feat: vision model (#489 )

* mongo init * perf: mongo connect * perf: tts perf: whisper and tts peref: tts whisper permission log reabase (#488) * perf: modal * i18n * perf: schema lean * feat: vision model format * perf: tts loading * perf: static data * perf: tts * feat: image * perf: image * perf: upload image and title * perf: image size * doc * perf: color * doc * speaking can not select file * doc
add image input (#486 )
2023-11-18 15:42:35 +08:00 · 2023-11-17 18:22:29 +08:00 · 2023-11-17 17:15:41 +08:00 · 2023-11-17 00:03:05 +08:00 · 2023-11-16 17:10:04 +08:00 · 2023-11-16 16:22:08 +08:00
718 changed files with 26305 additions and 15032 deletions
--- a/.github/ISSUE_TEMPLATE/bugs.md
+++ b/.github/ISSUE_TEMPLATE/bugs.md
@@ -11,7 +11,7 @@ assignees: ''
 [//]: # '方框内填 x 表示打钩'

 - [ ] 我已确认目前没有类似 issue
- [ ] 我已完整查看过项目 README，以及[项目文档](https://doc.fastgpt.run/docs/intro/)
+- [ ] 我已完整查看过项目 README，以及[项目文档](https://doc.fastgpt.in/docs/intro/)
 - [ ] 我使用了自己的 key，并确认我的 key 是可正常使用的
 - [ ] 我理解并愿意跟进此 issue，协助测试和提供反馈
 - [x] 我理解并认可上述内容，并理解项目维护者精力有限，**不遵循规则的 issue 可能会被无视或直接关闭**
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,5 +1,5 @@
 blank_issues_enabled: false
 contact_links:
  - name: 微信交流群
-    url: https://doc.fastgpt.run/wechat-fastgpt.webp
+    url: https://doc.fastgpt.in/wechat-fastgpt.webp
    about: FastGPT 全是问题群
--- a/.github/workflows/fastgpt-image-personal.yml
+++ b/.github/workflows/fastgpt-image-personal.yml
@@ -0,0 +1,52 @@
+name: Build FastGPT images in Personal warehouse
+on:
+  workflow_dispatch:
+  push:
+    paths:
+      - 'projects/app/**'
+      - 'packages/**'
+    branches:
+      - 'main'
+jobs:
+  build-fastgpt-images:
+    runs-on: ubuntu-20.04
+    if: github.repository != 'labring/FastGPT'
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+        with:
+          driver-opts: network=host
+      - name: Cache Docker layers
+        uses: actions/cache@v3
+        with:
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-${{ github.sha }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v2
+        with:
+          registry: ghcr.io
+          username: ${{ github.repository_owner }}
+          password: ${{ secrets.GH_PAT }}
+      - name: Set DOCKER_REPO_TAGGED based on branch or tag
+        run: |
+          echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt:latest" >> $GITHUB_ENV
+      - name: Build and publish image for main branch or tag push event
+        env:
+          DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
+        run: |
+          docker buildx build \
+          --build-arg name=app \
+          --label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/FastGPT" \
+          --label "org.opencontainers.image.description=fastgpt image" \
+          --push \
+          --cache-from=type=local,src=/tmp/.buildx-cache \
+          --cache-to=type=local,dest=/tmp/.buildx-cache \
+          -t ${DOCKER_REPO_TAGGED} \
+          -f Dockerfile \
+          .
--- a/.github/workflows/fastgpt-image.yml
+++ b/.github/workflows/fastgpt-image.yml
@@ -5,8 +5,6 @@ on:
    paths:
      - 'projects/app/**'
      - 'packages/**'
-    branches:
-      - 'main'
    tags:
      - 'v*.*.*'
 jobs:
@@ -53,9 +51,8 @@ jobs:
          docker buildx build \
          --build-arg name=app \
          --platform linux/amd64,linux/arm64 \
-          --label "org.opencontainers.image.source=  https://github.com/  ${{ github.repository_owner }}/FastGPT" \
+          --label "org.opencontainers.image.source=https://github.com/${{ github.repository_owner }}/FastGPT" \
          --label "org.opencontainers.image.description=fastgpt image" \
-          --label "org.opencontainers.image.licenses=Apache" \
          --push \
          --cache-from=type=local,src=/tmp/.buildx-cache \
          --cache-to=type=local,dest=/tmp/.buildx-cache \
--- a/.github/workflows/preview-image.yml
+++ b/.github/workflows/preview-image.yml
@@ -15,13 +15,16 @@ jobs:
      - name: Checkout
        uses: actions/checkout@v3
        with:
-          fetch-depth: 1
+          ref: ${{ github.event.pull_request.head.ref }}
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
+          submodules: recursive # Fetch submodules
+          fetch-depth: 0 # Fetch all history for .GitInfo and .Lastmod
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v2
        with:
          driver-opts: network=host
      - name: Cache Docker layers
-        uses: actions/cache@v2
+        uses: actions/cache@v3
        with:
          path: /tmp/.buildx-cache
          key: ${{ runner.os }}-buildx-${{ github.sha }}
@@ -45,6 +48,7 @@ jobs:
          --label "org.opencontainers.image.source=  https://github.com/  ${{ github.repository_owner }}/FastGPT" \
          --label "org.opencontainers.image.description=fastgpt-pr image" \
          --label "org.opencontainers.image.licenses=Apache" \
+          --push \
          --cache-from=type=local,src=/tmp/.buildx-cache \
          --cache-to=type=local,dest=/tmp/.buildx-cache \
          -t ${DOCKER_REPO_TAGGED} \
--- a/12
+++ b/12
@@ -1,5 +1,5 @@
 # Install dependencies only when needed
-FROM node:current-alpine AS deps
+FROM node:18.15-alpine AS deps
 # Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
 RUN apk add --no-cache libc6-compat && npm install -g pnpm
 WORKDIR /app
@@ -11,12 +11,12 @@ COPY package.json pnpm-lock.yaml pnpm-workspace.yaml ./
 COPY ./packages ./packages
 COPY ./projects/$name/package.json ./projects/$name/package.json

-RUN \
-  [ -f pnpm-lock.yaml ] && pnpm install || \
-  (echo "Lockfile not found." && exit 1)
+RUN [ -f pnpm-lock.yaml ] || (echo "Lockfile not found." && exit 1)
+
+RUN pnpm install

 # Rebuild the source code only when needed
-FROM node:current-alpine AS builder
+FROM node:18.15-alpine AS builder
 WORKDIR /app

