Compare commits

...

18 Commits

Author SHA1 Message Date
Archer
34602b25df 4.6.8-alpha (#804)
* perf: redirect request and err log replace

perf: dataset openapi

feat: session

fix: retry input error

feat: 468 doc

sub page

feat: standard sub

perf: rerank tip

perf: rerank tip

perf: api sdk

perf: openapi

sub plan

perf: sub ui

fix: ts

* perf: init log

* fix: variable select

* sub page

* icon

* perf: llm model config

* perf: menu ux

* perf: system store

* perf: publish app name

* fix: init data

* perf: flow edit ux

* fix: value type format and ux

* fix prompt editor default value (#13)

* fix prompt editor default value

* fix prompt editor update when not focus

* add key with variable

---------

Co-authored-by: Archer <545436317@qq.com>

* fix: value type

* doc

* i18n

* import path

* home page

* perf: mongo session running

* fix: ts

* perf: use toast

* perf: flow edit

* perf: sse response

* slider ui

* fetch error

* fix prompt editor rerender when not focus by key defaultvalue (#14)

* perf: prompt editor

* feat: dataset search concat

* perf: doc

* fix:ts

* perf: doc

* fix json editor onblur value (#15)

* faq

* vector model default config

* ipv6

---------

Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>
2024-02-01 21:57:41 +08:00
Fengrui Liu
fc19c4cf09 Add IPv6 support for hostnames (#807) 2024-02-01 21:44:29 +08:00
Nils Jacobsen
9188752b41 fix: badge points to right repo now (#799) 2024-01-29 19:11:14 +08:00
Nils Jacobsen
6b40062504 feat: add localization editor and ide extension (#786)
* feat: add localization editor and ide extension

* feat: add markdown status translation badge
2024-01-29 18:14:02 +08:00
Fengrui Liu
72d1503fa3 Fixes: Fix logic error when extraction result is "false". (#797)
BUG when the value of arg is 'false'
2024-01-29 12:03:03 +08:00
Archer
2c6dbe13d9 fix: retry input and whisper (#785) 2024-01-25 15:41:29 +08:00
Archer
318116627c perf: redirect request and err log replace (#768)
perf: dataset openapi

openapi
2024-01-23 10:46:02 +08:00
Archer
379673cae1 fix colection create api (#766)
Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>
2024-01-23 09:01:24 +08:00
Archer
aab6ee51eb V4.6.7-production (#759) 2024-01-22 13:48:55 +08:00
William Zhang
91b7d81c1a bugfix: correct time to real 7 days (#754) 2024-01-19 21:36:38 +08:00
Archer
5e2adb22f0 4.6.7 fix (#752) 2024-01-19 20:16:08 +08:00
Archer
c031e6dcc9 4.6.7-alpha commit (#743)
Co-authored-by: Archer <545436317@qq.com>
Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>
2024-01-19 11:17:28 +08:00
lolocoo
8ee7407c4c Update data_search.md (#745)
错别字
2024-01-18 11:25:58 +08:00
Archer
006ad17c6a 4.6.7 first pr (#726) 2024-01-10 23:35:04 +08:00
徒言
414b693303 Improve the i18n configuration of the chat page (#719) 2024-01-10 15:18:55 +08:00
Yao Yao
dfa6586e5e Docs: update SystemParams to systemEnv (#712)
* Ignore .idea directory

* docs: update SystemParams to systemEnv
2024-01-10 15:16:55 +08:00
Yao Yao
5968bfeb12 Ignore .idea directory (#711) 2024-01-10 15:16:32 +08:00
Archer
5876a47da6 Update rearanker code url. Add chat storage ip address (#717)
* save chat origin ip

* reranker code url
2024-01-09 12:09:36 +08:00
692 changed files with 62127 additions and 17567 deletions

5
.gitignore vendored
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@@ -36,4 +36,7 @@ dist/
docSite/public/
docSite/resources/_gen/
docSite/.vercel
*.local.*
*.local.*
.idea/

5
.vscode/extensions.json vendored Normal file
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@@ -0,0 +1,5 @@
{
"recommendations": [
"inlang.vs-code-extension"
]
}

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@@ -78,7 +78,7 @@ COPY --from=builder /app/projects/$name/package.json ./package.json
COPY --from=workerDeps /app/worker /app/worker
# copy config
COPY ./projects/$name/data /app/data
RUN chown -R nextjs:nodejs /app/data
ENV NODE_ENV production
ENV NEXT_TELEMETRY_DISABLED 1

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@@ -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 pdfdocxtxthtmlmdcsv
- [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
@@ -215,4 +215,4 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
1. 允许作为后台服务直接商用,但不允许提供 SaaS 服务。
2. 未经商业授权,任何形式的商用服务均需保留相关版权信息。
3. 完整请查看 [FastGPT Open Source License](./LICENSE)
4. 联系方式yujinlong@sealos.io[点击查看商业版定价策略](https://doc.fastgpt.in/docs/commercial)
4. 联系方式yujinlong@sealos.io[点击查看商业版定价策略](https://doc.fastgpt.in/docs/commercial)

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@@ -49,48 +49,52 @@ Cloud: [fastgpt.in](https://fastgpt.in/)
## 💡 Features
1. Powerful visual workflows: Effortlessly craft AI applications
`1` Application Orchestration Features
- [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
- [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. Extensive knowledge base preprocessing
`2` Knowledge Base Features
- [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
- [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. Multiple effect testing channels
`3` Application Debugging Features
- [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
- [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
`4` OpenAPI Interface
- [x] completions interface (aligned with GPT interface)
- [ ] Knowledge base CRUD
- [x] The completions interface (aligned with GPT's chat mode interface)
- [x] CRUD operations for the knowledge base
- [ ] CRUD operations for conversations
5. Operational functions
`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
- [x] Login-free sharing window
- [x] One-click embedding with Iframe
- [ ] Unified access to dialogue records
<a href="#readme">
<img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">

17
dev.md Normal file
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@@ -0,0 +1,17 @@
# 打包命令
```sh
# Build image, not proxy
docker build -t registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.4.7 --build-arg name=app .
# build image with proxy
docker build -t registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.4.7 --build-arg name=app --build-arg proxy=taobao .
```
# Pg 常用索引
```sql
CREATE INDEX IF NOT EXISTS modelData_dataset_id_index ON modeldata (dataset_id);
CREATE INDEX IF NOT EXISTS modelData_collection_id_index ON modeldata (collection_id);
CREATE INDEX IF NOT EXISTS modelData_teamId_index ON modeldata (team_id);
```

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@@ -44,15 +44,19 @@ FastGPT 商业版软件根据不同的部署方式,分为 3 类收费模式。
**特有服务**
{{< table "table-hover table-striped-columns" >}}
| 部署方式 | 特有服务 | 上线时长 | 价格 |
| 部署方式 | 特有服务 | 上线时长 | 标品价格 |
| ---- | ---- | ---- | ---- |
| Sealos全托管 | 1. 有效期内免费升级。<br>2. 免运维服务&数据库。 | 半天 | 3000元起/月3个月起<br>或<br>30000元起/年 |
| 自有服务器-单机版 | 1. 6个版本的升级服务。 | 14天内 | 60000元/套(不限时长) |
| 自有服务器-Sealos版 | 1. 6个版本的升级服务。 | 14天内 | 150000元/套(不限时长)|
| 自有服务器-高可用版 | 1. 6个版本的升级服务。 | 14天内 | 150000元/套(不限时长)|
{{< /table >}}
{{% alert icon="🤖 " context="success" %}}
6个版本的升级服务不是指只能用 6 个版本,而是指依赖 FastGPT 团队提供的升级服务。大部分时候,建议自行升级,也不麻烦。
- 6个版本的升级服务不是指只能用 6 个版本,而是指依赖 FastGPT 团队提供的升级服务。大部分时候,建议自行升级,也不麻烦。
- 全托管版本适合技术人员紧缺的团队,仅需关注业务推动,无需关心服务是否正常运行。
- 单机版和高可用版可以完全部署在自己服务器中。
- 单机版适合中小团队对内提供服务,需要自己维护数据库备份等。
- 高可用版适合对外提供在线服务,包含可视化监控、多副本、负载均衡、数据库自动备份等生产环境的基础设施。
{{% /alert %}}

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@@ -29,7 +29,7 @@ weight: 106
### 全文检索
用传统的全文检索方式。适合查找关键的主谓语等。
用传统的全文检索方式。适合查找关键的主谓语等。
### 混合检索
@@ -55,4 +55,4 @@ FastGPT 会使用 `RRF` 对重排结果、向量搜索结果、全文检索结
一个`0-1`的数值,会过滤掉一些低相关度的搜索结果。
该值仅在`语义检索`或使用`结果重排`时生效。
该值仅在`语义检索`或使用`结果重排`时生效。

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@@ -13,153 +13,7 @@ weight: 708
这个配置文件中包含了系统级参数、AI 对话的模型、function 模型等……
## 旧版本配置文件
以下配置适合 4.6.6-alpha 之前
```json
{
"SystemParams": {
"vectorMaxProcess": 15, // 向量生成最大进程,结合数据库性能和 key 来设置
"qaMaxProcess": 15, // QA 生成最大进程,结合数据库性能和 key 来设置
"pgHNSWEfSearch": 100 // pg vector 索引参数,越大精度高但速度慢
},
"ChatModels": [ // 对话模型
{
"model": "gpt-3.5-turbo-1106",
"name": "GPT35-1106",
"price": 0, // 除以 100000 后等于1个token的价格
"maxContext": 16000, // 最大上下文长度
"maxResponse": 4000, // 最大回复长度
"quoteMaxToken": 2000, // 最大引用内容长度
"maxTemperature": 1.2, // 最大温度值
"censor": false, // 是否开启敏感词过滤(商业版)
"vision": false, // 支持图片输入
"defaultSystemChatPrompt": ""
},
{
"model": "gpt-3.5-turbo-16k",
"name": "GPT35-16k",
"maxContext": 16000,
"maxResponse": 16000,
"price": 0,
"quoteMaxToken": 8000,
"maxTemperature": 1.2,
"censor": false,
"vision": false,
"defaultSystemChatPrompt": ""
},
{
"model": "gpt-4",
"name": "GPT4-8k",
"maxContext": 8000,
"maxResponse": 8000,
"price": 0,
"quoteMaxToken": 4000,
"maxTemperature": 1.2,
"censor": false,
"vision": false,
"defaultSystemChatPrompt": ""
},
{
"model": "gpt-4-vision-preview",
"name": "GPT4-Vision",
"maxContext": 128000,
"maxResponse": 4000,
"price": 0,
"quoteMaxToken": 100000,
"maxTemperature": 1.2,
"censor": false,
"vision": true,
"defaultSystemChatPrompt": ""
}
],
"QAModels": [ // QA 生成模型
{
"model": "gpt-3.5-turbo-16k",
"name": "GPT35-16k",
"maxContext": 16000,
"maxResponse": 16000,
"price": 0
}
],
"CQModels": [ // 问题分类模型
{
"model": "gpt-3.5-turbo-1106",
"name": "GPT35-1106",
"maxContext": 16000,
"maxResponse": 4000,
"price": 0,
"toolChoice": true, // 是否支持openai的 toolChoice 不支持的模型需要设置为 false会走提示词生成
"functionPrompt": ""
},
{
"model": "gpt-4",
"name": "GPT4-8k",
"maxContext": 8000,
"maxResponse": 8000,
"price": 0,
"toolChoice": true,
"functionPrompt": ""
}
],
"ExtractModels": [ // 内容提取模型
{
"model": "gpt-3.5-turbo-1106",
"name": "GPT35-1106",
"maxContext": 16000,
"maxResponse": 4000,
"price": 0,
"toolChoice": true,
"functionPrompt": ""
}
],
"QGModels": [ // 生成下一步指引
{
"model": "gpt-3.5-turbo-1106",
"name": "GPT35-1106",
"maxContext": 1600,
"maxResponse": 4000,
"price": 0
}
],
"VectorModels": [ // 向量模型
{
"model": "text-embedding-ada-002",
"name": "Embedding-2",
"price": 0.2,
"defaultToken": 700,
"maxToken": 3000
}
],
"ReRankModels": [], // 重排模型,暂时填空数组
"AudioSpeechModels": [
{
"model": "tts-1",
"name": "OpenAI TTS1",
"price": 0,
"baseUrl": "",
"key": "",
"voices": [
{ "label": "Alloy", "value": "alloy", "bufferId": "openai-Alloy" },
{ "label": "Echo", "value": "echo", "bufferId": "openai-Echo" },
{ "label": "Fable", "value": "fable", "bufferId": "openai-Fable" },
{ "label": "Onyx", "value": "onyx", "bufferId": "openai-Onyx" },
{ "label": "Nova", "value": "nova", "bufferId": "openai-Nova" },
{ "label": "Shimmer", "value": "shimmer", "bufferId": "openai-Shimmer" }
]
}
],
"WhisperModel": {
"model": "whisper-1",
"name": "Whisper1",
"price": 0
}
}
```
## 4.6.6-alpha 版本完整配置参数
## 4.6.8 以前版本完整配置参数
**使用时,请务必去除注释!**
@@ -277,7 +131,7 @@ weight: 708
"maxContext": 1600,
"maxResponse": 4000,
"inputPrice": 0,
"outputPrice": 0,
"outputPrice": 0
}
],
"vectorModels": [ // 向量模型
@@ -315,6 +169,134 @@ weight: 708
}
```
## 4.6.8 新配置文件
llm模型全部合并
```json
{
"systemEnv": {
"openapiPrefix": "fastgpt",
"vectorMaxProcess": 15,
"qaMaxProcess": 15,
"pgHNSWEfSearch": 100
},
"llmModels": [
{
"model": "gpt-3.5-turbo-1106", // 模型名
"name": "gpt-3.5-turbo", // 别名
"maxContext": 16000, // 最大上下文
"maxResponse": 4000, // 最大回复
"quoteMaxToken": 13000, // 最大引用内容
"maxTemperature": 1.2, // 最大温度
"inputPrice": 0,
"outputPrice": 0,
"censor": false,
"vision": false, // 是否支持图片输入
"datasetProcess": false, // 是否设置为知识库处理模型
"toolChoice": true, // 是否支持工具选择
"functionCall": false, // 是否支持函数调用
"customCQPrompt": "", // 自定义文本分类提示词(不支持工具和函数调用的模型
"customExtractPrompt": "", // 自定义内容提取提示词
"defaultSystemChatPrompt": "", // 对话默认携带的系统提示词
"defaultConfig":{} // 对话默认配置(比如 GLM4 的 top_p
},
{
"model": "gpt-3.5-turbo-16k",
"name": "gpt-3.5-turbo-16k",
"maxContext": 16000,
"maxResponse": 16000,
"quoteMaxToken": 13000,
"maxTemperature": 1.2,
"inputPrice": 0,
"outputPrice": 0,
"censor": false,
"vision": false,
"datasetProcess": true,
"toolChoice": true,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"defaultConfig":{}
},
{
"model": "gpt-4-0125-preview",
"name": "gpt-4-turbo",
"maxContext": 125000,
"maxResponse": 125000,
"quoteMaxToken": 100000,
"maxTemperature": 1.2,
"inputPrice": 0,
"outputPrice": 0,
"censor": false,
"vision": false,
"datasetProcess": false,
"toolChoice": true,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"defaultConfig":{}
},
{
"model": "gpt-4-vision-preview",
"name": "gpt-4-vision",
"maxContext": 128000,
"maxResponse": 4000,
"quoteMaxToken": 100000,
"maxTemperature": 1.2,
"inputPrice": 0,
"outputPrice": 0,
"censor": false,
"vision": false,
"datasetProcess": false,
"toolChoice": true,
"functionCall": false,
"customCQPrompt": "",
"customExtractPrompt": "",
"defaultSystemChatPrompt": "",
"defaultConfig":{}
}
],
"vectorModels": [
{
"model": "text-embedding-ada-002",
"name": "Embedding-2",
"inputPrice": 0,
"outputPrice": 0,
"defaultToken": 700,
"maxToken": 3000,
"weight": 100,
"defaultConfig":{} // 默认配置。例如,如果希望使用 embedding3-large 的话,可以传入 dimensions:1024来返回1024维度的向量。目前必须小于1536维度
}
],
"reRankModels": [],
"audioSpeechModels": [
{
"model": "tts-1",
"name": "OpenAI TTS1",
"inputPrice": 0,
"outputPrice": 0,
"voices": [
{ "label": "Alloy", "value": "alloy", "bufferId": "openai-Alloy" },
{ "label": "Echo", "value": "echo", "bufferId": "openai-Echo" },
{ "label": "Fable", "value": "fable", "bufferId": "openai-Fable" },
{ "label": "Onyx", "value": "onyx", "bufferId": "openai-Onyx" },
{ "label": "Nova", "value": "nova", "bufferId": "openai-Nova" },
{ "label": "Shimmer", "value": "shimmer", "bufferId": "openai-Shimmer" }
]
}
],
"whisperModel": {
"model": "whisper-1",
"name": "Whisper1",
"inputPrice": 0,
"outputPrice": 0
}
}
```
## 特殊模型
### ReRank 接入

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@@ -59,10 +59,10 @@ Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One
## 接入 FastGPT
修改 config.json 配置文件,在 ChatModels 中加入 chatglm2, 在 VectorModels 中加入 M3E 模型:
修改 config.json 配置文件,在 llmModels 中加入 chatglm2, 在 vectorModels 中加入 M3E 模型:
```json
"ChatModels": [
"llmModels": [
//其他对话模型
{
"model": "chatglm2",
@@ -74,7 +74,7 @@ Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One
"defaultSystemChatPrompt": ""
}
],
"VectorModels": [
"vectorModels": [
{
"model": "text-embedding-ada-002",
"name": "Embedding-2",

