4.8.9 test fix (#2291)

* perf: read file icon

* perf:icon

* fix: i18n

* perf: hide pro api

* perf: upload expired time

* perf: upload file frequency limit

* perf: upload file ux

* perf: input file tip

* perf: qa custom chunk size

* feat: dataset openapi

* fix: auth dataset list

* fix: openapi doc

* perf: zero temperature change to 0.01

* perf: read file prompt

* perf: read file prompt

* perf: free plan tip

* feat: cron job usage

.github/workflows/helm-release.yaml

@@ -24,11 +24,6 @@ jobs:
           export APP_VERSION=${{ steps.vars.outputs.tag }}
           export HELM_VERSION=${{ steps.vars.outputs.tag }}
           export HELM_REPO=ghcr.io/${{ github.repository_owner }}
-          if [[ ! "$line" =~ ^v ]]
-          then
-            unset APP_VERSION
-            unset HELM_VERSION
-          fi
           helm dependency update files/helm/fastgpt
           helm package files/helm/fastgpt --version ${HELM_VERSION}-helm --app-version ${APP_VERSION} -d bin
           helm push bin/fastgpt-${HELM_VERSION}-helm.tgz oci://${HELM_REPO}

.vscode/settings.json

@@ -21,6 +21,7 @@
     "i18n-ally.namespace": true,
     "i18n-ally.pathMatcher": "{locale}/{namespaces}.json",
     "i18n-ally.extract.targetPickingStrategy": "most-similar-by-key",
+    "i18n-ally.translate.engines": ["deepl", "google"],
     "[typescript]": {
         "editor.defaultFormatter": "esbenp.prettier-vscode"
     }

README.md

@@ -106,7 +106,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
 
   由于需要部署数据库，部署完后需要等待 2~4 分钟才能正常访问。默认用了最低配置，首次访问时会有些慢。相关使用教程可查看：[Sealos 部署 FastGPT](https://doc.fastgpt.in/docs/development/sealos/)
 
-* [快开始本地开发](https://doc.fastgpt.in/docs/development/intro/)
+* [快速开始本地开发](https://doc.fastgpt.in/docs/development/intro/)
 * [部署 FastGPT](https://doc.fastgpt.in/docs/development/sealos)
 * [系统配置文件说明](https://doc.fastgpt.in/docs/development/configuration/)
 * [多模型配置](https://doc.fastgpt.in/docs/development/one-api/)
@@ -122,7 +122,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
 
 扫码加入飞书话题群 (新开，逐渐弃用微信群)：
 
-![](https://oss.laf.run/otnvvf-imgs/1719505774252.jpg)
+![](https://oss.laf.run/otnvvf-imgs/飞书20240806-114214.png)
 
 <a href="#readme">
     <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">

docSite/content/zh-cn/docs/course/chat_input_guide.md

@@ -4,7 +4,7 @@ description: "FastGPT 对话问题引导"
 icon: "code"
 draft: false
 toc: true
-weight: 350
+weight: 108
 ---
 
 ![](/imgs/questionGuide.png)

docSite/content/zh-cn/docs/course/collection_tags.md

@@ -0,0 +1,50 @@
+---
+title: "知识库集合标签"
+description: "FastGPT 知识库集合标签使用说明"
+icon: "developer_guide"
+draft: false
+toc: true
+weight: 108
+---
+
+知识库集合标签是 FastGPT 商业版特有功能。它允许你对知识库中的数据集合添加标签进行分类，更高效地管理知识库数据。
+
+而进一步可以在问答中，搜索知识库时添加集合过滤，实现更精确的搜索。
+
+|                       |                       | |
+| --------------------- | --------------------- | --------------------- |
+| ![](/imgs/collection-tags-1.png) | ![](/imgs/collection-tags-2.png) | ![](/imgs/collection-tags-3.png) |
+
+## 标签基础操作说明
+
+在知识库详情页面，可以对标签进行管理，可执行的操作有
+
+- 创建标签
+- 修改标签名
+- 删除标签
+- 将一个标签赋给多个数据集合
+- 给一个数据集合添加多个标签
+
+也可以利用标签对数据集合进行筛选
+
+## 知识库搜索-集合过滤说明
+
+利用标签可以在知识库搜索时，通过填写「集合过滤」这一栏来实现更精确的搜索，具体的填写示例如下
+
+```json
+{
+  "tags": {
+    "$and": ["标签 1","标签 2"],
+    "$or": ["有 $and 标签时，and 生效，or 不生效"]
+  },
+  "createTime": {
+      "$gte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间大于该时间",
+      "$lte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间小于该时间,可和 $gte 共同使用"
+  }
+}
+```
+
+在填写时有两个注意的点，
+
+- 标签值可以为 `string` 类型的标签名，也可以为 `null`，而 `null` 代表着未设置标签的数据集合
+- 标签过滤有 `$and` 和 `$or` 两种条件类型，在同时设置了 `$and` 和 `$or` 的情况下，只有 `$and` 会生效

docSite/content/zh-cn/docs/development/custom-models/xinference.md

@@ -124,53 +124,36 @@ curl --location --request POST 'https://<oneapi_url>/v1/chat/completions' \
 
 ## 将本地模型接入 FastGPT
 
-修改 FastGPT 的 `config.json` 配置文件，其中 chatModels（对话模型）用于聊天对话，cqModels（问题分类模型）用来对问题进行分类，extractModels（内容提取模型）则用来进行工具选择。我们分别在 chatModels、cqModels 和 extractModels 中加入 qwen-chat 模型：
+修改 FastGPT 的 `config.json` 配置文件的 llmModels 部分加入 qwen-chat 模型：
 
 ```json
-{
-  "chatModels": [
-      ...
-      {
-        "model": "qwen-chat",
-        "name": "Qwen",
-        "maxContext": 2048,
-        "maxResponse": 2048, 
-        "quoteMaxToken": 2000,
-        "maxTemperature": 1, 
-        "vision": false, 
-        "defaultSystemChatPrompt": "" 
-      }
-      ...
+...
+  "llmModels": [
+    {
+      "model": "qwen-chat", // 模型名(对应OneAPI中渠道的模型名)
+      "name": "Qwen", // 模型别名
+      "avatar": "/imgs/model/Qwen.svg", // 模型的logo
+      "maxContext": 125000, // 最大上下文
+      "maxResponse": 4000, // 最大回复
+      "quoteMaxToken": 120000, // 最大引用内容
+      "maxTemperature": 1.2, // 最大温度
+      "charsPointsPrice": 0, // n积分/1k token（商业版）
+      "censor": false, // 是否开启敏感校验（商业版）
+      "vision": true, // 是否支持图片输入
+      "datasetProcess": true, // 是否设置为知识库处理模型（QA），务必保证至少有一个为true，否则知识库会报错
+      "usedInClassify": true, // 是否用于问题分类（务必保证至少有一个为true）
+      "usedInExtractFields": true, // 是否用于内容提取（务必保证至少有一个为true）
+      "usedInToolCall": true, // 是否用于工具调用（务必保证至少有一个为true）
+      "usedInQueryExtension": true, // 是否用于问题优化（务必保证至少有一个为true）
+      "toolChoice": true, // 是否支持工具选择（分类，内容提取，工具调用会用到。目前只有gpt支持）
+      "functionCall": false, // 是否支持函数调用（分类，内容提取，工具调用会用到。会优先使用 toolChoice，如果为false，则使用 functionCall，如果仍为 false，则使用提示词模式）
+      "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": {} // 请求API时，挟带一些默认配置（比如 GLM4 的 top_p）
+    }
   ],
-  "cqModels": [
-      ...
-      {
-        "model": "qwen-chat",
-        "name": "Qwen",
-        "maxContext": 2048,
-        "maxResponse": 2048,
-        "inputPrice": 0,
-        "outputPrice": 0,
-        "toolChoice": true,
-        "functionPrompt": ""
-      }
-      ...
-  ],    
-  "extractModels": [
-      ...
-      {
-        "model": "qwen-chat",
-        "name": "Qwen",
-        "maxContext": 2048,
-        "maxResponse": 2048,
-        "inputPrice": 0,
-        "outputPrice": 0,
-        "toolChoice": true,
-        "functionPrompt": ""
-      }
-      ...
-  ]
-}
+...
 ```
 
 然后重启 FastGPT 就可以在应用配置中选择 Qwen 模型进行对话：

docSite/content/zh-cn/docs/development/openapi/dataset.md

@@ -531,6 +531,8 @@ data 为集合的 ID。
 {{< tab tabName="请求示例" >}}
 {{< markdownify >}}
 