 ARG name
@@ -33,7 +33,7 @@ ENV NEXT_TELEMETRY_DISABLED 1
 RUN npm install -g pnpm
 RUN pnpm --filter=$name run build

-FROM node:current-alpine AS runner
+FROM node:18.15-alpine AS runner
 WORKDIR /app

 ARG name
--- a/README.md
+++ b/README.md
@@ -15,19 +15,19 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开

 <p align="center">
  <a href="https://fastgpt.run/">
-    <img height="21" src="https://img.shields.io/badge/在线使用-fff?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
+    <img height="21" src="https://img.shields.io/badge/在线使用-d4eaf7?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
  </a>
-  <a href="https://doc.fastgpt.run/docs/intro">
+  <a href="https://doc.fastgpt.in/docs/intro">
    <img height="21" src="https://img.shields.io/badge/相关文档-7d09f1?style=flat-square" alt="document">
  </a>
-  <a href="https://doc.fastgpt.run/docs/development">
-    <img height="21" src="https://img.shields.io/badge/本地开发-%23fff?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
+  <a href="https://doc.fastgpt.in/docs/development">
+    <img height="21" src="https://img.shields.io/badge/本地开发-%23d4eaf7?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
  </a>
  <a href="/#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">
    <img height="21" src="https://img.shields.io/badge/相关项目-7d09f1?style=flat-square" alt="project">
  </a>
  <a href="https://github.com/labring/FastGPT/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=fff&color=7d09f1" alt="license">
+    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
  </a>
 </p>

@@ -35,18 +35,23 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b

 ## 🛸 在线使用

-[fastgpt.run](https://fastgpt.run/)（服务器在新加坡，部分地区可能无法直连）
+- 🌐 国内版：[ai.fastgpt.in](https://ai.fastgpt.in/)
+- 🌍 海外版：[fastgpt.run](https://fastgpt.run/)

 |                                    |                                    |
 | ---------------------------------- | ---------------------------------- |
 | ![Demo](./.github/imgs/intro1.png) | ![Demo](./.github/imgs/intro2.png) |
 | ![Demo](./.github/imgs/intro3.png) | ![Demo](./.github/imgs/intro4.png) |

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 💡 功能

 1. 强大的可视化编排，轻松构建 AI 应用
   - [x] 提供简易模式，无需操作编排
-   - [x] 用户对话前引导, 全局字符串变量
+   - [x] 用户对话前引导，全局字符串变量
   - [x] 知识库搜索
   - [x] 多 LLM 模型对话
   - [x] 文本内容提取成结构化数据
@@ -55,12 +60,12 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
   - [x] 对话下一步指引
   - [ ] 对话多路线选择
   - [x] 源文件引用追踪
-   - [ ] 自定义文件阅读器
+   - [x] 模块封装，实现多级复用
 2. 丰富的知识库预处理
   - [x] 多库复用，混用
   - [x] chunk 记录修改和删除
-   - [x] 支持 手动输入, 直接分段, QA 拆分导入
-   - [x] 支持 url 读取、 CSV 批量导入
+   - [x] 支持手动输入，直接分段，QA 拆分导入
+   - [x] 支持 url 读取、CSV 批量导入
   - [x] 支持知识库单独设置向量模型
   - [x] 源文件存储
   - [ ] 文件学习 Agent
@@ -70,16 +75,20 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
   - [x] 完整上下文呈现
   - [x] 完整模块中间值呈现
 4. OpenAPI
-   - [x] completions 接口（对齐 GPT 接口）
+   - [x] completions 接口 (对齐 GPT 接口)
   - [ ] 知识库 CRUD
 5. 运营功能
   - [x] 免登录分享窗口
   - [x] Iframe 一键嵌入
   - [x] 统一查阅对话记录，并对数据进行标注

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 👨‍💻 开发

-项目技术栈: NextJs + TS + ChakraUI + Mongo + Postgres（Vector 插件）
+项目技术栈：NextJs + TS + ChakraUI + Mongo + Postgres (Vector 插件)

 - **⚡ 快速部署**

@@ -89,12 +98,17 @@ 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。

-* [快开始本地开发](https://doc.fastgpt.run/docs/development/intro/)
-* [部署 FastGPT](https://doc.fastgpt.run/docs/installation)
-* [系统配置文件说明](https://doc.fastgpt.run/docs/development/configuration/)
-* [多模型配置](https://doc.fastgpt.run/docs/installation/one-api/)
-* [版本更新/升级介绍](https://doc.fastgpt.run/docs/installation/upgrading)
-* [API 文档](https://doc.fastgpt.run/docs/development/openapi/)
+* [快开始本地开发](https://doc.fastgpt.in/docs/development/intro/)
+* [部署 FastGPT](https://doc.fastgpt.in/docs/installation)
+* [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/installation/one-api/)
+* [版本更新/升级介绍](https://doc.fastgpt.in/docs/installation/upgrading)
+* [OpenAPI API 文档](https://doc.fastgpt.in/docs/development/openapi/)
+* [知识库结构详解](https://doc.fastgpt.in/docs/use-cases/datasetengine/)
+
+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>

 ## 🏘️ 社区交流群

@@ -102,12 +116,20 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b

 ![](https://otnvvf-imgs.oss.laf.run/wx300.jpg)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 💪 相关项目

- [Laf: 3 分钟快速接入三方应用](https://github.com/labring/laf)
- [Sealos: 快速部署集群应用](https://github.com/labring/sealos)
- [One API: 多模型管理，支持 Azure、文心一言等](https://github.com/songquanpeng/one-api)
- [TuShan: 5 分钟搭建后台管理系统](https://github.com/msgbyte/tushan)
+- [Laf：3 分钟快速接入三方应用](https://github.com/labring/laf)
+- [Sealos：快速部署集群应用](https://github.com/labring/sealos)
+- [One API：多模型管理，支持 Azure、文心一言等](https://github.com/songquanpeng/one-api)
+- [TuShan：5 分钟搭建后台管理系统](https://github.com/msgbyte/tushan)
+
+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>

 ## 👀 其他

@@ -115,19 +137,31 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
 - [接入飞书](https://www.bilibili.com/video/BV1Su4y1r7R3/?spm_id_from=333.999.0.0)
 - [接入企微](https://www.bilibili.com/video/BV1Tp4y1n72T/?spm_id_from=333.999.0.0)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 🤝 第三方生态