View File

@@ -99,10 +99,10 @@ Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One
## 接入 FastGPT
修改 config.json 配置文件,在 ChatModels 中加入 chatglm2 模型:
修改 config.json 配置文件,在 llmModels 中加入 chatglm2 模型:
```json
"ChatModels": [
"llmModels": [
//已有模型
{
"model": "chatglm2",

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@@ -28,7 +28,7 @@ weight: 910
### 源码部署
1. 根据上面的环境配置配置好环境,具体教程自行 GPT
2. 下载 [python 文件](app.py)
2. 下载 [python 文件](https://github.com/labring/FastGPT/tree/main/python/reranker/bge-reranker-base)
3. 在命令行输入命令 `pip install -r requirments.txt`
4. 按照[https://huggingface.co/BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)下载模型仓库到app.py同级目录
5. 添加环境变量 `export ACCESS_TOKEN=XXXXXX` 配置 token这里的 token 只是加一层验证,防止接口被人盗用,默认值为 `ACCESS_TOKEN`

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@@ -17,6 +17,11 @@ weight: 707
| 500w 组向量 | 8c32g | 16c64g 200GB |
{{< /table >}}
## 部署架构图
![](/imgs/sealos-fastgpt.webp)
### 1. 准备好代理环境(国外服务器可忽略)
确保可以访问 OpenAI具体方案可以参考[代理方案](/docs/development/proxy/)。或直接在 Sealos 上 [部署 OneAPI](/docs/development/one-api),既解决代理问题也能实现多 Key 轮询、接入其他大模型。
@@ -89,10 +94,14 @@ curl -O https://raw.githubusercontent.com/labring/FastGPT/main/files/deploy/fast
curl -O https://raw.githubusercontent.com/labring/FastGPT/main/projects/app/data/config.json
```
## 三、修改 docker-compose.yml 的环境变量
## 三、启动容器
修改`docker-compose.yml`中的`OPENAI_BASE_URL`API 接口的地址,需要加/v1`CHAT_API_KEY`API 接口的凭证)。
修改`docker-compose.yml`中的`OPENAI_BASE_URL``CHAT_API_KEY`即可,对应为 API 的地址(别忘记加/v1)和 key。
使用 OneAPI 的话OPENAI_BASE_URL=OneAPI访问地址/v1CHAT_API_KEY=令牌
## 四、启动容器
```bash
# 在 docker-compose.yml 同级目录下执行
@@ -100,7 +109,39 @@ docker-compose pull
docker-compose up -d
```
## 四、访问 FastGPT
## 四、初始化 Mongo 副本集(4.6.8以前可忽略)
FastGPT 4.6.8 后使用了 MongoDB 的事务,需要运行在副本集上。副本集没法自动化初始化,需手动操作。
```bash
# 查看 mongo 容器是否正常运行
docker ps
# 进入容器
docker exec -it mongo bash
# 连接数据库
mongo
# 初始化副本集。
rs.initiate({
_id: "rs0",
members: [
{ _id: 0, host: "mongo:27017" }
]
})
# 检查状态。如果提示 rs0 状态,则代表运行成功
rs.status()
# 初始化用户
use admin
db.createUser({
user: "admin",
pwd: "password",
roles: [{ role: "root", db: "admin" }]
});
```
## 五、访问 FastGPT
目前可以通过 `ip:3000` 直接访问(注意防火墙)。登录用户名为 `root`,密码为`docker-compose.yml`环境变量里设置的 `DEFAULT_ROOT_PSW`

View File

@@ -19,13 +19,17 @@ images: []
## 通用问题
### 能否纯本地允许
可以。需要准备好向量模型和LLM模型。
### insufficient_user_quota user quota is not enough
OneAPI 账号的余额不足,默认 root 用户只有 200 刀,可以手动修改。
### xxx渠道找不到
OneAPI 中没有配置该模型渠道。
OneAPI 中没有配置该模型渠道。或者是修改了配置文件中一部分的模型,但没有全部修改。
### 页面中可以正常回复API 报错
@@ -35,6 +39,25 @@ OneAPI 中没有配置该模型渠道。
OneAPI 的 API Key 配置错误,需要修改`OPENAI_API_KEY`环境变量,并重启容器(先 stop 然后 rm 掉,最后再 up -d 运行一次)。可以`exec`进入容器,`env`查看环境变量是否生效。
### 其他模型没法进行问题分类/内容提取
需要给其他模型配置`toolChoice=false`就会默认走提示词模式。目前内置提示词仅针对了商业模型API进行测试国内外的商业模型基本都可用。
### 页面崩溃
1. 关闭翻译
2. 检查配置文件是否正常加载,如果没有正常加载会导致缺失系统信息,在某些操作下会导致空指针。
## 私有部署问题
### 知识库索引没有进度
先看日志报错信息。
1. 可以对话但是索引没有进度没有配置向量模型vectorModels
2. 不能对话也不能索引API调用失败。可能是没连上OneAPI或OenAI
3. 有进度但是非常慢api key不行OpenAI的免费号一分钟只有3次还是60次。一天上限200次。
## Docker 部署常见问题
### 如何更新?
@@ -96,7 +119,7 @@ mongo连接失败检查
### TypeError: Cannot read properties of null (reading 'useMemo' )
用 Node18 试试,可能最新的 Node 有问题。 本地开发流程:
删除所有的`node_modules`,用 Node18 重新 install 试试,可能最新的 Node 有问题。 本地开发流程:
1. 根目录: `pnpm i`
2. 复制 `config.json` -> `config.local.json`

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@@ -48,6 +48,8 @@ git clone git@github.com:<github_username>/FastGPT.git
第一次开发,需要先部署数据库,建议本地开发可以随便找一台 2C2G 的轻量小数据库实践。数据库部署教程:[Docker 快速部署](/docs/development/docker/)。部署完了,可以本地访问其数据库。
Mongo 数据库需要修改副本集的`host`,从原来的`mongo:27017`修改为`ip:27017`
### 4. 初始配置
以下文件均在 `projects/app` 路径下。
@@ -62,7 +64,7 @@ git clone git@github.com:<github_username>/FastGPT.git
**注意json 配置文件不能包含注释,介绍中为了方便看才加入的注释**
这个文件大部分时候不需要修改。只需要关注 SystemParams 里的参数:
这个文件大部分时候不需要修改。只需要关注 `systemEnv` 里的参数:
- `vectorMaxProcess`: 向量生成最大进程,根据数据库和 key 的并发数来决定,通常单个 120 号2c4g 服务器设置 10~15。
- `qaMaxProcess`: QA 生成最大进程