+使用代码上传时，请注意中文 filename 需要进行 encode 处理，否则容易乱码。
+
 ```bash
 curl --location --request POST 'http://localhost:3000/api/core/dataset/collection/create/localFile' \
 --header 'Authorization: Bearer {{authorization}}' \
@@ -1173,7 +1175,7 @@ curl --location --request PUT 'http://localhost:3000/api/core/dataset/data/updat
 --header 'Authorization: Bearer {{authorization}}' \
 --header 'Content-Type: application/json' \
 --data-raw '{
-    "id":"65abd4b29d1448617cba61db",
+    "dataId":"65abd4b29d1448617cba61db",
     "q":"测试111",
     "a":"sss",
     "indexes":[
@@ -1196,7 +1198,7 @@ curl --location --request PUT 'http://localhost:3000/api/core/dataset/data/updat
 {{< markdownify >}}
 
 {{% alert icon=" " context="success" %}}
-- id: 数据的id
+- dataId: 数据的id
 - q: 主要数据（选填）
 - a: 辅助数据（选填）
 - indexes: 自定义索引（选填），类型参考`为集合批量添加添加数据`。如果创建时候有自定义索引，

docSite/content/zh-cn/docs/development/upgrading/482.md

@@ -10,7 +10,7 @@ weight: 822
 ## Sealos 升级说明
 
 1. 在应用管理中新建一个应用，镜像为：registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt-sandbox:v4.8.1
-2. 无需外网访问地址
+2. 无需外网访问地址，端口号为3000
 3. 部署完后，复制应用的内网地址
 4. 点击变更`FastGPT - 修改环境变量，增加下面的环境变量即可
 
@@ -31,4 +31,4 @@ SANDBOX_URL=内网地址
 1. 新增 - js代码运行节点（更完整的type提醒，后续继续完善）
 2. 新增 - 内容提取节点支持数据类型选择
 3. 修复 - 新增的站点同步无法使用
-4. 修复 - 定时任务无法输入内容
+4. 修复 - 定时任务无法输入内容

docSite/content/zh-cn/docs/development/upgrading/488.md

@@ -1,5 +1,5 @@
 ---
-title: 'V4.8.8'
+title: 'V4.8.8(需要初始化)'
 description: 'FastGPT V4.8.8 更新说明'
 icon: 'upgrade'
 draft: false
@@ -13,7 +13,7 @@ weight: 816
 
 ### 2. 修改镜像
 
-- fastgpt 镜像 tag 修改成 v4.8.8
+- fastgpt 镜像 tag 修改成 v4.8.8-fix2
 - 商业版镜像 tag 修改成 v4.8.8
 
 ### 3. 执行初始化

docSite/content/zh-cn/docs/development/upgrading/489.md

@@ -0,0 +1,51 @@
+---
+title: 'V4.8.9（进行中）'
+description: 'FastGPT V4.8.9 更新说明'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 816
+---
+
+## 升级指南
+
+### 1. 做好数据库备份
+
+### 2. 修改镜像
+
+
+### 3. 执行初始化
+
+从任意终端，发起 1 个 HTTP 请求。其中 {{rootkey}} 替换成环境变量里的 `rootkey`；{{host}} 替换成**FastGPT 商业版域名**。
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/init/489' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+会初始化多租户的通知方式
+
+-------
+
+## V4.8.9 更新说明
+
+1. 新增 - 文件上传配置，不再依赖视觉模型决定是否可上传图片，而是通过系统配置决定。
+2. 新增 - AI 对话节点和工具调用支持选择“是否开启图片识别”，开启后会自动获取对话框上传的图片和“用户问题”中的图片链接。
+3. 新增 - 文档解析节点。
+4. 商业版新增 - 团队通知账号绑定，用于接收重要信息。
+5. 商业版新增 - 知识库集合标签功能，可以对知识库进行标签管理。
+6. 商业版新增 - 知识库搜索节点支持标签过滤和创建时间过滤。
+7. 商业版新增 - 转移 App owner 权限。
+8. 新增 - 删除所有对话引导内容。
+9. 新增 - QA 拆分支持自定义 chunk 大小，并优化 gpt4o-mini 拆分时，chunk 太大导致生成内容很少的问题。
+10. 优化 - 对话框信息懒加载，减少网络传输。
+11. 优化 - 清除选文件缓存，支持重复选择同一个文件。
+12. 修复 - 知识库上传文件，网络不稳定或文件较多情况下，进度无法到 100%。
+13. 修复 - 删除应用后回到聊天选择最后一次对话的应用为删除的应用时提示无该应用问题。
+14. 修复 - 插件动态变量配置默认值时，无法正常显示默认值。
+15. 修复 - 工具调用温度和最大回复值未生效。
+16. 修复 - 函数调用模式，assistant role 中，GPT 模型必须传入 content 参数。（不影响大部分模型，目前基本都改用用 ToolChoice 模式，FC 模式已弃用）。
+17. 修复 - 知识库文件上传进度更新可能异常。
+18. 修复 - 知识库 rebuilding 时候，页面总是刷新到第一页。
+19. 修复 - 知识库 list openapi 鉴权问题。

files/docker/docker-compose-milvus.yml

@@ -121,8 +121,8 @@ services:
     restart: always
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.8.8 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.8.8-fix2 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8-fix2 # 阿里云
     ports:
       - 3000:3000
     networks:
@@ -179,7 +179,7 @@ services:
       - ./mysql:/var/lib/mysql
   oneapi:
     container_name: oneapi
-    image: ghcr.io/songquanpeng/one-api:0.6.7
+    image: ghcr.io/songquanpeng/one-api:v0.6.7
     # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/one-api:v0.6.6 # 阿里云
     ports:
       - 3001:3000

files/docker/docker-compose-pgvector.yml

@@ -79,8 +79,8 @@ services:
     restart: always
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.8.8 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.8.8-fix2 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8-fix2 # 阿里云
     ports:
       - 3000:3000
     networks:
@@ -136,7 +136,7 @@ services:
       - ./mysql:/var/lib/mysql
   oneapi:
     container_name: oneapi
-    image: ghcr.io/songquanpeng/one-api:0.6.7
+    image: ghcr.io/songquanpeng/one-api:v0.6.7
     # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/one-api:v0.6.6 # 阿里云
     ports:
       - 3001:3000

files/docker/docker-compose-zilliz.yml

@@ -60,8 +60,8 @@ services:
     restart: always
   fastgpt:
     container_name: fastgpt
-    image: ghcr.io/labring/fastgpt:v4.8.8 # git
-    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8 # 阿里云
+    image: ghcr.io/labring/fastgpt:v4.8.8-fix2 # git
+    # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:v4.8.8-fix2 # 阿里云
     ports:
       - 3000:3000
     networks:
@@ -117,7 +117,7 @@ services:
       - ./mysql:/var/lib/mysql
   oneapi:
     container_name: oneapi
-    image: ghcr.io/songquanpeng/one-api:0.6.7
+    image: ghcr.io/songquanpeng/one-api:v0.6.7
     # image: registry.cn-hangzhou.aliyuncs.com/fastgpt/one-api:v0.6.6 # 阿里云
     ports:
       - 3001:3000

packages/global/common/error/code/app.ts

@@ -1,18 +1,28 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 502000 */
 export enum AppErrEnum {
   unExist = 'appUnExist',
-  unAuthApp = 'unAuthApp'
+  unAuthApp = 'unAuthApp',
+  invalidOwner = 'invalidOwner',
+  invalidAppType = 'invalidAppType'
 }
 const appErrList = [
   {
     statusText: AppErrEnum.unExist,
-    message: '应用不存在'
+    message: i18nT('common:code_error.app_error.not_exist')
   },
   {
     statusText: AppErrEnum.unAuthApp,
-    message: '无权操作该应用'
+    message: i18nT('common:code_error.app_error.un_auth_app')
+  },
+  {
+    statusText: AppErrEnum.invalidOwner,
+    message: i18nT('common:code_error.app_error.invalid_owner')
+  },
+  {
+    statusText: AppErrEnum.invalidAppType,
+    message: i18nT('common:code_error.app_error.invalid_app_type')
   }
 ];
 export default appErrList.reduce((acc, cur, index) => {

packages/global/common/error/code/chat.ts

@@ -1,5 +1,5 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 504000 */
 export enum ChatErrEnum {
   unAuthChat = 'unAuthChat'
@@ -7,7 +7,7 @@ export enum ChatErrEnum {
 const errList = [
   {
     statusText: ChatErrEnum.unAuthChat,
-    message: '无权操作该对话记录'
+    message: i18nT('common:code_error.chat_error.un_auth')
   }
 ];
 export default errList.reduce((acc, cur, index) => {

packages/global/common/error/code/openapi.ts

@@ -1,25 +1,27 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 506000 */
 export enum OpenApiErrEnum {
   unExist = 'openapiUnExist',
   unAuth = 'openapiUnAuth',
   exceedLimit = 'openapiExceedLimit'
 }
+
 const errList = [
   {
     statusText: OpenApiErrEnum.unExist,
-    message: 'Api Key 不存在'
+    message: i18nT('common:code_error.openapi_error.api_key_not_exist')
   },
   {
     statusText: OpenApiErrEnum.unAuth,
-    message: '无权操作该 Api Key'
+    message: i18nT('common:code_error.openapi_error.un_auth')
   },
   {
     statusText: OpenApiErrEnum.exceedLimit,
-    message: '最多 10 组 API 密钥'
+    message: i18nT('common:code_error.openapi_error.exceed_limit')
   }
 ];
+
 export default errList.reduce((acc, cur, index) => {
   return {
     ...acc,

packages/global/common/error/code/outLink.ts

@@ -1,32 +1,33 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 505000 */
 export enum OutLinkErrEnum {
   unExist = 'outlinkUnExist',
   unAuthLink = 'unAuthLink',
   linkUnInvalid = 'linkUnInvalid',
-
   unAuthUser = 'unAuthUser'
 }
+
 const errList = [
   {
     statusText: OutLinkErrEnum.unExist,
-    message: '分享链接不存在'
+    message: i18nT('common:code_error.outlink_error.link_not_exist')
   },
   {
     statusText: OutLinkErrEnum.unAuthLink,
-    message: '分享链接无效'
+    message: i18nT('common:code_error.outlink_error.invalid_link')
   },
   {
     code: 501,
     statusText: OutLinkErrEnum.linkUnInvalid,
-    message: '分享链接无效'
+    message: i18nT('common:code_error.outlink_error.invalid_link') // 使用相同的错误消息
   },
   {
     statusText: OutLinkErrEnum.unAuthUser,
-    message: '身份校验失败'
+    message: i18nT('common:code_error.outlink_error.un_auth_user')
   }
 ];
+
 export default errList.reduce((acc, cur, index) => {
   return {
     ...acc,

packages/global/common/error/code/plugin.ts

@@ -1,20 +1,22 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 508000 */
 export enum PluginErrEnum {
   unExist = 'pluginUnExist',
   unAuth = 'pluginUnAuth'
 }
+
 const errList = [
   {
     statusText: PluginErrEnum.unExist,
-    message: '插件不存在'
+    message: i18nT('common:code_error.plugin_error.not_exist')
   },
   {
     statusText: PluginErrEnum.unAuth,
-    message: '无权操作该插件'
+    message: i18nT('common:code_error.plugin_error.un_auth')
   }
 ];
+
 export default errList.reduce((acc, cur, index) => {
   return {
     ...acc,

packages/global/common/error/code/system.ts

@@ -1,15 +1,17 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* dataset: 509000 */
 export enum SystemErrEnum {
   communityVersionNumLimit = 'communityVersionNumLimit'
 }
+
 const systemErr = [
   {
     statusText: SystemErrEnum.communityVersionNumLimit,
-    message: '超出开源版数量限制，请升级商业版: https://fastgpt.in'
+    message: i18nT('common:code_error.system_error.community_version_num_limit')
   }
 ];
+
 export default systemErr.reduce((acc, cur, index) => {
   return {
     ...acc,

packages/global/common/error/code/team.ts

@@ -1,5 +1,5 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* team: 500000 */
 export enum TeamErrEnum {
   teamOverSize = 'teamOverSize',
@@ -12,17 +12,43 @@ export enum TeamErrEnum {
   websiteSyncNotEnough = 'websiteSyncNotEnough',
   reRankNotEnough = 'reRankNotEnough'
 }
+
 const teamErr = [
-  { statusText: TeamErrEnum.teamOverSize, message: 'error.team.overSize' },
-  { statusText: TeamErrEnum.unAuthTeam, message: '无权操作该团队' },
-  { statusText: TeamErrEnum.aiPointsNotEnough, message: '' },
-  { statusText: TeamErrEnum.datasetSizeNotEnough, message: '知识库容量不足，请先扩容~' },
-  { statusText: TeamErrEnum.datasetAmountNotEnough, message: '知识库数量已达上限~' },
-  { statusText: TeamErrEnum.appAmountNotEnough, message: '应用数量已达上限~' },
-  { statusText: TeamErrEnum.pluginAmountNotEnough, message: '插件数量已达上限~' },
-  { statusText: TeamErrEnum.websiteSyncNotEnough, message: '无权使用Web站点同步~' },
-  { statusText: TeamErrEnum.reRankNotEnough, message: '无权使用检索重排~' }
+  {
+    statusText: TeamErrEnum.teamOverSize,
+    message: i18nT('common:code_error.team_error.over_size')
+  },
+  { statusText: TeamErrEnum.unAuthTeam, message: i18nT('common:code_error.team_error.un_auth') },
+  {
+    statusText: TeamErrEnum.aiPointsNotEnough,
+    message: i18nT('common:code_error.team_error.ai_points_not_enough')
+  }, // 需要定义或留空
+  {
+    statusText: TeamErrEnum.datasetSizeNotEnough,
+    message: i18nT('common:code_error.team_error.dataset_size_not_enough')
+  },
+  {
+    statusText: TeamErrEnum.datasetAmountNotEnough,
+    message: i18nT('common:code_error.team_error.dataset_amount_not_enough')
+  },
+  {
+    statusText: TeamErrEnum.appAmountNotEnough,
+    message: i18nT('common:code_error.team_error.app_amount_not_enough')
+  },
+  {
+    statusText: TeamErrEnum.pluginAmountNotEnough,
+    message: i18nT('common:code_error.team_error.plugin_amount_not_enough')
+  },
+  {
+    statusText: TeamErrEnum.websiteSyncNotEnough,
+    message: i18nT('common:code_error.team_error.website_sync_not_enough')
+  },
+  {
+    statusText: TeamErrEnum.reRankNotEnough,
+    message: i18nT('common:code_error.team_error.re_rank_not_enough')
+  }
 ];
+
 export default teamErr.reduce((acc, cur, index) => {
   return {
     ...acc,

packages/global/common/error/code/user.ts

@@ -1,5 +1,5 @@
 import { ErrType } from '../errorCode';
-
+import { i18nT } from '../../../../web/i18n/utils';
 /* team: 503000 */
 export enum UserErrEnum {
   unAuthUser = 'unAuthUser',
@@ -8,10 +8,22 @@ export enum UserErrEnum {
   balanceNotEnough = 'balanceNotEnough'
 }
 const errList = [
-  { statusText: UserErrEnum.unAuthUser, message: '找不到该用户' },
-  { statusText: UserErrEnum.binVisitor, message: '您的身份校验未通过' },
-  { statusText: UserErrEnum.binVisitor, message: '您当前身份为游客，无权操作' },
-  { statusText: UserErrEnum.balanceNotEnough, message: '账号余额不足~' }
+  {
+    statusText: UserErrEnum.unAuthUser,
+    message: i18nT('common:code_error.user_error.un_auth_user')
+  },
+  {
+    statusText: UserErrEnum.binVisitor,
+    message: i18nT('common:code_error.user_error.bin_visitor')
+  }, // 身份校验未通过
+  {
+    statusText: UserErrEnum.binVisitor,
+    message: i18nT('common:code_error.user_error.bin_visitor_guest')
+  }, // 游客身份
+  {
+    statusText: UserErrEnum.balanceNotEnough,
+    message: i18nT('common:code_error.user_error.balance_not_enough')
+  }
 ];
 export default errList.reduce((acc, cur, index) => {
   return {

packages/global/common/error/errorCode.ts

@@ -8,24 +8,25 @@ import teamErr from './code/team';
 import userErr from './code/user';
 import commonErr from './code/common';
 import SystemErrEnum from './code/system';
+import { i18nT } from '../../../web/i18n/utils';
 
 export const ERROR_CODE: { [key: number]: string } = {
-  400: '请求失败',
-  401: '无权访问',
-  403: '紧张访问',
-  404: '请求不存在',
-  405: '请求方法错误',
-  406: '请求的格式错误',
-  410: '资源已删除',
-  422: '验证错误',
-  500: '服务器发生错误',
-  502: '网关错误',
-  503: '服务器暂时过载或维护',
-  504: '网关超时'
+  400: i18nT('common:code_error.error_code.400'),
+  401: i18nT('common:code_error.error_code.401'),
+  403: i18nT('common:code_error.error_code.403'),
+  404: i18nT('common:code_error.error_code.404'),
+  405: i18nT('common:code_error.error_code.405'),
+  406: i18nT('common:code_error.error_code.406'),
+  410: i18nT('common:code_error.error_code.410'),
+  422: i18nT('common:code_error.error_code.422'),
+  500: i18nT('common:code_error.error_code.500'),
+  502: i18nT('common:code_error.error_code.502'),
+  503: i18nT('common:code_error.error_code.503'),
+  504: i18nT('common:code_error.error_code.504')
 };
 
 export const TOKEN_ERROR_CODE: Record<number, string> = {
-  403: '登录状态无效,请重新登录'
+  403: i18nT('common:code_error.token_error_code.403')
 };
 
 export const proxyError: Record<string, boolean> = {
@@ -63,32 +64,31 @@ export const ERROR_RESPONSE: Record<
   [ERROR_ENUM.unAuthorization]: {
     code: 403,
     statusText: ERROR_ENUM.unAuthorization,
-    message: '凭证错误',
+    message: i18nT('common:code_error.error_message.403'),
     data: null
   },
   [ERROR_ENUM.insufficientQuota]: {
     code: 510,
     statusText: ERROR_ENUM.insufficientQuota,
-    message: '账号余额不足',
+    message: i18nT('common:code_error.error_message.510'),
     data: null
   },
   [ERROR_ENUM.unAuthModel]: {
     code: 511,
     statusText: ERROR_ENUM.unAuthModel,
-    message: '无权操作该模型',
+    message: i18nT('common:code_error.error_message.511'),
     data: null
   },
-
   [ERROR_ENUM.unAuthFile]: {
     code: 513,
     statusText: ERROR_ENUM.unAuthFile,
-    message: '无权阅读该文件',
+    message: i18nT('common:code_error.error_message.513'),
     data: null
   },
   [ERROR_ENUM.unAuthApiKey]: {
     code: 514,
     statusText: ERROR_ENUM.unAuthApiKey,
-    message: 'Api Key 不合法',
+    message: i18nT('common:code_error.error_message.514'),
     data: null
   },
   ...appErr,

packages/global/common/error/utils.ts

@@ -1,6 +1,6 @@
 import { replaceSensitiveText } from '../string/tools';
 
-export const getErrText = (err: any, def = '') => {
+export const getErrText = (err: any, def = ''): any => {
   const msg: string =
     typeof err === 'string'
       ? err

packages/global/common/file/constants.ts

@@ -1,11 +1,21 @@
+import { i18nT } from '../../../web/i18n/utils';
+
 /* mongo fs bucket */
 export enum BucketNameEnum {
-  dataset = 'dataset'
+  dataset = 'dataset',
+  chat = 'chat'
 }
 export const bucketNameMap = {
   [BucketNameEnum.dataset]: {
-    label: 'file.bucket.dataset'
+    label: i18nT('file:bucket_file'),
+    previewExpireMinutes: 30 // 30 minutes
+  },
+  [BucketNameEnum.chat]: {
+    label: i18nT('file:bucket_chat'),
+    previewExpireMinutes: 7 * 24 * 60 // 7 days
   }
 };
 
 export const ReadFileBaseUrl = '/api/common/file/read';
+
+export const documentFileType = '.txt, .docx, .csv, .xlsx, .pdf, .md, .html, .pptx';

packages/global/common/frequenctLimit/type.d.ts

@@ -0,0 +1,5 @@
+export type AuthFrequencyLimitProps = {
+  eventId: string;
+  maxAmount: number;
