
;
onStop
:
()
=>
void
;
isChatting
:
boolean
;
...
...
@@ -45,6 +45,8 @@ const MessageInput = ({
TextareaDom
:
React
.
MutableRefObject
<
HTMLTextAreaElement
|
null
>
;
resetInputVal
:
(
val
:
string
)
=>
void
;
})
=>
{
const
[,
startSts
]
=
useTransition
();
const
{
shareId
}
=
useRouter
().
query
as
{
shareId
?:
string
};
const
{
isSpeaking
,
...
...
@@ -330,17 +332,29 @@ ${images.map((img) => JSON.stringify({ src: img.src })).join('\n')}
const
textarea
=
e
.
target
;
textarea
.
style
.
height
=
textareaMinH
;
textarea
.
style
.
height
=
`${textarea.scrollHeight}px`
;
onChange
(
textarea
.
value
);
startSts
(()
=>
{
onChange
?.(
textarea
.
value
);
});
}
}
onKeyDown=
{
(
e
)
=>
{
// enter send.(pc or iframe && enter and unPress shift)
if
((
isPc
||
window
!==
parent
)
&&
e
.
keyCode
===
13
&&
!
e
.
shiftKey
)
{
handleSend
();
e
.
preventDefault
();
const
isEnter
=
e
.
keyCode
===
13
;
if
(
isEnter
&&
TextareaDom
.
current
&&
(
e
.
ctrlKey
||
e
.
altKey
))
{
TextareaDom
.
current
.
value
+=
'\n'
;
TextareaDom
.
current
.
style
.
height
=
textareaMinH
;
TextareaDom
.
current
.
style
.
height
=
`${TextareaDom.current.scrollHeight}px`
;
return
;
}
// 全选内容
// @ts-ignore
e
.
key
===
'a'
&&
e
.
ctrlKey
&&
e
.
target
?.
select
();
if
((
isPc
||
window
!==
parent
)
&&
e
.
keyCode
===
13
&&
!
e
.
shiftKey
)
{
handleSend
();
e
.
preventDefault
();
}
}
}
onPaste=
{
(
e
)
=>
{
const
clipboardData
=
e
.
clipboardData
;
...
...
projects/app/src/components/ChatBox/index.tsx
View file @
aab6ee51
...
...
@@ -36,7 +36,7 @@ import { adaptChat2GptMessages } from '@fastgpt/global/core/chat/adapt';
import
{
useMarkdown
}
from
'@/web/common/hooks/useMarkdown'
;
import
{
ModuleItemType
}
from
'@fastgpt/global/core/module/type.d'
;
import
{
VariableInputEnum
}
from
'@fastgpt/global/core/module/constants'
;
import
{
useForm
}
from
'react-hook-form'
;
import
{
UseFormReturn
,
useForm
}
from
'react-hook-form'
;
import
type
{
ChatMessageItemType
}
from
'@fastgpt/global/core/ai/type.d'
;
import
{
fileDownload
}
from
'@/web/common/file/utils'
;
import
{
htmlTemplate
}
from
'@/constants/common'
;
...
...
@@ -65,7 +65,7 @@ const SelectMarkCollection = dynamic(() => import('./SelectMarkCollection'));
import
styles
from
'./index.module.scss'
;
import
{
postQuestionGuide
}
from
'@/web/core/ai/api'
;
import
{
splitGuideModule
}
from
'@fastgpt/global/core/module/utils'
;
import
type
{
AppTTSConfigType
}
from
'@fastgpt/global/core/module/type.d'
;
import
type
{
AppTTSConfigType
,
VariableItemType
}
from
'@fastgpt/global/core/module/type.d'
;
import
MessageInput
from
'./MessageInput'
;
import
{
ModuleOutputKeyEnum
}
from
'@fastgpt/global/core/module/constants'
;
import
ChatBoxDivider
from
'../core/chat/Divider'
;
...
...
@@ -98,6 +98,15 @@ enum FeedbackTypeEnum {
hidden
=
'hidden'
}
const
MessageCardStyle
:
BoxProps
=
{
px
:
4
,
py
:
3
,
borderRadius
:
'0 8px 8px 8px'
,
boxShadow
:
'0 0 8px rgba(0,0,0,0.15)'
,
display
:
'inline-block'
,
maxW
:
[
'calc(100% - 25px)'
,
'calc(100% - 40px)'
]
};
type
Props
=
{
feedbackType
?:
`
${
FeedbackTypeEnum
}
`
;
showMarkIcon
?:
boolean
;
// admin mark dataset
...
...
@@ -157,7 +166,6 @@ const ChatBox = (
const
isNewChatReplace
=
useRef
(
false
);
const
[
refresh
,
setRefresh
]
=
useState
(
false
);
const
[
variables
,
setVariables
]
=
useState
<
Record
<
string
,
any
>>
({});
// settings variable
const
[
chatHistory
,
setChatHistory
]
=
useState
<
ChatSiteItemType
[]
>
([]);
const
[
feedbackId
,
setFeedbackId
]
=
useState
<
string
>
();
const
[
readFeedbackData
,
setReadFeedbackData
]
=
useState
<
{
...
...
@@ -180,7 +188,17 @@ const ChatBox = (
);
// compute variable input is finish.
const
[
variableInputFinish
,
setVariableInputFinish
]
=
useState
(
false
);
const
chatForm
=
useForm
<
{
variables
:
Record
<
string
,
any
>
;
}
>
({
defaultValues
:
{
variables
:
{}
}
});
const
{
setValue
,
watch
,
handleSubmit
}
=
chatForm
;
const
variables
=
watch
(
'variables'
);
const
[
variableInputFinish
,
setVariableInputFinish
]
=
useState
(
false
);
// clicked start chat button
const
variableIsFinish
=
useMemo
(()
=>
{
if
(
!
variableModules
||
variableModules
.
length
===
0
||
chatHistory
.
length
>
0
)
return
true
;
...
...
@@ -194,21 +212,15 @@ const ChatBox = (
return
variableInputFinish
;
},
[
chatHistory
.
length
,
variableInputFinish
,
variableModules
,
variables
]);
const
{
register
,
reset
,
getValues
,
setValue
,
handleSubmit
}
=
useForm
<
Record
<
string
,
any
>>
({
defaultValues
:
variables
});
// 滚动到底部
const
scrollToBottom
=
useCallback
(
(
behavior
:
'smooth'
|
'auto'
=
'smooth'
)
=>
{
if
(
!
ChatBoxRef
.
current
)
return
;
ChatBoxRef
.
current
.
scrollTo
({
top
:
ChatBoxRef
.
current
.
scrollHeight
,
behavior
});
},
[
ChatBoxRef
]
);
const
scrollToBottom
=
(
behavior
:
'smooth'
|
'auto'
=
'smooth'
)
=>
{
if
(
!
ChatBoxRef
.
current
)
return
;
ChatBoxRef
.
current
.
scrollTo
({
top
:
ChatBoxRef
.
current
.
scrollHeight
,
behavior
});
};
// 聊天信息生成中……获取当前滚动条位置，判断是否需要滚动到底部
const
generatingScroll
=
useCallback
(
throttle
(()
=>
{
...
...
@@ -222,28 +234,31 @@ const ChatBox = (
[]
);
// eslint-disable-next-line react-hooks/exhaustive-deps
const
generatingMessage
=
({
text
=
''
,
status
,
name
}:
generatingMessageProps
)
=>
{
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
...(
text
?
{
value
:
item
.
value
+
text
}
:
{}),
...(
status
&&
name
?
{
status
,
moduleName
:
name
}
:
{})
};
})
);
generatingScroll
();
};
const
generatingMessage
=
useCallback
(
({
text
=
''
,
status
,
name
}:
generatingMessageProps
)
=>
{
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
...(
text
?
{
value
:
item
.
value
+
text
}
:
{}),
...(
status
&&
name
?
{
status
,
moduleName
:
name
}
:
{})
};
})
);
generatingScroll
();
},
[
generatingScroll
]
);
// 重置输入内容
const
resetInputVal
=
useCallback
((
val
:
string
)
=>
{
...
...
@@ -284,149 +299,157 @@ const ChatBox = (
}
}
catch
(
error
)
{}
},
[
questionGuide
,
s
crollToBottom
,
s
hareId
]
[
questionGuide
,
shareId
]
);
/**
* user confirm send prompt
*/
const
sendPrompt
=
useCallback
(
async
(
variables
:
Record
<
string
,
any
>
=
{},
inputVal
=
''
,
history
=
chatHistory
)
=>
{
if
(
!
onStartChat
)
return
;
if
(
isChatting
)
{
toast
({
title
:
'正在聊天中...请等待结束'
,
status
:
'warning'
});
return
;
}
questionGuideController
.
current
?.
abort
(
'stop'
);
// get input value
const
val
=
inputVal
.
trim
();
if
(
!
val
)
{
toast
({
title
:
'内容为空'
,
status
:
'warning'
});
return
;
}
const
newChatList
:
ChatSiteItemType
[]
=
[
...
history
,
{
dataId
:
nanoid
(),
obj
:
'Human'
,
value
:
val
,
status
:
'finish'
},
{
dataId
:
nanoid
(),
obj
:
'AI'
,
value
:
''
,
status
:
'loading'
({
inputVal
=
''
,
history
=
chatHistory
}:
{
inputVal
?:
string
;
history
?:
ChatSiteItemType
[];
})
=>
{
handleSubmit
(
async
({
variables
})
=>
{
if
(
!
onStartChat
)
return
;
if
(
isChatting
)
{
toast
({
title
:
'正在聊天中...请等待结束'
,
status
:
'warning'
});
return
;
}
questionGuideController
.
current
?.
abort
(
'stop'
);
// get input value
const
val
=
inputVal
.
trim
();
if
(
!