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@@ -11,6 +11,10 @@ weight: 708
* [One API](https://github.com/songquanpeng/one-api) 是一个 OpenAI 接口管理 & 分发系统,可以通过标准的 OpenAI API 格式访问所有的大模型,开箱即用。
* FastGPT 可以通过接入 OneAPI 来实现对不同大模型的支持。OneAPI 的部署方法也很简单。
## FastGPT 与 OneAPI 关系
![](/imgs/sealos-fastgpt.webp)
## MySQL 版本
MySQL 版本支持多实例,高并发。
@@ -50,7 +54,14 @@ BATCH_UPDATE_ENABLED=true
BATCH_UPDATE_INTERVAL=60
```
## One API使用步骤
## One API使用教程
### 概念
1. 渠道:
1. OneApi 中一个渠道对应一个 `Api Key`,这个 `Api Key` 可以是GPT、微软、ChatGLM、文心一言的。一个`Api Key`通常可以调用同一个厂商的多个模型。
2. OneAPI 会根据请求传入的`模型`来决定使用哪一个`Key`,如果一个模型对应了多个`Key`,则会随机调用。
2. 令牌:访问 OneAPI 所需的凭证,只需要这`1`个凭证即可访问`OneAPI`上配置的模型。因此`FastGPT`中,只需要配置`OneAPI``baseurl``令牌`即可。
### 1. 登录 One API
@@ -68,7 +79,11 @@ BATCH_UPDATE_INTERVAL=60
创建一个令牌
![step7](/imgs/oneapi-step7.png)
### 3. 修改 FastGPT 的环境变量
### 3. 修改账号余额
OneAPI 默认 root 用户只有 200刀可以自行修改编辑。
### 4. 修改 FastGPT 的环境变量
有了 One API 令牌后FastGPT 可以通过修改 `baseurl``key` 去请求到 One API再由 One API 去请求不同的模型。修改下面两个环境变量:
@@ -92,21 +107,29 @@ CHAT_API_KEY=sk-xxxxxx
可以在 `/projects/app/src/data/config.json` 里找到配置文件(本地开发需要复制成 config.local.json配置文件中有一项是对话模型配置
```json
"ChatModels": [
"llmModels": [
...
{
"model": "ERNIE-Bot", // 这里的模型需要对应 One API 的模型
"name": "文心一言", // 对外展示的名称
"maxContext": 8000, // 最大下文 token无论什么模型都按 GPT35 的计算。GPT 外的模型需要自行大致计算下这个值。可以调用官方接口去比对 Token 的倍率,然后在这里粗略计算。
"maxResponse": 4000, // 最大回复 token
// 例如:文心一言的中英文 token 基本是 1:1而 GPT 的中文 Token 是 2:1如果文心一言官方最大 Token 是 4000那么这里就可以填 8000保险点就填 7000.
"quoteMaxToken": 2000, // 引用知识库的最大 Token
"maxTemperature": 1, // 最大温度
"vision": false, // 是否开启图片识别
"defaultSystemChatPrompt": "" // 默认的系统提示词
"maxContext": 16000, // 最大下文
"maxResponse": 4000, // 最大回复
"quoteMaxToken": 13000, // 最大引用内容
"maxTemperature": 1.2, // 最大温度
"inputPrice": 0,
"outputPrice": 0,
"censor": false,
"vision": false, // 是否支持图片输入
"datasetProcess": false, // 是否设置为知识库处理模型
"toolChoice": true, // 是否支持工具选择
"functionCall": false, // 是否支持函数调用
"customCQPrompt": "", // 自定义文本分类提示词(不支持工具和函数调用的模型
"customExtractPrompt": "", // 自定义内容提取提示词
"defaultSystemChatPrompt": "", // 对话默认携带的系统提示词
"defaultConfig":{} // 对话默认配置(比如 GLM4 的 top_p
}
...
],
```
添加完后,重启 FastGPT 即可在选择文心一言模型进行对话。
添加完后,重启 FastGPT 即可在选择文心一言模型进行对话。**添加向量模型也是类似操作,增加到 `vectorModels`里。**

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@@ -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 。
-`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。
-`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 %}}

File diff suppressed because it is too large Load Diff

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@@ -11,6 +11,13 @@ weight: 706
![](/imgs/sealos-fastgpt.webp)
## 多模型支持
FastGPT 使用了 one-api 项目来管理模型池,其可以兼容 OpenAI 、Azure 、国内主流模型和本地模型等。
可参考:[Sealos 快速部署 OneAPI](/docs/development/one-api)
## 一键部署
Sealos 的服务器在国外,不需要额外处理网络问题,无需服务器、无需魔法、无需域名,支持高并发 & 动态伸缩。点击以下按钮即可一键部署 👇

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@@ -0,0 +1,35 @@
---
title: 'V4.6.7(需要初始化)'
description: 'FastGPT V4.6.7'
icon: 'upgrade'
draft: false
toc: true
weight: 829
---
## 1。执行初始化 API
发起 1 个 HTTP 请求 ({{rootkey}} 替换成环境变量里的 `rootkey`{{host}} 替换成自己域名)
1. https://xxxxx/api/admin/initv464
```bash
curl --location --request POST 'https://{{host}}/api/admin/initv467' \
--header 'rootkey: {{rootkey}}' \
--header 'Content-Type: application/json'
```
初始化说明:
1. 将 images 重新关联到数据集
2. 设置 pg 表的 null 值。
## V4.6.7 更新说明
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](/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. 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。

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@@ -0,0 +1,27 @@
---
title: 'V4.6.8(进行中)'
description: 'FastGPT V4.6.7'
icon: 'upgrade'
draft: false
toc: true
weight: 828
---
## docker 部署 - 更新 Mongo
开启 Mongo 副本集模式。需要进入 mongo 执行一次 init参考[初始化Mongo副本集](/docs/development/docker/#四初始化-mongo-副本集),这个比较麻烦,初始化后可以用 mongoshell 之类的连接试试,看能不能连接上。
## Sealos 部署 - 无需更新 Mongo
## 修改配置文件
去除了重复的模型配置LLM模型都合并到一个属性中[点击查看最新的配置文件](/docs/development/configuration/)
## V4.6.8 更新说明
1. 新增 - 知识库搜索合并模块。
1. 优化 - LLM 模型配置,不再区分对话、分类、提取模型。同时支持模型的默认参数,避免不同模型参数冲突,可通过`defaultConfig`传入默认的配置。
2. 优化 - HTTP 模块支持输出字符串自动序列化JSON可自动转成字符串
3. 优化 - 流响应,参考了`ChatNextWeb`的流,更加丝滑。此外,之前提到的乱码、中断,刷新后又正常了,可能会修复)
4. 修复 - 语音输入文件无法上传。
5. 修复 - 对话框重新生成无法使用。

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@@ -25,7 +25,9 @@ FastGPT 采用了 RAG 中的 Embedding 方案构建知识库,要使用好 Fast
FastGPT 采用了 `PostgresSQL``PG Vector` 插件作为向量检索器,索引为`HNSW`。且`PostgresSQL`仅用于向量检索,`MongoDB`用于其他数据的存取。
`PostgresSQL`的表中,设置一个 `index` 字段用于存储向量,以及一个`data_id`用于在`MongoDB`中寻找对应的映射值。多个`index`可以对应一组`data_id`,也就是说,一组向量可以对应多组数据。在进行检索时,相同数据会进行合并。
`MongoDB``dataset.datas`表中,会存储向量原数据的信息,同时有一个`indexes`字段会记录其对应的向量ID这是一个数组,也就是说,一组向量可以对应多组数据。
`PostgresSQL`的表中,设置一个 `index` 字段用于存储向量。在检索时会先召回向量再根据向量的ID`MongoDB`中寻找原数据内容,如果对应了同一组原数据,则进行合并,向量得分取最高得分。
![](/imgs/datasetSetting1.png)

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@@ -30,20 +30,15 @@ FastGPT 从 V4 版本开始采用新的交互方式来构建 AI 应用。使用
### 模块分类
从功能上,模块可以分为 3 类:
从功能上,模块可以分为 2 类:
1. **只读模块**全局变量、用户引导。
2. **系统模块**聊天记录(无输入,直接从数据库取)、用户问题(流程入口)。
3. **功能模块**知识库搜索、AI 对话等剩余模块。(这些模块都有输入和输出,可以自由组合)。
1. **系统模块**:用户引导(配置一些对话框信息)、用户问题(流程入口)
2. **功能模块**知识库搜索、AI 对话等剩余模块。(这些模块都有输入和输出,可以自由组合)。
### 模块的组成
每个模块会包含 3 个核心部分:固定参数、外部输入(左边有个圆圈)和输出(右边有个圆圈)。
+ 对于只读模块,只需要根据提示填写即可,不参与流程运行。
+ 对于系统模块,通常只有固定参数和输出,主要需要关注输出到哪个位置。
+ 对于功能模块,通常这 3 部分都是重要的,以下图的 AI 对话为例:
![](/imgs/flow-intro3.png)
- 对话模型、温度、回复上限、系统提示词和限定词为固定参数,同时系统提示词和限定词也可以作为外部输入,意味着如果你有输入流向了系统提示词,那么原本填写的内容就会被**覆盖**。
@@ -56,6 +51,7 @@ FastGPT 从 V4 版本开始采用新的交互方式来构建 AI 应用。使用
1. 仅关心**已连接的**外部输入,即左边的圆圈被连接了。
2. 当连接内容都有值时触发。
3. **可以多个输出连接到一个输入,后续的值会覆盖前面的值。**
#### 示例 1
@@ -86,4 +82,17 @@ FastGPT 从 V4 版本开始采用新的交互方式来构建 AI 应用。使用
1. 建议从左往右阅读。
2.**用户问题** 模块开始。用户问题模块,代表的是用户发送了一段文本,触发任务开始。
3. 关注【AI 对话】和【指定回复】模块,这两个模块是输出答案的地方。
3. 关注【AI 对话】和【指定回复】模块,这两个模块是输出答案的地方。
## FAQ
### 想合并多个输出结果怎么实现?
1. 文本加工,可以对字符串进行合并。
2. 知识库搜索合并,可以合并多个知识库搜索结果
3. 其他结果,无法直接合并,可以考虑传入到`HTTP`模块中进行合并,使用`[Laf](https://laf.run/)`可以快速实现一个无服务器HTTP接口。
### 模块为什么有2个用户问题
左侧的`用户问题`是指该模块所需的输入。右侧的`用户问题`是为了方便后续的连线,输出的值和传入的用户问题一样。

View File

@@ -38,7 +38,7 @@ HTTP 模块会向对应的地址发送一个 `POST/GET` 请求,携带部分`
### 嵌套对象使用
**入参**
#### 入参
假设我们设计了`3个`输入。
@@ -58,7 +58,7 @@ HTTP 模块会向对应的地址发送一个 `POST/GET` 请求,携带部分`
}
```
**出参**
#### 出参
假设接口的输出结构为:
@@ -66,18 +66,46 @@ HTTP 模块会向对应的地址发送一个 `POST/GET` 请求,携带部分`
{
"message": "测试",
"data":{
"name": "name",
"age": 10
"user": {
"name": "xxx",
"age": 12
},
"list": [
{
"name": "xxx",
"age": 50
},
[{ "test": 22 }]
],
"psw": "xxx"
}
}
```
那么,自定出参的`key`可以设置为:
最终得到的解析为:
- message (string)
- data.name (string)
- data.age (number)
```json
{
"user": { "name": "xxx", "age": 12 },
"user.name": "xxx",
"user.age": 12,
"list": [ { "name": "xxx", "age": 50 }, [{ "test": 22 }] ],
"list[0]": { "name": "xxx", "age": 50 },
"list[0].name": "xxx",
"list[0].age": 50,
"list[1]": [ { "test": 22 } ],
"list[1][0]": { "test": 22 },
"list[1][0].test": 22,
"psw": "xxx"
}
```
你可以使用`json`里对应的`key`来获取值。
### 格式化输出
FastGPT v4.6.8 后,加入了出参格式化功能,主要以`json`格式化成`字符串`为主。如果你的输出类型选择了`字符串`,则会将`HTTP`对应`key`的值,转成`json`字符串进行输出。因此,未来你可以直接从`HTTP`接口输出内容至`文本加工`中,然后拼接适当的提示词,最终输入给`AI对话`
## POST 示例

View File

@@ -1,43 +0,0 @@
mixed-port: 7890
allow-lan: false
bind-address: '*'
mode: rule
log-level: warning
dns:
enable: true
ipv6: false
nameserver:
- 8.8.8.8
- 8.8.4.4
cache-size: 400
proxies:
proxy-groups:
- {
name: '♻️ 自动选择',
type: url-test,
proxies:
[
香港V02×1.5,
ABC,
印度01,
台湾03,
新加坡02,
新加坡03,
日本01,
日本02,
新加坡01,
美国01,
美国02,
台湾01,
台湾02
],
url: 'https://api.openai.com',
interval: 3600
}
rules:
- 'DOMAIN-SUFFIX,google.com,♻️ 自动选择'
- 'DOMAIN-SUFFIX,ai.fastgpt.in,♻️ 自动选择'
- 'DOMAIN-SUFFIX,openai.com,♻️ 自动选择'
- 'DOMAIN-SUFFIX,api.openai.com,♻️ 自动选择'
- 'MATCH,DIRECT'

View File

@@ -1,18 +0,0 @@
export ALL_PROXY=socks5://127.0.0.1:7891
export http_proxy=http://127.0.0.1:7890
export https_proxy=http://127.0.0.1:7890
export HTTP_PROXY=http://127.0.0.1:7890
export HTTPS_PROXY=http://127.0.0.1:7890
OLD_PROCESS=$(pgrep clash)
if [ ! -z "$OLD_PROCESS" ]; then
echo "Killing old process: $OLD_PROCESS"
kill $OLD_PROCESS
fi
sleep 2
cd /root/fastgpt/clash/fast
rm -f ./nohup.out || true
rm -f ./cache.db || true
nohup ./clash-linux-amd64-v3 -d ./ &
echo "Restart clash fast"

View File

@@ -1,10 +0,0 @@
export ALL_PROXY=''
export http_proxy=''
export https_proxy=''
export HTTP_PROXY=''
export HTTPS_PROXY=''
OLD_PROCESS=$(pgrep clash)
if [ ! -z "$OLD_PROCESS" ]; then
echo "Killing old process: $OLD_PROCESS"
kill $OLD_PROCESS
fi

View File

@@ -27,10 +27,7 @@ services:
- 27017:27017
networks:
- fastgpt
environment:
# 这里的配置只有首次运行生效。修改后,重启镜像是不会生效的。需要把持久化数据删除再重启,才有效果
- MONGO_INITDB_ROOT_USERNAME=username
- MONGO_INITDB_ROOT_PASSWORD=password
command: --replSet rs0
volumes:
- ./mongo/data:/data/db
fastgpt:
@@ -55,63 +52,11 @@ services:
- TOKEN_KEY=any
- ROOT_KEY=root_key
- FILE_TOKEN_KEY=filetoken
# mongo 配置,不需要改. 如果连不上,可能需要去掉 ?authSource=admin
- MONGODB_URI=mongodb://username:password@mongo:27017/fastgpt?authSource=admin
# mongo 配置,不需要改. 用户名admin,密码password是执行 db.createUser 时的账号和密码。
- MONGODB_URI=mongodb://admin:password@mongo:27017/fastgpt?authSource=admin
# pg配置. 不需要改
- PG_URL=postgresql://username:password@pg:5432/postgres
volumes:
- ./config.json:/app/data/config.json
networks:
fastgpt:
# host 版本, 不推荐。
# version: '3.3'
# services:
# pg:
# image: ankane/pgvector:v0.5.0 # dockerhub
# # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/pgvector:v0.5.0 # 阿里云
# container_name: pg
# restart: always
# ports: # 生产环境建议不要暴露
# - 5432:5432
# environment:
# # 这里的配置只有首次运行生效。修改后,重启镜像是不会生效的。需要把持久化数据删除再重启,才有效果
# - POSTGRES_USER=username
# - POSTGRES_PASSWORD=password
# - POSTGRES_DB=postgres
# volumes:
# - ./pg/data:/var/lib/postgresql/data
# mongo:
# image: mongo:5.0.18
# # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/mongo:5.0.18 # 阿里云
# container_name: mongo
# restart: always
# ports: # 生产环境建议不要暴露
# - 27017:27017
# environment:
# # 这里的配置只有首次运行生效。修改后,重启镜像是不会生效的。需要把持久化数据删除再重启,才有效果
# - MONGO_INITDB_ROOT_USERNAME=username
# - MONGO_INITDB_ROOT_PASSWORD=password
# volumes:
# - ./mongo/data:/data/db
# - ./mongo/logs:/var/log/mongodb
# fastgpt:
# # image: ghcr.io/labring/fastgpt:latest # github
# image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:latest # 阿里云
# network_mode: host
# restart: always
# container_name: fastgpt
# environment:
# # root 密码,用户名为: root
# - DEFAULT_ROOT_PSW=1234
# # 中转地址,如果是用官方号,不需要管
# - OPENAI_BASE_URL=https://api.openai.com/v1
# - CHAT_API_KEY=sk-xxxx
# - DB_MAX_LINK=5 # database max link
# # token加密凭证随便填作为登录凭证
# - TOKEN_KEY=any
# # root key, 最高权限,可以内部接口互相调用
# - ROOT_KEY=root_key
# # mongo 配置,不需要改
# - MONGODB_URI=mongodb://username:password@0.0.0.0:27017/fastgpt?authSource=admin
# # pg配置. 不需要改
# - PG_URL=postgresql://username:password@0.0.0.0:5432/postgres

View File

@@ -1,5 +1,7 @@
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);
};

View File

@@ -1,9 +1,15 @@
export type UploadImgProps = {
base64Img: string;
import { MongoImageTypeEnum } from './image/constants';
export type preUploadImgProps = {
type: `${MongoImageTypeEnum}`;
expiredTime?: Date;
metadata?: Record<string, any>;
shareId?: string;
};
export type UploadImgProps = preUploadImgProps & {
base64Img: string;
};
export type UrlFetchParams = {
urlList: string[];
@@ -11,6 +17,7 @@ export type UrlFetchParams = {
};
export type UrlFetchResponse = {
url: string;
title: string;
content: string;
selector?: string;
}[];

View File

@@ -1,12 +1,14 @@
export const fileImgs = [
{ suffix: 'pdf', src: '/imgs/files/pdf.svg' },
{ suffix: 'csv', src: '/imgs/files/csv.svg' },
{ suffix: '(doc|docs)', src: '/imgs/files/doc.svg' },
{ suffix: 'txt', src: '/imgs/files/txt.svg' },
{ suffix: 'md', src: '/imgs/files/markdown.svg' }
{ suffix: 'pdf', src: 'file/fill/pdf' },
{ suffix: 'csv', src: 'file/fill/csv' },
{ suffix: '(doc|docs)', src: 'file/fill/doc' },
{ suffix: 'txt', src: 'file/fill/txt' },
{ suffix: 'md', src: 'file/fill/markdown' },
{ suffix: 'html', src: 'file/fill/html' }
// { suffix: '.', src: '/imgs/files/file.svg' }
];
export function getFileIcon(name = '', defaultImg = '/imgs/files/file.svg') {
export function getFileIcon(name = '', defaultImg = 'file/fill/file') {
return fileImgs.find((item) => new RegExp(item.suffix, 'gi').test(name))?.src || defaultImg;
}

View File

@@ -0,0 +1,52 @@
export const imageBaseUrl = '/api/system/img/';