- [OnWeChat 个人微信/企微机器人](https://doc.fastgpt.run/docs/use-cases/onwechat/)
+- [OnWeChat 个人微信/企微机器人](https://doc.fastgpt.in/docs/use-cases/onwechat/)
+
+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>

 ## 🌟 Star History

 [![Star History Chart](https://api.star-history.com/svg?repos=labring/FastGPT&type=Date)](https://star-history.com/#labring/FastGPT&Date)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-返回顶部-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 使用协议

 本仓库遵循 [FastGPT Open Source License](./LICENSE) 开源协议。

-1. 允许作为后台服务直接商用，但不允许直接使用 SaaS 服务商用。
-2. 需保留相关版权信息。
+1. 允许作为后台服务直接商用，但不允许提供 SaaS 服务。
+2. 未经商业授权，任何形式的商用服务均需保留相关版权信息。
 3. 完整请查看 [FastGPT Open Source License](./LICENSE)
-4. 联系方式：yujinlong@sealos.io, [点击查看定价策略](https://doc.fastgpt.run/docs/commercial)
+4. 联系方式：yujinlong@sealos.io，[点击查看商业版定价策略](https://doc.fastgpt.in/docs/commercial)
--- a/README_en.md
+++ b/README_en.md
@@ -15,19 +15,19 @@ FastGPT is a knowledge-based Q&A system built on the LLM, offers out-of-the-box

 <p align="center">
  <a href="https://fastgpt.run/">
-    <img height="21" src="https://img.shields.io/badge/Website-fff?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
+    <img height="21" src="https://img.shields.io/badge/在线使用-d4eaf7?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
  </a>
  <a href="https://doc.fastgpt.run/docs/intro">
-    <img height="21" src="https://img.shields.io/badge/Docs-7d09f1?style=flat-square" alt="document">
+    <img height="21" src="https://img.shields.io/badge/相关文档-7d09f1?style=flat-square" alt="document">
  </a>
  <a href="https://doc.fastgpt.run/docs/development">
-    <img height="21" src="https://img.shields.io/badge/Development-%23fff?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
+    <img height="21" src="https://img.shields.io/badge/本地开发-%23d4eaf7?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
  </a>
  <a href="/#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">
-    <img height="21" src="https://img.shields.io/badge/Related Projects-7d09f1?style=flat-square" alt="project">
+    <img height="21" src="https://img.shields.io/badge/相关项目-7d09f1?style=flat-square" alt="project">
  </a>
  <a href="https://github.com/labring/FastGPT/blob/main/LICENSE">
-    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=fff&color=7d09f1" alt="license">
+    <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
  </a>
 </p>

@@ -41,6 +41,10 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
 | ![Demo](./.github/imgs/intro1.png) | ![Demo](./.github/imgs/intro2.png) |
 | ![Demo](./.github/imgs/intro3.png) | ![Demo](./.github/imgs/intro4.png) |

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 💡 Features

 1. Powerful visual workflows: Effortlessly craft AI applications
@@ -54,9 +58,9 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
   - [x] Extend with HTTP
   - [ ] Embed Laf for on-the-fly HTTP module crafting
   - [x] Directions for the next dialogue steps
-   - [ ] Multiple dialogue paths selection
   - [x] Tracking source file references
   - [ ] Custom file reader
+   - [ ] Modules are packaged into plug-ins to achieve reuse

 2. Extensive knowledge base preprocessing

@@ -86,6 +90,10 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
   - [x] One-click embedding with Iframe
   - [ ] Unified access to dialogue records

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 👨‍💻 Development

 Project tech stack: NextJs + TS + ChakraUI + Mongo + Postgres (Vector plugin)
@@ -111,6 +119,10 @@ Project tech stack: NextJs + TS + ChakraUI + Mongo + Postgres (Vector plugin)
 | ------------------------------------------------- | ---------------------------------------------- |
 | ![](https://otnvvf-imgs.oss.laf.run/wxqun300.jpg) | ![](https://otnvvf-imgs.oss.laf.run/wx300.jpg) | -->

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 👀 Others

 - [FastGPT FAQ](https://kjqvjse66l.feishu.cn/docx/HtrgdT0pkonP4kxGx8qcu6XDnGh)
@@ -118,6 +130,10 @@ Project tech stack: NextJs + TS + ChakraUI + Mongo + Postgres (Vector plugin)
 - [Official Account Integration Video Tutorial](https://www.bilibili.com/video/BV1xh4y1t7fy/)
 - [FastGPT Knowledge Base Demo](https://www.bilibili.com/video/BV1Wo4y1p7i1/)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 💪 Related Projects

 - [Laf: 3-minute quick access to third-party applications](https://github.com/labring/laf)
@@ -125,10 +141,18 @@ Project tech stack: NextJs + TS + ChakraUI + Mongo + Postgres (Vector plugin)
 - [One API: Multi-model management, supports Azure, Wenxin Yiyuan, etc.](https://github.com/songquanpeng/one-api)
 - [TuShan: Build a backend management system in 5 minutes](https://github.com/msgbyte/tushan)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 🤝 Third-party Ecosystem

 - [luolinAI: Enterprise WeChat bot, ready to use](https://github.com/luolin-ai/FastGPT-Enterprise-WeChatbot)

+<a href="#readme">
+    <img src="https://img.shields.io/badge/-Back_to_Top-7d09f1.svg" alt="#" align="right">
+</a>
+
 ## 🌟 Star History

 [![Star History Chart](https://api.star-history.com/svg?repos=labring/FastGPT&type=Date)](https://star-history.com/#labring/FastGPT&Date)
--- a/docSite/.zhlintignore
+++ b/docSite/.zhlintignore
@@ -0,0 +1 @@
+*.html
--- a/docSite/.zhlintrc
+++ b/docSite/.zhlintrc
@@ -0,0 +1,6 @@
+{
+  "preset": "default",
+  "rules": {
+    "adjustedFullWidthPunctuation": ""
+  }
+}
--- a/docSite/README.md
+++ b/docSite/README.md
@@ -3,7 +3,7 @@
 ## 本地运行