val
)
{
toast
({
title
:
'内容为空'
,
status
:
'warning'
});
return
;
}
];
// 插入内容
setChatHistory
(
newChatList
);
// 清空输入内容
resetInputVal
(
''
);
setQuestionGuide
([]);
setTimeout
(()
=>
{
scrollToBottom
();
},
100
);
try
{
// create abort obj
const
abortSignal
=
new
AbortController
();
chatController
.
current
=
abortSignal
;
const
messages
=
adaptChat2GptMessages
({
messages
:
newChatList
,
reserveId
:
true
});
const
{
responseData
,
responseText
,
isNewChat
=
false
}
=
await
onStartChat
({
chatList
:
newChatList
.
map
((
item
)
=>
({
dataId
:
item
.
dataId
,
obj
:
item
.
obj
,
value
:
item
.
value
,
status
:
item
.
status
,
moduleName
:
item
.
moduleName
})),
messages
,
controller
:
abortSignal
,
generatingMessage
,
variables
});
const
newChatList
:
ChatSiteItemType
[]
=
[
...
history
,
{
dataId
:
nanoid
(),
obj
:
'Human'
,
value
:
val
,
status
:
'finish'
},
{
dataId
:
nanoid
(),
obj
:
'AI'
,
value
:
''
,
status
:
'loading'
}
];
isNewChatReplace
.
current
=
isNewChat
;
// set finish status
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
status
:
'finish'
,
responseData
};
})
);
// 插入内容
setChatHistory
(
newChatList
);
// 清空输入内容
resetInputVal
(
''
);
setQuestionGuide
([]);
setTimeout
(()
=>
{
createQuestionGuide
({
history
:
newChatList
.
map
((
item
,
i
)
=>
i
===
newChatList
.
length
-
1
?
{
...
item
,
value
:
responseText
}
:
item
)
});
generatingScroll
();
isPc
&&
TextareaDom
.
current
?.
focus
();
scrollToBottom
();
},
100
);
}
catch
(
err
:
any
)
{
toast
({
title
:
t
(
getErrText
(
err
,
'core.chat.error.Chat error'
)),
status
:
'error'
,
duration
:
5000
,
isClosable
:
true
});
try
{
// create abort obj
const
abortSignal
=
new
AbortController
();
chatController
.
current
=
abortSignal
;
const
messages
=
adaptChat2GptMessages
({
messages
:
newChatList
,
reserveId
:
true
});
const
{
responseData
,
responseText
,
isNewChat
=
false
}
=
await
onStartChat
({
chatList
:
newChatList
.
map
((
item
)
=>
({
dataId
:
item
.
dataId
,
obj
:
item
.
obj
,
value
:
item
.
value
,
status
:
item
.
status
,
moduleName
:
item
.
moduleName
})),
messages
,
controller
:
abortSignal
,
generatingMessage
,
variables
});
if
(
!
err
?.
responseText
)
{
resetInputVal
(
inputVal
);
setChatHistory
(
newChatList
.
slice
(
0
,
newChatList
.
length
-
2
));
}
isNewChatReplace
.
current
=
isNewChat
;
// set finish status
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
status
:
'finish'
};
})
);
}
// set finish status
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
status
:
'finish'
,
responseData
};
})
);
setTimeout
(()
=>
{
createQuestionGuide
({
history
:
newChatList
.
map
((
item
,
i
)
=>
i
===
newChatList
.
length
-
1
?
{
...
item
,
value
:
responseText
}
:
item
)
});
generatingScroll
();
isPc
&&
TextareaDom
.
current
?.
focus
();
},
100
);
}
catch
(
err
:
any
)
{
toast
({
title
:
t
(
getErrText
(
err
,
'core.chat.error.Chat error'
)),
status
:
'error'
,
duration
:
5000
,
isClosable
:
true
});
if
(
!
err
?.
responseText
)
{
resetInputVal
(
inputVal
);
setChatHistory
(
newChatList
.
slice
(
0
,
newChatList
.
length
-
2
));
}
// set finish status
setChatHistory
((
state
)
=>
state
.
map
((
item
,
index
)
=>
{
if
(
index
!==
state
.
length
-
1
)
return
item
;
return
{
...
item
,
status
:
'finish'
};
})
);
}
})();
},
[
chatHistory
,
onStartChat
,
isChatting
,
resetInputVal
,
toast
,
scrollToBottom
,
generatingMessage
,
createQuestionGuide
,
generatingMessage
,
generatingScroll
,
handleSubmit
,
isChatting
,
isPc
,
t
onStartChat
,
resetInputVal
,
t
,
toast
]
);
...
...
@@ -444,11 +467,14 @@ const ChatBox = (
);
setChatHistory
((
state
)
=>
(
index
===
0
?
[]
:
state
.
slice
(
0
,
index
)));
sendPrompt
(
variables
,
delHistory
[
0
].
value
,
chatHistory
.
slice
(
0
,
index
));
sendPrompt
({
inputVal
:
delHistory
[
0
].
value
,
history
:
chatHistory
.
slice
(
0
,
index
)
});
}
catch
(
error
)
{}
setLoading
(
false
);
},
[
chatHistory
,
onDelMessage
,
sendPrompt
,
setLoading
,
variables
]
[
chatHistory
,
onDelMessage
,
sendPrompt
,
setLoading
]
);
// delete one message
const
delOneMessage
=
useCallback
(
...
...
@@ -471,27 +497,21 @@ const ChatBox = (
defaultVal
[
item
.
key
]
=
''
;
});
reset
(
e
||
defaultVal
);
setVariables
(
e
||
defaultVal
);
setValue
(
'variables'
,
e
||
defaultVal
);
},
resetHistory
(
e
)
{
setVariableInputFinish
(
!!
e
.
length
);
setChatHistory
(
e
);
},
scrollToBottom
,
sendPrompt
:
(
question
:
string
)
=>
handleSubmit
((
item
)
=>
sendPrompt
(
item
,
question
))()
sendPrompt
:
(
question
:
string
)
=>
{
sendPrompt
({
inputVal
:
question
});
}
}));
/* style start */
const
MessageCardStyle
:
BoxProps
=
{
px
:
4
,
py
:
3
,
borderRadius
:
'0 8px 8px 8px'
,
boxShadow
:
'0 0 8px rgba(0,0,0,0.15)'
,
display
:
'inline-block'
,
maxW
:
[
'calc(100% - 25px)'
,
'calc(100% - 40px)'
]
};
const
showEmpty
=
useMemo
(
()
=>
feConfigs
?.
show_emptyChat
&&
...
...
@@ -534,14 +554,18 @@ const ChatBox = (
useEffect
(()
=>
{
const
windowMessage
=
({
data
}:
MessageEvent
<
{
type
:
'sendPrompt'
;
text
:
string
}
>
)
=>
{
if
(
data
?.
type
===
'sendPrompt'
&&
data
?.
text
)
{
handleSubmit
((
item
)
=>
sendPrompt
(
item
,
data
.
text
))();
sendPrompt
({
inputVal
:
data
.
text
});
}
};
window
.
addEventListener
(
'message'
,
windowMessage
);
eventBus
.
on
(
EventNameEnum
.
sendQuestion
,
({
text
}:
{
text
:
string
})
=>
{
if
(
!
text
)
return
;
handleSubmit
((
data
)
=>
sendPrompt
(
data
,
text
))();
sendPrompt
({
inputVal
:
text
});
});
eventBus
.
on
(
EventNameEnum
.
editQuestion
,
({
text
}:
{
text
:
string
})
=>
{
if
(
!
text
)
return
;
...
...
@@ -553,140 +577,81 @@ const ChatBox = (
eventBus
.
off
(
EventNameEnum
.
sendQuestion
);
eventBus
.
off
(
EventNameEnum
.
editQuestion
);
};
},
[
handleSubmit
,
resetInputVal
,
sendPrompt
]);
},
[
resetInputVal
,
sendPrompt
]);
const
onSubmitVariables
=
useCallback
(
(
data
:
Record
<
string
,
any
>
)
=>
{
setVariableInputFinish
(
true
);
onUpdateVariable
?.(
data
);
},
[
onUpdateVariable
]
);
const
HumanChatCard
=
useCallback
(
({
item
,
index
}:
{
item
:
ChatSiteItemType
;
index
:
number
})
=>
{
return
(
<>
{
/* control icon */
}
<
Flex
w=
{
'100%'
}
alignItems=
{
'center'
}
justifyContent=
{
'flex-end'
}
>
<
ChatControllerComponent
chat=
{
item
}
onDelete=
{
onDelMessage
?
()
=>
{
delOneMessage
({
dataId
:
item
.
dataId
,
index
});
}
:
undefined
}
onRetry=
{
useCallback
(()
=>
retryInput
(
index
),
[
index
])
}
/>
<
ChatAvatar
src=
{
userAvatar
}
type=
{
'Human'
}
/>
</
Flex
>
{
/* content */
}
<
Box
mt=
{
[
'6px'
,
2
]
}
textAlign=
{
'right'
}
>
<
Card
className=
"markdown"
{
...
MessageCardStyle
}
bg=
{
'primary.200'
}
borderRadius=
{
'8px 0 8px 8px'
}
textAlign=
{
'left'
}
>
<
Markdown
source=
{
item
.
value
}
isChatting=
{
false
}
/>
</
Card
>
</
Box
>
</>
);
},
[]
);
return
(
<
Flex
flexDirection=
{
'column'
}
h=
{
'100%'
}
>
<
Script
src=
"/js/html2pdf.bundle.min.js"
strategy=
"lazyOnload"
></
Script
>
{
/* chat box container */
}
<
Box
ref=
{
ChatBoxRef
}
flex=
{
'1 0 0'
}
h=
{
0
}
w=
{
'100%'
}
overflow=
{
'overlay'
}
px=
{
[
4
,
0
]
}
pb=
{
3
}
>
<
Box
id=
"chat-container"
maxW=
{
[
'100%'
,
'92%'
]
}
h=
{
'100%'
}
mx=
{
'auto'
}
>
{
showEmpty
&&
<
Empty
/>
}
{
!!
welcomeText
&&
(
<
Box
py=
{
3
}
>
{
/* avatar */
}
<
ChatAvatar
src=
{
appAvatar
}
type=
{
'AI'
}
/>
{
/* message */
}
<
Box
textAlign=
{
'left'
}
>
<
Card
order=
{
2
}
mt=
{
2
}
{
...