export enum MongoImageTypeEnum {
systemAvatar = 'systemAvatar',
appAvatar = 'appAvatar',
pluginAvatar = 'pluginAvatar',
datasetAvatar = 'datasetAvatar',
userAvatar = 'userAvatar',
teamAvatar = 'teamAvatar',
chatImage = 'chatImage',
collectionImage = 'collectionImage'
}
export const mongoImageTypeMap = {
[MongoImageTypeEnum.systemAvatar]: {
label: 'common.file.type.appAvatar',
unique: true
},
[MongoImageTypeEnum.appAvatar]: {
label: 'common.file.type.appAvatar',
unique: true
},
[MongoImageTypeEnum.pluginAvatar]: {
label: 'common.file.type.pluginAvatar',
unique: true
},
[MongoImageTypeEnum.datasetAvatar]: {
label: 'common.file.type.datasetAvatar',
unique: true
},
[MongoImageTypeEnum.userAvatar]: {
label: 'common.file.type.userAvatar',
unique: true
},
[MongoImageTypeEnum.teamAvatar]: {
label: 'common.file.type.teamAvatar',
unique: true
},
[MongoImageTypeEnum.chatImage]: {
label: 'common.file.type.chatImage',
unique: false
},
[MongoImageTypeEnum.collectionImage]: {
label: 'common.file.type.collectionImage',
unique: false
}
};
export const uniqueImageTypeList = Object.entries(mongoImageTypeMap)
.filter(([key, value]) => value.unique)
.map(([key]) => key as `${MongoImageTypeEnum}`);

View File

@@ -0,0 +1,14 @@
import { MongoImageTypeEnum } from './constants';
export type MongoImageSchemaType = {
_id: string;
teamId: string;
binary: Buffer;
createTime: Date;
expiredTime?: Date;
type: `${MongoImageTypeEnum}`;
metadata?: {
relatedId?: string; // This id is associated with a set of images
};
};

View File

@@ -0,0 +1,18 @@
// The number of days left in the month is calculated as 30 days per month, and less than 1 day is calculated as 1 day
export const getMonthRemainingDays = (startDate = new Date()) => {
const year = startDate.getFullYear();
const month = startDate.getMonth();
const endDay = new Date(year, month + 1, 0, 0, 0, 0);
return calculateDaysBetweenDates(startDate, endDay);
};
export const calculateDaysBetweenDates = (date1: Date, date2: Date) => {
const oneDay = 24 * 60 * 60 * 1000;
const firstDate = new Date(date1).getTime();
const secondDate = new Date(date2).getTime();
const differenceInTime = Math.abs(secondDate - firstDate);
const differenceInDays = Math.floor(differenceInTime / oneDay);
return differenceInDays;
};

View File

@@ -15,10 +15,10 @@ export const simpleMarkdownText = (rawText: string) => {
return `[${cleanedLinkText}](${url})`;
});
// replace special \.* ……
const reg1 = /\\([-.!`_(){}\[\]])/g;
// replace special #\.* ……
const reg1 = /\\([#`!*()+-_\[\]{}\\.])/g;
if (reg1.test(rawText)) {
rawText = rawText.replace(/\\([`!*()+-_\[\]{}\\.])/g, '$1');
rawText = rawText.replace(reg1, '$1');
}
// replace \\n
@@ -45,14 +45,15 @@ export const uploadMarkdownBase64 = async ({
uploadImgController
}: {
rawText: string;
uploadImgController: (base64: string) => Promise<string>;
uploadImgController?: (base64: string) => Promise<string>;
}) => {
// match base64, upload and replace it
const base64Regex = /data:image\/.*;base64,([^\)]+)/g;
const base64Arr = rawText.match(base64Regex) || [];
// upload base64 and replace it
await Promise.all(
base64Arr.map(async (base64Img) => {
if (uploadImgController) {
// match base64, upload and replace it
const base64Regex = /data:image\/.*;base64,([^\)]+)/g;
const base64Arr = rawText.match(base64Regex) || [];
// upload base64 and replace it
for await (const base64Img of base64Arr) {
try {
const str = await uploadImgController(base64Img);
@@ -61,8 +62,8 @@ export const uploadMarkdownBase64 = async ({
rawText = rawText.replace(base64Img, '');
rawText = rawText.replace(/!\[.*\]\(\)/g, '');
}
})
);
}
}
// Remove white space on both sides of the picture
const trimReg = /(!\[.*\]\(.*\))\s*/g;
@@ -70,5 +71,20 @@ export const uploadMarkdownBase64 = async ({
rawText = rawText.replace(trimReg, '$1');
}
return simpleMarkdownText(rawText);
return rawText;
};
export const markdownProcess = async ({
rawText,
uploadImgController
}: {
rawText: string;
uploadImgController?: (base64: string) => Promise<string>;
}) => {
const imageProcess = await uploadMarkdownBase64({
rawText,
uploadImgController
});
return simpleMarkdownText(imageProcess);
};

View File

@@ -13,13 +13,12 @@ export const splitText2Chunks = (props: {
chunkLen: number;
overlapRatio?: number;
customReg?: string[];
countTokens?: boolean;
}): {
chunks: string[];
tokens: number;
chars: number;
overlapRatio?: number;
} => {
let { text = '', chunkLen, overlapRatio = 0.2, customReg = [], countTokens = true } = props;
let { text = '', chunkLen, overlapRatio = 0.2, customReg = [] } = props;
const splitMarker = 'SPLIT_HERE_SPLIT_HERE';
const codeBlockMarker = 'CODE_BLOCK_LINE_MARKER';
const overlapLen = Math.round(chunkLen * overlapRatio);
@@ -240,13 +239,11 @@ export const splitText2Chunks = (props: {
mdTitle: ''
}).map((chunk) => chunk?.replaceAll(codeBlockMarker, '\n') || ''); // restore code block
const tokens = countTokens
? chunks.reduce((sum, chunk) => sum + countPromptTokens(chunk, 'system'), 0)
: 0;
const chars = chunks.reduce((sum, chunk) => sum + chunk.length, 0);
return {
chunks,
tokens
chars
};
} catch (err) {
throw new Error(getErrText(err));

View File

@@ -33,6 +33,12 @@ export function countPromptTokens(
) {
const enc = getTikTokenEnc();
const text = `${role}\n${prompt}`;
// too large a text will block the thread
if (text.length > 15000) {
return text.length * 1.7;
}
try {
const encodeText = enc.encode(text);
return encodeText.length + role.length; // 补充 role 估算值

View File

@@ -1,3 +1,4 @@
import dayjs from 'dayjs';
export const formatTime2YMDHM = (time: Date) => dayjs(time).format('YYYY-MM-DD HH:mm');
export const formatTime2YMDHM = (time?: Date) =>
time ? dayjs(time).format('YYYY-MM-DD HH:mm') : '';

View File

@@ -1,4 +1,5 @@
import crypto from 'crypto';
import { customAlphabet } from 'nanoid';
/* check string is a web link */
export function strIsLink(str?: string) {
@@ -36,3 +37,13 @@ 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)();
};

View File

@@ -1,3 +1,4 @@
import { StandSubPlanLevelMapType, SubPlanType } from '../../../support/wallet/sub/type';
import type {
ChatModelItemType,
FunctionModelItemType,
@@ -7,16 +8,14 @@ import type {
WhisperModelType,
ReRankModelItemType
} from '../../../core/ai/model.d';
import { SubTypeEnum } from '../../../support/wallet/sub/constants';
/* fastgpt main */
export type FastGPTConfigFileType = {
feConfigs: FastGPTFeConfigsType;
systemEnv: SystemEnvType;
chatModels: ChatModelItemType[];
qaModels: LLMModelItemType[];
cqModels: FunctionModelItemType[];
extractModels: FunctionModelItemType[];
qgModels: LLMModelItemType[];
subPlans?: SubPlanType;
llmModels: ChatModelItemType[];
vectorModels: VectorModelItemType[];
reRankModels: ReRankModelItemType[];
audioSpeechModels: AudioSpeechModelType[];
@@ -51,6 +50,8 @@ export type FastGPTFeConfigsType = {
favicon?: string;
customApiDomain?: string;
customSharePageDomain?: string;
uploadFileMaxSize?: number;
};
export type SystemEnvType = {
@@ -60,7 +61,8 @@ export type SystemEnvType = {
pgHNSWEfSearch: number;
};
declare global {
var feConfigs: FastGPTFeConfigsType;
var systemEnv: SystemEnvType;
}
// declare global {
// var feConfigs: FastGPTFeConfigsType;
// var systemEnv: SystemEnvType;
// var systemInitd: boolean;
// }

View File

@@ -3,20 +3,24 @@ export type LLMModelItemType = {
name: string;
maxContext: number;
maxResponse: number;
inputPrice: number;
outputPrice: number;
};
export type ChatModelItemType = LLMModelItemType & {
quoteMaxToken: number;
maxTemperature: number;
inputPrice: number;
outputPrice: number;
censor?: boolean;
vision?: boolean;
defaultSystemChatPrompt?: string;
};
datasetProcess?: boolean;
export type FunctionModelItemType = LLMModelItemType & {
functionCall: boolean;
toolChoice: boolean;
functionPrompt: string;
customCQPrompt: string;
customExtractPrompt: string;
defaultSystemChatPrompt?: string;
defaultConfig?: Record<string, any>;
};
export type VectorModelItemType = {
@@ -27,6 +31,8 @@ export type VectorModelItemType = {
outputPrice: number;
maxToken: number;
weight: number;
hidden?: boolean;
defaultConfig?: Record<string, any>;
};
export type ReRankModelItemType = {

View File

@@ -3,11 +3,22 @@ import type { LLMModelItemType, VectorModelItemType } from './model.d';
export const defaultQAModels: LLMModelItemType[] = [
{
model: 'gpt-3.5-turbo-16k',
name: 'GPT35-16k',
name: 'gpt-3.5-turbo-16k',
maxContext: 16000,
maxResponse: 16000,
quoteMaxToken: 13000,
maxTemperature: 1.2,
inputPrice: 0,
outputPrice: 0
outputPrice: 0,
censor: false,
vision: false,
datasetProcess: true,
toolChoice: true,
functionCall: false,
customCQPrompt: '',
customExtractPrompt: '',
defaultSystemChatPrompt: '',
defaultConfig: {}
}
];

View File

@@ -1,4 +1,4 @@
import type { ChatModelItemType } from '../ai/model.d';
import type { LLMModelItemType } from '../ai/model.d';
import { AppTypeEnum } from './constants';
import { AppSchema, AppSimpleEditFormType } from './type';
@@ -22,5 +22,5 @@ export interface AppUpdateParams {
export type FormatForm2ModulesProps = {
formData: AppSimpleEditFormType;
chatModelMaxToken: number;
chatModelList: ChatModelItemType[];
llmModelList: LLMModelItemType[];
};

View File

@@ -4,7 +4,7 @@ import { PermissionTypeEnum } from '../../support/permission/constant';
import type { AIChatModuleProps, DatasetModuleProps } from '../module/node/type.d';
import { VariableInputEnum } from '../module/constants';
import { SelectedDatasetType } from '../module/api';
import { DatasetSearchModeEnum } from '../dataset/constant';
import { DatasetSearchModeEnum } from '../dataset/constants';
export interface AppSchema {
_id: string;

View File

@@ -4,7 +4,7 @@ import { ModuleOutputKeyEnum, ModuleInputKeyEnum } from '../module/constants';
import type { FlowNodeInputItemType } from '../module/node/type.d';
import { getGuideModule, splitGuideModule } from '../module/utils';
import { ModuleItemType } from '../module/type.d';
import { DatasetSearchModeEnum } from '../dataset/constant';
import { DatasetSearchModeEnum } from '../dataset/constants';
export const getDefaultAppForm = (templateId = 'fastgpt-universal'): AppSimpleEditFormType => {
return {
@@ -91,7 +91,7 @@ export const appModules2Form = ({
);
defaultAppForm.dataset.limit = findInputValueByKey(
module.inputs,
ModuleInputKeyEnum.datasetLimit
ModuleInputKeyEnum.datasetMaxTokens
);
defaultAppForm.dataset.searchMode =
findInputValueByKey(module.inputs, ModuleInputKeyEnum.datasetSearchMode) ||

View File

@@ -31,16 +31,16 @@ export enum ChatSourceEnum {
}
export const ChatSourceMap = {
[ChatSourceEnum.test]: {
name: 'chat.logs.test'
name: 'core.chat.logs.test'
},
[ChatSourceEnum.online]: {
name: 'chat.logs.online'
name: 'core.chat.logs.online'
},
[ChatSourceEnum.share]: {
name: 'chat.logs.share'
name: 'core.chat.logs.share'
},
[ChatSourceEnum.api]: {
name: 'chat.logs.api'
name: 'core.chat.logs.api'
}
};

View File

@@ -4,7 +4,7 @@ import { ChatRoleEnum, ChatSourceEnum, ChatStatusEnum } from './constants';
import { FlowNodeTypeEnum } from '../module/node/constant';
import { ModuleOutputKeyEnum } from '../module/constants';
import { AppSchema } from '../app/type';
import { DatasetSearchModeEnum } from '../dataset/constant';
import { DatasetSearchModeEnum } from '../dataset/constants';
export type ChatSchema = {
_id: string;
@@ -22,6 +22,7 @@ export type ChatSchema = {
shareId?: string;
outLinkUid?: string;
content: ChatItemType[];
metadata?: Record<string, any>;
};
export type ChatWithAppSchema = Omit<ChatSchema, 'appId'> & {
@@ -91,6 +92,7 @@ export type moduleDispatchResType = {
runningTime?: number;
inputTokens?: number;
outputTokens?: number;
charsLength?: number;
model?: string;
query?: string;
contextTotalLen?: number;

View File

@@ -1,5 +1,5 @@
import { DatasetDataIndexItemType, DatasetSchemaType } from './type';
import { DatasetCollectionTrainingModeEnum, DatasetCollectionTypeEnum } from './constant';
import { TrainingModeEnum, DatasetCollectionTypeEnum } from './constants';
import type { LLMModelItemType } from '../ai/model.d';
/* ================= dataset ===================== */
@@ -16,28 +16,47 @@ export type DatasetUpdateBody = {
};
/* ================= collection ===================== */
export type CreateDatasetCollectionParams = {
datasetId: string;
export type DatasetCollectionChunkMetadataType = {