 1. 安装 go 语言环境。
-2. 安装 hugo。 [二进制下载](https://github.com/gohugoio/hugo/releases/tag/v0.117.0)，注意需要安装 extended 版本。
+2. 安装 hugo。[二进制下载](https://github.com/gohugoio/hugo/releases/tag/v0.117.0)，注意需要安装 extended 版本。
 3. cd docSite
 4. hugo serve
 5. 访问 http://localhost:1313
--- a/docSite/assets/imgs/datasetEngine1.png
+++ b/docSite/assets/imgs/datasetEngine1.png
--- a/docSite/assets/imgs/datasetEngine10.png
+++ b/docSite/assets/imgs/datasetEngine10.png
--- a/docSite/assets/imgs/datasetEngine11.png
+++ b/docSite/assets/imgs/datasetEngine11.png
--- a/docSite/assets/imgs/datasetEngine2.png
+++ b/docSite/assets/imgs/datasetEngine2.png
--- a/docSite/assets/imgs/datasetEngine3.png
+++ b/docSite/assets/imgs/datasetEngine3.png
--- a/docSite/assets/imgs/datasetEngine4.png
+++ b/docSite/assets/imgs/datasetEngine4.png
--- a/docSite/assets/imgs/datasetEngine5.png
+++ b/docSite/assets/imgs/datasetEngine5.png
--- a/docSite/assets/imgs/datasetEngine6.png
+++ b/docSite/assets/imgs/datasetEngine6.png
--- a/docSite/assets/imgs/datasetEngine7.png
+++ b/docSite/assets/imgs/datasetEngine7.png
--- a/docSite/assets/imgs/datasetEngine8.png
+++ b/docSite/assets/imgs/datasetEngine8.png
--- a/docSite/assets/imgs/datasetEngine9.png
+++ b/docSite/assets/imgs/datasetEngine9.png
--- a/docSite/assets/imgs/datasetSetting1.png
+++ b/docSite/assets/imgs/datasetSetting1.png
--- a/docSite/assets/imgs/datasetprompt1.png
+++ b/docSite/assets/imgs/datasetprompt1.png
--- a/docSite/assets/imgs/datasetprompt2.png
+++ b/docSite/assets/imgs/datasetprompt2.png
--- a/docSite/assets/imgs/datasetprompt3.png
+++ b/docSite/assets/imgs/datasetprompt3.png
--- a/docSite/assets/imgs/datasetprompt4.png
+++ b/docSite/assets/imgs/datasetprompt4.png
--- a/docSite/assets/imgs/datasetprompt5.png
+++ b/docSite/assets/imgs/datasetprompt5.png
--- a/docSite/assets/imgs/datasetprompt6.png
+++ b/docSite/assets/imgs/datasetprompt6.png
--- a/docSite/assets/imgs/datasetprompt7.png
+++ b/docSite/assets/imgs/datasetprompt7.png
--- a/docSite/assets/imgs/datasetprompt8.png
+++ b/docSite/assets/imgs/datasetprompt8.png
--- a/docSite/assets/imgs/datasetprompt9.png
+++ b/docSite/assets/imgs/datasetprompt9.png
--- a/docSite/assets/imgs/getDatasetId.png
+++ b/docSite/assets/imgs/getDatasetId.png
--- a/docSite/assets/imgs/getfile_id.png
+++ b/docSite/assets/imgs/getfile_id.png
--- a/docSite/assets/imgs/wechat-fastgpt.webp
+++ b/docSite/assets/imgs/wechat-fastgpt.webp
--- a/docSite/assets/imgs/wechat1.png
+++ b/docSite/assets/imgs/wechat1.png
--- a/docSite/assets/imgs/wechat10.png
+++ b/docSite/assets/imgs/wechat10.png
--- a/docSite/assets/imgs/wechat2.png
+++ b/docSite/assets/imgs/wechat2.png
--- a/docSite/assets/imgs/wechat3.png
+++ b/docSite/assets/imgs/wechat3.png
--- a/docSite/assets/imgs/wechat4.png
+++ b/docSite/assets/imgs/wechat4.png
--- a/docSite/assets/imgs/wechat5.png
+++ b/docSite/assets/imgs/wechat5.png
--- a/docSite/assets/imgs/wechat6.png
+++ b/docSite/assets/imgs/wechat6.png
--- a/docSite/assets/imgs/wechat7.png
+++ b/docSite/assets/imgs/wechat7.png
--- a/docSite/assets/imgs/wechat8.png
+++ b/docSite/assets/imgs/wechat8.png
--- a/docSite/assets/imgs/wechat9.png
+++ b/docSite/assets/imgs/wechat9.png
--- a/docSite/content/docs/development/configuration.md
+++ b/docSite/content/docs/development/configuration.md
@@ -21,62 +21,76 @@ weight: 520
 ```json
 {
  "SystemParams": {
+    "pluginBaseUrl": "", // 商业版接口地址
    "vectorMaxProcess": 15, // 向量生成最大进程，结合数据库性能和 key 来设置
    "qaMaxProcess": 15,  // QA 生成最大进程，结合数据库性能和 key 来设置
-    "pgHNSWEfSearch": 40  // pg vector 索引参数，越大精度高但速度慢
+    "pgHNSWEfSearch": 100  // pg vector 索引参数，越大精度高但速度慢
  },
  "ChatModels": [
    {
-      "model": "gpt-3.5-turbo", // 实际调用的模型
-      "name": "GPT35-4k", // 展示的名字
-      "maxToken": 4000, // 最大token，均按 gpt35 计算
-      "quoteMaxToken": 2000, // 引用内容最大 token
-      "maxTemperature": 1.2, // 最大温度
-      "price": 0,
+      "model": "gpt-3.5-turbo-1106",
+      "name": "GPT35-1106",
+      "price": 0, // 除以 100000 后等于1个token的价格
+      "maxContext": 16000, // 最大上下文长度