+  expiredTime: Date;
+};

packages/global/common/string/time.ts

@@ -1,5 +1,10 @@
 import dayjs from 'dayjs';
 import cronParser from 'cron-parser';
+import utc from 'dayjs/plugin/utc';
+import timezone from 'dayjs/plugin/timezone';
+
+dayjs.extend(utc);
+dayjs.extend(timezone);
 
 export const formatTime2YMDHMW = (time?: Date) => dayjs(time).format('YYYY-MM-DD HH:mm:ss dddd');
 export const formatTime2YMDHM = (time?: Date) =>

packages/global/common/string/tools.ts

@@ -91,3 +91,10 @@ export const sliceJsonStr = (str: string) => {
 
   return jsonStr;
 };
+
+export const sliceStrStartEnd = (str: string, start: number, end: number) => {
+  const overSize = str.length > start + end;
+  const startContent = str.slice(0, start);
+  const endContent = overSize ? str.slice(-end) : '';
+  return startContent + (overSize ? ` ...... ` : '') + endContent;
+};

packages/global/common/type/utils.ts

@@ -0,0 +1,9 @@
+export type RequireAtLeastOne<T, Keys extends keyof T = keyof T> = Omit<T, Keys> &
+  {
+    [K in Keys]-?: Required<Pick<T, K>> & Partial<Omit<T, K>>;
+  }[Keys];
+
+export type RequireOnlyOne<T, Keys extends keyof T = keyof T> = Omit<T, Keys> &
+  {
+    [K in Keys]-?: Required<Pick<T, K>> & Partial<Record<Exclude<Keys, K>, undefined>>;
+  }[Keys];

packages/global/core/ai/prompt/AIChat.ts

@@ -119,3 +119,10 @@ export const Prompt_QuotePromptList: PromptTemplateItem[] = [
 问题:"""{{question}}"""`
   }
 ];
+
+// Document quote prompt
+export const Prompt_DocumentQuote = `将 <Quote></Quote> 中的内容作为你的知识:
+<Quote>
+{{quote}}
+</Quote>
+`;

packages/global/core/ai/prompt/agent.ts

@@ -1,9 +1,9 @@
 export const Prompt_AgentQA = {
   description: `<Context></Context> 标记中是一段文本，学习和分析它，并整理学习成果：
 - 提出问题并给出每个问题的答案。
-- 答案需详细完整，尽可能保留原文描述。
+- 答案需详细完整，尽可能保留原文描述，可以适当扩展答案描述。
 - 答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 Markdown 元素。
-- 最多提出 30 个问题。
+- 最多提出 50 个问题。
 `,
   fixedText: `请按以下格式整理学习成果:
 <Context>

packages/global/core/ai/type.d.ts

@@ -2,23 +2,46 @@ import openai from 'openai';
 import type {
   ChatCompletionMessageToolCall,
   ChatCompletionChunk,
-  ChatCompletionMessageParam,
+  ChatCompletionMessageParam as SdkChatCompletionMessageParam,
   ChatCompletionToolMessageParam,
-  ChatCompletionAssistantMessageParam
+  ChatCompletionAssistantMessageParam,
+  ChatCompletionContentPart as SdkChatCompletionContentPart,
+  ChatCompletionUserMessageParam as SdkChatCompletionUserMessageParam
 } from 'openai/resources';
 import { ChatMessageTypeEnum } from './constants';
 
 export * from 'openai/resources';
 
-export type ChatCompletionMessageParam = ChatCompletionMessageParam & {
+// Extension of ChatCompletionMessageParam, Add file url type
+export type ChatCompletionContentPartFile = {
+  type: 'file_url';
+  name: string;
+  url: string;
+};
+// Rewrite ChatCompletionContentPart, Add file type
+export type ChatCompletionContentPart =
+  | SdkChatCompletionContentPart
+  | ChatCompletionContentPartFile;
+type CustomChatCompletionUserMessageParam = {
+  content: string | Array<ChatCompletionContentPart>;
+  role: 'user';
+  name?: string;
+};
+
+export type ChatCompletionMessageParam = (
+  | Exclude<SdkChatCompletionMessageParam, SdkChatCompletionUserMessageParam>
+  | CustomChatCompletionUserMessageParam
+) & {
   dataId?: string;
 };
+export type SdkChatCompletionMessageParam = SdkChatCompletionMessageParam;
+
+/* ToolChoice and functionCall extension */
 export type ChatCompletionToolMessageParam = ChatCompletionToolMessageParam & { name: string };
 export type ChatCompletionAssistantToolParam = {
   role: 'assistant';
   tool_calls: ChatCompletionMessageToolCall[];
 };
-
 export type ChatCompletionMessageToolCall = ChatCompletionMessageToolCall & {
   toolName?: string;
   toolAvatar?: string;
@@ -28,13 +51,16 @@ export type ChatCompletionMessageFunctionCall = ChatCompletionAssistantMessagePa
   toolName?: string;
   toolAvatar?: string;
 };
+
+// Stream response
 export type StreamChatType = Stream<ChatCompletionChunk>;
 
+export default openai;
+export * from 'openai';
+
+// Other
 export type PromptTemplateItem = {
   title: string;
   desc: string;
   value: string;
 };
-
-export default openai;
-export * from 'openai';

packages/global/core/app/constants.ts

@@ -1,4 +1,4 @@
-import { AppTTSConfigType, AppWhisperConfigType } from './type';
+import { AppTTSConfigType, AppFileSelectConfigType, AppWhisperConfigType } from './type';
 
 export enum AppTypeEnum {
   folder = 'folder',
@@ -23,3 +23,9 @@ export const defaultChatInputGuideConfig = {
   textList: [],
   customUrl: ''
 };
+
+export const defaultAppSelectFileConfig: AppFileSelectConfigType = {
+  canSelectFile: false,
+  canSelectImg: false,
+  maxFiles: 10
+};

packages/global/core/app/plugin/utils.ts

@@ -5,6 +5,20 @@ import { FlowNodeTypeEnum } from '../../workflow/node/constant';
 export const getPluginInputsFromStoreNodes = (nodes: StoreNodeItemType[]) => {
   return nodes.find((node) => node.flowNodeType === FlowNodeTypeEnum.pluginInput)?.inputs || [];
 };
-export const getPluginRunContent = (e: { pluginInputs: FlowNodeInputItemType[] }) => {
-  return JSON.stringify(e);
+export const getPluginRunContent = ({
+  pluginInputs,
+  variables
+}: {
+  pluginInputs: FlowNodeInputItemType[];
+  variables: Record<string, any>;
+}) => {
+  const pluginInputsWithValue = pluginInputs.map((input) => {
+    const { key } = input;
+    const value = variables?.hasOwnProperty(key) ? variables[key] : input.defaultValue;
+    return {
+      ...input,
+      value
+    };
+  });
+  return JSON.stringify(pluginInputsWithValue);
 };

packages/global/core/app/type.d.ts

@@ -1,7 +1,7 @@
 import type { FlowNodeTemplateType, StoreNodeItemType } from '../workflow/type/node';
 import { AppTypeEnum } from './constants';
 import { PermissionTypeEnum } from '../../support/permission/constant';
-import { VariableInputEnum } from '../workflow/constants';
+import { NodeInputKeyEnum, VariableInputEnum } from '../workflow/constants';
 import { SelectedDatasetType } from '../workflow/api';
 import { DatasetSearchModeEnum } from '../dataset/constants';
 import { TeamTagSchema as TeamTagsSchemaType } from '@fastgpt/global/support/user/team/type.d';
@@ -91,6 +91,7 @@ export type AppChatConfigType = {
   whisperConfig?: AppWhisperConfigType;
   scheduledTriggerConfig?: AppScheduledTriggerConfigType;
   chatInputGuide?: ChatInputGuideConfigType;
+  fileSelectConfig?: AppFileSelectConfigType;
 };
 export type SettingAIDataType = {
   model: string;
@@ -98,6 +99,7 @@ export type SettingAIDataType = {
   maxToken: number;
   isResponseAnswerText?: boolean;
   maxHistories?: number;
+  [NodeInputKeyEnum.aiChatVision]?: boolean; // Is open vision mode
 };
 
 // variable
@@ -134,3 +136,9 @@ export type AppScheduledTriggerConfigType = {
   timezone: string;
   defaultPrompt: string;
 };
+// File
+export type AppFileSelectConfigType = {
+  canSelectFile: boolean;
+  canSelectImg: boolean;
+  maxFiles: number;
+};

packages/global/core/chat/adapt.ts

@@ -56,16 +56,21 @@ export const chats2GPTMessages = ({
               text: item.text?.content || ''
             };
           }
-          if (
-            item.type === ChatItemValueTypeEnum.file &&
-            item.file?.type === ChatFileTypeEnum.image
-          ) {
-            return {
-              type: 'image_url',
-              image_url: {
+          if (item.type === ChatItemValueTypeEnum.file) {
+            if (item.file?.type === ChatFileTypeEnum.image) {
+              return {
+                type: 'image_url',
+                image_url: {
+                  url: item.file?.url || ''
+                }
+              };
+            } else if (item.file?.type === ChatFileTypeEnum.file) {
+              return {
+                type: 'file_url',
+                name: item.file?.name || '',
                 url: item.file?.url || ''
-              }
-            };
+              };
+            }
           }
         })
         .filter(Boolean) as ChatCompletionContentPart[];
@@ -175,6 +180,16 @@ export const GPTMessages2Chats = (
                   url: item.image_url.url
                 }
               });
+            } else if (item.type === 'file_url') {
+              value.push({
+                // @ts-ignore
+                type: ChatItemValueTypeEnum.file,
+                file: {
+                  type: ChatFileTypeEnum.file,
+                  name: item.name,
+                  url: item.url
+                }
+              });
             }
           });
         }

packages/global/core/chat/type.d.ts

@@ -106,17 +106,26 @@ export type AdminFbkType = {
 };
 
 /* --------- chat item ---------- */
-export type ChatItemType = (UserChatItemType | SystemChatItemType | AIChatItemType) & {
-  dataId?: string;
+export type ResponseTagItemType = {
+  totalRunningTime?: number;
+  totalQuoteList?: SearchDataResponseItemType[];
+  llmModuleAccount?: number;
+  historyPreviewLength?: number;
 };
 
+export type ChatItemType = (UserChatItemType | SystemChatItemType | AIChatItemType) & {
+  dataId?: string;
+} & ResponseTagItemType;
+
+// Frontend type
 export type ChatSiteItemType = (UserChatItemType | SystemChatItemType | AIChatItemType) & {
   dataId: string;
   status: `${ChatStatusEnum}`;
   moduleName?: string;
   ttsBuffer?: Uint8Array;
   responseData?: ChatHistoryItemResType[];
-} & ChatBoxInputType;
+} & ChatBoxInputType &
+  ResponseTagItemType;
 
 /* --------- team chat --------- */
 export type ChatAppListSchema = {

packages/global/core/chat/utils.ts

@@ -2,6 +2,7 @@ import { DispatchNodeResponseType } from '../workflow/runtime/type';
 import { FlowNodeTypeEnum } from '../workflow/node/constant';
 import { ChatItemValueTypeEnum, ChatRoleEnum } from './constants';
 import { ChatHistoryItemResType, ChatItemType, UserChatItemValueItemType } from './type.d';
+import { sliceStrStartEnd } from '../../common/string/tools';
 
 // Concat 2 -> 1, and sort by role
 export const concatHistories = (histories1: ChatItemType[], histories2: ChatItemType[]) => {
@@ -25,6 +26,7 @@ export const getChatTitleFromChatMessage = (message?: ChatItemType, defaultValue
   return defaultValue;
 };
 
+// Keep the first n and last n characters
 export const getHistoryPreview = (
   completeMessages: ChatItemType[]
 ): {
@@ -32,30 +34,44 @@ export const getHistoryPreview = (
   value: string;
 }[] => {
   return completeMessages.map((item, i) => {
-    if (item.obj === ChatRoleEnum.System || i >= completeMessages.length - 2) {
-      return {
-        obj: item.obj,
-        value: item.value?.[0]?.text?.content || ''
-      };
-    }
+    const n = item.obj === ChatRoleEnum.System || i >= completeMessages.length - 2 ? 80 : 40;
 
-    const content = item.value
-      .map((item) => {
-        if (item.text?.content) {
-          const content =
-            item.text.content.length > 20
-              ? `${item.text.content.slice(0, 20)}...`
-              : item.text.content;
-          return content;
-        }
-        return '';
-      })
-      .filter(Boolean)
-      .join('\n');
+    // Get message text content
+    const rawText = (() => {
+      if (item.obj === ChatRoleEnum.System) {
+        return item.value?.map((item) => item.text?.content).join('') || '';
+      } else if (item.obj === ChatRoleEnum.Human) {
+        return (
+          item.value
+            ?.map((item) => {
+              if (item?.text?.content) return item?.text?.content;
+              if (item.file?.type === 'image') return 'Input an image';
+              return '';
+            })
+            .filter(Boolean)
+            .join('\n') || ''
+        );
+      } else if (item.obj === ChatRoleEnum.AI) {
+        return (
+          item.value
+            ?.map((item) => {
+              return (
+                item.text?.content || item?.tools?.map((item) => item.toolName).join(',') || ''
+              );
+            })
+            .join('') || ''
+        );
+      }
+      return '';
+    })();
+
+    const startContent = rawText.slice(0, n);
+    const endContent = rawText.length > 2 * n ? rawText.slice(-n) : '';
+    const content = startContent + (rawText.length > n ? ` ...... ` : '') + endContent;
 
     return {
       obj: item.obj,
-      value: content
+      value: sliceStrStartEnd(content, 80, 80)
     };
   });
 };

packages/global/core/dataset/api.d.ts

@@ -74,6 +74,23 @@ export type ExternalFileCreateDatasetCollectionParams = ApiCreateDatasetCollecti
   filename?: string;
 };
 
+/* ================= tag ===================== */
+export type CreateDatasetCollectionTagParams = {
+  datasetId: string;
+  tag: string;
+};
+export type AddTagsToCollectionsParams = {
+  originCollectionIds: string[];
+  collectionIds: string[];
+  datasetId: string;
+  tag: string;
+};
+export type UpdateDatasetCollectionTagParams = {
+  datasetId: string;
+  tagId: string;
+  tag: string;
+};
+
 /* ================= data ===================== */
 export type PgSearchRawType = {
   id: string;

packages/global/core/dataset/type.d.ts

@@ -69,6 +69,13 @@ export type DatasetCollectionSchemaType = {
   };
 };
 
+export type DatasetCollectionTagsSchemaType = {
+  _id: string;
+  teamId: string;
+  datasetId: string;
+  tag: string;
+};
+
 export type DatasetDataIndexItemType = {
   defaultIndex: boolean;
   dataId: string; // pg data id
@@ -144,6 +151,17 @@ export type DatasetItemType = Omit<DatasetSchemaType, 'vectorModel' | 'agentMode
   permission: DatasetPermission;
 };
 
+/* ================= tag ===================== */
+export type DatasetTagType = {
+  _id: string;
+  tag: string;
+};
+
+export type TagUsageType = {
+  tagId: string;
+  collections: string[];
+};
+
 /* ================= collection ===================== */
 export type DatasetCollectionItemType = CollectionWithDatasetType & {
   sourceName: string;

packages/global/core/workflow/constants.ts

@@ -75,6 +75,8 @@ export enum NodeInputKeyEnum {
   aiChatQuoteTemplate = 'quoteTemplate',
   aiChatQuotePrompt = 'quotePrompt',
   aiChatDatasetQuote = 'quoteQA',
+  aiChatVision = 'aiChatVision',
+  stringQuoteText = 'stringQuoteText',
 
   // dataset
   datasetSelectList = 'datasets',
@@ -85,6 +87,7 @@ export enum NodeInputKeyEnum {
   datasetSearchUsingExtensionQuery = 'datasetSearchUsingExtensionQuery',
   datasetSearchExtensionModel = 'datasetSearchExtensionModel',
   datasetSearchExtensionBg = 'datasetSearchExtensionBg',
+  collectionFilterMatch = 'collectionFilterMatch',
 
   // concat dataset
   datasetQuoteList = 'system_datasetQuoteList',
@@ -117,7 +120,10 @@ export enum NodeInputKeyEnum {
 
   // code
   code = 'code',
-  codeType = 'codeType' // js|py
+  codeType = 'codeType', // js|py
+
+  // read files
+  fileUrlList = 'fileUrlList'
 }
 
 export enum NodeOutputKeyEnum {
@@ -132,6 +138,9 @@ export enum NodeOutputKeyEnum {
   addOutputParam = 'system_addOutputParam',
   rawResponse = 'system_rawResponse',
 
+  // start
+  userFiles = 'userFiles',
+
   // dataset
   datasetQuoteQA = 'quoteQA',
 

packages/global/core/workflow/node/constant.ts

@@ -117,7 +117,8 @@ export enum FlowNodeTypeEnum {
   variableUpdate = 'variableUpdate',
   code = 'code',
   textEditor = 'textEditor',
-  customFeedback = 'customFeedback'
+  customFeedback = 'customFeedback',
+  readFiles = 'readFiles'
 }
 
 // node IO value type

packages/global/core/workflow/runtime/type.d.ts

@@ -16,10 +16,12 @@ import { UserModelSchema } from '../../../support/user/type';
 import { AppDetailType, AppSchema } from '../../app/type';
 import { RuntimeNodeItemType } from '../runtime/type';
 import { RuntimeEdgeItemType } from './edge';
+import { ReadFileNodeResponse } from '../template/system/readFiles/type';
 
 /* workflow props */
 export type ChatDispatchProps = {
   res?: NextApiResponse;
+  requestOrigin?: string;
   mode: 'test' | 'chat' | 'debug';
   teamId: string;
   tmbId: string;
@@ -30,6 +32,7 @@ export type ChatDispatchProps = {
   histories: ChatItemType[];
   variables: Record<string, any>; // global variable
   query: UserChatItemValueItemType[]; // trigger query
+  chatConfig: AppSchema['chatConfig'];
   stream: boolean;