MessageCardStyle
}
bg=
{
'white'
}
>
<
Markdown
source=
{
`~~~guide \n${welcomeText}`
}
isChatting=
{
false
}
/>
</
Card
>
</
Box
>
</
Box
>
)
}
{
!!
welcomeText
&&
<
WelcomeText
appAvatar=
{
appAvatar
}
welcomeText=
{
welcomeText
}
/>
}
{
/* variable input */
}
{
!!
variableModules
?.
length
&&
(
<
Box
py=
{
3
}
>
{
/* avatar */
}
<
ChatAvatar
src=
{
appAvatar
}
type=
{
'AI'
}
/>
{
/* message */
}
<
Box
textAlign=
{
'left'
}
>
<
Card
order=
{
2
}
mt=
{
2
}
bg=
{
'white'
}
w=
{
'400px'
}
{
...
MessageCardStyle
}
>
{
variableModules
.
map
((
item
)
=>
(
<
Box
key=
{
item
.
id
}
mb=
{
4
}
>
<
VariableLabel
required=
{
item
.
required
}
>
{
item
.
label
}
</
VariableLabel
>
{
item
.
type
===
VariableInputEnum
.
input
&&
(
<
Input
isDisabled=
{
variableIsFinish
}
bg=
{
'myWhite.400'
}
{
...
register
(
item
.
key
,
{
required
:
item
.
required
})}
/>
)
}
{
item
.
type
===
VariableInputEnum
.
textarea
&&
(
<
Textarea
isDisabled=
{
variableIsFinish
}
bg=
{
'myWhite.400'
}
{
...
register
(
item
.
key
,
{
required
:
item
.
required
})}
rows=
{
5
}
maxLength=
{
4000
}
/>
)
}
{
item
.
type
===
VariableInputEnum
.
select
&&
(
<
MySelect
width=
{
'100%'
}
isDisabled=
{
variableIsFinish
}
list=
{
(
item
.
enums
||
[]).
map
((
item
)
=>
({
label
:
item
.
value
,
value
:
item
.
value
}))
}
{
...
register
(
item
.
key
,
{
required
:
item
.
required
})}
value=
{
getValues
(
item
.
key
)
}
onchange=
{
(
e
)
=>
{
setValue
(
item
.
key
,
e
);
setRefresh
(
!
refresh
);
}
}
/>
)
}
</
Box
>
))
}
{
!
variableIsFinish
&&
(
<
Button
leftIcon=
{
<
MyIcon
name=
{
'core/chat/chatFill'
}
w=
{
'16px'
}
/>
}
size=
{
'sm'
}
maxW=
{
'100px'
}
onClick=
{
handleSubmit
((
data
)
=>
{
onUpdateVariable
?.(
data
);
setVariables
(
data
);
setVariableInputFinish
(
true
);
})
}
>
{
t
(
'core.chat.Start Chat'
)
}
</
Button
>
)
}
</
Card
>
</
Box
>
</
Box
>
<
VariableInput
appAvatar=
{
appAvatar
}
variableModules=
{
variableModules
}
variableIsFinish=
{
variableIsFinish
}
chatForm=
{
chatForm
}
onSubmitVariables=
{
onSubmitVariables
}
/>
)
}
{
/* chat history */
}
<
Box
id=
{
'history'
}
>
{
chatHistory
.
map
((
item
,
index
)
=>
(
<
Box
key=
{
item
.
dataId
}
py=
{
5
}
>
{
item
.
obj
===
'Human'
&&
(
<>
{
/* control icon */
}
<
Flex
w=
{
'100%'
}
alignItems=
{
'center'
}
justifyContent=
{
'flex-end'
}
>
<
ChatController
chat=
{
item
}
onDelete=
{
onDelMessage
?
()
=>
{
delOneMessage
({
dataId
:
item
.
dataId
,
index
});
}
:
undefined
}
onRetry=
{
()
=>
retryInput
(
index
)
}
/>
<
ChatAvatar
src=
{
userAvatar
}
type=
{
'Human'
}
/>
</
Flex
>
{
/* content */
}
<
Box
mt=
{
[
'6px'
,
2
]
}
textAlign=
{
'right'
}
>
<
Card
className=
"markdown"
{
...
MessageCardStyle
}
bg=
{
'primary.200'
}
borderRadius=
{
'8px 0 8px 8px'
}
textAlign=
{
'left'
}
>
<
Markdown
source=
{
item
.
value
}
isChatting=
{
false
}
/>
</
Card
>
</
Box
>
</>
)
}
{
item
.
obj
===
'Human'
&&
<
HumanChatCard
item=
{
item
}
index=
{
index
}
/>
}
{
item
.
obj
===
'AI'
&&
(
<>
{
/* control icon */
}
<
Flex
w=
{
'100%'
}
alignItems=
{
'center'
}
>
<
ChatAvatar
src=
{
appAvatar
}
type=
{
'AI'
}
/>
<
ChatController
{
/* control icon */
}
<
ChatControllerComponent
ml=
{
2
}
chat=
{
item
}
setChatHistory=
{
setChatHistory
}
...
...
@@ -723,36 +688,35 @@ const ChatBox = (
}
:
undefined
}
onAddUserLike=
{
(()
=>
{
if
(
feedbackType
!==
FeedbackTypeEnum
.
user
||
item
.
userBadFeedback
)
{
return
;
}
return
()
=>
{
if
(
!
item
.
dataId
||
!
chatId
||
!
appId
)
return
;
const
isGoodFeedback
=
!!
item
.
userGoodFeedback
;
setChatHistory
((
state
)
=>
state
.
map
((
chatItem
)
=>
chatItem
.
dataId
===
item
.
dataId
?
{
...
chatItem
,
userGoodFeedback
:
isGoodFeedback
?
undefined
:
'yes'
}
:
chatItem
)
);
try
{
updateChatUserFeedback
({
appId
,
chatId
,
chatItemId
:
item
.
dataId
,
shareId
,
outLinkUid
,
userGoodFeedback
:
isGoodFeedback
?
undefined
:
'yes'
});
}
catch
(
error
)
{}
};
})()
}
onAddUserLike=
{
feedbackType
!==
FeedbackTypeEnum
.
user
||
item
.
userBadFeedback
?
undefined
:
()
=>
{
if
(
!
item
.
dataId
||
!
chatId
||
!
appId
)
return
;
const
isGoodFeedback
=
!!
item
.
userGoodFeedback
;
setChatHistory
((
state
)
=>
state
.
map
((
chatItem
)
=>
chatItem
.
dataId
===
item
.
dataId
?
{
...
chatItem
,
userGoodFeedback
:
isGoodFeedback
?
undefined
:
'yes'
}
:
chatItem
)
);
try
{
updateChatUserFeedback
({
appId
,
chatId
,
chatItemId
:
item
.
dataId
,
shareId
,
outLinkUid
,
userGoodFeedback
:
isGoodFeedback
?
undefined
:
'yes'
});
}
catch
(
error
)
{}
}
}
onCloseUserLike=
{
feedbackType
===
FeedbackTypeEnum
.
admin
?
()
=>
{
...
...
@@ -931,13 +895,12 @@ const ChatBox = (
</
Box
>
</
Box
>
{
/* message input */
}
{
onStartChat
&&
variableIsFinish
&&
active
?
(
{
onStartChat
&&
variableIsFinish
&&
active
&&
(
<
MessageInput
onChange=
{
(
e
)
=>
{
setRefresh
(
!
refresh
);
}
}
onSendMessage=
{
(
e
)
=>
{
handleSubmit
((
data
)
=>
sendPrompt
(
data
,
e
))();
onSendMessage=
{
(
inputVal
)
=>
{
sendPrompt
({
inputVal
});
}
}
onStop=
{
()
=>
chatController
.
current
?.
abort
(
'stop'
)
}
isChatting=
{
isChatting
}
...
...
@@ -945,7 +908,7 @@ const ChatBox = (
resetInputVal=
{
resetInputVal
}
showFileSelector=
{
showFileSelector
}
/>
)
:
null
}
)
}
{
/* user feedback modal */
}
{
!!
feedbackId
&&
chatId
&&
appId
&&
(
<
FeedbackModal
...
...