parentId?: string;
trainingType?: `${TrainingModeEnum}`;
chunkSize?: number;
chunkSplitter?: string;
qaPrompt?: string;
metadata?: Record<string, any>;
};
export type CreateDatasetCollectionParams = DatasetCollectionChunkMetadataType & {
datasetId: string;
name: string;
type: `${DatasetCollectionTypeEnum}`;
trainingType?: `${DatasetCollectionTrainingModeEnum}`;
chunkSize?: number;
fileId?: string;
rawLink?: string;
qaPrompt?: string;
rawTextLength?: number;
hashRawText?: string;
metadata?: Record<string, any>;
};
export type ApiCreateDatasetCollectionParams = DatasetCollectionChunkMetadataType & {
datasetId: string;
};
export type TextCreateDatasetCollectionParams = ApiCreateDatasetCollectionParams & {
name: string;
text: string;
};
export type LinkCreateDatasetCollectionParams = ApiCreateDatasetCollectionParams & {
link: string;
};
export type FileCreateDatasetCollectionParams = ApiCreateDatasetCollectionParams & {
name: string;
rawTextLength: number;
hashRawText: string;
fileMetadata?: Record<string, any>;
collectionMetadata?: Record<string, any>;
};
/* ================= data ===================== */
export type PgSearchRawType = {
id: string;
team_id: string;
tmb_id: string;
collection_id: string;
data_id: string;
score: number;
};
export type PushDatasetDataChunkProps = {
@@ -51,3 +70,14 @@ export type PostWebsiteSyncParams = {
datasetId: string;
billId: string;
};
export type PushDatasetDataProps = {
collectionId: string;
data: PushDatasetDataChunkProps[];
trainingMode: `${TrainingModeEnum}`;
prompt?: string;
billId?: string;
};
export type PushDatasetDataResponse = {
insertLen: number;
};

View File

@@ -6,7 +6,7 @@ export enum DatasetTypeEnum {
}
export const DatasetTypeMap = {
[DatasetTypeEnum.folder]: {
icon: 'core/dataset/folderDataset',
icon: 'common/folderFill',
label: 'core.dataset.Folder Dataset',
collectionLabel: 'common.Folder'
},
@@ -53,23 +53,7 @@ export const DatasetCollectionTypeMap = {
name: 'core.dataset.link'
},
[DatasetCollectionTypeEnum.virtual]: {
name: 'core.dataset.Virtual File'
}
};
export enum DatasetCollectionTrainingModeEnum {
manual = 'manual',
chunk = 'chunk',
qa = 'qa'
}
export const DatasetCollectionTrainingTypeMap = {
[DatasetCollectionTrainingModeEnum.manual]: {
label: 'core.dataset.collection.training.type manual'
},
[DatasetCollectionTrainingModeEnum.chunk]: {
label: 'core.dataset.collection.training.type chunk'
},
[DatasetCollectionTrainingModeEnum.qa]: {
label: 'core.dataset.collection.training.type qa'
name: 'core.dataset.Manual collection'
}
};
@@ -120,10 +104,12 @@ export enum TrainingModeEnum {
export const TrainingTypeMap = {
[TrainingModeEnum.chunk]: {
label: 'core.dataset.training.type chunk'
label: 'core.dataset.training.Chunk mode',
tooltip: 'core.dataset.import.Chunk Split Tip'
},
[TrainingModeEnum.qa]: {
label: 'core.dataset.training.type qa'
label: 'core.dataset.training.QA mode',
tooltip: 'core.dataset.import.QA Import Tip'
}
};
@@ -184,4 +170,8 @@ export const SearchScoreTypeMap = {
}
};
export const FolderAvatarSrc = '/imgs/files/folder.svg';
export const FolderIcon = 'file/fill/folder';
export const FolderImgUrl = '/imgs/files/folder.svg';
export const CustomCollectionIcon = 'common/linkBlue';
export const LinkCollectionIcon = 'common/linkBlue';

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@@ -21,7 +21,7 @@ export type UpdateDatasetDataProps = {
};
export type PatchIndexesProps = {
type: 'create' | 'update' | 'delete';
type: 'create' | 'update' | 'delete' | 'unChange';
index: Omit<DatasetDataIndexItemType, 'dataId'> & {
dataId?: string;
};

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@@ -0,0 +1,95 @@
import { countPromptTokens } from '../../../common/string/tiktoken';
import { SearchScoreTypeEnum } from '../constants';
import { SearchDataResponseItemType } from '../type';
/* dataset search result concat */
export const datasetSearchResultConcat = (
arr: { k: number; list: SearchDataResponseItemType[] }[]
): SearchDataResponseItemType[] => {
arr = arr.filter((item) => item.list.length > 0);
if (arr.length === 0) return [];
if (arr.length === 1) return arr[0].list;
const map = new Map<string, SearchDataResponseItemType & { rrfScore: number }>();
// rrf
arr.forEach((item) => {
const k = item.k;
item.list.forEach((data, index) => {
const rank = index + 1;
const score = 1 / (k + rank);
const record = map.get(data.id);
if (record) {
// 合并两个score,有相同type的score,取最大值
const concatScore = [...record.score];
for (const dataItem of data.score) {
const sameScore = concatScore.find((item) => item.type === dataItem.type);
if (sameScore) {
sameScore.value = Math.max(sameScore.value, dataItem.value);
} else {
concatScore.push(dataItem);
}
}
map.set(data.id, {
...record,
score: concatScore,
rrfScore: record.rrfScore + score
});
} else {
map.set(data.id, {
...data,
rrfScore: score
});
}
});
});
// sort
const mapArray = Array.from(map.values());
const results = mapArray.sort((a, b) => b.rrfScore - a.rrfScore);
return results.map((item, index) => {
// if SearchScoreTypeEnum.rrf exist, reset score
const rrfScore = item.score.find((item) => item.type === SearchScoreTypeEnum.rrf);
if (rrfScore) {
rrfScore.value = item.rrfScore;
rrfScore.index = index;
} else {
item.score.push({
type: SearchScoreTypeEnum.rrf,
value: item.rrfScore,
index
});
}
// @ts-ignore
delete item.rrfScore;
return item;
});
};
export const filterSearchResultsByMaxChars = (
list: SearchDataResponseItemType[],
maxTokens: number
) => {
const results: SearchDataResponseItemType[] = [];
let totalTokens = 0;
for (let i = 0; i < list.length; i++) {
const item = list[i];
totalTokens += countPromptTokens(item.q + item.a);
if (totalTokens > maxTokens + 500) {
break;
}
results.push(item);
if (totalTokens > maxTokens) {
break;
}
}
return results.length === 0 ? list.slice(0, 1) : results;
};

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@@ -8,7 +8,7 @@ import {
DatasetTypeEnum,
SearchScoreTypeEnum,
TrainingModeEnum
} from './constant';
} from './constants';
/* schema */
export type DatasetSchemaType = {
@@ -42,15 +42,21 @@ export type DatasetCollectionSchemaType = {
type: `${DatasetCollectionTypeEnum}`;
createTime: Date;
updateTime: Date;
trainingType: `${TrainingModeEnum}`;
chunkSize: number;
chunkSplitter?: string;
qaPrompt?: string;
fileId?: string;
rawLink?: string;
qaPrompt?: string;
rawTextLength?: number;
hashRawText?: string;
metadata?: {
webPageSelector?: string;
relatedImgId?: string; // The id of the associated image collections
[key: string]: any;
};
};

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@@ -1,4 +1,4 @@
import { DatasetCollectionTypeEnum, DatasetDataIndexTypeEnum } from './constant';
import { TrainingModeEnum, DatasetCollectionTypeEnum, DatasetDataIndexTypeEnum } from './constants';
import { getFileIcon } from '../../common/file/icon';
import { strIsLink } from '../../common/string/tools';
@@ -7,18 +7,13 @@ export function getCollectionIcon(
name = ''
) {
if (type === DatasetCollectionTypeEnum.folder) {
return '/imgs/files/folder.svg';
return 'common/folderFill';
}
if (type === DatasetCollectionTypeEnum.link) {
return '/imgs/files/link.svg';
return 'common/linkBlue';
}
if (type === DatasetCollectionTypeEnum.virtual) {
if (name === '手动录入') {
return '/imgs/files/manual.svg';
} else if (name === '手动标注') {
return '/imgs/files/mark.svg';
}
return '/imgs/files/collection.svg';
return 'file/fill/manual';
}
return getFileIcon(name);
}
@@ -30,21 +25,17 @@ export function getSourceNameIcon({
sourceId?: string;
}) {
if (strIsLink(sourceId)) {
return '/imgs/files/link.svg';
return 'common/linkBlue';
}
const fileIcon = getFileIcon(sourceName, '');
if (fileIcon) {
return fileIcon;
}
if (sourceName === '手动录入') {
return '/imgs/files/manual.svg';
} else if (sourceName === '手动标注') {
return '/imgs/files/mark.svg';
}
return '/imgs/files/collection.svg';
return 'file/fill/manual';
}
/* get dataset data default index */
export function getDefaultIndex(props?: { q?: string; a?: string; dataId?: string }) {
const { q = '', a, dataId } = props || {};
const qaStr = `${q}\n${a}`.trim();
@@ -55,3 +46,8 @@ export function getDefaultIndex(props?: { q?: string; a?: string; dataId?: strin
dataId
};
}
export const predictDataLimitLength = (mode: `${TrainingModeEnum}`, data: any[]) => {
if (mode === TrainingModeEnum.qa) return data.length * 20;
return data.length;
};

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@@ -61,7 +61,7 @@ export enum ModuleInputKeyEnum {
// dataset
datasetSelectList = 'datasets',
datasetSimilarity = 'similarity',
datasetLimit = 'limit',
datasetMaxTokens = 'limit',
datasetSearchMode = 'searchMode',
datasetSearchUsingReRank = 'usingReRank',
datasetParamsModal = 'datasetParamsModal',
@@ -113,5 +113,16 @@ export enum VariableInputEnum {
textarea = 'textarea',
select = 'select'
}
export const variableMap = {
[VariableInputEnum.input]: {
icon: 'core/app/variable/input'
},
[VariableInputEnum.textarea]: {
icon: 'core/app/variable/textarea'
},
[VariableInputEnum.select]: {
icon: 'core/app/variable/select'
}
};
export const DYNAMIC_INPUT_KEY = 'DYNAMIC_INPUT_KEY';

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@@ -45,7 +45,10 @@ export enum FlowNodeTypeEnum {
questionInput = 'questionInput',
historyNode = 'historyNode',
chatNode = 'chatNode',
datasetSearchNode = 'datasetSearchNode',
datasetConcatNode = 'datasetConcatNode',
answerNode = 'answerNode',
classifyQuestion = 'classifyQuestion',
contentExtract = 'contentExtract',
@@ -54,10 +57,9 @@ export enum FlowNodeTypeEnum {
pluginModule = 'pluginModule',
pluginInput = 'pluginInput',
pluginOutput = 'pluginOutput',
cfr = 'cfr',
cfr = 'cfr'
// abandon
variable = 'variable'
}
export const EDGE_TYPE = 'default';

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@@ -106,6 +106,6 @@ export type AIChatModuleProps = {
export type DatasetModuleProps = {
[ModuleInputKeyEnum.datasetSelectList]: SelectedDatasetType;
[ModuleInputKeyEnum.datasetSimilarity]: number;
[ModuleInputKeyEnum.datasetLimit]: number;
[ModuleInputKeyEnum.datasetMaxTokens]: number;
[ModuleInputKeyEnum.datasetStartReRank]: boolean;
};

View File

@@ -7,6 +7,7 @@ export const Input_Template_Switch: FlowNodeInputItemType = {
key: ModuleInputKeyEnum.switch,
type: FlowNodeInputTypeEnum.target,
label: 'core.module.input.label.switch',
description: 'core.module.input.description.Trigger',
valueType: ModuleIOValueTypeEnum.any,
showTargetInApp: true,
showTargetInPlugin: true
@@ -27,8 +28,8 @@ export const Input_Template_History: FlowNodeInputItemType = {
export const Input_Template_UserChatInput: FlowNodeInputItemType = {
key: ModuleInputKeyEnum.userChatInput,
type: FlowNodeInputTypeEnum.hidden,
label: 'core.module.input.label.user question',
type: FlowNodeInputTypeEnum.custom,
label: '',
required: true,
valueType: ModuleIOValueTypeEnum.string,
showTargetInApp: true,
@@ -56,3 +57,13 @@ export const Input_Template_DynamicInput: FlowNodeInputItemType = {
showTargetInPlugin: true,
hideInApp: true
};
export const Input_Template_Dataset_Quote: FlowNodeInputItemType = {
key: ModuleInputKeyEnum.aiChatDatasetQuote,
type: FlowNodeInputTypeEnum.target,
label: '知识库引用',
description: 'core.module.Dataset quote.Input description',
valueType: ModuleIOValueTypeEnum.datasetQuote,
showTargetInApp: true,
showTargetInPlugin: true
};

View File

@@ -1,52 +0,0 @@
import {
FlowNodeInputTypeEnum,
FlowNodeOutputTypeEnum,
FlowNodeTypeEnum
} from '../../../node/constant';
import { FlowModuleTemplateType } from '../../../type';
import {
ModuleIOValueTypeEnum,
ModuleInputKeyEnum,
ModuleTemplateTypeEnum
} from '../../../constants';
export const HistoryModule: FlowModuleTemplateType = {
id: FlowNodeTypeEnum.historyNode,
templateType: ModuleTemplateTypeEnum.systemInput,
flowType: FlowNodeTypeEnum.historyNode,
avatar: '/imgs/module/history.png',
name: '聊天记录(弃用)',
intro: '聊天记录,该模块已被弃用',
inputs: [
{
key: ModuleInputKeyEnum.historyMaxAmount,
type: FlowNodeInputTypeEnum.numberInput,
label: '最长记录数',
description:
'该记录数不代表模型可接收这么多的历史记录具体可接收多少历史记录取决于模型的能力通常建议不要超过20条。',
value: 6,
valueType: ModuleIOValueTypeEnum.number,
min: 0,
max: 100,
showTargetInApp: false,
showTargetInPlugin: false
},
{
key: ModuleInputKeyEnum.history,
type: FlowNodeInputTypeEnum.hidden,
valueType: ModuleIOValueTypeEnum.chatHistory,
label: '聊天记录',
showTargetInApp: false,