   detail: boolean; // response detail
   maxRunTimes: number;
@@ -146,6 +149,10 @@ export type DispatchNodeResponseType = {
 
   // plugin
   pluginOutput?: Record<string, any>;
+
+  // read files
+  readFilesResult?: string;
+  readFiles?: ReadFileNodeResponse;
 };
 
 export type DispatchNodeResultType<T> = {
@@ -166,4 +173,6 @@ export type AIChatNodeProps = {
   [NodeInputKeyEnum.aiChatIsResponseText]: boolean;
   [NodeInputKeyEnum.aiChatQuoteTemplate]?: string;
   [NodeInputKeyEnum.aiChatQuotePrompt]?: string;
+  [NodeInputKeyEnum.aiChatVision]?: boolean;
+  [NodeInputKeyEnum.stringQuoteText]?: string;
 };

packages/global/core/workflow/template/constants.ts

@@ -25,6 +25,7 @@ import { VariableUpdateNode } from './system/variableUpdate';
 import { CodeNode } from './system/sandbox';
 import { TextEditorNode } from './system/textEditor';
 import { CustomFeedbackNode } from './system/customFeedback';
+import { ReadFilesNodes } from './system/readFiles';
 
 const systemNodes: FlowNodeTemplateType[] = [
   AiChatModule,
@@ -36,6 +37,7 @@ const systemNodes: FlowNodeTemplateType[] = [
   StopToolNode,
   ClassifyQuestionModule,
   ContextExtractModule,
+  ReadFilesNodes,
   HttpNode468,
   AiQueryExtension,
   LafModule,

packages/global/core/workflow/template/input.ts

@@ -3,6 +3,7 @@ import { FlowNodeInputTypeEnum } from '../node/constant';
 import { WorkflowIOValueTypeEnum } from '../constants';
 import { chatNodeSystemPromptTip } from './tip';
 import { FlowNodeInputItemType } from '../type/io';
+import { i18nT } from '../../../../web/i18n/utils';
 
 export const Input_Template_History: FlowNodeInputItemType = {
   key: NodeInputKeyEnum.history,
@@ -64,3 +65,11 @@ export const Input_Template_Dataset_Quote: FlowNodeInputItemType = {
   description: '',
   valueType: WorkflowIOValueTypeEnum.datasetQuote
 };
+export const Input_Template_Text_Quote: FlowNodeInputItemType = {
+  key: NodeInputKeyEnum.stringQuoteText,
+  renderTypeList: [FlowNodeInputTypeEnum.reference, FlowNodeInputTypeEnum.textarea],
+  label: i18nT('app:document_quote'),
+  debugLabel: i18nT('app:document_quote'),
+  description: i18nT('app:document_quote_tip'),
+  valueType: WorkflowIOValueTypeEnum.string
+};

packages/global/core/workflow/template/system/aiChat.ts

@@ -15,10 +15,12 @@ import {
   Input_Template_Dataset_Quote,
   Input_Template_History,
   Input_Template_System_Prompt,
-  Input_Template_UserChatInput
+  Input_Template_UserChatInput,
+  Input_Template_Text_Quote
 } from '../input';
 import { chatNodeSystemPromptTip } from '../tip';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const AiChatModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.chatNode,
@@ -27,8 +29,8 @@ export const AiChatModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/aiChat',
-  name: 'AI 对话',
-  intro: 'AI 大模型对话',
+  name: i18nT('workflow:template.ai_chat'),
+  intro: i18nT('workflow:template.ai_chat_intro'),
   showStatus: true,
   isTool: true,
   version: '481',
@@ -40,20 +42,14 @@ export const AiChatModule: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
       label: '',
       value: 0,
-      valueType: WorkflowIOValueTypeEnum.number,
-      min: 0,
-      max: 10,
-      step: 1
+      valueType: WorkflowIOValueTypeEnum.number
     },
     {
       key: NodeInputKeyEnum.aiChatMaxToken,
       renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
       label: '',
       value: 2000,
-      valueType: WorkflowIOValueTypeEnum.number,
-      min: 100,
-      max: 4000,
-      step: 50
+      valueType: WorkflowIOValueTypeEnum.number
     },
     {
       key: NodeInputKeyEnum.aiChatIsResponseText,
@@ -74,6 +70,13 @@ export const AiChatModule: FlowNodeTemplateType = {
       label: '',
       valueType: WorkflowIOValueTypeEnum.string
     },
+    {
+      key: NodeInputKeyEnum.aiChatVision,
+      renderTypeList: [FlowNodeInputTypeEnum.hidden],
+      label: '',
+      valueType: WorkflowIOValueTypeEnum.boolean,
+      value: true
+    },
     // settings modal ---
     {
       ...Input_Template_System_Prompt,
@@ -82,8 +85,9 @@ export const AiChatModule: FlowNodeTemplateType = {
       placeholder: chatNodeSystemPromptTip
     },
     Input_Template_History,
-    { ...Input_Template_UserChatInput, toolDescription: '用户问题' },
-    Input_Template_Dataset_Quote
+    Input_Template_Dataset_Quote,
+    Input_Template_Text_Quote,
+    { ...Input_Template_UserChatInput, toolDescription: '用户问题' }
   ],
   outputs: [
     {

packages/global/core/workflow/template/system/datasetSearch.ts

@@ -13,9 +13,9 @@ import {
 import { Input_Template_UserChatInput } from '../input';
 import { DatasetSearchModeEnum } from '../../../dataset/constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
-export const Dataset_SEARCH_DESC =
-  '调用“语义检索”和“全文检索”能力，从“知识库”中查找可能与问题相关的参考内容';
+export const Dataset_SEARCH_DESC = i18nT('workflow:template.dataset_search_intro');
 
 export const DatasetSearchModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.datasetSearchNode,
@@ -24,7 +24,7 @@ export const DatasetSearchModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, true, true),
   targetHandle: getHandleConfig(true, true, true, true),
   avatar: 'core/workflow/template/datasetSearch',
-  name: '知识库搜索',
+  name: i18nT('workflow:template.dataset_search'),
   intro: Dataset_SEARCH_DESC,
   showStatus: true,
   isTool: true,
@@ -90,6 +90,25 @@ export const DatasetSearchModule: FlowNodeTemplateType = {
     {
       ...Input_Template_UserChatInput,
       toolDescription: '需要检索的内容'
+    },
+    {
+      key: NodeInputKeyEnum.collectionFilterMatch,
+      renderTypeList: [FlowNodeInputTypeEnum.JSONEditor, FlowNodeInputTypeEnum.reference],
+      label: '集合元数据过滤',
+      valueType: WorkflowIOValueTypeEnum.object,
+      isPro: true,
+      description: `目前支持标签和创建时间过滤，需按照以下格式填写：
+{
+  "tags": {
+    "$and": ["标签 1","标签 2"],
+    "$or": ["有 $and 标签时，and 生效，or 不生效"]
+  },
+  "createTime": {
+      "$gte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间大于该时间",
+      "$lte": "YYYY-MM-DD HH:mm 格式即可，集合的创建时间小于该时间,可和 $gte 共同使用"
+  }
+}
+      `
     }
   ],
   outputs: [

packages/global/core/workflow/template/system/readFiles/index.tsx

@@ -0,0 +1,48 @@
+import { i18nT } from '../../../../../../web/i18n/utils';
+import {
+  FlowNodeTemplateTypeEnum,
+  NodeInputKeyEnum,
+  NodeOutputKeyEnum,
+  WorkflowIOValueTypeEnum
+} from '../../../constants';
+import {
+  FlowNodeInputTypeEnum,
+  FlowNodeOutputTypeEnum,
+  FlowNodeTypeEnum
+} from '../../../node/constant';
+import { FlowNodeTemplateType } from '../../../type/node';
+import { getHandleConfig } from '../../utils';
+
+export const ReadFilesNodes: FlowNodeTemplateType = {
+  id: FlowNodeTypeEnum.readFiles,
+  templateType: FlowNodeTemplateTypeEnum.tools,
+  flowNodeType: FlowNodeTypeEnum.readFiles,
+  sourceHandle: getHandleConfig(true, true, true, true),
+  targetHandle: getHandleConfig(true, true, true, true),
+  avatar: 'core/workflow/template/readFiles',
+  name: i18nT('app:workflow.read_files'),
+  intro: i18nT('app:workflow.read_files_tip'),
+  showStatus: true,
+  version: '489',
+  isTool: true,
+  inputs: [
+    {
+      key: NodeInputKeyEnum.fileUrlList,
+      renderTypeList: [FlowNodeInputTypeEnum.reference],
+      valueType: WorkflowIOValueTypeEnum.arrayString,
+      label: i18nT('app:workflow.file_url'),
+      required: true,
+      value: []
+    }
+  ],
+  outputs: [
+    {
+      id: NodeOutputKeyEnum.text,
+      key: NodeOutputKeyEnum.text,
+      label: i18nT('app:workflow.read_files_result'),
+      description: i18nT('app:workflow.read_files_result_desc'),
+      valueType: WorkflowIOValueTypeEnum.string,
+      type: FlowNodeOutputTypeEnum.static
+    }
+  ]
+};

packages/global/core/workflow/template/system/readFiles/type.d.ts

@@ -0,0 +1,4 @@
+export type ReadFileNodeResponse = {
+  url: string;
+  name: string;
+}[];

packages/global/core/workflow/template/system/systemConfig.ts

@@ -2,6 +2,7 @@ import { FlowNodeTypeEnum } from '../../node/constant';
 import { FlowNodeTemplateType } from '../../type/node.d';
 import { FlowNodeTemplateTypeEnum } from '../../constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const SystemConfigNode: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.systemConfig,
@@ -10,8 +11,8 @@ export const SystemConfigNode: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(false, false, false, false),
   targetHandle: getHandleConfig(false, false, false, false),
   avatar: 'core/workflow/template/systemConfig',
-  name: '系统配置',
-  intro: '可以配置应用的系统参数。',
+  name: i18nT('workflow:template.system_config'),
+  intro: '',
   unique: true,
   forbidDelete: true,
   version: '481',

packages/global/core/workflow/template/system/tools.ts

@@ -19,6 +19,7 @@ import {
 import { chatNodeSystemPromptTip } from '../tip';
 import { LLMModelTypeEnum } from '../../../ai/constants';
 import { getHandleConfig } from '../utils';
+import { i18nT } from '../../../../../web/i18n/utils';
 
 export const ToolModule: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.tools,
@@ -27,8 +28,8 @@ export const ToolModule: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(true, true, false, true),
   targetHandle: getHandleConfig(true, true, false, true),
   avatar: 'core/workflow/template/toolCall',
-  name: '工具调用',
-  intro: '通过AI模型自动选择一个或多个功能块进行调用，也可以对插件进行调用。',
+  name: i18nT('workflow:template.tool_call'),
+  intro: i18nT('workflow:template.tool_call_intro'),
   showStatus: true,
   version: '481',
   inputs: [
@@ -41,21 +42,23 @@ export const ToolModule: FlowNodeTemplateType = {
       renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
       label: '',
       value: 0,
-      valueType: WorkflowIOValueTypeEnum.number,
-      min: 0,
-      max: 10,
-      step: 1
+      valueType: WorkflowIOValueTypeEnum.number
     },
     {
       key: NodeInputKeyEnum.aiChatMaxToken,
       renderTypeList: [FlowNodeInputTypeEnum.hidden], // Set in the pop-up window
       label: '',
       value: 2000,
-      valueType: WorkflowIOValueTypeEnum.number,
-      min: 100,
-      max: 4000,
-      step: 50
+      valueType: WorkflowIOValueTypeEnum.number
     },
+    {
+      key: NodeInputKeyEnum.aiChatVision,
+      renderTypeList: [FlowNodeInputTypeEnum.hidden],
+      label: '',
+      valueType: WorkflowIOValueTypeEnum.boolean,
+      value: true
+    },
+
     {
       ...Input_Template_System_Prompt,
       label: 'core.ai.Prompt',

packages/global/core/workflow/template/system/workflowStart.ts

@@ -7,6 +7,17 @@ import {
 } from '../../constants';
 import { getHandleConfig } from '../utils';
 import { Input_Template_UserChatInput } from '../input';
+import { i18nT } from '../../../../../web/i18n/utils';
+import { FlowNodeOutputItemType } from '../../type/io';
+
+export const userFilesInput: FlowNodeOutputItemType = {
+  id: NodeOutputKeyEnum.userFiles,
+  key: NodeOutputKeyEnum.userFiles,
+  label: i18nT('app:workflow.user_file_input'),
+  description: i18nT('app:workflow.user_file_input_desc'),
+  type: FlowNodeOutputTypeEnum.static,
+  valueType: WorkflowIOValueTypeEnum.arrayString
+};
 
 export const WorkflowStart: FlowNodeTemplateType = {
   id: FlowNodeTypeEnum.workflowStart,
@@ -15,7 +26,7 @@ export const WorkflowStart: FlowNodeTemplateType = {
   sourceHandle: getHandleConfig(false, true, false, false),
   targetHandle: getHandleConfig(false, false, false, false),
   avatar: 'core/workflow/template/workflowStart',
-  name: '流程开始',
+  name: i18nT('workflow:template.workflow_start'),
   intro: '',
   forbidDelete: true,
   unique: true,
@@ -25,7 +36,7 @@ export const WorkflowStart: FlowNodeTemplateType = {
     {
       id: NodeOutputKeyEnum.userChatInput,
       key: NodeOutputKeyEnum.userChatInput,
-      label: 'core.module.input.label.user question',
+      label: i18nT('common:core.module.input.label.user question'),
       type: FlowNodeOutputTypeEnum.static,
       valueType: WorkflowIOValueTypeEnum.string
     }

packages/global/core/workflow/type/io.d.ts

@@ -52,6 +52,7 @@ export type FlowNodeInputItemType = InputComponentPropsType & {
 
   // render components params
   canEdit?: boolean; // dynamic inputs
+  isPro?: boolean; // Pro version field
 };
 
 export type FlowNodeOutputItemType = {

packages/global/core/workflow/utils.ts

@@ -82,6 +82,8 @@ export const splitGuideModule = (guideModules?: StoreNodeItemType) => {
     chatInputGuide
   };
 };
+
+// Get app chat config: db > nodes
 export const getAppChatConfig = ({
   chatConfig,
   systemConfigNode,
@@ -125,6 +127,7 @@ export const getAppChatConfig = ({
 
 export const getOrInitModuleInputValue = (input: FlowNodeInputItemType) => {
   if (input.value !== undefined || !input.valueType) return input.value;
+  if (input.defaultValue !== undefined) return input.defaultValue;
 
   const map: Record<string, any> = {
     [WorkflowIOValueTypeEnum.boolean]: false,

packages/global/support/user/auth/constants.ts

@@ -1,11 +1,13 @@
 export enum UserAuthTypeEnum {
   register = 'register',
   findPassword = 'findPassword',
-  wxLogin = 'wxLogin'
+  wxLogin = 'wxLogin',
+  bindNotification = 'bindNotification'
 }
 
 export const userAuthTypeMap = {
   [UserAuthTypeEnum.register]: 'register',
   [UserAuthTypeEnum.findPassword]: 'findPassword',
-  [UserAuthTypeEnum.wxLogin]: 'wxLogin'
+  [UserAuthTypeEnum.wxLogin]: 'wxLogin',
+  [UserAuthTypeEnum.bindNotification]: 'bindNotification'
 };

packages/global/support/user/inform/constants.ts

@@ -15,3 +15,14 @@ export const InformLevelMap = {
     label: '紧急'
   }
 };
+
+export enum SendInformTemplateCodeEnum {
+  EXPIRE_SOON = 'EXPIRE_SOON',
+  EXPIRED = 'EXPIRED',
+  FREE_CLEAN = 'FREE_CLEAN',
+  REGISTER = 'REGISTER',
+  RESET_PASSWORD = 'RESET_PASSWORD',
+  BIND_NOTIFICATION = 'BIND_NOTIFICATION',
+  LACK_OF_POINTS = 'LACK_OF_POINTS',
+  CUSTOM = 'CUSTOM'
+}

packages/global/support/user/inform/type.d.ts

@@ -1,13 +1,16 @@
-import { InformLevelEnum } from './constants';
+import { InformLevelEnum, SendInformTemplateCodeEnum } from './constants';
 
 export type SendInformProps = {
-  title: string;
-  content: string;
   level: `${InformLevelEnum}`;
+  templateCode: `${SendInformTemplateCodeEnum}`;
+  templateParam: Record<string, any>;
+  customLockMinutes?: number; // custom lock minutes
 };
+
 export type SendInform2UserProps = SendInformProps & {
-  tmbId: string;
+  teamId: string;
 };
+
 export type SendInform2User = SendInformProps & {
   type: `${InformTypeEnum}`;
   tmbId: string;

packages/global/support/user/team/type.d.ts

@@ -18,7 +18,9 @@ export type TeamSchema = {
   };
   lafAccount: LafAccountType;
   defaultPermission: PermissionValueType;
+  notificationAccount?: string;
 };
+
 export type tagsType = {
   label: string;
   key: string;
@@ -63,6 +65,7 @@ export type TeamTmbItemType = {
   role: `${TeamMemberRoleEnum}`;
   status: `${TeamMemberStatusEnum}`;
   lafAccount?: LafAccountType;
+  notificationAccount?: string;
   permission: TeamPermission;
 };
 
@@ -72,7 +75,6 @@ export type TeamMemberItemType = {
   teamId: string;
   memberName: string;
   avatar: string;
-  // TODO: this should be deprecated.
   role: `${TeamMemberRoleEnum}`;
   status: `${TeamMemberStatusEnum}`;
   permission: TeamPermission;

packages/global/support/user/type.d.ts

@@ -1,12 +1,10 @@
+import { TeamPermission } from '../permission/user/controller';
 import { UserStatusEnum } from './constant';
 import { TeamTmbItemType } from './team/type';
 
 export type UserModelSchema = {
   _id: string;
   username: string;
-  email?: string;
-  phonePrefix?: number;
-  phone?: string;
   password: string;
   avatar: string;
   promotionRate: number;
@@ -31,4 +29,6 @@ export type UserType = {
   openaiAccount: UserModelSchema['openaiAccount'];
   team: TeamTmbItemType;
   standardInfo?: standardInfoType;
+  notificationAccount?: string;
+  permission: TeamPermission;
 };

packages/global/support/wallet/usage/constants.ts

@@ -1,8 +1,11 @@
+import { i18nT } from '../../../../web/i18n/utils';
+
 export enum UsageSourceEnum {
   fastgpt = 'fastgpt',
   api = 'api',
   shareLink = 'shareLink',
-  training = 'training'
+  training = 'training',