@@ -1115,30 +1078,125 @@ export const useChatBox = () => {
};
};
function VariableLabel
({
required = false
,
children
const WelcomeText = React.memo(function Welcome
({
appAvatar
,
welcomeText
}: {
required?: boolean
;
children: React.ReactNode |
string;
appAvatar?: string
;
welcomeText:
string;
}) {
return (
<Box as={'label'} display={'inline-block'} position={'relative'} mb={1}>
{children}
{required && (
<Box
position={'absolute'}
top={'-2px'}
right={'-10px'}
color={'red.500'}
fontWeight={'bold'}
>
*
</Box>
)}
<Box py={3}>
{/* avatar */}
<ChatAvatar src={appAvatar} type={'AI'} />
{/* message */}
<Box textAlign={'left'}>
<Card order={2} mt={2} {...MessageCardStyle} bg={'white'}>
<Markdown source={`~~~guide \n${welcomeText}`} isChatting={false} />
</Card>
</Box>
</Box>
);
}
});
const VariableInput = React.memo(function VariableInput({
appAvatar,
variableModules,
variableIsFinish,
chatForm,
onSubmitVariables
}: {
appAvatar?: string;
variableModules: VariableItemType[];
variableIsFinish: boolean;
onSubmitVariables: (e: Record<string, any>) => void;
chatForm: UseFormReturn<{
variables: Record<string, any>;
}>;
}) {
const { t } = useTranslation();
const { register, setValue, handleSubmit: handleSubmitChat, watch } = chatForm;
const variables = watch('variables');
return (
<Box py={3}>
{/* avatar */}
<ChatAvatar src={appAvatar} type={'AI'} />
{/* message */}
<Box textAlign={'left'}>
<Card order={2} mt={2} bg={'white'} w={'400px'} {...MessageCardStyle}>
{variableModules.map((item) => (
<Box key={item.id} mb={4}>
<Box as={'label'} display={'inline-block'} position={'relative'} mb={1}>
{item.label}
{item.required && (
<Box
position={'absolute'}
top={'-2px'}
right={'-10px'}
color={'red.500'}
fontWeight={'bold'}
>
*
</Box>
)}
</Box>
{item.type === VariableInputEnum.input && (
<Input
isDisabled={variableIsFinish}
bg={'myWhite.400'}
{...register(`variables.${item.key}`, {
required: item.required
})}
/>
)}
{item.type === VariableInputEnum.textarea && (
<Textarea
isDisabled={variableIsFinish}
bg={'myWhite.400'}
{...register(`variables.${item.key}`, {
required: item.required
})}
rows={5}
maxLength={4000}
/>
)}
{item.type === VariableInputEnum.select && (
<MySelect
width={'100%'}
isDisabled={variableIsFinish}
list={(item.enums || []).map((item) => ({
label: item.value,
value: item.value
}))}
{...register(`variables.${item.key}`, {
required: item.required
})}
value={variables[item.key]}
onchange={(e) => {
setValue(`variables.${item.key}`, e);
}}
/>
)}
</Box>
))}
{!variableIsFinish && (
<Button
leftIcon={<MyIcon name={'core/chat/chatFill'} w={'16px'} />}
size={'sm'}
maxW={'100px'}
onClick={handleSubmitChat((data) => {
onSubmitVariables(data);
})}
>
{t('core.chat.Start Chat')}
</Button>
)}
</Card>
</Box>
</Box>
);
});
function ChatAvatar({ src, type }: { src?: string; type: 'Human' | 'AI' }) {
const theme = useTheme();
return (
...
...
@@ -1173,7 +1231,7 @@ function Empty() {
);
}
function ChatController
({
const ChatControllerComponent = React.memo(function ChatControllerComponent
({
chat,
setChatHistory,
display,
...
...
@@ -1226,7 +1284,7 @@ function ChatController({
return (
<Flex {...controlContainerStyle} ml={ml} mr={mr} display={display}>
<MyTooltip label={
'复制'
}>
<MyTooltip label={
t('common.Copy')
}>
<MyIcon
{...controlIconStyle}
name={'copy'}
...
...
@@ -1246,7 +1304,7 @@ function ChatController({
/>
</MyTooltip>
)}
<MyTooltip label={
'删除'
}>
<MyTooltip label={
t('common.Delete')
}>
<MyIcon
{...controlIconStyle}
name={'delete'}
...
...
@@ -1259,7 +1317,7 @@ function ChatController({
{showVoiceIcon &&
hasAudio &&
(audioLoading ? (
<MyTooltip label={
'加载中...'
}>
<MyTooltip label={
t('common.Loading')
}>
<MyIcon {...controlIconStyle} name={'common/loading'} />
</MyTooltip>
) : audioPlaying ? (
...
...
@@ -1372,4 +1430,4 @@ function ChatController({
)}
</Flex>
);
}
}
);
projects/app/src/components/Markdown/index.tsx
View file @
aab6ee51
...
...
@@ -35,36 +35,79 @@ export enum CodeClassName {
img
=
'img'
}
function
Code
({
inline
,
className
,
children
}:
any
)
{
const
match
=
/language-
(\w
+
)
/
.
exec
(
className
||
''
);
const
codeType
=
match
?.[
1
];
const
Markdown
=
({
source
,
isChatting
=
false
}:
{
source
:
string
;
isChatting
?:
boolean
})
=>
{
const
components
=
useMemo
<
any
>
(
()
=>
({
img
:
Image
,
pre
:
'div'
,
p
:
(
pProps
:
any
)
=>
<
p
{
...
pProps
}
dir=
"auto"
/>,
code
:
Code
,
a
:
A
}),
[]
);
if
(
codeType
===
CodeClassName
.
mermaid
)
{
return
<
MermaidCodeBlock
code=
{
String
(
children
)
}
/>;
}
const
formatSource
=
source
.
replace
(
/
\\
n/g
,
'\n '
)
.
replace
(
/
(
http
[
s
]?
:
\/\/[^\s
，。
]
+
)([
。，
])
/g
,
'$1 $2'
)
.
replace
(
/
\n
*
(\[
QUOTE SIGN
\]\(
.*
\))
/g
,
'$1'
);
if
(
codeType
===
CodeClassName
.
guide
)
{
return
<
ChatGuide
text=
{
String
(
children
)
}
/>;
}
if
(
codeType
===
CodeClassName
.
questionGuide
)
{
return
<
QuestionGuide
text=
{
String
(
children
)
}
/>;
}
if
(
codeType
===
CodeClassName
.
echarts
)
{
return
<
EChartsCodeBlock
code=
{
String
(
children
)
}
/>;
}
if
(
codeType
===
CodeClassName
.
img
)
{
return
<
ImageBlock
images=
{
String
(
children
)
}
/>;
}
return
(
<
CodeLight
className=
{
className
}
inline=
{
inline
}
match=
{
match
}
>
{
children
}
</
CodeLight
>
<
ReactMarkdown
className=
{
`markdown ${styles.markdown}
${isChatting ? `
$
{
formatSource
?
styles
.
waitingAnimation
:
styles
.
animation
}
` : ''}
`
}
remarkPlugins=
{
[
RemarkMath
,
RemarkGfm
,
RemarkBreaks
]
}
rehypePlugins=
{
[
RehypeKatex
]
}
components=
{
components
}
linkTarget=
{
'_blank'
}
>
{
formatSource
}
</
ReactMarkdown
>
);
}
function
Image
({
src
}:
{
src
?:
string
})
{
};
export
default
React
.
memo
(
Markdown
);
const
Code
=
React
.
memo
(
function
Code
(
e
:
any
)
{
const
{
inline
,
className
,
children
}
=
e
;
const
match
=
/language-
(\w
+
)
/
.
exec
(
className
||
''
);
const
codeType
=
match
?.[
1
];
const
strChildren
=
String
(
children
);
const
Component
=
useMemo
(()
=>
{
if
(
codeType
===
CodeClassName
.
mermaid
)
{
return
<
MermaidCodeBlock
code=
{
strChildren
}
/>;
}
if
(
codeType
===
CodeClassName
.
guide
)
{
return
<
ChatGuide
text=
{
strChildren
}
/>;
}
if
(
codeType
===
CodeClassName
.
questionGuide
)
{
return
<
QuestionGuide
text=
{
strChildren
}
/>;
}
if
(
codeType
===
CodeClassName
.
echarts
)
{
return
<
EChartsCodeBlock
code=
{
strChildren
}
/>;
}
if
(
codeType
===
CodeClassName
.
img
)
{
return
<
ImageBlock
images=
{
strChildren
}
/>;
}
return
(
<
CodeLight
className=
{
className
}
inline=
{
inline
}
match=
{
match
}
>
{
children
}
</
CodeLight
>
);
},
[
codeType
,
className
,
inline
,
match
,
strChildren
]);
return
Component
;
});
const
Image
=
React
.
memo
(
function
Image
({
src
}:
{
src
?:
string
})
{
return
<
MdImage
src=
{
src
}
/>;
}
function
A
({
children
,
...
props
}:
any
)
{
}
);
const
A
=
React
.
memo
(
function
A
({
children
,
...
props
}:
any
)
{
const
{
t
}
=
useTranslation
();
// empty href link
...
...
@@ -109,38 +152,4 @@ function A({ children, ...props }: any) {
}
return
<
Link
{
...
props
}
>
{
children
}
</
Link
>;
}
const
Markdown
=
({
source
,
isChatting
=
false
}:
{
source
:
string
;
isChatting
?:
boolean
})
=>
{
const
components
=
useMemo
<
any
>
(
()
=>
({
img
:
Image
,
pre
:
'div'
,
p
:
(
pProps
:
any
)
=>
<
p
{
...
pProps
}
dir=
"auto"
/>,
code
:
Code
,
a
:
A
}),
[]
);
const
formatSource
=
source
.
replace
(
/
\\
n/g
,
'\n '
)
.
replace
(
/
(
http
[
s
]?
:
\/\/[^\s
，。
]
+
)([
。，
])
/g
,
'$1 $2'
)
.
replace
(
/
\n
*
(\[
QUOTE SIGN
\]\(
.*
\))
/g
,
'$1'
);
return
(
<
ReactMarkdown
className=
{
`markdown ${styles.markdown}
${isChatting ? `
$
{
formatSource
?
styles
.
waitingAnimation
:
styles
.
animation
}
` : ''}
`
}
remarkPlugins=
{
[
RemarkMath
,
RemarkGfm
,
RemarkBreaks
]
}
rehypePlugins=
{
[
RehypeKatex
]
}
components=
{
components
}
linkTarget=
{
'_blank'
}
>
{
formatSource
}
</
ReactMarkdown
>
);
};
export
default
React
.
memo
(
Markdown
);
});
projects/app/src/components/common/Textarea/TagTextarea.tsx
View file @
aab6ee51
...
...
@@ -79,6 +79,8 @@ const TagTextarea = ({ defaultValues, onUpdate, ...props }: Props) => {
ref=
{
InputRef
}
variant=
{
'unstyled'
}
display=
{
'inline-block'
}
h=
{
'24px'
}
borderRadius=
{
'none'
}
w=
"auto"
onBlur=
{
(
e
)
=>
{
const
value
=
e
.
target
.
value
;
...