+      "maxResponse": 4000, // 最大回复长度
+      "quoteMaxToken": 2000, // 最大引用内容长度
+      "maxTemperature": 1.2, // 最大温度值
+      "censor": false, // 是否开启敏感词过滤(商业版)
+      "vision": false, // 支持图片输入
      "defaultSystemChatPrompt": ""
    },
    {
      "model": "gpt-3.5-turbo-16k",
      "name": "GPT35-16k",
-      "maxToken": 16000,
+      "maxContext": 16000,
+      "maxResponse": 16000,
+      "price": 0,
      "quoteMaxToken": 8000,
      "maxTemperature": 1.2,
-      "price": 0,
+      "censor": false,
+      "vision": false,
      "defaultSystemChatPrompt": ""
    },
    {
      "model": "gpt-4",
      "name": "GPT4-8k",
-      "maxToken": 8000,
+      "maxContext": 8000,
+      "maxResponse": 8000,
+      "price": 0,
      "quoteMaxToken": 4000,
      "maxTemperature": 1.2,
+      "censor": false,
+      "vision": false,
+      "defaultSystemChatPrompt": ""
+    },
+    {
+      "model": "gpt-4-vision-preview",
+      "name": "GPT4-Vision",
+      "maxContext": 128000,
+      "maxResponse": 4000,
      "price": 0,
+      "quoteMaxToken": 100000,
+      "maxTemperature": 1.2,
+      "censor": false,
+      "vision": true,
      "defaultSystemChatPrompt": ""
    }
  ],
-  "QAModel": [ // QA 拆分模型
-    { 
+  "QAModels": [
+    {
      "model": "gpt-3.5-turbo-16k",
      "name": "GPT35-16k",
-      "maxToken": 16000,
+      "maxContext": 16000,
+      "maxResponse": 16000,
      "price": 0
    }
  ],
-  "ExtractModels": [ // 内容提取模型
-    { 
-      "model": "gpt-3.5-turbo-16k",
-      "name": "GPT35-16k",
-      "maxToken": 16000, 
-      "price": 0,
-      "functionCall": true, // 是否支持 function call
-      "functionPrompt": "" // 自定义非 function call 提示词
-    }
-  ],
-  "CQModels": [ // Classify Question: 问题分类模型
+  "CQModels": [
    {
-      "model": "gpt-3.5-turbo-16k",
-      "name": "GPT35-16k",
-      "maxToken": 16000,
+      "model": "gpt-3.5-turbo-1106",
+      "name": "GPT35-1106",
+      "maxContext": 16000,
+      "maxResponse": 4000,
      "price": 0,
      "functionCall": true,
      "functionPrompt": ""
@@ -84,17 +98,30 @@ weight: 520
    {
      "model": "gpt-4",
      "name": "GPT4-8k",
-      "maxToken": 8000,
+      "maxContext": 8000,
+      "maxResponse": 8000,
      "price": 0,
      "functionCall": true,
      "functionPrompt": ""
    }
  ],
-  "QGModels": [ // Question Generation: 生成下一步指引模型
-    { 
-      "model": "gpt-3.5-turbo",
-      "name": "GPT35-4k",
-      "maxToken": 4000,
+  "ExtractModels": [
+    {
+      "model": "gpt-3.5-turbo-1106",
+      "name": "GPT35-1106",
+      "maxContext": 16000,
+      "maxResponse": 4000,
+      "price": 0,
+      "functionCall": true,
+      "functionPrompt": ""
+    }
+  ],
+  "QGModels": [
+    {
+      "model": "gpt-3.5-turbo-1106",
+      "name": "GPT35-1106",
+      "maxContext": 1600,
+      "maxResponse": 4000,
      "price": 0
    }
  ],
@@ -102,10 +129,32 @@ weight: 520
    {
      "model": "text-embedding-ada-002",
      "name": "Embedding-2",
-      "price": 0,
-      "defaultToken": 500,
+      "price": 0.2,
+      "defaultToken": 700,
      "maxToken": 3000
    }
-  ]
+  ],
+  "AudioSpeechModels": [
+    {
+      "model": "tts-1",
+      "name": "OpenAI TTS1",
+      "price": 0,
+      "baseUrl": "",
+      "key": "",
+      "voices": [
+        { "label": "Alloy", "value": "alloy", "bufferId": "openai-Alloy" },
+        { "label": "Echo", "value": "echo", "bufferId": "openai-Echo" },
+        { "label": "Fable", "value": "fable", "bufferId": "openai-Fable" },
+        { "label": "Onyx", "value": "onyx", "bufferId": "openai-Onyx" },
+        { "label": "Nova", "value": "nova", "bufferId": "openai-Nova" },
+        { "label": "Shimmer", "value": "shimmer", "bufferId": "openai-Shimmer" }
+      ]
+    }
+  ],
+  "WhisperModel": {
+    "model": "whisper-1",
+    "name": "Whisper1",
+    "price": 0
+  }
 }
 ```
--- a/docSite/content/docs/development/intro.md
+++ b/docSite/content/docs/development/intro.md
@@ -54,7 +54,7 @@ git clone git@github.com:<github_username>/FastGPT.git