+  cronJob = 'cronJob'
 }
 
 export const UsageSourceMap = {
@@ -17,5 +20,8 @@ export const UsageSourceMap = {
   },
   [UsageSourceEnum.training]: {
     label: 'dataset.Training Name'
+  },
+  [UsageSourceEnum.cronJob]: {
+    label: i18nT('common:cron_job_run_app')
   }
 };

packages/plugins/src/duckduckgo/search/index.ts

@@ -1,6 +1,7 @@
 import { search, SafeSearchType } from 'duck-duck-scrape';
 import { delay } from '@fastgpt/global/common/system/utils';
 import { addLog } from '@fastgpt/service/common/system/log';
+import { getErrText } from '@fastgpt/global/common/error/utils';
 
 type Props = {
   query: string;
@@ -35,7 +36,7 @@ const main = async (props: Props, retry = 3): Response => {
     if (retry <= 0) {
       addLog.warn('DuckDuckGo error', { error });
       return {
-        result: 'Failed to fetch data'
+        result: getErrText(error, 'Failed to fetch data from DuckDuckGo')
       };
     }
 

packages/plugins/src/duckduckgo/searchImg/index.ts

@@ -1,6 +1,7 @@
 import { searchImages, SafeSearchType } from 'duck-duck-scrape';
 import { delay } from '@fastgpt/global/common/system/utils';
 import { addLog } from '@fastgpt/service/common/system/log';
+import { getErrText } from '@fastgpt/global/common/error/utils';
 
 type Props = {
   query: string;
@@ -33,7 +34,7 @@ const main = async (props: Props, retry = 3): Response => {
     if (retry <= 0) {
       addLog.warn('DuckDuckGo error', { error });
       return {
-        result: 'Failed to fetch data'
+        result: getErrText(error, 'Failed to fetch data from DuckDuckGo')
       };
     }
 

packages/plugins/src/duckduckgo/searchNews/index.ts

@@ -1,6 +1,7 @@
 import { searchNews, SafeSearchType } from 'duck-duck-scrape';
 import { delay } from '@fastgpt/global/common/system/utils';
 import { addLog } from '@fastgpt/service/common/system/log';
+import { getErrText } from '@fastgpt/global/common/error/utils';
 
 type Props = {
   query: string;
@@ -34,7 +35,7 @@ const main = async (props: Props, retry = 3): Response => {
     if (retry <= 0) {
       addLog.warn('DuckDuckGo error', { error });
       return {
-        result: 'Failed to fetch data'
+        result: getErrText(error, 'Failed to fetch data from DuckDuckGo')
       };
     }
 

packages/plugins/src/duckduckgo/searchVideo/index.ts

@@ -1,6 +1,7 @@
 import { searchVideos, SafeSearchType } from 'duck-duck-scrape';
 import { delay } from '@fastgpt/global/common/system/utils';
 import { addLog } from '@fastgpt/service/common/system/log';
+import { getErrText } from '@fastgpt/global/common/error/utils';
 
 type Props = {
   query: string;
@@ -34,7 +35,7 @@ const main = async (props: Props, retry = 3): Response => {
     if (retry <= 0) {
       addLog.warn('DuckDuckGo error', { error });
       return {
-        result: 'Failed to fetch data'
+        result: getErrText(error, 'Failed to fetch data from DuckDuckGo')
       };
     }
 

packages/plugins/src/template/template.json

@@ -1,6 +1,6 @@
 {
   "author": "",
-  "version": "486",
+  "version": "489",
   "name": "文本加工",
   "avatar": "/imgs/workflow/textEditor.svg",
   "intro": "可对固定或传入的文本进行加工后输出，非字符串类型数据最终会转成字符串类型。",

packages/service/common/buffer/rawText/schema.ts

@@ -1,5 +1,5 @@
-import { connectionMongo, getMongoModel, type Model } from '../../mongo';
-const { Schema, model, models } = connectionMongo;
+import { connectionMongo, getMongoModel } from '../../mongo';
+const { Schema } = connectionMongo;
 import { RawTextBufferSchemaType } from './type';
 
 export const collectionName = 'buffer_rawtexts';

packages/service/common/file/gridfs/controller.ts

@@ -3,16 +3,19 @@ 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 { MongoFileSchema } from './schema';
+import { MongoChatFileSchema, MongoDatasetFileSchema } from './schema';
 import { detectFileEncoding } from '@fastgpt/global/common/file/tools';
 import { CommonErrEnum } from '@fastgpt/global/common/error/code/common';
 import { MongoRawTextBuffer } from '../../buffer/rawText/schema';
 import { readRawContentByFileBuffer } from '../read/utils';
 import { gridFsStream2Buffer, stream2Encoding } from './utils';
 import { addLog } from '../../system/log';
+import { readFromSecondary } from '../../mongo/utils';
 
 export function getGFSCollection(bucket: `${BucketNameEnum}`) {
-  MongoFileSchema;
+  MongoDatasetFileSchema;
+  MongoChatFileSchema;
+
   return connectionMongo.connection.db.collection(`${bucket}.files`);
 }
 export function getGridBucket(bucket: `${BucketNameEnum}`) {
@@ -49,6 +52,7 @@ export async function uploadFile({
 
   const { stream: readStream, encoding } = await stream2Encoding(fs.createReadStream(path));
 
+  // Add default metadata
   metadata.teamId = teamId;
   metadata.tmbId = tmbId;
   metadata.encoding = encoding;
@@ -103,7 +107,9 @@ export async function delFileByFileIdList({
   try {
     const bucket = getGridBucket(bucketName);
 
-    await Promise.all(fileIdList.map((id) => bucket.delete(new Types.ObjectId(id))));
+    for await (const fileId of fileIdList) {
+      await bucket.delete(new Types.ObjectId(fileId));
+    }
   } catch (error) {
     if (retry > 0) {
       return delFileByFileIdList({ bucketName, fileIdList, retry: retry - 1 });
@@ -138,7 +144,9 @@ export const readFileContentFromMongo = async ({
   filename: string;
 }> => {
   // read buffer
-  const fileBuffer = await MongoRawTextBuffer.findOne({ sourceId: fileId }).lean();
+  const fileBuffer = await MongoRawTextBuffer.findOne({ sourceId: fileId }, undefined, {
+    ...readFromSecondary
+  }).lean();
   if (fileBuffer) {
     return {
       rawText: fileBuffer.rawText,

packages/service/common/file/gridfs/schema.ts

@@ -1,13 +1,17 @@
 import { connectionMongo, getMongoModel, type Model } from '../../mongo';
-const { Schema, model, models } = connectionMongo;
+const { Schema } = connectionMongo;
 
-const FileSchema = new Schema({});
+const DatasetFileSchema = new Schema({});
+const ChatFileSchema = new Schema({});
 
 try {
-  FileSchema.index({ 'metadata.teamId': 1 });
-  FileSchema.index({ 'metadata.uploadDate': -1 });
+  DatasetFileSchema.index({ uploadDate: -1 });
+
+  ChatFileSchema.index({ uploadDate: -1 });
+  ChatFileSchema.index({ 'metadata.chatId': 1 });
 } catch (error) {
   console.log(error);
 }
 
-export const MongoFileSchema = getMongoModel('dataset.files', FileSchema);
+export const MongoDatasetFileSchema = getMongoModel('dataset.files', DatasetFileSchema);
+export const MongoChatFileSchema = getMongoModel('chat.files', ChatFileSchema);

packages/service/common/file/gridfs/utils.ts

@@ -45,7 +45,7 @@ export const stream2Encoding = async (stream: NodeJS.ReadableStream) => {
   })();
 
   const enc = detectFileEncoding(buffer);
-  console.log('Get encoding time', Date.now() - start, enc);
+
   return {
     encoding: enc,
     stream: copyStream

packages/service/common/file/multer.ts

@@ -3,7 +3,6 @@ import multer from 'multer';
 import path from 'path';
 import { BucketNameEnum, bucketNameMap } from '@fastgpt/global/common/file/constants';
 import { getNanoid } from '@fastgpt/global/common/string/tools';
-import { tmpFileDirPath } from './constants';
 
 type FileType = {
   fieldname: string;

packages/service/common/file/read/utils.ts

@@ -8,28 +8,6 @@ import fs from 'fs';
 import { detectFileEncoding } from '@fastgpt/global/common/file/tools';
 import type { ReadFileResponse } from '../../../worker/readFile/type';
 
-// match md img text and upload to db
-export const matchMdImgTextAndUpload = ({
-  teamId,
-  md,
-  metadata
-}: {
-  md: string;
-  teamId: string;
-  metadata?: Record<string, any>;
-}) =>
-  markdownProcess({
-    rawText: md,
-    uploadImgController: (base64Img) =>
-      uploadMongoImg({
-        type: MongoImageTypeEnum.collectionImage,
-        base64Img,
-        teamId,
-        metadata,
-        expiredTime: addHours(new Date(), 2)
-      })
-  });
-
 export type readRawTextByLocalFileParams = {
   teamId: string;
   path: string;
@@ -72,6 +50,28 @@ export const readRawContentByFileBuffer = async ({
   encoding: string;
   metadata?: Record<string, any>;
 }) => {
+  // Upload image in markdown
+  const matchMdImgTextAndUpload = ({
+    teamId,
+    md,
+    metadata
+  }: {
+    md: string;
+    teamId: string;
+    metadata?: Record<string, any>;
+  }) =>
+    markdownProcess({
+      rawText: md,
+      uploadImgController: (base64Img) =>
+        uploadMongoImg({
+          type: MongoImageTypeEnum.collectionImage,
+          base64Img,
+          teamId,
+          metadata,
+          expiredTime: addHours(new Date(), 1)
+        })
+    });
+
   let { rawText, formatText } = await runWorker<ReadFileResponse>(WorkerNameEnum.readFile, {
     extension,
     encoding,

packages/service/common/file/utils.ts

@@ -18,7 +18,17 @@ export const guessBase64ImageType = (str: string) => {
     i: 'image/png',
     R: 'image/gif',
     U: 'image/webp',
-    Q: 'image/bmp'
+    Q: 'image/bmp',
+    P: 'image/svg+xml',
+    T: 'image/tiff',
+    J: 'image/jp2',
+    S: 'image/x-tga',
+    I: 'image/ief',
+    V: 'image/vnd.microsoft.icon',
+    W: 'image/vnd.wap.wbmp',
+    X: 'image/x-xbitmap',
+    Z: 'image/x-xpixmap',
+    Y: 'image/x-xwindowdump'
   };
 
   const defaultType = 'image/jpeg';
@@ -30,6 +40,11 @@ export const guessBase64ImageType = (str: string) => {
   return imageTypeMap[firstChar] || defaultType;
 };
 
+export const getFileContentTypeFromHeader = (header: string): string | undefined => {
+  const contentType = header.split(';')[0];
+  return contentType;
+};
+
 export const clearDirFiles = (dirPath: string) => {
   if (!fs.existsSync(dirPath)) {
     return;

packages/service/common/system/timerLock/constants.ts

@@ -4,14 +4,21 @@ export enum TimerIdEnum {
   checkInvalidVector = 'checkInvalidVector',
   clearExpiredSubPlan = 'clearExpiredSubPlan',
   updateStandardPlan = 'updateStandardPlan',
-  scheduleTriggerApp = 'scheduleTriggerApp'
+  scheduleTriggerApp = 'scheduleTriggerApp',
+  notification = 'notification'
 }
 
-export const timerIdMap = {
-  [TimerIdEnum.checkInValidDatasetFiles]: 'checkInValidDatasetFiles',
-  [TimerIdEnum.checkInvalidDatasetData]: 'checkInvalidDatasetData',
-  [TimerIdEnum.checkInvalidVector]: 'checkInvalidVector',
-  [TimerIdEnum.clearExpiredSubPlan]: 'clearExpiredSubPlan',
-  [TimerIdEnum.updateStandardPlan]: 'updateStandardPlan',
-  [TimerIdEnum.scheduleTriggerApp]: 'scheduleTriggerApp'
-};
+export enum LockNotificationEnum {
+  NotificationExpire = 'notification_expire',
+  NotificationFreeClean = 'notification_free_clean',
+  NotificationLackOfPoints = 'notification_lack_of_points'
+}
+
+export type LockType = `${LockNotificationEnum}`;
+
+// add a new type enum example:
+// export enum ExampleLockEnum {
+//    ExampleLockType1 = 'example_lock_type1'
+// }
+//
+// export type LockType = `${NotificationLockEnum}` | `${ExampleLockEnum}`

packages/service/common/system/timerLock/schema.ts

@@ -1,6 +1,5 @@
-import { connectionMongo, getMongoModel, type Model } from '../../mongo';
-import { timerIdMap } from './constants';
-const { Schema, model, models } = connectionMongo;
+import { connectionMongo, getMongoModel } from '../../mongo';
+const { Schema } = connectionMongo;
 import { TimerLockSchemaType } from './type.d';
 
 export const collectionName = 'systemtimerlocks';
@@ -9,8 +8,7 @@ const TimerLockSchema = new Schema({
   timerId: {
     type: String,
     required: true,
-    unique: true,
-    enum: Object.keys(timerIdMap)
+    unique: true
   },
   expiredTime: {
     type: Date,

packages/service/common/system/timerLock/utils.ts

@@ -1,4 +1,3 @@
-import { TimerIdEnum } from './constants';
 import { MongoTimerLock } from './schema';
 import { addMinutes } from 'date-fns';
 
@@ -9,7 +8,7 @@ export const checkTimerLock = async ({
   timerId,
   lockMinuted
 }: {
-  timerId: TimerIdEnum;
+  timerId: string;
   lockMinuted: number;
 }) => {
   try {

packages/service/common/vectorStore/controller.d.ts

@@ -26,9 +26,7 @@ export type EmbeddingRecallProps = {
   datasetIds: string[];
 
   forbidCollectionIdList: string[];
-  // forbidEmbIndexIdList: string[];
-  // similarity?: number;
-  // efSearch?: number;
+  filterCollectionIdList?: string[];
 };
 export type EmbeddingRecallCtrlProps = EmbeddingRecallProps & {
   vector: number[];

packages/service/common/vectorStore/milvus/class.ts

@@ -213,19 +213,50 @@ export class MilvusCtrl {
   };
   embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
     const client = await this.getClient();
-    const { teamId, datasetIds, vector, limit, forbidCollectionIdList, retry = 2 } = props;
+    const {
+      teamId,
+      datasetIds,
+      vector,
+      limit,
+      forbidCollectionIdList,
+      filterCollectionIdList,
+      retry = 2
+    } = props;
 
+    // Forbid collection
+    const formatForbidCollectionIdList = (() => {
+      if (!filterCollectionIdList) return forbidCollectionIdList;
+      const list = forbidCollectionIdList
+        .map((id) => String(id))
+        .filter((id) => !filterCollectionIdList.includes(id));
+      return list;
+    })();
     const forbidColQuery =
-      forbidCollectionIdList.length > 0
-        ? `and (collectionId not in [${forbidCollectionIdList.map((id) => `"${String(id)}"`).join(',')}])`
+      formatForbidCollectionIdList.length > 0
+        ? `and (collectionId not in [${formatForbidCollectionIdList.map((id) => `"${id}"`).join(',')}])`
         : '';
 
+    // filter collection id
+    const formatFilterCollectionId = (() => {
+      if (!filterCollectionIdList) return;
+      return filterCollectionIdList
+        .map((id) => String(id))
+        .filter((id) => !forbidCollectionIdList.includes(id));
+    })();
+    const collectionIdQuery = formatFilterCollectionId
+      ? `and (collectionId in [${formatFilterCollectionId.map((id) => `"${id}"`)}])`
+      : ``;
+    // Empty data
+    if (formatFilterCollectionId && formatFilterCollectionId.length === 0) {
+      return { results: [] };
+    }
+
     try {
       const { results } = await client.search({
         collection_name: DatasetVectorTableName,
         data: vector,
         limit,
-        filter: `(teamId == "${teamId}") and (datasetId in [${datasetIds.map((id) => `"${String(id)}"`).join(',')}]) ${forbidColQuery}`,
+        filter: `(teamId == "${teamId}") and (datasetId in [${datasetIds.map((id) => `"${id}"`).join(',')}]) ${collectionIdQuery} ${forbidColQuery}`,
         output_fields: ['collectionId']
       });
 

packages/service/common/vectorStore/pg/class.ts

@@ -119,14 +119,44 @@ export class PgVectorCtrl {
     }
   };
   embRecall = async (props: EmbeddingRecallCtrlProps): Promise<EmbeddingRecallResponse> => {
-    const { teamId, datasetIds, vector, limit, forbidCollectionIdList, retry = 2 } = props;
+    const {
+      teamId,
+      datasetIds,
+      vector,
+      limit,
+      forbidCollectionIdList,
+      filterCollectionIdList,
+      retry = 2
+    } = props;
 
+    // Get forbid collection
+    const formatForbidCollectionIdList = (() => {
+      if (!filterCollectionIdList) return forbidCollectionIdList;
+      const list = forbidCollectionIdList
+        .map((id) => String(id))
+        .filter((id) => !filterCollectionIdList.includes(id));
+      return list;
+    })();
     const forbidCollectionSql =
-      forbidCollectionIdList.length > 0
-        ? `AND collection_id NOT IN (${forbidCollectionIdList.map((id) => `'${String(id)}'`).join(',')})`
-        : 'AND collection_id IS NOT NULL';
-    // const forbidDataSql =
-    //   forbidEmbIndexIdList.length > 0 ? `AND id NOT IN (${forbidEmbIndexIdList.join(',')})` : '';
+      formatForbidCollectionIdList.length > 0
+        ? `AND collection_id NOT IN (${formatForbidCollectionIdList.map((id) => `'${id}'`).join(',')})`
+        : '';
+
+    // Filter by collectionId
+    const formatFilterCollectionId = (() => {
+      if (!filterCollectionIdList) return;
+
+      return filterCollectionIdList
+        .map((id) => String(id))
+        .filter((id) => !forbidCollectionIdList.includes(id));
+    })();
+    const filterCollectionIdSql = formatFilterCollectionId