...
projects/app/src/components/core/module/AIChatSettingsModal.tsx
View file @
aab6ee51
...
...
@@ -66,7 +66,6 @@ const AIChatSettingsModal = ({
},
[
getValues
]);
const
quoteTemplateVariables
=
(()
=>
[
...
pickerMenu
,
{
key
:
'q'
,
label
:
'q'
,
...
...
@@ -91,15 +90,21 @@ const AIChatSettingsModal = ({
key
:
'index'
,
label
:
t
(
'core.dataset.search.Quote index'
),
icon
:
'core/app/simpleMode/variable'
}
},
...
pickerMenu
])();
const
quotePromptVariables
=
(()
=>
[
...
pickerMenu
,
{
key
:
'quote'
,
label
:
t
(
'core.app.Quote templates'
),
icon
:
'core/app/simpleMode/variable'
}
},
{
key
:
'question'
,
label
:
t
(
'core.module.input.label.user question'
),
icon
:
'core/app/simpleMode/variable'
},
...
pickerMenu
])();
const
LabelStyles
:
BoxProps
=
{
...
...
projects/app/src/components/support/user/team/TeamManageModal/InviteModal.tsx
View file @
aab6ee51
...
...
@@ -55,11 +55,13 @@ const InviteModal = ({
openConfirm
(
()
=>
onClose
(),
undefined
,
t
(
'user.team.Invite Member Success Tip'
,
{
success
:
res
.
invite
.
length
,
inValid
:
res
.
inValid
.
map
((
item
)
=>
item
.
username
).
join
(
', '
),
inTeam
:
res
.
inTeam
.
map
((
item
)
=>
item
.
username
).
join
(
', '
)
})
<
Box
whiteSpace=
{
'pre-wrap'
}
>
{
t
(
'user.team.Invite Member Success Tip'
,
{
success
:
res
.
invite
.
length
,
inValid
:
res
.
inValid
.
map
((
item
)
=>
item
.
username
).
join
(
', '
),
inTeam
:
res
.
inTeam
.
map
((
item
)
=>
item
.
username
).
join
(
', '
)
})
}
</
Box
>
)();
},
errorToast
:
t
(
'user.team.Invite Member Failed Tip'
)
...
...
projects/app/src/components/support/user/team/TeamManageModal/index.tsx
View file @
aab6ee51
...
...
@@ -75,7 +75,7 @@ const TeamManageModal = ({ onClose }: { onClose: () => void }) => {
const
{
mutate
:
onSwitchTeam
,
isLoading
:
isSwitchTeam
}
=
useRequest
({
mutationFn
:
async
(
teamId
:
string
)
=>
{
const
token
=
await
putSwitchTeam
(
teamId
);
setToken
(
token
);
token
&&
setToken
(
token
);
return
initUserInfo
();
},
errorToast
:
t
(
'user.team.Switch Team Failed'
)
...
...
@@ -286,13 +286,7 @@ const TeamManageModal = ({ onClose }: { onClose: () => void }) => {
size=
"sm"
borderRadius=
{
'md'
}
ml=
{
3
}
leftIcon=
{
<
MyIcon
name=
{
'support/account/loginoutLight'
}
w=
{
'14px'
}
color=
{
'primary.500'
}
/>
}
leftIcon=
{
<
MyIcon
name=
{
'support/account/loginoutLight'
}
w=
{
'14px'
}
/>
}
onClick=
{
()
=>
{
openLeaveConfirm
(()
=>
onLeaveTeam
(
userInfo
?.
team
?.
teamId
))();
}
}
...
...
projects/app/src/pages/account/components/Info.tsx
View file @
aab6ee51
...
...
@@ -271,28 +271,32 @@ const UserInfo = () => {
)
}
</
Flex
>
</
Box
>
<
Box
mt=
{
6
}
whiteSpace=
{
'nowrap'
}
w=
{
[
'85%'
,
'300px'
]
}
>
<
Flex
alignItems=
{
'center'
}
>
<
Box
flex=
{
'1 0 0'
}
fontSize=
{
'md'
}
>
{
t
(
'support.user.team.Dataset usage'
)
}
:
{
datasetUsageMap
.
usedSize
}
/
{
datasetSub
.
maxSize
}
{
feConfigs
?.
show_pay
&&
(
<
Box
mt=
{
6
}
whiteSpace=
{
'nowrap'
}
w=
{
[
'85%'
,
'300px'
]
}
>
<
Flex
alignItems=
{
'center'
}
>
<
Box
flex=
{
'1 0 0'
}
fontSize=
{
'md'
}
>
{
t
(
'support.user.team.Dataset usage'
)
}
:
{
datasetUsageMap
.
usedSize
}
/
{
datasetSub
.
maxSize
}
</
Box
>
{
userInfo
?.
team
?.
canWrite
&&
(
<
Button
size=
{
'sm'
}
onClick=
{
onOpenSubDatasetModal
}
>
{
t
(
'support.wallet.Buy more'
)
}
</
Button
>
)
}
</
Flex
>
<
Box
mt=
{
1
}
>
<
Progress
value=
{
datasetUsageMap
.
value
}
colorScheme=
{
datasetUsageMap
.
colorScheme
}
borderRadius=
{
'md'
}
isAnimated
hasStripe
borderWidth=
{
'1px'
}
borderColor=
{
'borderColor.base'
}
/>
</
Box
>
<
Button
size=
{
'sm'
}
onClick=
{
onOpenSubDatasetModal
}
>
{
t
(
'support.wallet.Buy more'
)
}
</
Button
>
</
Flex
>
<
Box
mt=
{
1
}
>
<
Progress
value=
{
datasetUsageMap
.
value
}
colorScheme=
{
datasetUsageMap
.
colorScheme
}
borderRadius=
{
'md'
}
isAnimated
hasStripe
borderWidth=
{
'1px'
}
borderColor=
{
'borderColor.base'
}
/>
</
Box
>
</
Box
>
)
}
</>
)
}
...
...
projects/app/src/pages/api/core/dataset/collection/create/file.ts
deleted
100644 → 0
View file @
91b7d81c
import
type
{
NextApiRequest
,
NextApiResponse
}
from
'next'
;
import
{
jsonRes
}
from
'@fastgpt/service/common/response'
;
import
{
connectToDatabase
}
from
'@/service/mongo'
;
import
{
uploadFile
}
from
'@fastgpt/service/common/file/gridfs/controller'
;
import
{
getUploadModel
}
from
'@fastgpt/service/common/file/multer'
;
import
{
authDataset
}
from
'@fastgpt/service/support/permission/auth/dataset'
;
import
{
FileCreateDatasetCollectionParams
}
from
'@fastgpt/global/core/dataset/api'
;
import
{
removeFilesByPaths
}
from
'@fastgpt/service/common/file/utils'
;
import
{
createOneCollection
}
from
'@fastgpt/service/core/dataset/collection/controller'
;
import
{
DatasetCollectionTypeEnum
}
from
'@fastgpt/global/core/dataset/constants'
;
/**
* Creates the multer uploader
*/
const
upload
=
getUploadModel
({
maxSize
:
500
*
1024
*
1024
});
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
<
any
>
)
{
let
filePaths
:
string
[]
=
[];
const
{
datasetId
}
=
req
.
query
as
{
datasetId
:
string
};
try
{
await
connectToDatabase
();
const
{
teamId
,
tmbId
}
=
await
authDataset
({
req
,
authToken
:
true
,
authApiKey
:
true
,
per
:
'w'
,
datasetId
});
const
{
file
,
bucketName
,
data
}
=
await
upload
.
doUpload
<
FileCreateDatasetCollectionParams
>
(
req
,
res
);
filePaths
=
[
file
.
path
];
if
(
!
file
||
!
bucketName
)
{
throw
new
Error
(
'file is empty'
);
}
const
{
fileMetadata
,
collectionMetadata
,
...
collectionData
}
=
data
;
// upload file and create collection
const
fileId
=
await
uploadFile
({
teamId
,
tmbId
,
bucketName
,
path
:
file
.
path
,
filename
:
file
.
originalname
,
contentType
:
file
.
mimetype
,
metadata
:
fileMetadata
});
// create collection
const
collectionId
=
await
createOneCollection
({
...
collectionData
,
metadata
:
collectionMetadata
,
teamId
,
tmbId
,
type
:
DatasetCollectionTypeEnum
.
file
,
fileId
});
jsonRes
(
res
,
{
data
:
collectionId
});
}
catch
(
error
)
{
jsonRes
(
res
,
{
code
:
500
,
error
});
}
removeFilesByPaths
(
filePaths
);
}
export
const
config
=
{
api
:
{
bodyParser
:
false
}
};
projects/app/src/pages/api/core/dataset/collection/create/link.ts
deleted
100644 → 0
View file @
91b7d81c
/*
Create one dataset collection
*/
import
type
{
NextApiRequest
,
NextApiResponse
}
from
'next'
;
import
{
jsonRes
}
from
'@fastgpt/service/common/response'
;
import
{
connectToDatabase
}
from
'@/service/mongo'
;
import
type
{
LinkCreateDatasetCollectionParams
}
from
'@fastgpt/global/core/dataset/api.d'
;
import
{
authDataset
}
from
'@fastgpt/service/support/permission/auth/dataset'
;
import
{
createOneCollection
}
from
'@fastgpt/service/core/dataset/collection/controller'
;
import
{
TrainingModeEnum
,
DatasetCollectionTypeEnum
}
from
'@fastgpt/global/core/dataset/constants'
;
import
{
checkDatasetLimit
}
from
'@fastgpt/service/support/permission/limit/dataset'
;
import
{
predictDataLimitLength
}
from
'@fastgpt/global/core/dataset/utils'
;
import
{
createTrainingBill
}
from
'@fastgpt/service/support/wallet/bill/controller'
;
import
{
BillSourceEnum
}
from
'@fastgpt/global/support/wallet/bill/constants'
;
import
{
getQAModel
,
getVectorModel
}
from
'@/service/core/ai/model'
;
import
{
reloadCollectionChunks
}
from
'@fastgpt/service/core/dataset/collection/utils'
;
import
{
startQueue
}
from
'@/service/utils/tools'
;
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
<
any
>
)
{
try
{
await
connectToDatabase
();
const
{
link
,
trainingType
=
TrainingModeEnum
.