 **环境变量**

-复制.env.template 文件，生成一个.env.local 环境变量文件夹，修改.env.local 里内容才是有效的变量。变量说明见 .env.template
+复制.env.template 文件，在同级目录下生成一个.env.local 文件，修改.env.local 里内容才是有效的变量。变量说明见 .env.template

 **config 配置文件**

--- a/docSite/content/docs/development/openApi.md
+++ b/docSite/content/docs/development/openApi.md
@@ -120,7 +120,7 @@ event: answer
 data: [DONE]

 event: appStreamResponse
-data: [{"moduleName":"KB Search","price":1.2000000000000002,"model":"Embedding-2","tokens":6,"similarity":0.61,"limit":3},{"moduleName":"AI Chat","price":463.5,"model":"FastAI-4k","tokens":309,"question":"导演是谁","answer":"电影《铃芽之旅》的导演是新海诚。","maxToken":2050,"quoteList":[{"kb_id":"646627f4f7b896cfd8910e38","id":"8099","q":"本作的主人公是谁？","a":"本作的主人公是名叫铃芽的少女。","source":"手动修改"},{"kb_id":"646627f4f7b896cfd8910e38","id":"8686","q":"电影《铃芽之旅》男主角是谁？","a":"电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。","source":""},{"kb_id":"646627f4f7b896cfd8910e38","id":"19339","q":"电影《铃芽之旅》的导演是谁？22","a":"电影《铃芽之旅》的导演是新海诚。","source":"手动修改"}],"completeMessages":[{"obj":"System","value":"下面是知识库内容:\n1. [本作的主人公是谁？\n本作的主人公是名叫铃芽的少女。]\n2. [电影《铃芽之旅》男主角是谁？\n电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。]\n3. [电影《铃芽之旅》的导演是谁？22\n电影《铃芽之旅》的导演是新海诚。]\n"},{"obj":"System","value":"1.请记住，你的身份是百度的下一代知识增强语言模型，能够完全根据知识库提供的内容回答问题。\n\n2. 你忘记了关于电影《铃芽之旅》以外的内容。"},{"obj":"System","value":"你仅回答关于电影《玲芽之旅》的问题，其余问题直接回复: 我不清楚。"},{"obj":"Human","value":"导演是谁"},{"obj":"AI","value":"电影《铃芽之旅》的导演是新海诚。"}]}]
+data: [{"moduleName":"KB Search","price":1.2000000000000002,"model":"Embedding-2","tokens":6,"similarity":0.61,"limit":3},{"moduleName":"AI Chat","price":463.5,"model":"FastAI-4k","tokens":309,"question":"导演是谁","answer":"电影《铃芽之旅》的导演是新海诚。","maxToken":2050,"quoteList":[{"dataset_id":"646627f4f7b896cfd8910e38","id":"8099","q":"本作的主人公是谁？","a":"本作的主人公是名叫铃芽的少女。","source":"手动修改"},{"dataset_id":"646627f4f7b896cfd8910e38","id":"8686","q":"电影《铃芽之旅》男主角是谁？","a":"电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。","source":""},{"dataset_id":"646627f4f7b896cfd8910e38","id":"19339","q":"电影《铃芽之旅》的导演是谁？22","a":"电影《铃芽之旅》的导演是新海诚。","source":"手动修改"}],"completeMessages":[{"obj":"System","value":"下面是知识库内容:\n1. [本作的主人公是谁？\n本作的主人公是名叫铃芽的少女。]\n2. [电影《铃芽之旅》男主角是谁？\n电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。]\n3. [电影《铃芽之旅》的导演是谁？22\n电影《铃芽之旅》的导演是新海诚。]\n"},{"obj":"System","value":"1.请记住，你的身份是百度的下一代知识增强语言模型，能够完全根据知识库提供的内容回答问题。\n\n2. 你忘记了关于电影《铃芽之旅》以外的内容。"},{"obj":"System","value":"你仅回答关于电影《玲芽之旅》的问题，其余问题直接回复: 我不清楚。"},{"obj":"Human","value":"导演是谁"},{"obj":"AI","value":"电影《铃芽之旅》的导演是新海诚。"}]}]

 ```
 {{< /markdownify >}}
@@ -150,21 +150,21 @@ data: [{"moduleName":"KB Search","price":1.2000000000000002,"model":"Embedding-2
            "maxToken": 2050,
            "quoteList": [
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "8099",
                    "q": "本作的主人公是谁？",
                    "a": "本作的主人公是名叫铃芽的少女。",
                    "source": "手动修改"
                },
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "8686",
                    "q": "电影《铃芽之旅》男主角是谁？",
                    "a": "电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。",
                    "source": ""
                },
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "19339",
                    "q": "电影《铃芽之旅》的导演是谁？22",
                    "a": "电影《铃芽之旅》的导演是新海诚。",
@@ -225,9 +225,9 @@ data: [{"moduleName":"KB Search","price":1.2000000000000002,"model":"Embedding-2
 此部分 API 需使用全局通用的 API Key。
 {{% /alert %}}

-| 如何获取知识库ID（kbId） | 如何获取文件ID（file_id） |
+| 如何获取知识库ID（datasetId） | 如何获取文件ID（file_id） |
 | --------------------- | --------------------- |
-| ![](/imgs/getKbId.png) | ![](/imgs/getfile_id.png) |
+| ![](/imgs/getDatasetId.png) | ![](/imgs/getfile_id.png) |


 ### 知识库添加数据
@@ -241,7 +241,7 @@ curl --location --request POST 'https://fastgpt.run/api/core/dataset/data/pushDa
 --header 'Authorization: Bearer apikey' \
 --header 'Content-Type: application/json' \
 --data-raw '{
-    "kbId": "64663f451ba1676dbdef0499",
+    "collectionId": "64663f451ba1676dbdef0499",
    "mode": "index",
    "prompt": "qa 拆分引导词，index 模式下可以忽略",
    "billId": "可选。如果有这个值，本次的数据会被聚合到一个订单中，这个值可以重复使用。可以参考 [创建训练订单] 获取该值。",
@@ -268,7 +268,7 @@ curl --location --request POST 'https://fastgpt.run/api/core/dataset/data/pushDa

 ```json
 {
-    "kbId": "知识库的ID，可以在知识库详情查看。",
+    "collectionId": "文件的ID，参考上面的第二张图",
    "mode": "index | qa ", //  index 模式: 直接将 q 转成向量存起来，a 直接入库。qa 模式: 只关注 data 里的 q，将 q 丢给大模型，让其根据 prompt 拆分成 qa 问答对。  
    "prompt": "拆分提示词，需严格按照模板，建议不要传入。",
    "data": [
@@ -351,7 +351,7 @@ curl --location --request POST 'https://fastgpt.run/api/core/dataset/searchTest'
 --header 'Authorization: Bearer apiKey' \
 --header 'Content-Type: application/json' \
 --data-raw '{
-    "kbId": "xxxxx",
+    "datasetId": "知识库的ID",
    "text": "导演是谁"
 }'
 ```
@@ -393,7 +393,7 @@ curl --location --request POST 'https://fastgpt.run/api/core/dataset/searchTest'
 **请求示例**

 ```bash
-curl --location --request POST 'https://fastgpt.run/api/common/bill/createTrainingBill' \
+curl --location --request POST 'https://fastgpt.run/api/support/wallet/bill/createTrainingBill' \
 --header 'Authorization: Bearer {{apikey}}' \
 --header 'Content-Type: application/json' \
 --data-raw ''
@@ -487,21 +487,21 @@ curl --location --request POST '{{host}}/shareAuth/finish' \
            "maxToken": 2050,
            "quoteList": [
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "8099",
                    "q": "本作的主人公是谁？",
                    "a": "本作的主人公是名叫铃芽的少女。",
                    "source": "手动修改"
                },
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "8686",
                    "q": "电影《铃芽之旅》男主角是谁？",
                    "a": "电影《铃芽之旅》男主角是宗像草太，由松村北斗配音。",
                    "source": ""
                },
                {
-                    "kb_id": "646627f4f7b896cfd8910e38",
+                    "dataset_id": "646627f4f7b896cfd8910e38",
                    "id": "19339",
                    "q": "电影《铃芽之旅》的导演是谁？22",
                    "a": "电影《铃芽之旅》的导演是新海诚。",
--- a/docSite/content/docs/installation/one-api.md
+++ b/docSite/content/docs/installation/one-api.md
@@ -99,9 +99,9 @@ CHAT_API_KEY=sk-xxxxxx
    {
      "model": "ERNIE-Bot", // 这里的模型需要对应 One API 的模型
      "name": "文心一言", // 对外展示的名称
-      "maxToken": 4000, // 最大长下文 token，无论什么模型都按 GPT35 的计算。GPT 外的模型需要自行大致计算下这个值。可以调用官方接口去比对 Token 的倍率，然后在这里粗略计算。
+      "maxContext": 8000, // 最大长下文 token，无论什么模型都按 GPT35 的计算。GPT 外的模型需要自行大致计算下这个值。可以调用官方接口去比对 Token 的倍率，然后在这里粗略计算。
+      "maxResponse": 4000, // 最大回复 token
      // 例如：文心一言的中英文 token 基本是 1:1，而 GPT 的中文 Token 是 2:1，如果文心一言官方最大 Token 是 4000，那么这里就可以填 8000，保险点就填 7000.
-      "price": 0, // 1个token 价格 => 1.5 / 100000 * 1000 = 0.015元/1k token
      "quoteMaxToken": 2000, // 引用知识库的最大 Token
      "maxTemperature": 1, // 最大温度
      "defaultSystemChatPrompt": "" // 默认的系统提示词
--- a/docSite/content/docs/installation/upgrading/447.md
+++ b/docSite/content/docs/installation/upgrading/447.md
@@ -1,5 +1,5 @@
 ---
-title: 'V4.4.7'
+title: 'V4.4.7（需执行升级脚本）'
 description: 'FastGPT V4.4.7 更新（需执行升级脚本）'
 icon: 'upgrade'
 draft: false
--- a/docSite/content/docs/installation/upgrading/45.md
+++ b/docSite/content/docs/installation/upgrading/45.md
@@ -29,6 +29,9 @@ ALTER EXTENSION vector UPDATE;
 alter system set maintenance_work_mem = '2400MB'; 
 select pg_reload_conf();