+      ? `AND collection_id IN (${formatFilterCollectionId.map((id) => `'${id}'`).join(',')})`
+      : '';
+    // Empty data
+    if (formatFilterCollectionId && formatFilterCollectionId.length === 0) {
+      return { results: [] };
+    }
 
     try {
       // const explan: any = await PgClient.query(
@@ -150,6 +180,7 @@ export class PgVectorCtrl {
             from ${DatasetVectorTableName} 
             where team_id='${teamId}'
               AND dataset_id IN (${datasetIds.map((id) => `'${String(id)}'`).join(',')})
+              ${filterCollectionIdSql}
               ${forbidCollectionSql}
             order by score limit ${limit};
         COMMIT;`

packages/service/core/ai/functions/createQuestionGuide.ts

@@ -1,6 +1,7 @@
 import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type.d';
 import { getAIApi } from '../config';
 import { countGptMessagesTokens } from '../../../common/string/tiktoken/index';
+import { loadRequestMessages } from '../../chat/utils';
 
 export const Prompt_QuestionGuide = `你是一个AI智能助手，可以回答和解决我的问题。请结合前面的对话记录，帮我生成 3 个问题，引导我继续提问。问题的长度应小于20个字符，按 JSON 格式返回: ["问题1", "问题2", "问题3"]`;
 
@@ -25,7 +26,10 @@ export async function createQuestionGuide({
     model: model,
     temperature: 0.1,
     max_tokens: 200,
-    messages: concatMessages,
+    messages: await loadRequestMessages({
+      messages: concatMessages,
+      useVision: false
+    }),
     stream: false
   });
 

packages/service/core/ai/utils.ts

@@ -0,0 +1,39 @@
+import { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
+import { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type';
+import { countGptMessagesTokens } from '../../common/string/tiktoken';
+
+export const computedMaxToken = async ({
+  maxToken,
+  model,
+  filterMessages = []
+}: {
+  maxToken: number;
+  model: LLMModelItemType;
+  filterMessages: ChatCompletionMessageParam[];
+}) => {
+  maxToken = Math.min(maxToken, model.maxResponse);
+  const tokensLimit = model.maxContext;
+
+  /* count response max token */
+  const promptsToken = await countGptMessagesTokens(filterMessages);
+  maxToken = promptsToken + maxToken > tokensLimit ? tokensLimit - promptsToken : maxToken;
+
+  if (maxToken <= 0) {
+    maxToken = 200;
+  }
+  return maxToken;
+};
+
+// FastGPT temperature range: [0,10], ai temperature:[0,2],{0,1]……
+export const computedTemperature = ({
+  model,
+  temperature
+}: {
+  model: LLMModelItemType;
+  temperature: number;
+}) => {
+  temperature = +(model.maxTemperature * (temperature / 10)).toFixed(2);
+  temperature = Math.max(temperature, 0.01);
+
+  return temperature;
+};

packages/service/core/app/schema.ts

@@ -17,7 +17,8 @@ export const chatConfigType = {
   ttsConfig: Object,
   whisperConfig: Object,
   scheduledTriggerConfig: Object,
-  chatInputGuide: Object
+  chatInputGuide: Object,
+  fileSelectConfig: Object
 };
 
 // schema

packages/service/core/chat/controller.ts

@@ -2,6 +2,9 @@ import type { ChatItemType, ChatItemValueItemType } from '@fastgpt/global/core/c
 import { MongoChatItem } from './chatItemSchema';
 import { addLog } from '../../common/system/log';
 import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
+import { delFileByFileIdList, getGFSCollection } from '../../common/file/gridfs/controller';
+import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
+import { MongoChat } from './chatSchema';
 
 export async function getChatItems({
   appId,
@@ -75,3 +78,40 @@ export const addCustomFeedbacks = async ({
     addLog.error('addCustomFeedbacks error', error);
   }
 };
+
+/* 
+  Delete chat files
+  1. ChatId: Delete one chat files
+  2. AppId: Delete all the app's chat files
+*/
+export const deleteChatFiles = async ({
+  chatIdList,
+  appId
+}: {
+  chatIdList?: string[];
+  appId?: string;
+}) => {
+  if (!appId && !chatIdList) return Promise.reject('appId or chatIdList is required');
+
+  const appChatIdList = await (async () => {
+    if (appId) {
+      const appChatIdList = await MongoChat.find({ appId }, { chatId: 1 });
+      return appChatIdList.map((item) => String(item.chatId));
+    } else if (chatIdList) {
+      return chatIdList;
+    }
+    return [];
+  })();
+
+  const collection = getGFSCollection(BucketNameEnum.chat);
+  const where = {
+    'metadata.chatId': { $in: appChatIdList }
+  };
+
+  const files = await collection.find(where, { projection: { _id: 1 } }).toArray();
+
+  await delFileByFileIdList({
+    bucketName: BucketNameEnum.chat,
+    fileIdList: files.map((item) => String(item._id))
+  });
+};

packages/service/core/chat/utils.ts

@@ -1,13 +1,13 @@
 import { countGptMessagesTokens } from '../../common/string/tiktoken/index';
 import type {
   ChatCompletionContentPart,
-  ChatCompletionMessageParam
+  ChatCompletionMessageParam,
+  SdkChatCompletionMessageParam
 } from '@fastgpt/global/core/ai/type.d';
 import axios from 'axios';
 import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
-import { guessBase64ImageType } from '../../common/file/utils';
+import { getFileContentTypeFromHeader, guessBase64ImageType } from '../../common/file/utils';
 import { serverRequestBaseUrl } from '../../common/api/serverRequest';
-import { cloneDeep } from 'lodash';
 
 /* slice chat context by tokens */
 const filterEmptyMessages = (messages: ChatCompletionMessageParam[]) => {
@@ -96,89 +96,183 @@ export const filterGPTMessageByMaxTokens = async ({
   return filterEmptyMessages([...systemPrompts, ...chats]);
 };
 
-export const formatGPTMessagesInRequestBefore = (messages: ChatCompletionMessageParam[]) => {
-  return messages
-    .map((item) => {
-      if (!item.content) return;
-      if (typeof item.content === 'string') {
-        return {
-          ...item,
-          content: item.content.trim()
-        };
-      }
-
-      // array
-      if (item.content.length === 0) return;
-      if (item.content.length === 1 && item.content[0].type === 'text') {
-        return {
-          ...item,
-          content: item.content[0].text
-        };
-      }
-
-      return item;
-    })
-    .filter(Boolean) as ChatCompletionMessageParam[];
-};
-
-/* Load user chat content.
-  Img: to base 64
+/* 
+  Format requested messages
+  1. If not useVision, only retain text.
+  2. Remove file_url
+  3. If useVision, parse url from question, and load image from url(Local url)
 */
-export const loadChatImgToBase64 = async (content: string | ChatCompletionContentPart[]) => {
-  if (typeof content === 'string') {
-    return content;
-  }
+export const loadRequestMessages = async ({
+  messages,
+  useVision = true,
+  origin
+}: {
+  messages: ChatCompletionMessageParam[];
+  useVision?: boolean;
+  origin?: string;
+}) => {
+  // Split question text and image
+  function parseStringWithImages(input: string): ChatCompletionContentPart[] {
+    if (!useVision) {
+      return [{ type: 'text', text: input || '' }];
+    }
 
-  return Promise.all(
-    content.map(async (item) => {
-      if (item.type === 'text') return item;
+    // 正则表达式匹配图片URL
+    const imageRegex = /(https?:\/\/.*\.(?:png|jpe?g|gif|webp|bmp|tiff?|svg|ico|heic|avif))/i;
 
-      if (!item.image_url.url) return item;
+    const result: { type: 'text' | 'image'; value: string }[] = [];
+    let lastIndex = 0;
+    let match;
 
-      /* 
-        1. From db: Get it from db
-        2. From web: Not update
-      */
-      if (item.image_url.url.startsWith('/')) {
-        const response = await axios.get(item.image_url.url, {
-          baseURL: serverRequestBaseUrl,
-          responseType: 'arraybuffer'
-        });
-        const base64 = Buffer.from(response.data).toString('base64');
-        let imageType = response.headers['content-type'];
-        if (imageType === undefined) {
-          imageType = guessBase64ImageType(base64);
-        }
-        return {
-          ...item,
-          image_url: {
-            ...item.image_url,
-            url: `data:${imageType};base64,${base64}`
-          }
-        };
+    // 使用正则表达式查找所有匹配项
+    while ((match = imageRegex.exec(input.slice(lastIndex))) !== null) {
+      const textBefore = input.slice(lastIndex, lastIndex + match.index);
+
+      // 如果图片URL前有文本，添加文本部分
+      if (textBefore) {
+        result.push({ type: 'text', value: textBefore });
       }
 
-      return item;
-    })
-  );
-};
-export const loadRequestMessages = async (messages: ChatCompletionMessageParam[]) => {
+      // 添加图片URL
+      result.push({ type: 'image', value: match[0] });
+
+      lastIndex += match.index + match[0].length;
+    }
+
+    // 添加剩余的文本（如果有的话）
+    if (lastIndex < input.length) {
+      result.push({ type: 'text', value: input.slice(lastIndex) });
+    }
+
+    return result
+      .map((item) => {
+        if (item.type === 'text') {
+          return { type: 'text', text: item.value };
+        }
+        if (item.type === 'image') {
+          return {
+            type: 'image_url',
+            image_url: {
+              url: item.value
+            }
+          };
+        }
+        return { type: 'text', text: item.value };
+      })
+      .filter(Boolean) as ChatCompletionContentPart[];
+  }
+  // Load image
+  const parseUserContent = async (content: string | ChatCompletionContentPart[]) => {
+    if (typeof content === 'string') {
+      return parseStringWithImages(content);
+    }
+
+    const result = await Promise.all(
+      content.map(async (item) => {
+        if (item.type === 'text') return parseStringWithImages(item.text);
+        if (item.type === 'file_url') return;
+
+        if (!item.image_url.url) return item;
+
+        // Remove url origin
+        const imgUrl = (() => {
+          if (origin && item.image_url.url.startsWith(origin)) {
+            return item.image_url.url.replace(origin, '');
+          }
+          return item.image_url.url;
+        })();
+
+        /* Load local image */
+        if (imgUrl.startsWith('/')) {
+          const response = await axios.get(imgUrl, {
+            baseURL: serverRequestBaseUrl,
+            responseType: 'arraybuffer'
+          });
+          const base64 = Buffer.from(response.data, 'binary').toString('base64');
+          const imageType =
+            getFileContentTypeFromHeader(response.headers['content-type']) ||
+            guessBase64ImageType(base64);
+
+          return {
+            ...item,
+            image_url: {
+              ...item.image_url,
+              url: `data:${imageType};base64,${base64}`
+            }
+          };
+        }
+
+        return item;
+      })
+    );
+
+    return result.flat().filter(Boolean);
+  };
+  // format GPT messages, concat text messages
+  const clearInvalidMessages = (messages: ChatCompletionMessageParam[]) => {
+    return messages
+      .map((item) => {
+        if (item.role === ChatCompletionRequestMessageRoleEnum.System && !item.content) {
+          return;
+        }
+        if (item.role === ChatCompletionRequestMessageRoleEnum.User) {
+          if (!item.content) return;
+
+          if (typeof item.content === 'string') {
+            return {
+              ...item,
+              content: item.content.trim()
+            };
+          }
+
+          // array
+          if (item.content.length === 0) return;
+          if (item.content.length === 1 && item.content[0].type === 'text') {
+            return {
+              ...item,
+              content: item.content[0].text
+            };
+          }
+        }
+
+        return item;
+      })
+      .filter(Boolean) as ChatCompletionMessageParam[];
+  };
+
   if (messages.length === 0) {
     return Promise.reject('core.chat.error.Messages empty');
   }
 
-  const loadMessages = await Promise.all(
-    messages.map(async (item) => {
+  // filter messages file
+  const filterMessages = messages.map((item) => {
+    // If useVision=false, only retain text.
+    if (
+      item.role === ChatCompletionRequestMessageRoleEnum.User &&
+      Array.isArray(item.content) &&
+      !useVision
+    ) {
+      return {
+        ...item,
+        content: item.content.filter((item) => item.type === 'text')
+      };
+    }
+
+    return item;
+  });
+
+  const loadMessages = (await Promise.all(
+    filterMessages.map(async (item) => {
       if (item.role === ChatCompletionRequestMessageRoleEnum.User) {
         return {
           ...item,
-          content: await loadChatImgToBase64(item.content)
+          content: await parseUserContent(item.content)
         };
       } else {
         return item;
       }
     })
-  );
+  )) as ChatCompletionMessageParam[];
 
-  return loadMessages;
+  return clearInvalidMessages(loadMessages) as SdkChatCompletionMessageParam[];
 };

packages/service/core/dataset/collection/controller.ts

@@ -1,7 +1,4 @@
-import {
-  TrainingModeEnum,
-  DatasetCollectionTypeEnum
-} from '@fastgpt/global/core/dataset/constants';
+import { TrainingModeEnum } from '@fastgpt/global/core/dataset/constants';
 import type { CreateDatasetCollectionParams } from '@fastgpt/global/core/dataset/api.d';
 import { MongoDatasetCollection } from './schema';
 import {
@@ -15,6 +12,7 @@ import { deleteDatasetDataVector } from '../../../common/vectorStore/controller'
 import { delFileByFileIdList } from '../../../common/file/gridfs/controller';
 import { BucketNameEnum } from '@fastgpt/global/common/file/constants';
 import { ClientSession } from '../../../common/mongo';
+import { createOrGetCollectionTags } from './utils';
 
 export async function createOneCollection({
   teamId,
@@ -39,6 +37,7 @@ export async function createOneCollection({
   rawTextLength,
   metadata = {},
   session,
+  tags,
   ...props
 }: CreateDatasetCollectionParams & {
   teamId: string;
@@ -46,6 +45,7 @@ export async function createOneCollection({
   [key: string]: any;
   session?: ClientSession;
 }) {
+  const collectionTags = await createOrGetCollectionTags({ tags, teamId, datasetId, session });
   const [collection] = await MongoDatasetCollection.create(
     [
       {
@@ -69,7 +69,8 @@ export async function createOneCollection({
 
         rawTextLength,
         hashRawText,
-        metadata
+        metadata,
+        tags: collectionTags
       }
     ],
     { session }

packages/service/core/dataset/collection/schema.ts

@@ -106,8 +106,10 @@ try {
     updateTime: -1
   });
 
-  // get forbid
-  // DatasetCollectionSchema.index({ teamId: 1, datasetId: 1, forbid: 1 });
+  // Tag filter
+  DatasetCollectionSchema.index({ teamId: 1, datasetId: 1, tags: 1 });
+  // create time filter
+  DatasetCollectionSchema.index({ teamId: 1, datasetId: 1, createTime: 1 });
 } catch (error) {
   console.log(error);
 }

packages/service/core/dataset/collection/utils.ts

@@ -11,6 +11,7 @@ import {
 import { hashStr } from '@fastgpt/global/common/string/tools';
 import { ClientSession } from '../../../common/mongo';
 import { PushDatasetDataResponse } from '@fastgpt/global/core/dataset/api';
+import { MongoDatasetCollectionTags } from '../tag/schema';
 
 /**
  * get all collection by top collectionId
@@ -200,3 +201,36 @@ export const reloadCollectionChunks = async ({
     insertLen: result.length
   };
 };
+
+export const createOrGetCollectionTags = async ({
+  tags = [],
+  datasetId,
+  teamId,
+  session
+}: {
+  tags?: string[];
+  datasetId: string;
+  teamId: string;
+  session?: ClientSession;
+}): Promise<string[]> => {
+  if (!tags.length) return [];
+  const existingTags = await MongoDatasetCollectionTags.find({
+    teamId,
+    datasetId,
+    $expr: { $in: ['$tag', tags] }
+  });
+
+  const existingTagContents = existingTags.map((tag) => tag.tag);
+  const newTagContents = tags.filter((tag) => !existingTagContents.includes(tag));
+
+  const newTags = await MongoDatasetCollectionTags.insertMany(
+    newTagContents.map((tagContent) => ({
+      teamId,
+      datasetId,
+      tag: tagContent
+    })),
+    { session }
+  );
+
+  return [...existingTags.map((tag) => tag._id), ...newTags.map((tag) => tag._id)];
+};

packages/service/core/dataset/search/controller.ts

@@ -20,6 +20,9 @@ import { hashStr } from '@fastgpt/global/common/string/tools';
 import { jiebaSplit } from '../../../common/string/jieba';