chunk
,
chunkSize
=
512
,
chunkSplitter
,
qaPrompt
,
...
body
}
=
req
.
body
as
LinkCreateDatasetCollectionParams
;
const
{
teamId
,
tmbId
,
dataset
}
=
await
authDataset
({
req
,
authToken
:
true
,
authApiKey
:
true
,
datasetId
:
body
.
datasetId
,
per
:
'w'
});
// 1. check dataset limit
await
checkDatasetLimit
({
teamId
,
freeSize
:
global
.
feConfigs
?.
subscription
?.
datasetStoreFreeSize
,
insertLen
:
predictDataLimitLength
(
trainingType
,
new
Array
(
10
))
});
// 2. create collection
const
collectionId
=
await
createOneCollection
({
...
body
,
name
:
link
,
teamId
,
tmbId
,
type
:
DatasetCollectionTypeEnum
.
link
,
trainingType
,
chunkSize
,
chunkSplitter
,
qaPrompt
,
rawLink
:
link
});
// 3. create bill and start sync
const
{
billId
}
=
await
createTrainingBill
({
teamId
,
tmbId
,
appName
:
'core.dataset.collection.Sync Collection'
,
billSource
:
BillSourceEnum
.
training
,
vectorModel
:
getVectorModel
(
dataset
.
vectorModel
).
name
,
agentModel
:
getQAModel
(
dataset
.
agentModel
).
name
});
await
reloadCollectionChunks
({
collectionId
,
tmbId
,
billId
});
startQueue
();
jsonRes
(
res
,
{
data
:
{
collectionId
}
});
}
catch
(
err
)
{
jsonRes
(
res
,
{
code
:
500
,
error
:
err
});
}
}
projects/app/src/pages/api/core/dataset/collection/create/text.ts
deleted
100644 → 0
View file @
91b7d81c
/*
Create one dataset collection
*/
import
type
{
NextApiRequest
,
NextApiResponse
}
from
'next'
;
import
{
jsonRes
}
from
'@fastgpt/service/common/response'
;
import
{
connectToDatabase
}
from
'@/service/mongo'
;
import
type
{
TextCreateDatasetCollectionParams
}
from
'@fastgpt/global/core/dataset/api.d'
;
import
{
authDataset
}
from
'@fastgpt/service/support/permission/auth/dataset'
;
import
{
createOneCollection
}
from
'@fastgpt/service/core/dataset/collection/controller'
;
import
{
TrainingModeEnum
,
DatasetCollectionTypeEnum
}
from
'@fastgpt/global/core/dataset/constants'
;
import
{
splitText2Chunks
}
from
'@fastgpt/global/common/string/textSplitter'
;
import
{
checkDatasetLimit
}
from
'@fastgpt/service/support/permission/limit/dataset'
;
import
{
predictDataLimitLength
}
from
'@fastgpt/global/core/dataset/utils'
;
import
{
pushDataToTrainingQueue
}
from
'@/service/core/dataset/data/controller'
;
import
{
hashStr
}
from
'@fastgpt/global/common/string/tools'
;
import
{
createTrainingBill
}
from
'@fastgpt/service/support/wallet/bill/controller'
;
import
{
BillSourceEnum
}
from
'@fastgpt/global/support/wallet/bill/constants'
;
import
{
getQAModel
,
getVectorModel
}
from
'@/service/core/ai/model'
;
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
<
any
>
)
{
try
{
await
connectToDatabase
();
const
{
name
,
text
,
trainingType
=
TrainingModeEnum
.
chunk
,
chunkSize
=
512
,
chunkSplitter
,
qaPrompt
,
...
body
}
=
req
.
body
as
TextCreateDatasetCollectionParams
;
const
{
teamId
,
tmbId
,
dataset
}
=
await
authDataset
({
req
,
authToken
:
true
,
authApiKey
:
true
,
datasetId
:
body
.
datasetId
,
per
:
'w'
});
// 1. split text to chunks
const
{
chunks
}
=
splitText2Chunks
({
text
,
chunkLen
:
chunkSize
,
overlapRatio
:
trainingType
===
TrainingModeEnum
.
chunk
?
0.2
:
0
,
customReg
:
chunkSplitter
?
[
chunkSplitter
]
:
[]
});
// 2. check dataset limit
await
checkDatasetLimit
({
teamId
,
freeSize
:
global
.
feConfigs
?.
subscription
?.
datasetStoreFreeSize
,
insertLen
:
predictDataLimitLength
(
trainingType
,
chunks
)
});
// 3. create collection and training bill
const
[
collectionId
,
{
billId
}]
=
await
Promise
.
all
([
createOneCollection
({
...
body
,
teamId
,
tmbId
,
type
:
DatasetCollectionTypeEnum
.
virtual
,
name
,
trainingType
,
chunkSize
,
chunkSplitter
,
qaPrompt
,
hashRawText
:
hashStr
(
text
),
rawTextLength
:
text
.
length
}),
createTrainingBill
({
teamId
,
tmbId
,
appName
:
name
,
billSource
:
BillSourceEnum
.
training
,
vectorModel
:
getVectorModel
(
dataset
.
vectorModel
)?.
name
,
agentModel
:
getQAModel
(
dataset
.
agentModel
)?.
name
})
]);
// 4. push chunks to training queue
const
insertResults
=
await
pushDataToTrainingQueue
({
teamId
,
tmbId
,
collectionId
,
trainingMode
:
trainingType
,
prompt
:
qaPrompt
,
billId
,
data
:
chunks
.
map
((
text
,
index
)
=>
({
q
:
text
,
chunkIndex
:
index
}))
});
jsonRes
(
res
,
{
data
:
{
collectionId
,
results
:
insertResults
}
});
}
catch
(
err
)
{
jsonRes
(
res
,
{
code
:
500
,
error
:
err
});
}
}
export
const
config
=
{
api
:
{
bodyParser
:
{
sizeLimit
:
'10mb'
}
}
};
projects/app/src/pages/api/core/dataset/create.ts
View file @
aab6ee51
...
...
@@ -6,6 +6,7 @@ import type { CreateDatasetParams } from '@/global/core/dataset/api.d';
import
{
createDefaultCollection
}
from
'@fastgpt/service/core/dataset/collection/controller'
;
import
{
authUserNotVisitor
}
from
'@fastgpt/service/support/permission/auth/user'
;
import
{
DatasetTypeEnum
}
from
'@fastgpt/global/core/dataset/constants'
;
import
{
getQAModel
,
getVectorModel
}
from
'@/service/core/ai/model'
;
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
<
any
>
)
{
try
{
...
...
@@ -13,18 +14,18 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
const
{
parentId
,
name
,
type
,
type
=
DatasetTypeEnum
.
dataset
,
avatar
,
vectorModel
=
global
.
vectorModels
[
0
].
model
,
agentModel
=
global
.
qaModels
[
0
].
model
}
=
req
.
body
as
CreateDatasetParams
;
// auth
const
{
teamId
,
tmbId
}
=
await
authUserNotVisitor
({
req
,
authToken
:
true
});
const
{
teamId
,
tmbId
}
=
await
authUserNotVisitor
({
req
,
authToken
:
true
,
authApiKey
:
true
});
// check model valid
const
vectorModelStore
=
g
lobal
.
vectorModels
.
find
((
item
)
=>
item
.
model
===
vectorModel
);
const
agentModelStore
=
g
lobal
.
qaModels
.
find
((
item
)
=>
item
.
model
===
agentModel
);
const
vectorModelStore
=
g
etVectorModel
(
vectorModel
);
const
agentModelStore
=
g
etQAModel
(
agentModel
);
if
(
!
vectorModelStore
||
!
agentModelStore
)
{
throw
new
Error
(
'vectorModel or qaModel is invalid'
);
}
...
...
projects/app/src/pages/api/core/dataset/delete.ts
View file @
aab6ee51
...
...
@@ -18,7 +18,13 @@ export default async function handler(req: NextApiRequest, res: NextApiResponse<
}
// auth owner
const
{
teamId
}
=
await
authDataset
({
req
,
authToken
:
true
,
datasetId
,
per
:
'owner'
});
const
{
teamId
}
=
await
authDataset
({
req
,
authToken
:
true
,
authApiKey
:
true
,
datasetId
,
per
:
'owner'
});
const
datasets
=
await
findDatasetAndAllChildren
({
teamId
,
...
...
projects/app/src/pages/api/plusApi/[...path].ts
deleted
100644 → 0
View file @
91b7d81c
import
type
{
NextApiRequest
,
NextApiResponse
}
from
'next'
;
import
{
jsonRes
}
from
'@fastgpt/service/common/response'
;
import
{
request
}
from
'@fastgpt/service/common/api/plusRequest'
;
import
type
{
Method
}
from
'axios'
;
import
{
setCookie
}
from
'@fastgpt/service/support/permission/controller'
;
import
{
connectToDatabase
}
from
'@/service/mongo'
;
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
)
{
try
{
await
connectToDatabase
();
const
method
=
(
req
.
method
||
'POST'
)
as
Method
;
const
{
path
=
[],
...
query
}
=
req
.
query
as
any
;
const
url
=
`/
${
path
?.
join
(
'/'
)}?