showTargetInPlugin: false
}
],
outputs: [
{
key: ModuleInputKeyEnum.history,
label: '聊天记录',
valueType: ModuleIOValueTypeEnum.chatHistory,
type: FlowNodeOutputTypeEnum.source,
targets: []
}
]
};

View File

@@ -11,6 +11,7 @@ import {
ModuleTemplateTypeEnum
} from '../../constants';
import {
Input_Template_Dataset_Quote,
Input_Template_History,
Input_Template_Switch,
Input_Template_UserChatInput
@@ -23,15 +24,15 @@ export const AiChatModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.textAnswer,
flowType: FlowNodeTypeEnum.chatNode,
avatar: '/imgs/module/AI.png',
name: 'AI 对话',
intro: 'AI 大模型对话',
name: 'core.module.template.Ai chat',
intro: 'core.module.template.Ai chat intro',
showStatus: true,
inputs: [
Input_Template_Switch,
{
key: ModuleInputKeyEnum.aiModel,
type: FlowNodeInputTypeEnum.selectChatModel,
label: '对话模型',
label: 'core.module.input.label.aiModel',
required: true,
valueType: ModuleIOValueTypeEnum.string,
showTargetInApp: false,
@@ -41,42 +42,31 @@ export const AiChatModule: FlowModuleTemplateType = {
{
key: ModuleInputKeyEnum.aiChatTemperature,
type: FlowNodeInputTypeEnum.hidden, // Set in the pop-up window
label: '温度',
label: '',
value: 0,
valueType: ModuleIOValueTypeEnum.number,
min: 0,
max: 10,
step: 1,
markList: [
{ label: '严谨', value: 0 },
{ label: '发散', value: 10 }
],
showTargetInApp: false,
showTargetInPlugin: false
},
{
key: ModuleInputKeyEnum.aiChatMaxToken,
type: FlowNodeInputTypeEnum.hidden, // Set in the pop-up window
label: '回复上限',
label: '',
value: 2000,
valueType: ModuleIOValueTypeEnum.number,
min: 100,
max: 4000,
step: 50,
markList: [
{ label: '100', value: 100 },
{
label: `${4000}`,
value: 4000
}
],
showTargetInApp: false,
showTargetInPlugin: false
},
{
key: ModuleInputKeyEnum.aiChatIsResponseText,
type: FlowNodeInputTypeEnum.hidden,
label: '返回AI内容',
label: '',
value: true,
valueType: ModuleIOValueTypeEnum.boolean,
showTargetInApp: false,
@@ -85,7 +75,7 @@ export const AiChatModule: FlowModuleTemplateType = {
{
key: ModuleInputKeyEnum.aiChatQuoteTemplate,
type: FlowNodeInputTypeEnum.hidden,
label: '引用内容模板',
label: '',
valueType: ModuleIOValueTypeEnum.string,
showTargetInApp: false,
showTargetInPlugin: false
@@ -93,7 +83,7 @@ export const AiChatModule: FlowModuleTemplateType = {
{
key: ModuleInputKeyEnum.aiChatQuotePrompt,
type: FlowNodeInputTypeEnum.hidden,
label: '引用内容提示词',
label: '',
valueType: ModuleIOValueTypeEnum.string,
showTargetInApp: false,
showTargetInPlugin: false
@@ -110,7 +100,7 @@ export const AiChatModule: FlowModuleTemplateType = {
{
key: ModuleInputKeyEnum.aiSystemPrompt,
type: FlowNodeInputTypeEnum.textarea,
label: '系统提示词',
label: 'core.ai.Prompt',
max: 300,
valueType: ModuleIOValueTypeEnum.string,
description: chatNodeSystemPromptTip,
@@ -119,31 +109,23 @@ export const AiChatModule: FlowModuleTemplateType = {
showTargetInPlugin: true
},
Input_Template_History,
{
key: ModuleInputKeyEnum.aiChatDatasetQuote,
type: FlowNodeInputTypeEnum.target,
label: '引用内容',
description: "对象数组格式,结构:\n [{q:'问题',a:'回答'}]",
valueType: ModuleIOValueTypeEnum.datasetQuote,
showTargetInApp: true,
showTargetInPlugin: true
},
Input_Template_UserChatInput
Input_Template_UserChatInput,
Input_Template_Dataset_Quote
],
outputs: [
Output_Template_UserChatInput,
{
key: ModuleOutputKeyEnum.history,
label: '新的上下文',
description: '将本次回复内容拼接上历史记录,作为新的上下文返回',
label: 'core.module.output.label.New context',
description: 'core.module.output.description.New context',
valueType: ModuleIOValueTypeEnum.chatHistory,
type: FlowNodeOutputTypeEnum.source,
targets: []
},
{
key: ModuleOutputKeyEnum.answerText,
label: 'AI回复内容',
description: '将在 stream 回复完毕后触发',
label: 'core.module.output.label.Ai response content',
description: 'core.module.output.description.Ai response content',
valueType: ModuleIOValueTypeEnum.string,
type: FlowNodeOutputTypeEnum.source,
targets: []

View File

@@ -9,19 +9,17 @@ export const AssignedAnswerModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.textAnswer,
flowType: FlowNodeTypeEnum.answerNode,
avatar: '/imgs/module/reply.png',
name: '指定回复',
intro: '该模块可以直接回复一段指定的内容。常用于引导、提示',
name: 'core.module.template.Assigned reply',
intro: 'core.module.template.Assigned reply intro',
inputs: [
Input_Template_Switch,
{
key: ModuleInputKeyEnum.answerText,
type: FlowNodeInputTypeEnum.textarea,
valueType: ModuleIOValueTypeEnum.any,
label: '回复的内容',
description:
'可以使用 \\n 来实现连续换行。\n可以通过外部模块输入实现回复外部模块输入时会覆盖当前填写的内容。\n如传入非字符串类型数据将会自动转成字符串',
placeholder:
'可以使用 \\n 来实现连续换行。\n可以通过外部模块输入实现回复外部模块输入时会覆盖当前填写的内容。\n如传入非字符串类型数据将会自动转成字符串',
label: 'core.module.input.label.Response content',
description: 'core.module.input.description.Response content',
placeholder: 'core.module.input.description.Response content',
showTargetInApp: true,
showTargetInPlugin: true
}

View File

@@ -17,12 +17,8 @@ export const ClassifyQuestionModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.functionCall,
flowType: FlowNodeTypeEnum.classifyQuestion,
avatar: '/imgs/module/cq.png',
name: '问题分类',
intro: `根据用户的历史记录和当前问题判断该次提问的类型。可以添加多组问题类型,下面是一个模板例子:
类型1: 打招呼
类型2: 关于商品“使用”问题
类型3: 关于商品“购买”问题
类型4: 其他问题`,
name: 'core.module.template.Classify question',
intro: `core.module.template.Classify question intro`,
showStatus: true,
inputs: [
Input_Template_Switch,
@@ -30,7 +26,7 @@ export const ClassifyQuestionModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.aiModel,
type: FlowNodeInputTypeEnum.selectCQModel,
valueType: ModuleIOValueTypeEnum.string,
label: '分类模型',
label: 'core.module.input.label.Classify model',
required: true,
showTargetInApp: false,
showTargetInPlugin: false
@@ -39,11 +35,9 @@ export const ClassifyQuestionModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.aiSystemPrompt,
type: FlowNodeInputTypeEnum.textarea,
valueType: ModuleIOValueTypeEnum.string,
label: '背景知识',
description:
'你可以添加一些特定内容的介绍,从而更好的识别用户的问题类型。这个内容通常是给模型介绍一个它不知道的内容。',
placeholder:
'例如: \n1. AIGC人工智能生成内容是指使用人工智能技术自动或半自动地生成数字内容如文本、图像、音乐、视频等。\n2. AIGC技术包括但不限于自然语言处理、计算机视觉、机器学习和深度学习。这些技术可以创建新内容或修改现有内容以满足特定的创意、教育、娱乐或信息需求。',
label: 'core.module.input.label.Background',
description: 'core.module.input.description.Background',
placeholder: 'core.module.input.placeholder.Classify background',
showTargetInApp: true,
showTargetInPlugin: true
},

View File

@@ -17,8 +17,8 @@ export const ContextExtractModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.functionCall,
flowType: FlowNodeTypeEnum.contentExtract,
avatar: '/imgs/module/extract.png',
name: '文本内容提取',
intro: '可从文本中提取指定的数据例如sql语句、搜索关键词、代码等',
name: 'core.module.template.Extract field',
intro: 'core.module.template.Extract field intro',
showStatus: true,
inputs: [
Input_Template_Switch,
@@ -26,7 +26,7 @@ export const ContextExtractModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.aiModel,
type: FlowNodeInputTypeEnum.selectExtractModel,
valueType: ModuleIOValueTypeEnum.string,
label: '提取模型',
label: 'core.module.input.label.LLM',
required: true,
showTargetInApp: false,
showTargetInPlugin: false

View File

@@ -0,0 +1,54 @@
import {
FlowNodeInputTypeEnum,
FlowNodeOutputTypeEnum,
FlowNodeTypeEnum
} from '../../node/constant';
import { FlowModuleTemplateType } from '../../type.d';
import {
ModuleIOValueTypeEnum,
ModuleInputKeyEnum,
ModuleOutputKeyEnum,
ModuleTemplateTypeEnum
} from '../../constants';
import { Input_Template_Dataset_Quote, Input_Template_Switch } from '../input';
import { Output_Template_Finish } from '../output';
import { getNanoid } from '../../../../common/string/tools';
export const getOneQuoteInputTemplate = (key = getNanoid()) => ({
...Input_Template_Dataset_Quote,
key,
type: FlowNodeInputTypeEnum.hidden
});
export const DatasetConcatModule: FlowModuleTemplateType = {
id: FlowNodeTypeEnum.datasetConcatNode,
flowType: FlowNodeTypeEnum.datasetConcatNode,
templateType: ModuleTemplateTypeEnum.tools,
avatar: '/imgs/module/concat.svg',
name: '知识库搜索引用合并',
intro: 'core.module.template.Dataset search result concat intro',
showStatus: false,
inputs: [
Input_Template_Switch,
{
key: ModuleInputKeyEnum.datasetMaxTokens,
type: FlowNodeInputTypeEnum.custom,
label: '最大 Tokens',
value: 1500,
valueType: ModuleIOValueTypeEnum.number,
showTargetInApp: false,
showTargetInPlugin: false
},
getOneQuoteInputTemplate('defaultQuote')
],
outputs: [
{
key: ModuleOutputKeyEnum.datasetQuoteQA,
label: 'core.module.Dataset quote.label',
type: FlowNodeOutputTypeEnum.source,
valueType: ModuleIOValueTypeEnum.datasetQuote,
targets: []
},
Output_Template_Finish
]
};

View File

@@ -12,22 +12,22 @@ import {
} from '../../constants';
import { Input_Template_Switch, Input_Template_UserChatInput } from '../input';
import { Output_Template_Finish, Output_Template_UserChatInput } from '../output';
import { DatasetSearchModeEnum } from '../../../dataset/constant';
import { DatasetSearchModeEnum } from '../../../dataset/constants';
export const DatasetSearchModule: FlowModuleTemplateType = {
id: FlowNodeTypeEnum.datasetSearchNode,
templateType: ModuleTemplateTypeEnum.functionCall,
flowType: FlowNodeTypeEnum.datasetSearchNode,
avatar: '/imgs/module/db.png',
name: '知识库搜索',
intro: '去知识库中搜索对应的答案。可作为 AI 对话引用参考。',
name: 'core.module.template.Dataset search',
intro: 'core.module.template.Dataset search intro',
showStatus: true,
inputs: [
Input_Template_Switch,
{
key: ModuleInputKeyEnum.datasetSelectList,
type: FlowNodeInputTypeEnum.selectDataset,
label: '关联的知识库',
label: 'core.module.input.label.Select dataset',
value: [],
valueType: ModuleIOValueTypeEnum.selectDataset,
list: [],
@@ -38,7 +38,7 @@ export const DatasetSearchModule: FlowModuleTemplateType = {
{
key: ModuleInputKeyEnum.datasetSimilarity,
type: FlowNodeInputTypeEnum.hidden,
label: '最低相关性',
label: '',
value: 0.4,
valueType: ModuleIOValueTypeEnum.number,
min: 0,
@@ -52,10 +52,9 @@ export const DatasetSearchModule: FlowModuleTemplateType = {
showTargetInPlugin: false
},
{
key: ModuleInputKeyEnum.datasetLimit,
key: ModuleInputKeyEnum.datasetMaxTokens,
type: FlowNodeInputTypeEnum.hidden,
label: '引用上限',
description: '单次搜索最大的 Tokens 数量中文约1字=1.7Tokens英文约1字=1Tokens',
label: '',
value: 1500,
valueType: ModuleIOValueTypeEnum.number,
showTargetInApp: false,
@@ -93,23 +92,21 @@ export const DatasetSearchModule: FlowModuleTemplateType = {
Output_Template_UserChatInput,
{
key: ModuleOutputKeyEnum.datasetIsEmpty,
label: '搜索结果为空',
label: 'core.module.output.label.Search result empty',
type: FlowNodeOutputTypeEnum.source,
valueType: ModuleIOValueTypeEnum.boolean,
targets: []
},
{
key: ModuleOutputKeyEnum.datasetUnEmpty,
label: '搜索结果不为空',
label: 'core.module.output.label.Search result not empty',
type: FlowNodeOutputTypeEnum.source,
valueType: ModuleIOValueTypeEnum.boolean,
targets: []
},
{
key: ModuleOutputKeyEnum.datasetQuoteQA,
label: '引用内容',
description:
'始终返回数组,如果希望搜索结果为空时执行额外操作,需要用到上面的两个输入以及目标模块的触发器',
label: 'core.module.Dataset quote.label',
type: FlowNodeOutputTypeEnum.source,
valueType: ModuleIOValueTypeEnum.datasetQuote,
targets: []

View File

@@ -17,8 +17,8 @@ export const HttpModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.externalCall,
flowType: FlowNodeTypeEnum.httpRequest,
avatar: '/imgs/module/http.png',
name: 'HTTP模块',
intro: '可以发出一个 HTTP POST 请求,实现更为复杂的操作(联网搜索、数据库查询等)',
name: 'core.module.template.Http request',
intro: 'core.module.template.Http request intro',
showStatus: true,
inputs: [
Input_Template_Switch,

View File

@@ -22,8 +22,8 @@ export const RunAppModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.externalCall,
flowType: FlowNodeTypeEnum.runApp,
avatar: '/imgs/module/app.png',
name: '应用调用',
intro: '可以选择一个其他应用进行调用',
name: 'core.module.template.Running app',
intro: 'core.module.template.Running app intro',
showStatus: true,
inputs: [
Input_Template_Switch,

View File

@@ -6,9 +6,9 @@ export const RunPluginModule: FlowModuleTemplateType = {
id: FlowNodeTypeEnum.pluginModule,
templateType: ModuleTemplateTypeEnum.externalCall,
flowType: FlowNodeTypeEnum.pluginModule,
avatar: '/imgs/module/custom.png',
avatar: '',
intro: '',
name: '自定义模块',
name: '',
showStatus: false,
inputs: [], // [{key:'pluginId'},...]
outputs: []

View File

@@ -8,14 +8,14 @@ export const UserGuideModule: FlowModuleTemplateType = {
templateType: ModuleTemplateTypeEnum.userGuide,
flowType: FlowNodeTypeEnum.userGuide,
avatar: '/imgs/module/userGuide.png',
name: '用户引导',
name: 'core.module.template.User guide',