 import { getCollectionSourceData } from '@fastgpt/global/core/dataset/collection/utils';
 import { Types } from '../../../common/mongo';
+import json5 from 'json5';
+import { MongoDatasetCollectionTags } from '../tag/schema';
+import { readFromSecondary } from '../../../common/mongo/utils';
 
 type SearchDatasetDataProps = {
   teamId: string;
@@ -31,6 +34,20 @@ type SearchDatasetDataProps = {
   usingReRank?: boolean;
   reRankQuery: string;
   queries: string[];
+
+  /* 
+    {
+      tags: {
+        $and: ["str1","str2"],
+        $or: ["str1","str2",null] null means no tags
+      },
+      createTime: {
+        $gte: 'xx',
+        $lte: 'xxx'
+      }
+    }
+  */
+  collectionFilterMatch?: string;
 };
 
 export async function searchDatasetData(props: SearchDatasetDataProps) {
@@ -43,7 +60,8 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
     limit: maxTokens,
     searchMode = DatasetSearchModeEnum.embedding,
     usingReRank = false,
-    datasetIds = []
+    datasetIds = [],
+    collectionFilterMatch
   } = props;
 
   /* init params */
@@ -87,14 +105,148 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
       forbidCollectionIdList: collections.map((item) => String(item._id))
     };
   };
+  /* 
+    Collection metadata filter
+    标签过滤：
+    1. and 先生效
+    2. and 标签和 null 不能共存，否则返回空数组
+  */
+  const filterCollectionByMetadata = async (): Promise<string[] | undefined> => {
+    if (!collectionFilterMatch || !global.feConfigs.isPlus) return;
+
+    let tagCollectionIdList: string[] | undefined = undefined;
+    let createTimeCollectionIdList: string[] | undefined = undefined;
+
+    try {
+      const jsonMatch = json5.parse(collectionFilterMatch);
+
+      // Tag
+      let andTags = jsonMatch?.tags?.$and as (string | null)[] | undefined;
+      let orTags = jsonMatch?.tags?.$or as (string | null)[] | undefined;
+
+      // get andTagIds
+      if (andTags && andTags.length > 0) {
+        // tag 去重
+        andTags = Array.from(new Set(andTags));
+
+        if (andTags.includes(null) && andTags.some((tag) => typeof tag === 'string')) {
+          return [];
+        }
+
+        if (andTags.every((tag) => typeof tag === 'string')) {
+          // Get tagId by tag string
+          const andTagIdList = await MongoDatasetCollectionTags.find(
+            {
+              teamId,
+              datasetId: { $in: datasetIds },
+              tag: { $in: andTags }
+            },
+            '_id',
+            {
+              ...readFromSecondary
+            }
+          ).lean();
+
+          // If you enter a tag that does not exist, none will be found
+          if (andTagIdList.length !== andTags.length) return [];
+
+          // Get collectionId by tagId
+          const collections = await MongoDatasetCollection.find(
+            {
+              teamId,
+              datasetId: { $in: datasetIds },
+              tags: { $all: andTagIdList.map((item) => String(item._id)) }
+            },
+            '_id',
+            {
+              ...readFromSecondary
+            }
+          ).lean();
+          tagCollectionIdList = collections.map((item) => String(item._id));
+        } else if (andTags.every((tag) => tag === null)) {
+          const collections = await MongoDatasetCollection.find(
+            {
+              teamId,
+              datasetId: { $in: datasetIds },
+              $or: [{ tags: { $size: 0 } }, { tags: { $exists: false } }]
+            },
+            '_id',
+            {
+              ...readFromSecondary
+            }
+          ).lean();
+          tagCollectionIdList = collections.map((item) => String(item._id));
+        }
+      } else if (orTags && orTags.length > 0) {
+        // Get tagId by tag string
+        const orTagArray = await MongoDatasetCollectionTags.find(
+          {
+            teamId,
+            datasetId: { $in: datasetIds },
+            tag: { $in: orTags.filter((tag) => tag !== null) }
+          },
+          '_id',
+          { ...readFromSecondary }
+        ).lean();
+        const orTagIds = orTagArray.map((item) => String(item._id));
+
+        // Get collections by tagId
+        const collections = await MongoDatasetCollection.find(
+          {
+            teamId,
+            datasetId: { $in: datasetIds },
+            $or: [
+              { tags: { $in: orTagIds } },
+              ...(orTags.includes(null) ? [{ tags: { $size: 0 } }] : [])
+            ]
+          },
+          '_id',
+          { ...readFromSecondary }
+        ).lean();
+
+        tagCollectionIdList = collections.map((item) => String(item._id));
+      }
+
+      // time
+      const getCreateTime = jsonMatch?.createTime?.$gte as string | undefined;
+      const lteCreateTime = jsonMatch?.createTime?.$lte as string | undefined;
+      if (getCreateTime || lteCreateTime) {
+        const collections = await MongoDatasetCollection.find(
+          {
+            teamId,
+            datasetId: { $in: datasetIds },
+            createTime: {
+              ...(getCreateTime && { $gte: new Date(getCreateTime) }),
+              ...(lteCreateTime && {
+                $lte: new Date(lteCreateTime)
+              })
+            }
+          },
+          '_id'
+        );
+        createTimeCollectionIdList = collections.map((item) => String(item._id));
+      }
+
+      // Concat tag and time
+      if (tagCollectionIdList && createTimeCollectionIdList) {
+        return tagCollectionIdList.filter((id) => createTimeCollectionIdList!.includes(id));
+      } else if (tagCollectionIdList) {
+        return tagCollectionIdList;
+      } else if (createTimeCollectionIdList) {
+        return createTimeCollectionIdList;
+      }
+    } catch (error) {}
+  };
   const embeddingRecall = async ({
     query,
     limit,
-    forbidCollectionIdList
+    forbidCollectionIdList,
+    filterCollectionIdList
   }: {
     query: string;
     limit: number;
     forbidCollectionIdList: string[];
+    filterCollectionIdList?: string[];
   }) => {
     const { vectors, tokens } = await getVectorsByText({
       model: getVectorModel(model),
@@ -107,7 +259,8 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
       datasetIds,
       vector: vectors[0],
       limit,
-      forbidCollectionIdList
+      forbidCollectionIdList,
+      filterCollectionIdList
     });
 
     // get q and a
@@ -165,10 +318,12 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
   };
   const fullTextRecall = async ({
     query,
-    limit
+    limit,
+    filterCollectionIdList
   }: {
     query: string;
     limit: number;
+    filterCollectionIdList?: string[];
   }): Promise<{
     fullTextRecallResults: SearchDataResponseItemType[];
     tokenLen: number;
@@ -188,7 +343,14 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
               $match: {
                 teamId: new Types.ObjectId(teamId),
                 datasetId: new Types.ObjectId(id),
-                $text: { $search: jiebaSplit({ text: query }) }
+                $text: { $search: jiebaSplit({ text: query }) },
+                ...(filterCollectionIdList && filterCollectionIdList.length > 0
+                  ? {
+                      collectionId: {
+                        $in: filterCollectionIdList.map((id) => new Types.ObjectId(id))
+                      }
+                    }
+                  : {})
               }
             },
             {
@@ -327,7 +489,10 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
     const fullTextRecallResList: SearchDataResponseItemType[][] = [];
     let totalTokens = 0;
 
-    const { forbidCollectionIdList } = await getForbidData();
+    const [{ forbidCollectionIdList }, filterCollectionIdList] = await Promise.all([
+      getForbidData(),
+      filterCollectionByMetadata()
+    ]);
 
     await Promise.all(
       queries.map(async (query) => {
@@ -335,11 +500,13 @@ export async function searchDatasetData(props: SearchDatasetDataProps) {
           embeddingRecall({
             query,
             limit: embeddingLimit,
-            forbidCollectionIdList
+            forbidCollectionIdList,
+            filterCollectionIdList
           }),
           fullTextRecall({
             query,
-            limit: fullTextLimit
+            limit: fullTextLimit,
+            filterCollectionIdList
           })
         ]);
         totalTokens += tokens;

packages/service/core/dataset/tag/schema.ts

@@ -0,0 +1,35 @@
+import { TeamCollectionName } from '@fastgpt/global/support/user/team/constant';
+import { connectionMongo, getMongoModel, type Model } from '../../../common/mongo';
+import { DatasetCollectionName } from '../schema';
+import { DatasetCollectionTagsSchemaType } from '@fastgpt/global/core/dataset/type';
+const { Schema } = connectionMongo;
+
+export const DatasetCollectionTagsName = 'dataset_collection_tags';
+
+const DatasetCollectionTagsSchema = new Schema({
+  teamId: {
+    type: Schema.Types.ObjectId,
+    ref: TeamCollectionName,
+    required: true
+  },
+  datasetId: {
+    type: Schema.Types.ObjectId,
+    ref: DatasetCollectionName,
+    required: true
+  },
+  tag: {
+    type: String,
+    required: true
+  }
+});
+
+try {
+  DatasetCollectionTagsSchema.index({ teamId: 1, datasetId: 1, tag: 1 });
+} catch (error) {
+  console.log(error);
+}
+
+export const MongoDatasetCollectionTags = getMongoModel<DatasetCollectionTagsSchemaType>(
+  DatasetCollectionTagsName,
+  DatasetCollectionTagsSchema
+);

packages/service/core/workflow/dispatch/agent/classifyQuestion.ts

@@ -16,6 +16,7 @@ import { formatModelChars2Points } from '../../../../support/wallet/usage/utils'
 import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
 import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
 import { getHandleId } from '@fastgpt/global/core/workflow/utils';
+import { loadRequestMessages } from '../../../chat/utils';
 
 type Props = ModuleDispatchProps<{
   [NodeInputKeyEnum.aiModel]: string;
@@ -113,6 +114,10 @@ const completions = async ({
       ]
     }
   ];
+  const requestMessages = await loadRequestMessages({
+    messages: chats2GPTMessages({ messages, reserveId: false }),
+    useVision: false
+  });
 
   const ai = getAIApi({
     userKey: user.openaiAccount,
@@ -122,7 +127,7 @@ const completions = async ({
   const data = await ai.chat.completions.create({
     model: cqModel.model,
     temperature: 0.01,
-    messages: chats2GPTMessages({ messages, reserveId: false }),
+    messages: requestMessages,
     stream: false
   });
   const answer = data.choices?.[0].message?.content || '';

packages/service/core/workflow/dispatch/agent/extract.ts

@@ -1,5 +1,5 @@
 import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
-import { filterGPTMessageByMaxTokens } from '../../../chat/utils';
+import { filterGPTMessageByMaxTokens, loadRequestMessages } from '../../../chat/utils';
 import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
 import {
   countMessagesTokens,
@@ -173,6 +173,10 @@ ${description ? `- ${description}` : ''}
     messages: adaptMessages,
     maxTokens: extractModel.maxContext
   });
+  const requestMessages = await loadRequestMessages({
+    messages: filterMessages,
+    useVision: false
+  });
 
   const properties: Record<
     string,
@@ -200,7 +204,7 @@ ${description ? `- ${description}` : ''}
   };
 
   return {
-    filterMessages,
+    filterMessages: requestMessages,
     agentFunction
   };
 };
@@ -224,7 +228,7 @@ const toolChoice = async (props: ActionProps) => {
 
   const response = await ai.chat.completions.create({
     model: extractModel.model,
-    temperature: 0,
+    temperature: 0.01,
     messages: filterMessages,
     tools,
     tool_choice: { type: 'function', function: { name: agentFunName } }
@@ -269,7 +273,7 @@ const functionCall = async (props: ActionProps) => {
 
   const response = await ai.chat.completions.create({
     model: extractModel.model,
-    temperature: 0,
+    temperature: 0.01,
     messages: filterMessages,
     function_call: {
       name: agentFunName
@@ -338,6 +342,10 @@ Human: ${content}`
       ]
     }
   ];
+  const requestMessages = await loadRequestMessages({
+    messages: chats2GPTMessages({ messages, reserveId: false }),
+    useVision: false
+  });
 
   const ai = getAIApi({
     userKey: user.openaiAccount,
@@ -346,7 +354,7 @@ Human: ${content}`
   const data = await ai.chat.completions.create({
     model: extractModel.model,
     temperature: 0.01,
-    messages: chats2GPTMessages({ messages, reserveId: false }),
+    messages: requestMessages,
     stream: false
   });
   const answer = data.choices?.[0].message?.content || '';

packages/service/core/workflow/dispatch/agent/runTool/constants.ts

@@ -1,3 +1,5 @@
+import { replaceVariable } from '@fastgpt/global/common/string/tools';
+
 export const Prompt_Tool_Call = `<Instruction>
 你是一个智能机器人，除了可以回答用户问题外，你还掌握工具的使用能力。有时候，你可以依赖工具的运行结果，来更准确的回答用户。
 
@@ -32,6 +34,8 @@ TOOL_RESPONSE: """
 ANSWER: 0: 今天杭州是晴天，适合去西湖、灵隐寺、千岛湖等地玩。
 </Instruction>
 
+------
+
 现在，我们开始吧！下面是你本次可以使用的工具：
 
 """
@@ -42,3 +46,16 @@ ANSWER: 0: 今天杭州是晴天，适合去西湖、灵隐寺、千岛湖等地
 
 USER: {{question}}
 ANSWER: `;
+
+export const getMultiplePrompt = (obj: {
+  fileCount: number;
+  imgCount: number;
+  question: string;
+}) => {
+  const prompt = `Number of session file inputs：
+Document：{{fileCount}}
+Image：{{imgCount}}
+------
+{{question}}`;
+  return replaceVariable(prompt, obj);
+};

packages/service/core/workflow/dispatch/agent/runTool/functionCall.ts

@@ -9,7 +9,7 @@ import {
   ChatCompletionMessageFunctionCall,
   ChatCompletionFunctionMessageParam,
   ChatCompletionAssistantMessageParam
-} from '@fastgpt/global/core/ai/type';
+} from '@fastgpt/global/core/ai/type.d';
 import { NextApiResponse } from 'next';
 import {
   responseWrite,
@@ -24,10 +24,11 @@ import { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './ty
 import json5 from 'json5';
 import { DispatchFlowResponse } from '../../type';
 import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/index';
-import { getNanoid } from '@fastgpt/global/common/string/tools';
+import { getNanoid, sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
 import { AIChatItemType } from '@fastgpt/global/core/chat/type';
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import { updateToolInputValue } from './utils';
+import { computedMaxToken, computedTemperature } from '../../../../ai/utils';
 
 type FunctionRunResponseType = {
   toolRunResponse: DispatchFlowResponse;
@@ -42,7 +43,18 @@ export const runToolWithFunctionCall = async (
   },
   response?: RunToolResponse
 ): Promise<RunToolResponse> => {
-  const { toolModel, toolNodes, messages, res, runtimeNodes, detail = false, node, stream } = props;
+  const {
+    toolModel,
+    toolNodes,
+    messages,
+    res,
+    requestOrigin,
+    runtimeNodes,
+    detail = false,
+    node,
+    stream,
+    params: { temperature = 0, maxToken = 4000, aiChatVision }
+  } = props;
   const assistantResponses = response?.assistantResponses || [];
 
   const functions: ChatCompletionCreateParams.Function[] = toolNodes.map((item) => {
@@ -72,44 +84,60 @@ export const runToolWithFunctionCall = async (
     };
   });
 
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages,
-    maxTokens: toolModel.maxContext - 500 // filter token. not response maxToken
-  });
-  const formativeMessages = filterMessages.map((item) => {
+  const filterMessages = (
+    await filterGPTMessageByMaxTokens({
+      messages,
+      maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
+    })
+  ).map((item) => {
     if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant && item.function_call) {
       return {
         ...item,
         function_call: {
           name: item.function_call?.name,
           arguments: item.function_call?.arguments
-        }
+        },
+        content: ''
       };
     }
     return item;
   });
-  const requestMessages = await loadRequestMessages(formativeMessages);
+  const [requestMessages, max_tokens] = await Promise.all([
+    loadRequestMessages({
+      messages: filterMessages,
+      useVision: toolModel.vision && aiChatVision,
+      origin: requestOrigin
+    }),