$
{
new
URLSearchParams
(
query
).
toString
()}
`;
if (!url) {
throw new Error('url is empty');
}
const data = req.body || query;
const repose = await request(
url,
data,
{
headers: {
...req.headers,
// @ts-ignore
rootkey: undefined
}
},
method
);
/* special response */
// response cookie
if (repose?.cookie) {
setCookie(res, repose.cookie);
return jsonRes(res, {
data: repose?.cookie
});
}
jsonRes(res, {
data: repose
});
} catch (error) {
jsonRes(res, {
code: 500,
error
});
}
}
export const config = {
api: {
bodyParser: {
sizeLimit: '10mb'
},
responseLimit: '10mb'
}
};
projects/app/src/pages/api/proApi/[...path].ts
0 → 100644
View file @
aab6ee51
import
type
{
NextApiRequest
,
NextApiResponse
}
from
'next'
;
import
{
jsonRes
}
from
'@fastgpt/service/common/response'
;
import
{
connectToDatabase
}
from
'@/service/mongo'
;
import
{
request
}
from
'http'
;
import
{
FastGPTProUrl
}
from
'@fastgpt/service/common/system/constants'
;
import
url
from
'url'
;
export
default
async
function
handler
(
req
:
NextApiRequest
,
res
:
NextApiResponse
)
{
try
{
await
connectToDatabase
();
const
{
path
=
[],
...
query
}
=
req
.
query
as
any
;
const
requestPath
=
`/api/
${
path
?.
join
(
'/'
)}?
$
{
new
URLSearchParams
(
query
).
toString
()}
`;
if (!requestPath) {
throw new Error('url is empty');
}
const parsedUrl = url.parse(FastGPTProUrl);
delete req.headers?.rootkey;
const requestResult = request({
protocol: parsedUrl.protocol,
hostname: parsedUrl.hostname,
port: parsedUrl.port,
path: requestPath,
method: req.method,
headers: req.headers
});
req.pipe(requestResult);
requestResult.on('response', (response) => {
Object.keys(response.headers).forEach((key) => {
// @ts-ignore
res.setHeader(key, response.headers[key]);
});
response.statusCode && res.writeHead(response.statusCode);
response.pipe(res);
});
requestResult.on('error', (e) => {
res.send(e);
res.end();
});
} catch (error) {
jsonRes(res, {
code: 500,
error
});
}
}
export const config = {
api: {
bodyParser: false
}
};
projects/app/src/pages/dataset/detail/index.tsx
View file @
aab6ee51
...
...
@@ -87,7 +87,7 @@ const Detail = ({ datasetId, currentTab }: { datasetId: string; currentTab: `${T
onError
(
err
:
any
)
{
router
.
replace
(
`/dataset/list`
);
toast
({
title
:
getErrText
(
err
,
t
(
'common.Load Failed'
)),
title
:
t
(
getErrText
(
err
,
t
(
'common.Load Failed'
)
)),
status
:
'error'
});
}
...
...
projects/app/src/pages/dataset/list/index.tsx
View file @
aab6ee51
...
...
@@ -46,13 +46,15 @@ import { PermissionTypeEnum } from '@fastgpt/global/support/permission/constant'
import
{
DatasetItemType
}
from
'@fastgpt/global/core/dataset/type'
;
import
ParentPaths
from
'@/components/common/ParentPaths'
;
import
DatasetTypeTag
from
'@/components/core/dataset/DatasetTypeTag'
;
import
{
useToast
}
from
'@/web/common/hooks/useToast'
;
import
{
getErrText
}
from
'@fastgpt/global/common/error/utils'
;
const
CreateModal
=
dynamic
(()
=>
import
(
'./component/CreateModal'
),
{
ssr
:
false
});
const
MoveModal
=
dynamic
(()
=>
import
(
'./component/MoveModal'
),
{
ssr
:
false
});
const
Kb
=
()
=>
{
const
{
t
}
=
useTranslation
();
const
theme
=
useTheme
();
const
{
toast
}
=
useToast
();
const
router
=
useRouter
();
const
{
parentId
}
=
router
.
query
as
{
parentId
:
string
};
const
{
setLoading
}
=
useSystemStore
();
...
...
@@ -115,9 +117,20 @@ const Kb = () => {
errorToast
:
t
(
'dataset.Export Dataset Limit Error'
)
});
const
{
data
,
refetch
,
isFetching
}
=
useQuery
([
'loadDataset'
,
parentId
],
()
=>
{
return
Promise
.
all
([
loadDatasets
(
parentId
),
getDatasetPaths
(
parentId
)]);
});
const
{
data
,
refetch
,
isFetching
}
=
useQuery
(
[
'loadDataset'
,
parentId
],
()
=>
{
return
Promise
.
all
([
loadDatasets
(
parentId
),
getDatasetPaths
(
parentId
)]);
},
{
onError
(
err
)
{
toast
({
status
:
'error'
,
title
:
t
(
getErrText
(
err
))
});
}
}
);
const
paths
=
data
?.[
1
]
||
[];
...
...
projects/app/src/pages/login/provider.tsx
View file @
aab6ee51
...
...
@@ -106,9 +106,9 @@ const provider = ({ code, state, error }: { code: string; state: string; error?:
export
async
function
getServerSideProps
(
content
:
any
)
{
return
{
props
:
{
code
:
content
?.
query
?.
code
,
state
:
content
?.
query
?.
state
,
error
:
content
?.
query
?.
error
,
code
:
content
?.
query
?.
code
||
''
,
state
:
content
?.
query
?.
state
||
''
,
error
:
content
?.
query
?.
error
||
''
,
...(
await
serviceSideProps
(
content
))
}
};
...
...
projects/app/src/web/common/hooks/useConfirm.tsx
View file @
aab6ee51
import
{
useCallback
,
useEffect
,
useMemo
,
useRef
,
useState
}
from
'react'
;
import
React
,
{
useCallback
,
useEffect
,
useMemo
,
useRef
,
useState
}
from
'react'
;
import
{
useDisclosure
,
Button
,
ModalBody
,
ModalFooter
}
from
'@chakra-ui/react'
;
import
{
useTranslation
}
from
'next-i18next'
;
import
MyModal
from
'@/components/MyModal'
;
...
...
@@ -35,7 +35,7 @@ export const useConfirm = (props?: {
content
,
showCancel
=
true
}
=
props
||
{};
const
[
customContent
,
setCustomContent
]
=
useState
(
content
);
const
[
customContent
,
setCustomContent
]
=
useState
<
string
|
React
.
ReactNode
>
(
content
);
const
{
isOpen
,
onOpen
,
onClose
}
=
useDisclosure
();
...
...
@@ -44,7 +44,7 @@ export const useConfirm = (props?: {
return
{
openConfirm
:
useCallback
(
(
confirm
?:
any
,
cancel
?:
any
,
customContent
?:
string
)
=>
{
(
confirm
?:
any
,
cancel
?:
any
,
customContent
?:
string
|
React
.
ReactNode
)
=>
{
confirmCb
.
current
=
confirm
;
cancelCb
.
current
=
cancel
;
...
...
projects/app/src/web/core/dataset/api.ts
View file @
aab6ee51
...
...
@@ -58,7 +58,7 @@ export const putDatasetById = (data: DatasetUpdateBody) => PUT<void>(`/core/data
export
const
delDatasetById
=
(
id
:
string
)
=>
DELETE
(
`/core/dataset/delete?id=
${
id
}
`
);
export
const
postWebsiteSync
=
(
data
:
PostWebsiteSyncParams
)
=>
POST
(
`/p
lus
Api/core/dataset/websiteSync`
,
data
,
{
POST
(
`/p
ro
Api/core/dataset/websiteSync`
,
data
,
{
timeout
:
600000
}).
catch
();
...
...
@@ -76,7 +76,7 @@ export const getDatasetCollectionById = (id: string) =>
export
const
postDatasetCollection
=
(
data
:
CreateDatasetCollectionParams
)
=>
POST
<
string
>
(
`/core/dataset/collection/create`
,
data
);
export
const
postCreateDatasetLinkCollection
=
(
data
:
LinkCreateDatasetCollectionParams
)
=>
POST
<
{
collectionId
:
string
}
>
(
`/core/dataset/collection/create/link`
,
data
);
POST
<
{
collectionId
:
string
}
>
(
`/
proApi/
core/dataset/collection/create/link`
,
data
);
export
const
putDatasetCollectionById
=
(
data
:
UpdateDatasetCollectionParams
)
=>
POST
(
`/core/dataset/collection/update`
,
data
);
...
...
projects/app/src/web/core/dataset/utils.ts
View file @
aab6ee51
...
...
@@ -27,18 +27,22 @@ export const fileCollectionCreate = ({
form
.
append
(
'bucketName'
,
BucketNameEnum
.
dataset
);
form
.
append
(
'file'
,
file
,
encodeURIComponent
(
file
.
name
));
return
POST
<
string
>
(
`/core/dataset/collection/create/file?datasetId=
${
data
.
datasetId
}
`
,
form
,
{
timeout
:
480000
,
onUploadProgress
:
(
e
)
=>
{
if
(
!
e
.
total
)
return
;
return
POST
<
string
>
(
`/proApi/core/dataset/collection/create/emptyFile?datasetId=
${
data
.
datasetId
}
`
,
form
,
{
timeout
:
480000
,
onUploadProgress
:
(
e
)
=>
{
if
(
!
e
.
total
)
return
;
const
percent
=
Math
.
round
((
e
.
loaded
/
e
.
total
)
*
100
);
percentListen
&&
percentListen
(
percent
);
},
headers
:
{
'Content-Type'
:
'multipart/form-data; charset=utf-8'
const
percent
=
Math
.
round
((
e
.
loaded
/
e
.
total
)
*
100
);
percentListen
&&
percentListen
(
percent
);
},
headers
:
{
'Content-Type'
:
'multipart/form-data; charset=utf-8'
}
}
}
);
);
};
export
async
function
chunksUpload
({
...
...
projects/app/src/web/support/activity/promotion/api.ts
View file @
aab6ee51
...
...