intro: userGuideTip,
inputs: [
{
key: ModuleInputKeyEnum.welcomeText,
type: FlowNodeInputTypeEnum.hidden,
valueType: ModuleIOValueTypeEnum.string,
label: '开场白',
label: 'core.app.Welcome Text',
showTargetInApp: false,
showTargetInPlugin: false
},
@@ -23,7 +23,7 @@ export const UserGuideModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.variables,
type: FlowNodeInputTypeEnum.hidden,
valueType: ModuleIOValueTypeEnum.any,
label: '对话框变量',
label: 'core.module.Variable',
value: [],
showTargetInApp: false,
showTargetInPlugin: false
@@ -32,7 +32,7 @@ export const UserGuideModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.questionGuide,
valueType: ModuleIOValueTypeEnum.boolean,
type: FlowNodeInputTypeEnum.switch,
label: '问题引导',
label: '',
showTargetInApp: false,
showTargetInPlugin: false
},
@@ -40,7 +40,7 @@ export const UserGuideModule: FlowModuleTemplateType = {
key: ModuleInputKeyEnum.tts,
type: FlowNodeInputTypeEnum.hidden,
valueType: ModuleIOValueTypeEnum.any,
label: '语音播报',
label: '',
showTargetInApp: false,
showTargetInPlugin: false
}

View File

@@ -15,15 +15,15 @@ export const UserInputModule: FlowModuleTemplateType = {
id: FlowNodeTypeEnum.questionInput,
templateType: ModuleTemplateTypeEnum.systemInput,
flowType: FlowNodeTypeEnum.questionInput,
avatar: '/imgs/module/userChatInput.png',
name: '用户问题(入口)',
intro: '用户输入的内容。该模块通常作为应用的入口,用户在发送消息后会首先执行该模块。',
avatar: '/imgs/module/userChatInput.svg',
name: 'core.module.template.Chat entrance',
intro: 'core.module.template.Chat entrance intro',
inputs: [
{
key: ModuleInputKeyEnum.userChatInput,
type: FlowNodeInputTypeEnum.systemInput,
valueType: ModuleIOValueTypeEnum.string,
label: '用户问题',
label: 'core.module.input.label.user question',
showTargetInApp: false,
showTargetInPlugin: false
}
@@ -31,7 +31,7 @@ export const UserInputModule: FlowModuleTemplateType = {
outputs: [
{
key: ModuleOutputKeyEnum.userChatInput,
label: '用户问题',
label: 'core.module.input.label.user question',
type: FlowNodeOutputTypeEnum.source,
valueType: ModuleIOValueTypeEnum.string,
targets: []

View File

@@ -1,7 +1,4 @@
export const chatNodeSystemPromptTip =
'模型固定的引导词,通过调整该内容,可以引导模型聊天方向。该内容会被固定在上下文的开头。可使用变量,例如 {{language}}';
export const userGuideTip = '可以在对话前设置引导语,设置全局变量,设置下一步指引';
export const welcomeTextTip =
'每次对话开始前,发送一个初始内容。支持标准 Markdown 语法,可使用的额外标记:\n[快捷按键]: 用户点击后可以直接发送该问题';
export const variableTip =
'可以在对话开始前,要求用户填写一些内容作为本轮对话的特定变量。该模块位于开场引导之后。\n变量可以通过 {{变量key}} 的形式注入到其他模块 string 类型的输入中,例如:提示词、限定词等';
export const chatNodeSystemPromptTip = 'core.app.tip.chatNodeSystemPromptTip';
export const userGuideTip = 'core.app.tip.userGuideTip';
export const welcomeTextTip = 'core.app.tip.welcomeTextTip';
export const variableTip = 'core.app.tip.variableTip';

View File

@@ -3,9 +3,9 @@ import { ModuleIOValueTypeEnum, ModuleTemplateTypeEnum, VariableInputEnum } from
import { FlowNodeInputItemType, FlowNodeOutputItemType } from './node/type';
export type FlowModuleTemplateType = {
id: string;
id: string; // module id, unique
templateType: `${ModuleTemplateTypeEnum}`;
flowType: `${FlowNodeTypeEnum}`; // unique
flowType: `${FlowNodeTypeEnum}`; // render node card
avatar?: string;
name: string;
intro: string; // template list intro
@@ -85,12 +85,14 @@ export type RunningModuleItemType = {
inputs: {
key: string;
value?: any;
valueType?: `${ModuleIOValueTypeEnum}`;
}[];
outputs: {
key: string;
answer?: boolean;
response?: boolean;
value?: any;
valueType?: `${ModuleIOValueTypeEnum}`;
targets: {
moduleId: string;
key: string;
@@ -115,5 +117,6 @@ export type ChatDispatchProps = {
export type ModuleDispatchProps<T> = ChatDispatchProps & {
outputs: RunningModuleItemType['outputs'];
inputs: T;
inputs: RunningModuleItemType['inputs'];
params: T;
};

View File

@@ -1,8 +1,14 @@
import { FlowNodeInputTypeEnum, FlowNodeTypeEnum } from './node/constant';
import { ModuleIOValueTypeEnum, ModuleInputKeyEnum } from './constants';
import {
ModuleIOValueTypeEnum,
ModuleInputKeyEnum,
VariableInputEnum,
variableMap
} from './constants';
import { FlowNodeInputItemType, FlowNodeOutputItemType } from './node/type';
import { AppTTSConfigType, ModuleItemType, VariableItemType } from './type';
import { Input_Template_Switch } from './template/input';
import { EditorVariablePickerType } from '../../../web/components/common/Textarea/PromptEditor/type';
export const getGuideModule = (modules: ModuleItemType[]) =>
modules.find((item) => item.flowType === FlowNodeTypeEnum.userGuide);
@@ -68,7 +74,7 @@ export function plugin2ModuleIO(
// plugin id
key: ModuleInputKeyEnum.pluginId,
type: FlowNodeInputTypeEnum.hidden,
label: 'pluginId',
label: '',
value: pluginId,
valueType: ModuleIOValueTypeEnum.string,
connected: true,
@@ -94,3 +100,12 @@ export function plugin2ModuleIO(
: []
};
}
export const formatEditorVariablePickerIcon = (
variables: { key: string; label: string; type?: `${VariableInputEnum}` }[]
): EditorVariablePickerType[] => {
return variables.map((item) => ({
...item,
icon: item.type ? variableMap[item.type]?.icon : variableMap['input'].icon
}));
};

View File

@@ -7,7 +7,7 @@
"encoding": "^0.1.13",
"js-tiktoken": "^1.0.7",
"openai": "4.23.0",
"pdfjs-dist": "^4.0.269",
"nanoid": "^4.0.1",
"timezones-list": "^3.0.2"
},
"devDependencies": {

View File

@@ -3,7 +3,6 @@ import { OAuthEnum } from './constant';
export type PostLoginProps = {
username: string;
password: string;
tmbId?: string;
};
export type OauthLoginProps = {

View File

@@ -9,7 +9,6 @@ export type TeamSchema = {
createTime: Date;
balance: number;
maxSize: number;
lastDatasetBillTime: Date;
limit: {
lastExportDatasetTime: Date;
lastWebsiteSyncTime: Date;

View File

@@ -13,6 +13,7 @@ export type UserModelSchema = {
createTime: number;
timezone: string;
status: `${UserStatusEnum}`;
lastLoginTmbId?: string;
openaiAccount?: {
key: string;
baseUrl: string;

View File

@@ -3,8 +3,7 @@ import { BillListItemCountType, BillListItemType } from './type';
export type CreateTrainingBillProps = {
name: string;
vectorModel?: string;
agentModel?: string;
datasetId: string;
};
export type ConcatBillProps = BillListItemCountType & {

View File

@@ -7,13 +7,28 @@ export enum BillSourceEnum {
api = 'api',
shareLink = 'shareLink',
training = 'training',
datasetStore = 'datasetStore'
standSubPlan = 'standSubPlan',
extraDatasetSub = 'extraDatasetSub'
}
export const BillSourceMap: Record<`${BillSourceEnum}`, string> = {
[BillSourceEnum.fastgpt]: '在线使用',
[BillSourceEnum.api]: 'Api',
[BillSourceEnum.shareLink]: '免登录链接',
[BillSourceEnum.training]: '数据训练',
[BillSourceEnum.datasetStore]: '知识库存储'
export const BillSourceMap = {
[BillSourceEnum.fastgpt]: {
label: '在线使用'
},
[BillSourceEnum.api]: {
label: 'Api'
},
[BillSourceEnum.shareLink]: {
label: '免登录链接'
},
[BillSourceEnum.training]: {
label: 'dataset.Training Name'
},
[BillSourceEnum.standSubPlan]: {
label: 'support.wallet.subscription.type.standard'
},
[BillSourceEnum.extraDatasetSub]: {
label: 'support.wallet.subscription.type.extraDatasetSize'
}
};

View File

@@ -4,9 +4,11 @@ import { BillSourceEnum } from './constants';
export type BillListItemCountType = {
inputTokens?: number;
outputTokens?: number;
textLen?: number;
charsLength?: number;
duration?: number;
dataLen?: number;
// sub
datasetSize?: number;
// abandon
tokenLen?: number;
@@ -24,7 +26,7 @@ export type BillSchema = CreateBillProps & {
export type BillItemType = {
id: string;
memberName: string;
// memberName: string;
time: Date;
appName: string;
source: BillSchema['source'];

View File

@@ -0,0 +1,41 @@
export enum PayTypeEnum {
balance = 'balance',
subStandard = 'subStandard',
subExtraDatasetSize = 'subExtraDatasetSize',
subExtraPoints = 'subExtraPoints'
}
export const payTypeMap = {
[PayTypeEnum.balance]: {
label: 'support.user.team.pay.type.balance'
},
[PayTypeEnum.subStandard]: {
label: 'support.wallet.subscription.type.standard'
},
[PayTypeEnum.subExtraDatasetSize]: {
label: 'support.wallet.subscription.type.extraDatasetSize'
},
[PayTypeEnum.subExtraPoints]: {
label: 'support.wallet.subscription.type.extraPoints'
}
};
export enum PayStatusEnum {
SUCCESS = 'SUCCESS',
REFUND = 'REFUND',
NOTPAY = 'NOTPAY',
CLOSED = 'CLOSED'
}
export const payStatusMap = {
[PayStatusEnum.SUCCESS]: {
label: 'support.user.team.pay.status.success'
},
[PayStatusEnum.REFUND]: {
label: 'support.user.team.pay.status.refund'
},
[PayStatusEnum.NOTPAY]: {
label: 'support.user.team.pay.status.notpay'
},
[PayStatusEnum.CLOSED]: {
label: 'support.user.team.pay.status.closed'
}
};

View File

@@ -1,10 +1,18 @@
import { SubModeEnum, SubTypeEnum } from '../sub/constants';
import { PayTypeEnum } from './constants';
export type PaySchema = {
_id: string;
userId: string;
teamId: string;
tmbId: string;
createTime: Date;
price: number;
orderId: string;
status: 'SUCCESS' | 'REFUND' | 'NOTPAY' | 'CLOSED';
type: `${PayType}`;
price: number;
payWay: 'balance' | 'wx';
subMetadata: {};
};

View File

@@ -0,0 +1,36 @@
import { StandardSubLevelEnum, SubModeEnum } from './constants';
import { TeamSubSchema } from './type.d';
export type SubDatasetSizeParams = {
size: number;
};
export type StandardSubPlanParams = {
level: `${StandardSubLevelEnum}`;
mode: `${SubModeEnum}`;
};
export type SubDatasetSizePreviewCheckResponse = {
payForNewSub: boolean; // Does this change require payment
newSubSize: number; // new sub dataset size
alreadySubSize: number; // old sub dataset size
payPrice: number; // this change require payment
newPlanPrice: number; // the new sub price
newSubStartTime: Date;
newSubExpiredTime: Date;
balanceEnough: boolean; // team balance is enough
};
export type StandardSubPlanUpdateResponse = {
balanceEnough: boolean; // team balance is enough
payPrice?: number;
planPrice: number;
planPointPrice: number;
currentMode: `${SubModeEnum}`;
nextMode: `${SubModeEnum}`;
currentSubLevel: `${StandardSubLevelEnum}`;
nextSubLevel: `${StandardSubLevelEnum}`;
totalPoints: number;
surplusPoints: number;
planStartTime: Date;
planExpiredTime: Date;
};

View File

@@ -0,0 +1,80 @@
export const POINTS_SCALE = 10000;
export enum SubTypeEnum {
standard = 'standard',
extraDatasetSize = 'extraDatasetSize',
extraPoints = 'extraPoints'
}
export const subTypeMap = {
[SubTypeEnum.standard]: {
label: 'support.wallet.subscription.type.standard'
},
[SubTypeEnum.extraDatasetSize]: {
label: 'support.wallet.subscription.type.extraDatasetSize'
},
[SubTypeEnum.extraPoints]: {
label: 'support.wallet.subscription.type.extraPoints'
}
};
export enum SubStatusEnum {
active = 'active',
canceled = 'canceled'
}
export const subStatusMap = {
[SubStatusEnum.active]: {
label: 'support.wallet.subscription.status.active'
},
[SubStatusEnum.canceled]: {
label: 'support.wallet.subscription.status.canceled'
}
};
export const subSelectMap = {
true: SubStatusEnum.active,
false: SubStatusEnum.canceled
};
export enum SubModeEnum {
month = 'month',
year = 'year'
}
export const subModeMap = {
[SubModeEnum.month]: {
label: 'support.wallet.subscription.mode.month',
durationMonth: 1
},
[SubModeEnum.year]: {
label: 'support.wallet.subscription.mode.year',
durationMonth: 12
}
};
export enum StandardSubLevelEnum {
free = 'free',
experience = 'experience',
team = 'team',
enterprise = 'enterprise',
custom = 'custom'
}
export const standardSubLevelMap = {
[StandardSubLevelEnum.free]: {
label: 'support.wallet.subscription.standardSubLevel.free',
desc: 'support.wallet.subscription.standardSubLevel.free desc'
},
[StandardSubLevelEnum.experience]: {
label: 'support.wallet.subscription.standardSubLevel.experience',
desc: 'support.wallet.subscription.standardSubLevel.experience desc'
},
[StandardSubLevelEnum.team]: {
label: 'support.wallet.subscription.standardSubLevel.team',
desc: ''
},
[StandardSubLevelEnum.enterprise]: {
label: 'support.wallet.subscription.standardSubLevel.enterprise',
desc: ''
},
[StandardSubLevelEnum.custom]: {
label: 'support.wallet.subscription.standardSubLevel.custom',
desc: ''
}
};

View File

@@ -0,0 +1,73 @@
import { StandardSubLevelEnum, SubModeEnum, SubStatusEnum, SubTypeEnum } from './constants';
// Content of plan
export type TeamStandardSubPlanItemType = {
price: number; // read price
pointPrice: number; // read price/ one ten thousand
maxTeamMember: number;
maxAppAmount: number; // max app or plugin amount
maxDatasetAmount: number;
chatHistoryStoreDuration: number; // n day