+    computedMaxToken({
+      model: toolModel,
+      maxToken,
+      filterMessages
+    })
+  ]);
+  const requestBody: any = {
+    ...toolModel?.defaultConfig,
+    model: toolModel.model,
+    temperature: computedTemperature({
+      model: toolModel,
+      temperature
+    }),
+    max_tokens,
+    stream,
+    messages: requestMessages,
+    functions,
+    function_call: 'auto'
+  };
 
+  // console.log(JSON.stringify(requestBody, null, 2));
   /* Run llm */
   const ai = getAIApi({
     timeout: 480000
   });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: requestMessages,
-      functions,
-      function_call: 'auto'
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
+  const aiResponse = await ai.chat.completions.create(requestBody, {
+    headers: {
+      Accept: 'application/json, text/plain, */*'
     }
-  );
+  });
 
   const { answer, functionCalls } = await (async () => {
     if (res && stream) {
@@ -198,7 +226,7 @@ export const runToolWithFunctionCall = async (
                 toolName: '',
                 toolAvatar: '',
                 params: '',
-                response: stringToolResponse
+                response: sliceStrStartEnd(stringToolResponse, 300, 300)
               }
             })
           });
@@ -222,7 +250,7 @@ export const runToolWithFunctionCall = async (
       function_call: functionCall
     };
     const concatToolMessages = [
-      ...filterMessages,
+      ...requestMessages,
       assistantToolMsgParams
     ] as ChatCompletionMessageParam[];
     const tokens = await countGptMessagesTokens(concatToolMessages, undefined, functions);

packages/service/core/workflow/dispatch/agent/runTool/index.ts

@@ -8,7 +8,7 @@ import { ModelTypeEnum, getLLMModel } from '../../../../ai/model';
 import { filterToolNodeIdByEdges, getHistories } from '../../utils';
 import { runToolWithToolChoice } from './toolChoice';
 import { DispatchToolModuleProps, ToolNodeItemType } from './type.d';
-import { ChatItemType } from '@fastgpt/global/core/chat/type';
+import { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/core/chat/type';
 import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
 import {
   GPTMessages2Chats,
@@ -22,12 +22,46 @@ import { getHistoryPreview } from '@fastgpt/global/core/chat/utils';
 import { runToolWithFunctionCall } from './functionCall';
 import { runToolWithPromptCall } from './promptCall';
 import { replaceVariable } from '@fastgpt/global/common/string/tools';
-import { Prompt_Tool_Call } from './constants';
+import { getMultiplePrompt, Prompt_Tool_Call } from './constants';
+import { filterToolResponseToPreview } from './utils';
 
 type Response = DispatchNodeResultType<{
   [NodeOutputKeyEnum.answerText]: string;
 }>;
 
+/* 
+  Tool call， auth add file prompt to question。
+  Guide the LLM to call tool.
+*/
+export const toolCallMessagesAdapt = ({
+  userInput
+}: {
+  userInput: UserChatItemValueItemType[];
+}) => {
+  const files = userInput.filter((item) => item.type === 'file');
+
+  if (files.length > 0) {
+    return userInput.map((item) => {
+      if (item.type === 'text') {
+        const filesCount = files.filter((file) => file.file?.type === 'file').length;
+        const imgCount = files.filter((file) => file.file?.type === 'image').length;
+        const text = item.text?.content || '';
+
+        return {
+          ...item,
+          text: {
+            content: getMultiplePrompt({ fileCount: filesCount, imgCount, question: text })
+          }
+        };
+      }
+
+      return item;
+    });
+  }
+
+  return userInput;
+};
+
 export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<Response> => {
   const {
     node: { nodeId, name },
@@ -62,16 +96,31 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
 
   const messages: ChatItemType[] = [
     ...getSystemPrompt(systemPrompt),
-    ...chatHistories,
+    // Add file input prompt to histories
+    ...chatHistories.map((item) => {
+      if (item.obj === ChatRoleEnum.Human) {
+        return {
+          ...item,
+          value: toolCallMessagesAdapt({
+            userInput: item.value
+          })
+        };
+      }
+      return item;
+    }),
     {
       obj: ChatRoleEnum.Human,
-      value: runtimePrompt2ChatsValue({
-        text: userChatInput,
-        files: chatValue2RuntimePrompt(query).files
+      value: toolCallMessagesAdapt({
+        userInput: runtimePrompt2ChatsValue({
+          text: userChatInput,
+          files: chatValue2RuntimePrompt(query).files
+        })
       })
     }
   ];
 
+  // console.log(JSON.stringify(messages, null, 2));
+
   const {
     dispatchFlowResponse, // tool flow response
     totalTokens,
@@ -98,14 +147,24 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
     }
 
     const lastMessage = adaptMessages[adaptMessages.length - 1];
-    if (typeof lastMessage.content !== 'string') {
-      return Promise.reject('暂时只支持纯文本');
+    if (typeof lastMessage.content === 'string') {
+      lastMessage.content = replaceVariable(Prompt_Tool_Call, {
+        question: lastMessage.content
+      });
+    } else if (Array.isArray(lastMessage.content)) {
+      // array, replace last element
+      const lastText = lastMessage.content[lastMessage.content.length - 1];
+      if (lastText.type === 'text') {
+        lastMessage.content = replaceVariable(Prompt_Tool_Call, {
+          question: lastText.text
+        });
+      } else {
+        return Promise.reject('Prompt call invalid input');
+      }
+    } else {
+      return Promise.reject('Prompt call invalid input');
     }
 
-    lastMessage.content = replaceVariable(Prompt_Tool_Call, {
-      question: userChatInput
-    });
-
     return runToolWithPromptCall({
       ...props,
       toolNodes,
@@ -132,12 +191,14 @@ export const dispatchRunTools = async (props: DispatchToolModuleProps): Promise<
     }, 0);
   const flatUsages = dispatchFlowResponse.map((item) => item.flowUsages).flat();
 
+  const previewAssistantResponses = filterToolResponseToPreview(assistantResponses);
+
   return {
-    [NodeOutputKeyEnum.answerText]: assistantResponses
+    [NodeOutputKeyEnum.answerText]: previewAssistantResponses
       .filter((item) => item.text?.content)
       .map((item) => item.text?.content || '')
       .join(''),
-    [DispatchNodeResponseKeyEnum.assistantResponses]: assistantResponses,
+    [DispatchNodeResponseKeyEnum.assistantResponses]: previewAssistantResponses,
     [DispatchNodeResponseKeyEnum.nodeResponse]: {
       totalPoints: totalPointsUsage,
       toolCallTokens: totalTokens,

packages/service/core/workflow/dispatch/agent/runTool/promptCall.ts

@@ -20,10 +20,16 @@ import { dispatchWorkFlow } from '../../index';
 import { DispatchToolModuleProps, RunToolResponse, ToolNodeItemType } from './type.d';
 import json5 from 'json5';
 import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/index';
-import { getNanoid, replaceVariable, sliceJsonStr } from '@fastgpt/global/common/string/tools';
+import {
+  getNanoid,
+  replaceVariable,
+  sliceJsonStr,
+  sliceStrStartEnd
+} from '@fastgpt/global/common/string/tools';
 import { AIChatItemType } from '@fastgpt/global/core/chat/type';
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import { updateToolInputValue } from './utils';
+import { computedMaxToken, computedTemperature } from '../../../../ai/utils';
 
 type FunctionCallCompletion = {
   id: string;
@@ -43,7 +49,18 @@ export const runToolWithPromptCall = async (
   },
   response?: RunToolResponse
 ): Promise<RunToolResponse> => {
-  const { toolModel, toolNodes, messages, res, runtimeNodes, detail = false, node, stream } = props;
+  const {
+    toolModel,
+    toolNodes,
+    messages,
+    res,
+    requestOrigin,
+    runtimeNodes,
+    detail = false,
+    node,
+    stream,
+    params: { temperature = 0, maxToken = 4000, aiChatVision }
+  } = props;
   const assistantResponses = response?.assistantResponses || [];
 
   const toolsPrompt = JSON.stringify(
@@ -77,7 +94,7 @@ export const runToolWithPromptCall = async (
 
   const lastMessage = messages[messages.length - 1];
   if (typeof lastMessage.content !== 'string') {
-    return Promise.reject('暂时只支持纯文本');
+    return Promise.reject('Prompt call invalid input');
   }
   lastMessage.content = replaceVariable(lastMessage.content, {
     toolsPrompt
@@ -87,27 +104,40 @@ export const runToolWithPromptCall = async (
     messages,
     maxTokens: toolModel.maxContext - 500 // filter token. not response maxToken
   });
-  const requestMessages = await loadRequestMessages(filterMessages);
+  const [requestMessages, max_tokens] = await Promise.all([
+    loadRequestMessages({
+      messages: filterMessages,
+      useVision: toolModel.vision && aiChatVision,
+      origin: requestOrigin
+    }),
+    computedMaxToken({
+      model: toolModel,
+      maxToken,
+      filterMessages
+    })
+  ]);
+  const requestBody = {
+    ...toolModel?.defaultConfig,
+    model: toolModel.model,
+    temperature: computedTemperature({
+      model: toolModel,
+      temperature
+    }),
+    max_tokens,
+    stream,
+    messages: requestMessages
+  };
 
-  // console.log(JSON.stringify(filterMessages, null, 2));
+  // console.log(JSON.stringify(requestBody, null, 2));
   /* Run llm */
   const ai = getAIApi({
     timeout: 480000
   });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: requestMessages
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
+  const aiResponse = await ai.chat.completions.create(requestBody, {
+    headers: {
+      Accept: 'application/json, text/plain, */*'
     }
-  );
+  });
 
   const answer = await (async () => {
     if (res && stream) {
@@ -225,7 +255,7 @@ export const runToolWithPromptCall = async (
             toolName: '',
             toolAvatar: '',
             params: '',
-            response: stringToolResponse
+            response: sliceStrStartEnd(stringToolResponse, 300, 300)
           }
         })
       });
@@ -250,7 +280,7 @@ export const runToolWithPromptCall = async (
     function_call: toolJson
   };
   const concatToolMessages = [
-    ...filterMessages,
+    ...requestMessages,
     assistantToolMsgParams
   ] as ChatCompletionMessageParam[];
   const tokens = await countGptMessagesTokens(concatToolMessages, undefined);

packages/service/core/workflow/dispatch/agent/runTool/toolChoice.ts

@@ -28,6 +28,8 @@ import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/in
 import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
 import { AIChatItemType } from '@fastgpt/global/core/chat/type';
 import { updateToolInputValue } from './utils';
+import { computedMaxToken, computedTemperature } from '../../../../ai/utils';
+import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
 
 type ToolRunResponseType = {
   toolRunResponse: DispatchFlowResponse;
@@ -49,7 +51,18 @@ export const runToolWithToolChoice = async (
   },
   response?: RunToolResponse
 ): Promise<RunToolResponse> => {
-  const { toolModel, toolNodes, messages, res, runtimeNodes, detail = false, node, stream } = props;
+  const {
+    toolModel,
+    toolNodes,
+    messages,
+    res,
+    requestOrigin,
+    runtimeNodes,
+    detail = false,
+    node,
+    stream,
+    params: { temperature = 0, maxToken = 4000, aiChatVision }
+  } = props;
   const assistantResponses = response?.assistantResponses || [];
 
   const tools: ChatCompletionTool[] = toolNodes.map((item) => {
@@ -81,12 +94,13 @@ export const runToolWithToolChoice = async (
       }
     };
   });
-
-  const filterMessages = await filterGPTMessageByMaxTokens({
-    messages,
-    maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
-  });
-  const formativeMessages = filterMessages.map((item) => {
+  // Filter histories by maxToken
+  const filterMessages = (
+    await filterGPTMessageByMaxTokens({
+      messages,
+      maxTokens: toolModel.maxContext - 300 // filter token. not response maxToken
+    })
+  ).map((item) => {
     if (item.role === 'assistant' && item.tool_calls) {
       return {
         ...item,
@@ -99,43 +113,43 @@ export const runToolWithToolChoice = async (
     }
     return item;
   });
-  const requestMessages = await loadRequestMessages(formativeMessages);
 
-  // console.log(
-  //   JSON.stringify(
-  //     {
-  //       ...toolModel?.defaultConfig,
-  //       model: toolModel.model,
-  //       temperature: 0,
-  //       stream,
-  //       messages: requestMessages,
-  //       tools,
-  //       tool_choice: 'auto'
-  //     },
-  //     null,
-  //     2
-  //   )
-  // );
+  const [requestMessages, max_tokens] = await Promise.all([
+    loadRequestMessages({
+      messages: filterMessages,
+      useVision: toolModel.vision && aiChatVision,
+      origin: requestOrigin
+    }),
+    computedMaxToken({
+      model: toolModel,
+      maxToken,
+      filterMessages
+    })
+  ]);
+  const requestBody: any = {
+    ...toolModel?.defaultConfig,
+    model: toolModel.model,
+    temperature: computedTemperature({
+      model: toolModel,
+      temperature
+    }),
+    max_tokens,
+    stream,
+    messages: requestMessages,
+    tools,
+    tool_choice: 'auto'
+  };
+
+  // console.log(JSON.stringify(requestBody, null, 2));
   /* Run llm */
   const ai = getAIApi({
     timeout: 480000
   });
-  const aiResponse = await ai.chat.completions.create(
-    {
-      ...toolModel?.defaultConfig,
-      model: toolModel.model,
-      temperature: 0,
-      stream,
-      messages: requestMessages,
-      tools,
-      tool_choice: 'auto'
-    },
-    {
-      headers: {
-        Accept: 'application/json, text/plain, */*'
-      }
+  const aiResponse = await ai.chat.completions.create(requestBody, {
+    headers: {
+      Accept: 'application/json, text/plain, */*'
     }
-  );
+  });
 
   const { answer, toolCalls } = await (async () => {
     if (res && stream) {
@@ -221,7 +235,7 @@ export const runToolWithToolChoice = async (
                 toolName: '',
                 toolAvatar: '',
                 params: '',
-                response: stringToolResponse
+                response: sliceStrStartEnd(stringToolResponse, 300, 300)
               }
             })
           });
@@ -243,7 +257,7 @@ export const runToolWithToolChoice = async (
       tool_calls: toolCalls
     };
     const concatToolMessages = [
-      ...filterMessages,
+      ...requestMessages,
       assistantToolMsgParams
     ] as ChatCompletionMessageParam[];
     const tokens = await countGptMessagesTokens(concatToolMessages, tools);

packages/service/core/workflow/dispatch/agent/runTool/type.d.ts

@@ -11,9 +11,13 @@ import { AIChatItemValueItemType, ChatItemValueItemType } from '@fastgpt/global/
 
 export type DispatchToolModuleProps = ModuleDispatchProps<{
   [NodeInputKeyEnum.history]?: ChatItemType[];
+  [NodeInputKeyEnum.userChatInput]: string;
+
   [NodeInputKeyEnum.aiModel]: string;
   [NodeInputKeyEnum.aiSystemPrompt]: string;
-  [NodeInputKeyEnum.userChatInput]: string;
+  [NodeInputKeyEnum.aiChatTemperature]: number;
+  [NodeInputKeyEnum.aiChatMaxToken]: number;
+  [NodeInputKeyEnum.aiChatVision]?: boolean;
 }>;
 
 export type RunToolResponse = {

packages/service/core/workflow/dispatch/agent/runTool/utils.ts

@@ -1,3 +1,6 @@
+import { sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
+import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
+import { AIChatItemValueItemType } from '@fastgpt/global/core/chat/type';
 import { FlowNodeInputItemType } from '@fastgpt/global/core/workflow/type/io';
 
 export const updateToolInputValue = ({
@@ -12,3 +15,22 @@ export const updateToolInputValue = ({
     value: params[input.key] ?? input.value
   }));
 };
+
+export const filterToolResponseToPreview = (response: AIChatItemValueItemType[]) => {
+  return response.map((item) => {
+    if (item.type === ChatItemValueTypeEnum.tool) {
+      const formatTools = item.tools?.map((tool) => {
+        return {
+          ...tool,
+          response: sliceStrStartEnd(tool.response, 500, 500)
+        };
+      });
+      return {
+        ...item,
+        tools: formatTools
+      };
+    }
+
+    return item;
+  });
+};