@@ -7,8 +7,8 @@ export const getPromotionInitData = () =>
GET
<
{
invitedAmount
:
number
;
earningsAmount
:
number
;
}
>
(
'/p
lus
Api/support/activity/promotion/getPromotionData'
);
}
>
(
'/p
ro
Api/support/activity/promotion/getPromotionData'
);
/* promotion records */
export
const
getPromotionRecords
=
(
data
:
RequestPaging
)
=>
POST
<
PromotionRecordType
>
(
`/p
lus
Api/support/activity/promotion/getPromotions`
,
data
);
POST
<
PromotionRecordType
>
(
`/p
ro
Api/support/activity/promotion/getPromotions`
,
data
);
projects/app/src/web/support/user/api.ts
View file @
aab6ee51
...
...
@@ -14,14 +14,14 @@ export const sendAuthCode = (data: {
username
:
string
;
type
:
`
${
UserAuthTypeEnum
}
`
;
googleToken
:
string
;
})
=>
POST
(
`/p
lus
Api/support/user/inform/sendAuthCode`
,
data
);
})
=>
POST
(
`/p
ro
Api/support/user/inform/sendAuthCode`
,
data
);
export
const
getTokenLogin
=
()
=>
GET
<
UserType
>
(
'/support/user/account/tokenLogin'
,
{},
{
maxQuantity
:
1
});
export
const
oauthLogin
=
(
params
:
OauthLoginProps
)
=>
POST
<
ResLogin
>
(
'/p
lus
Api/support/user/account/login/oauth'
,
params
);
POST
<
ResLogin
>
(
'/p
ro
Api/support/user/account/login/oauth'
,
params
);
export
const
postFastLogin
=
(
params
:
FastLoginProps
)
=>
POST
<
ResLogin
>
(
'/p
lus
Api/support/user/account/login/fastLogin'
,
params
);
POST
<
ResLogin
>
(
'/p
ro
Api/support/user/account/login/fastLogin'
,
params
);
export
const
postRegister
=
({
username
,
...
...
@@ -34,7 +34,7 @@ export const postRegister = ({
password
:
string
;
inviterId
?:
string
;
})
=>
POST
<
ResLogin
>
(
`/p
lus
Api/support/user/account/register/emailAndPhone`
,
{
POST
<
ResLogin
>
(
`/p
ro
Api/support/user/account/register/emailAndPhone`
,
{
username
,
code
,
inviterId
,
...
...
@@ -50,7 +50,7 @@ export const postFindPassword = ({
code
:
string
;
password
:
string
;
})
=>
POST
<
ResLogin
>
(
`/p
lus
Api/support/user/account/password/updateByCode`
,
{
POST
<
ResLogin
>
(
`/p
ro
Api/support/user/account/password/updateByCode`
,
{
username
,
code
,
password
:
hashStr
(
password
)
...
...
projects/app/src/web/support/user/inform/api.ts
View file @
aab6ee51
...
...
@@ -3,7 +3,7 @@ import type { PagingData, RequestPaging } from '@/types';
import
type
{
UserInformSchema
}
from
'@fastgpt/global/support/user/inform/type'
;
export
const
getInforms
=
(
data
:
RequestPaging
)
=>
POST
<
PagingData
<
UserInformSchema
>>
(
`/p
lus
Api/support/user/inform/list`
,
data
);
POST
<
PagingData
<
UserInformSchema
>>
(
`/p
ro
Api/support/user/inform/list`
,
data
);
export
const
getUnreadCount
=
()
=>
GET
<
number
>
(
`/p
lus
Api/support/user/inform/countUnread`
);
export
const
readInform
=
(
id
:
string
)
=>
GET
(
`/p
lus
Api/support/user/inform/read`
,
{
id
});
export
const
getUnreadCount
=
()
=>
GET
<
number
>
(
`/p
ro
Api/support/user/inform/countUnread`
);
export
const
readInform
=
(
id
:
string
)
=>
GET
(
`/p
ro
Api/support/user/inform/read`
,
{
id
});
projects/app/src/web/support/user/team/api.ts
View file @
aab6ee51
...
...
@@ -16,29 +16,29 @@ import {
/* --------------- team ---------------- */
export
const
getTeamList
=
(
status
:
`
${
TeamMemberSchema
[
'status'
]}
`
)
=>
GET
<
TeamItemType
[]
>
(
`/p
lus
Api/support/user/team/list`
,
{
status
});
GET
<
TeamItemType
[]
>
(
`/p
ro
Api/support/user/team/list`
,
{
status
});
export
const
postCreateTeam
=
(
data
:
CreateTeamProps
)
=>
POST
<
string
>
(
`/p
lus
Api/support/user/team/create`
,
data
);
POST
<
string
>
(
`/p
ro
Api/support/user/team/create`
,
data
);
export
const
putUpdateTeam
=
(
data
:
UpdateTeamProps
)
=>
PUT
(
`/p
lus
Api/support/user/team/update`
,
data
);
PUT
(
`/p
ro
Api/support/user/team/update`
,
data
);
export
const
putSwitchTeam
=
(
teamId
:
string
)
=>
PUT
<
string
>
(
`/p
lus
Api/support/user/team/switch`
,
{
teamId
});
PUT
<
string
>
(
`/p
ro
Api/support/user/team/switch`
,
{
teamId
});
/* --------------- team member ---------------- */
export
const
getTeamMembers
=
(
teamId
:
string
)
=>
GET
<
TeamMemberItemType
[]
>
(
`/p
lus
Api/support/user/team/member/list`
,
{
teamId
});
GET
<
TeamMemberItemType
[]
>
(
`/p
ro
Api/support/user/team/member/list`
,
{
teamId
});
export
const
postInviteTeamMember
=
(
data
:
InviteMemberProps
)
=>
POST
<
InviteMemberResponse
>
(
`/p
lus
Api/support/user/team/member/invite`
,
data
);
POST
<
InviteMemberResponse
>
(
`/p
ro
Api/support/user/team/member/invite`
,
data
);
export
const
putUpdateMember
=
(
data
:
UpdateTeamMemberProps
)
=>
PUT
(
`/p
lus
Api/support/user/team/member/update`
,
data
);
PUT
(
`/p
ro
Api/support/user/team/member/update`
,
data
);
export
const
putUpdateMemberName
=
(
name
:
string
)
=>
PUT
(
`/p
lus
Api/support/user/team/member/updateName`
,
{
name
});
PUT
(
`/p
ro
Api/support/user/team/member/updateName`
,
{
name
});
export
const
delRemoveMember
=
(
props
:
DelMemberProps
)
=>
DELETE
(
`/p
lus
Api/support/user/team/member/delete`
,
props
);
DELETE
(
`/p
ro
Api/support/user/team/member/delete`
,
props
);
export
const
updateInviteResult
=
(
data
:
UpdateInviteProps
)
=>
PUT
(
'/p
lus
Api/support/user/team/member/updateInvite'
,
data
);
PUT
(
'/p
ro
Api/support/user/team/member/updateInvite'
,
data
);
export
const
delLeaveTeam
=
(
teamId
:
string
)
=>
DELETE
(
'/p
lus
Api/support/user/team/member/leave'
,
{
teamId
});
DELETE
(
'/p
ro
Api/support/user/team/member/leave'
,
{
teamId
});
/* team limit */
export
const
checkTeamExportDatasetLimit
=
(
datasetId
:
string
)
=>
...
...
projects/app/src/web/support/wallet/bill/api.ts
View file @
aab6ee51
...
...
@@ -4,7 +4,7 @@ import type { PagingData, RequestPaging } from '@/types';
import
type
{
BillItemType
}
from
'@fastgpt/global/support/wallet/bill/type'
;
export
const
getUserBills
=
(
data
:
RequestPaging
)
=>
POST
<
PagingData
<
BillItemType
>>
(
`/p
lus
Api/support/wallet/bill/getBill`
,
data
);
POST
<
PagingData
<
BillItemType
>>
(
`/p
ro
Api/support/wallet/bill/getBill`
,
data
);
export
const
postCreateTrainingBill
=
(
data
:
CreateTrainingBillProps
)
=>
POST
<
string
>
(
`/support/wallet/bill/createTrainingBill`
,
data
);
projects/app/src/web/support/wallet/pay/api.ts
View file @
aab6ee51
import
{
GET
}
from
'@/web/common/api/request'
;
import
type
{
PaySchema
}
from
'@fastgpt/global/support/wallet/pay/type.d'
;
export
const
getPayOrders
=
()
=>
GET
<
PaySchema
[]
>
(
`/p
lus
Api/support/wallet/pay/getPayOrders`
);
export
const
getPayOrders
=
()
=>
GET
<
PaySchema
[]
>
(
`/p
ro
Api/support/wallet/pay/getPayOrders`
);
export
const
getPayCode
=
(
amount
:
number
)
=>
GET
<
{
codeUrl
:
string
;
payId
:
string
;
}
>
(
`/p
lus
Api/support/wallet/pay/getPayCode`
,
{
amount
});
}
>
(
`/p
ro
Api/support/wallet/pay/getPayCode`
,
{
amount
});
export
const
checkPayResult
=
(
payId
:
string
)
=>
GET
<
string
>
(
`/p
lus
Api/support/wallet/pay/checkPayResult`
,
{
payId
}).
then
((
data
)
=>
{
GET
<
string
>
(
`/p
ro
Api/support/wallet/pay/checkPayResult`
,
{
payId
}).
then
((
data
)
=>
{
try
{
GET
(
'/common/system/unlockTask'
);
}
catch
(
error
)
{}
...
...
projects/app/src/web/support/wallet/sub/api.ts
View file @
aab6ee51
...
...
@@ -10,4 +10,4 @@ export const getTeamDatasetValidSub = () =>
}
>
(
`/support/wallet/sub/getDatasetSub`
);
export
const
postExpandTeamDatasetSub
=
(
data
:
SubDatasetSizeParams
)
=>
POST
(
'/p
lus
Api/support/wallet/sub/datasetSize/expand'
,
data
);
POST
(
'/p
ro
Api/support/wallet/sub/datasetSize/expand'
,
data
);
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