+-- 重构数据库索引和排序
+REINDEX DATABASE postgres;
+
 -- 开始构建索引，该索引构建时间非常久，直接点击右上角的叉，退出 Terminal 即可
 CREATE INDEX CONCURRENTLY vector_index ON modeldata USING hnsw (vector vector_ip_ops) WITH (m = 16, ef_construction = 64);
 -- 可以再次点击一键链接，进入 Terminal，输入下方命令，如果看到 "vector_index" hnsw (vector vector_ip_ops) WITH (m='16', ef_construction='64') 则代表构建完成（注意，后面没有 INVALID）
@@ -62,10 +65,16 @@ ALTER EXTENSION vector UPDATE;
 alter system set maintenance_work_mem = '2400MB'; 
 select pg_reload_conf();

+-- 重构数据库索引和排序
+REINDEX DATABASE postgres;
+ALTER DATABASE postgres REFRESH COLLATION VERSION;
+
 -- 开始构建索引，该索引构建时间非常久，直接关掉终端即可，不要使用 ctrl+c 关闭
 CREATE INDEX CONCURRENTLY vector_index ON modeldata USING hnsw (vector vector_ip_ops) WITH (m = 16, ef_construction = 64);
 -- 可以再次连接数据库，输入下方命令。如果看到 "vector_index" hnsw (vector vector_ip_ops) WITH (m='16', ef_construction='64') 则代表构建完成（注意，后面没有 INVALID）
 \d modeldata
+
+
 ```

 ## 版本新功能介绍
--- a/docSite/content/docs/installation/upgrading/451.md
+++ b/docSite/content/docs/installation/upgrading/451.md
@@ -0,0 +1,35 @@
+---
+title: 'V4.5.1(需进行初始化)'
+description: 'FastGPT V4.5.1 更新'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 838
+---
+
+## 执行初始化 API
+
+发起 1 个 HTTP 请求（{{rootkey}} 替换成环境变量里的`rootkey`，{{host}}替换成自己域名）
+
+1. https://xxxxx/api/admin/initv451
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv451' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+初始化内容：
+1. rename 数据库字段
+2. 初始化 Mongo APP 表中知识库的相关字段
+3. 初始化 PG 和 Mongo 的内容，为每个文件创建一个集合（存储 Mongo 中），并反馈赋值给 PG。
+
+**该初始化接口可能速度很慢，返回超时不用管，注意看日志即可**
+
+## 功能介绍
+
+### Fast GPT V4.5.1
+
+1. 新增知识库文件夹管理
+2. 修复了 openai4.x sdk 无法兼容 oneapi 的智谱和阿里的接口。
+3. 修复部分模块无法触发完成事件
--- a/docSite/content/docs/installation/upgrading/452.md
+++ b/docSite/content/docs/installation/upgrading/452.md
@@ -0,0 +1,15 @@
+---
+title: 'V4.5.2'
+description: 'FastGPT V4.5.2 更新'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 837
+---
+
+## 功能介绍
+
+### Fast GPT V4.5.2
+
+1. 新增 - 模块插件，允许自行组装插件进行模块复用。
+2. 优化 - 知识库引用提示。
--- a/docSite/content/docs/installation/upgrading/46.md
+++ b/docSite/content/docs/installation/upgrading/46.md
@@ -0,0 +1,67 @@
+---
+title: 'V4.6(需要初始化)'
+description: 'FastGPT V4.6 更新'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 836
+---
+
+**V4.6 版本加入了简单的团队功能，可以邀请其他用户进来管理资源。该版本升级后无法执行旧的升级脚本，且无法回退。**
+
+## 1. 更新镜像并变更配置文件
+
+更新镜像至 latest 或者 v4.6 版本。商业版镜像更新至 V0.2.1
+
+最新配置可参考: [V46版本最新 config.json](/docs/development/configuration)，商业镜像配置文件也更新，参考最新的飞书文档。
+
+
+## 2. 执行初始化 API
+
+发起 2 个 HTTP 请求（{{rootkey}} 替换成环境变量里的`rootkey`，{{host}}替换成自己域名）
+
+**该初始化接口可能速度很慢，返回超时不用管，注意看日志即可，需要注意的是，需确保initv46成功后，在执行initv46-2**
+
+1. https://xxxxx/api/admin/initv46
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv46' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+2. https://xxxxx/api/admin/initv46-2
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv46-2' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+初始化内容：
+1. 创建默认团队
+2. 初始化 Mongo 所有资源的团队字段
+3. 初始化 Pg 的字段
+4. 初始化 Mongo Data
+
+
+## V4.6功能介绍
+
+1. 新增 - 团队空间
+2. 新增 - 多路向量（多个向量映射一组数据）
+3. 新增 - tts语音
+4. 新增 - 支持知识库配置文本预处理模型
+5. 线上环境新增 - ReRank向量召回，提高召回精度
+6. 优化 - 知识库导出，可直接触发流下载，无需等待转圈圈
+
+## 4.6缺陷修复
+
+旧的 4.6 版本由于缺少一个字段，导致文件导入时知识库数据无法显示，可执行下面的脚本：
+
+https://xxxxx/api/admin/initv46-fix
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv46-fix' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
--- a/docSite/content/docs/installation/upgrading/461.md
+++ b/docSite/content/docs/installation/upgrading/461.md
@@ -0,0 +1,16 @@
+---
+title: 'V4.6.1'
+description: 'FastGPT V4.6 .1'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 835
+---
+
+
+## V4.6.1 功能介绍
+
+1. 新增 - GPT4-v 模型支持
+2. 新增 - whisper 语音输入
+3. 优化 - TTS 流传输
+4. 优化 - TTS 缓存
--- a/docSite/content/docs/pricing.md
+++ b/docSite/content/docs/pricing.md
@@ -1,10 +1,10 @@
 ---
-title: '定价'
-description: 'FastGPT 的定价'
+title: '线上版定价'
+description: 'FastGPT 线上版定价'
 icon: 'currency_yen'
 draft: false
 toc: true
-weight: 10
+weight: 11
 ---

 ## Tokens 说明
@@ -15,7 +15,7 @@ weight: 10

 ## FastGPT 线上计费

-目前，FastGPT 线上计费也仅按 Tokens 使用数量为准。以下是详细的计费表（最新定价以线上表格为准，可在点击充值后实时获取）：
+使用: [https://fastgpt.run](https://fastgpt.run) 或  [https://ai.fastgpt.in](https://ai.fastgpt.in) 只需仅按 Tokens 使用数量扣费即可。可在 账号-使用记录 中查看具体使用情况，以下是详细的计费表（最新定价以线上表格为准，可在点击充值后实时获取）：

 {{< table "table-hover table-striped-columns" >}}
 | 计费项                 | 价格: 元/ 1K tokens（包含上下文） |
--- a/docSite/content/docs/use-cases/ai_settings.md
+++ b/docSite/content/docs/use-cases/ai_settings.md
@@ -9,23 +9,23 @@ weight: 310

 在 FastGPT 的 AI 对话模块中，有一个 AI 高级配置，里面包含了 AI 模型的参数配置，本文详细介绍这些配置的含义。

-# 返回AI内容
+## 返回AI内容

 这是一个开关，打开的时候，当 AI 对话模块运行时，会将其输出的内容返回到浏览器（API响应）；如果关闭，AI 输出的内容不会返回到浏览器，但是生成的内容仍可以通过【AI回复】进行输出。你可以将【AI回复】连接到其他模块中。

-# 温度
+## 温度

 可选范围0-10，约大代表生成的内容约自由扩散，越小代表约严谨。调节能力有限，知识库问答场景通常设置为0。

-# 回复上限
+## 回复上限

 控制 AI 回复的最大 Tokens，较小的值可以一定程度上减少 AI 的废话，但也可能导致 AI 回复不完整。

-# 引用模板 & 引用提示词
+## 引用模板 & 引用提示词

 这两个参数与知识库问答场景相关，可以控制知识库相关的提示词。

-## AI 对话消息组成
+### AI 对话消息组成

 想使用明白这两个变量，首先要了解传递传递给 AI 模型的消息格式。它是一个数组，FastGPT 中这个数组的组成形式为：

@@ -39,16 +39,18 @@ weight: 310
 ```