maxDatasetSize: number;
customApiKey: boolean;
customCopyright: boolean; // feature
websiteSyncInterval: number; // n hours
trainingWeight: number; // 1~4
reRankWeight: number; // 1~4
totalPoints: number; // n ten thousand
};
export type StandSubPlanLevelMapType = Record<
`${StandardSubLevelEnum}`,
TeamStandardSubPlanItemType
>;
export type SubPlanType = {
[SubTypeEnum.standard]: StandSubPlanLevelMapType;
[SubTypeEnum.extraDatasetSize]: {
price: number;
};
};
export type TeamSubSchema = {
_id: string;
teamId: string;
type: `${SubTypeEnum}`;
status: `${SubStatusEnum}`;
currentMode: `${SubModeEnum}`;
nextMode: `${SubModeEnum}`;
startTime: Date;
expiredTime: Date;
price: number;
currentSubLevel: `${StandardSubLevelEnum}`;
nextSubLevel: `${StandardSubLevelEnum}`;
pointPrice: number;
totalPoints: number;
currentExtraDatasetSize: number;
nextExtraDatasetSize: number;
currentExtraPoints: number;
nextExtraPoints: number;
surplusPoints: number;
// abandon
datasetStoreAmount?: number;
renew?: boolean;
};
export type FeTeamSubType = {
[SubTypeEnum.standard]?: TeamSubSchema;
[SubTypeEnum.extraDatasetSize]?: TeamSubSchema;
[SubTypeEnum.extraPoints]?: TeamSubSchema;
standardMaxDatasetSize: number;
totalPoints: number;
usedPoints: number;
standardMaxPoints: number;
datasetMaxSize: number;
usedDatasetSize: number;
};

View File

@@ -0,0 +1,6 @@
import path from 'path';
export const tmpFileDirPath =
process.env.NODE_ENV === 'production' ? '/app/tmp' : path.join(process.cwd(), 'tmp');
export const previewMaxCharCount = 3000;

View File

@@ -3,9 +3,10 @@ import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
import fsp from 'fs/promises';
import fs from 'fs';
import { DatasetFileSchema } from '@fastgpt/global/core/dataset/type';
import { delImgByFileIdList } from '../image/controller';
import { MongoFileSchema } from './schema';
export function getGFSCollection(bucket: `${BucketNameEnum}`) {
MongoFileSchema;
return connectionMongo.connection.db.collection(`${bucket}.files`);
}
export function getGridBucket(bucket: `${BucketNameEnum}`) {
@@ -21,6 +22,7 @@ export async function uploadFile({
tmbId,
path,
filename,
contentType,
metadata = {}
}: {
bucketName: `${BucketNameEnum}`;
@@ -28,6 +30,7 @@ export async function uploadFile({
tmbId: string;
path: string;
filename: string;
contentType?: string;
metadata?: Record<string, any>;
}) {
if (!path) return Promise.reject(`filePath is empty`);
@@ -44,7 +47,7 @@ export async function uploadFile({
const stream = bucket.openUploadStream(filename, {
metadata,
contentType: metadata?.contentType
contentType
});
// save to gridfs
@@ -96,40 +99,6 @@ export async function delFileByFileIdList({
}
}
}
// delete file by metadata(datasetId)
export async function delFileByMetadata({
bucketName,
datasetId
}: {
bucketName: `${BucketNameEnum}`;
datasetId?: string;
}) {
const bucket = getGridBucket(bucketName);
const files = await bucket
.find(
{
...(datasetId && { 'metadata.datasetId': datasetId })
},
{
projection: {
_id: 1
}
}
)
.toArray();
const idList = files.map((item) => String(item._id));
// delete img
await delImgByFileIdList(idList);
// delete file
await delFileByFileIdList({
bucketName,
fileIdList: idList
});
}
export async function getDownloadStream({
bucketName,

View File

@@ -0,0 +1,15 @@
import { connectionMongo, type Model } from '../../mongo';
const { Schema, model, models } = connectionMongo;
const FileSchema = new Schema({});
try {
FileSchema.index({ 'metadata.teamId': 1 });
FileSchema.index({ 'metadata.uploadDate': -1 });
} catch (error) {
console.log(error);
}
export const MongoFileSchema = models['dataset.files'] || model('dataset.files', FileSchema);
MongoFileSchema.syncIndexes();

View File

@@ -1 +0,0 @@
export const imageBaseUrl = '/api/system/img/';

View File

@@ -1,6 +1,7 @@
import { UploadImgProps } from '@fastgpt/global/common/file/api';
import { imageBaseUrl } from './constant';
import { imageBaseUrl } from '@fastgpt/global/common/file/image/constants';
import { MongoImage } from './schema';
import { ClientSession } from '../../../common/mongo';
export function getMongoImgUrl(id: string) {
return `${imageBaseUrl}${id}`;
@@ -8,10 +9,13 @@ export function getMongoImgUrl(id: string) {
export const maxImgSize = 1024 * 1024 * 12;
export async function uploadMongoImg({
type,
base64Img,
teamId,
expiredTime,
metadata
metadata,
shareId
}: UploadImgProps & {
teamId: string;
}) {
@@ -20,12 +24,16 @@ export async function uploadMongoImg({
}
const base64Data = base64Img.split(',')[1];
const binary = Buffer.from(base64Data, 'base64');
const { _id } = await MongoImage.create({
type,
teamId,
binary: Buffer.from(base64Data, 'base64'),
binary,
expiredTime: expiredTime,
metadata
metadata,
shareId
});
return getMongoImgUrl(String(_id));
@@ -39,8 +47,22 @@ export async function readMongoImg({ id }: { id: string }) {
return data?.binary;
}
export async function delImgByFileIdList(fileIds: string[]) {
return MongoImage.deleteMany({
'metadata.fileId': { $in: fileIds.map((item) => String(item)) }
});
export async function delImgByRelatedId({
teamId,
relateIds,
session
}: {
teamId: string;
relateIds: string[];
session: ClientSession;
}) {
if (relateIds.length === 0) return;
return MongoImage.deleteMany(
{
teamId,
'metadata.relatedId': { $in: relateIds.map((id) => String(id)) }
},
{ session }
);
}

View File

@@ -1,5 +1,7 @@
import { TeamCollectionName } from '@fastgpt/global/support/user/team/constant';
import { connectionMongo, type Model } from '../../mongo';
import { MongoImageSchemaType } from '@fastgpt/global/common/file/image/type.d';
import { mongoImageTypeMap } from '@fastgpt/global/common/file/image/constants';
const { Schema, model, models } = connectionMongo;
const ImageSchema = new Schema({
@@ -12,12 +14,18 @@ const ImageSchema = new Schema({
type: Date,
default: () => new Date()
},
binary: {
type: Buffer
},
expiredTime: {
type: Date
},
binary: {
type: Buffer
},
type: {
type: String,
enum: Object.keys(mongoImageTypeMap),
required: true
},
metadata: {
type: Object
}
@@ -25,14 +33,14 @@ const ImageSchema = new Schema({
try {
ImageSchema.index({ expiredTime: 1 }, { expireAfterSeconds: 60 });
ImageSchema.index({ type: 1 });
ImageSchema.index({ createTime: 1 });
ImageSchema.index({ teamId: 1, 'metadata.relatedId': 1 });
} catch (error) {
console.log(error);
}
export const MongoImage: Model<{
teamId: string;
binary: Buffer;
metadata?: { fileId?: string };
}> = models['image'] || model('image', ImageSchema);
export const MongoImage: Model<MongoImageSchemaType> =
models['image'] || model('image', ImageSchema);
MongoImage.syncIndexes();

View File

@@ -1,11 +1,8 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { customAlphabet } from 'nanoid';
import multer from 'multer';
import path from 'path';
import { BucketNameEnum, bucketNameMap } from '@fastgpt/global/common/file/constants';
import fs from 'fs';
const nanoid = customAlphabet('1234567890abcdef', 12);
import { getNanoid } from '@fastgpt/global/common/string/tools';
type FileType = {
fieldname: string;
@@ -17,7 +14,7 @@ type FileType = {
size: number;
};
export function getUploadModel({ maxSize = 500 }: { maxSize?: number }) {
export const getUploadModel = ({ maxSize = 500 }: { maxSize?: number }) => {
maxSize *= 1024 * 1024;
class UploadModel {
uploader = multer({
@@ -26,17 +23,25 @@ export function getUploadModel({ maxSize = 500 }: { maxSize?: number }) {
},
preservePath: true,
storage: multer.diskStorage({
filename: (_req, file, cb) => {
// destination: (_req, _file, cb) => {
// cb(null, tmpFileDirPath);
// },
filename: async (req, file, cb) => {
const { ext } = path.parse(decodeURIComponent(file.originalname));
cb(null, nanoid() + ext);
cb(null, `${getNanoid()}${ext}`);
}
})
}).any();
}).single('file');
async doUpload<T = Record<string, any>>(req: NextApiRequest, res: NextApiResponse) {
async doUpload<T = Record<string, any>>(
req: NextApiRequest,
res: NextApiResponse,
originBuckerName?: `${BucketNameEnum}`
) {
return new Promise<{
files: FileType[];
metadata: T;
file: FileType;
metadata: Record<string, any>;
data: T;
bucketName?: `${BucketNameEnum}`;
}>((resolve, reject) => {
// @ts-ignore
@@ -46,25 +51,33 @@ export function getUploadModel({ maxSize = 500 }: { maxSize?: number }) {
}
// check bucket name
const bucketName = req.body?.bucketName as `${BucketNameEnum}`;
const bucketName = (req.body?.bucketName || originBuckerName) as `${BucketNameEnum}`;
if (bucketName && !bucketNameMap[bucketName]) {
return reject('BucketName is invalid');
}
// @ts-ignore
const file = req.file as FileType;
resolve({
...req.body,
files:
// @ts-ignore
req.files?.map((file) => ({
...file,
originalname: decodeURIComponent(file.originalname)
})) || [],
file: {
...file,
originalname: decodeURIComponent(file.originalname)
},
bucketName,
metadata: (() => {
if (!req.body?.metadata) return {};
try {
return JSON.parse(req.body.metadata);
} catch (error) {
console.log(error);
return {};
}
})(),
data: (() => {
if (!req.body?.data) return {};
try {
return JSON.parse(req.body.data);
} catch (error) {
return {};
}
})()
@@ -75,14 +88,4 @@ export function getUploadModel({ maxSize = 500 }: { maxSize?: number }) {
}
return new UploadModel();
}
export const removeFilesByPaths = (paths: string[]) => {
paths.forEach((path) => {
fs.unlink(path, (err) => {
if (err) {
console.error(err);
}
});
});
};

View File

@@ -0,0 +1,11 @@
import fs from 'fs';
export const removeFilesByPaths = (paths: string[]) => {
paths.forEach((path) => {
fs.unlink(path, (err) => {
if (err) {
// console.error(err);
}
});
});
};

View File

@@ -1,21 +1,21 @@
import mongoose, { connectionMongo } from './index';
import { connectionMongo, ClientSession } from './index';
export async function mongoSessionTask(
fn: (session: mongoose.mongo.ClientSession) => Promise<any>
) {
export const mongoSessionRun = async <T = unknown>(fn: (session: ClientSession) => Promise<T>) => {
const session = await connectionMongo.startSession();
session.startTransaction();
try {
session.startTransaction();
await fn(session);
const result = await fn(session);
await session.commitTransaction();
await session.endSession();
session.endSession();
return result as T;
} catch (error) {
console.log(error);
await session.abortTransaction();
await session.endSession();
console.error(error);
session.endSession();
return Promise.reject(error);
}
}
};

View File

@@ -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) })
});
};

View File

@@ -50,8 +50,11 @@ export const cheerioToHtml = ({
.get()
.join('\n');
const title = $('head title').text() || $('h1:first').text() || fetchUrl;
return {
html,
title,
usedSelector
};
};
@@ -61,39 +64,39 @@ export const urlsFetch = async ({
}: UrlFetchParams): Promise<UrlFetchResponse> => {
urlList = urlList.filter((url) => /^(http|https):\/\/[^ "]+$/.test(url));
const response = (
await Promise.all(
urlList.map(async (url) => {
try {
const fetchRes = await axios.get(url, {
timeout: 30000
});
const response = await Promise.all(
urlList.map(async (url) => {
try {
const fetchRes = await axios.get(url, {
timeout: 30000
});
const $ = cheerio.load(fetchRes.data);
const { html, usedSelector } = cheerioToHtml({
fetchUrl: url,
$,
selector
});
const md = await htmlToMarkdown(html);
const $ = cheerio.load(fetchRes.data);
const { title, html, usedSelector } = cheerioToHtml({
fetchUrl: url,
$,
selector
});
const md = await htmlToMarkdown(html);
return {
url,
content: md,
selector: usedSelector
};
} catch (error) {
console.log(error, 'fetch error');
return {
url,
title,
content: md,
selector: usedSelector
};
} catch (error) {
console.log(error, 'fetch error');
return {
url,
content: '',
selector: ''
};
}
})
)
).filter((item) => item.content);
return {
url,
title: '',
content: '',
selector: ''
};
}
})
);
return response;
};

View File

@@ -15,7 +15,9 @@ export const htmlToMarkdown = (html?: string | null) =>
worker.on('message', (md: string) => {
worker.terminate();
resolve(simpleMarkdownText(md));
let rawText = simpleMarkdownText(md);
resolve(rawText);
});
worker.on('error', (err) => {
worker.terminate();

View File

@@ -0,0 +1,6 @@
import nodeCron from 'node-cron';
export const setCron = (time: string, cb: () => void) => {
// second minute hour day month week
return nodeCron.schedule(time, cb);
};

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