 {{% alert icon="🍅" context="success" %}}
-Tips: 可以通过点击上下文按键查看完整的
+Tips: 可以通过点击上下文按键查看完整的上下文组成，便于调试。
 {{% /alert %}}

-## 引用模板和提示词设计
+### 引用模板和提示词设计

 引用模板和引用提示词通常是成对出现，引用提示词依赖引用模板。

-FastGPT 知识库采用 QA 对(不一定都是问答格式，仅代表两个变量)的格式存储，在转义成字符串时候会根据**引用模板**来进行格式化。知识库包含 3 个变量： q, a, file_id, index, source，可以通过 {{q}} {{a}} {{file_id}} {{index}} {{source}} 按需引入。下面一个模板例子：
+FastGPT 知识库采用 QA 对(不一定都是问答格式，仅代表两个变量)的格式存储，在转义成字符串时候会根据**引用模板**来进行格式化。知识库包含多个可用变量： q, a, sourceId（数据的ID）, index(第n个数据), source(数据的集合名、文件名)，score(距离得分，0-1) 可以通过 {{q}} {{a}} {{sourceId}} {{index}} {{source}} {{score}} 按需引入。下面一个模板例子：

-**引用模板**
+可以通过 [知识库结构讲解](/docs/use-cases/datasetEngine/) 了解详细的知识库的结构。
+
+#### 引用模板

 ```
 {instruction:"{{q}}",output:"{{a}}",source:"{{source}}"}
@@ -62,7 +64,7 @@ FastGPT 知识库采用 QA 对(不一定都是问答格式，仅代表两个变
 {instruction:"电影《铃芽之旅》的编剧是谁？22",output:"新海诚是本片的编剧。",source:"手动输入"}
 ```

-**引用提示词**
+#### 引用提示词

 引用模板需要和引用提示词一起使用，提示词中可以写引用模板的格式说明以及对话的要求等。可以使用 {{quote}} 来使用 **引用模板**，使用 {{question}} 来引入问题。例如：

@@ -91,4 +93,42 @@ FastGPT 知识库采用 QA 对(不一定都是问答格式，仅代表两个变
 2. 使用背景知识回答问题。
 3. 背景知识无法回答问题时，你可以礼貌的的回答用户问题。
 我的问题是:"{{question}}"
-```
+```
+
+#### 总结
+
+引用模板规定了搜索出来的内容如何组成一句话，其由 q,a,index,source 多个变量组成。
+
+引用提示词由`引用模板`和`提示词`组成，提示词通常是对引用模板的一个描述，加上对模型的要求。
+
+### 引用模板和提示词设计 示例
+
+#### 通用模板与问答模板对比
+
+我们通过一组`你是谁`的手动数据，对通用模板与问答模板的效果进行对比。此处特意打了个搞笑的答案，通用模板下 GPT35 就变得不那么听话了，而问答模板下 GPT35 依然能够回答正确。这是由于结构化的提示词，在大语言模型中具有更强的引导作用。
+
+{{% alert icon="🍅" context="success" %}}
+Tips: 建议根据不同的场景，每种知识库仅选择1类数据类型，这样有利于充分发挥提示词的作用。
+{{% /alert %}}
+
+| 通用模板配置及效果 | 问答模板配置及效果 |
+| --- | --- |
+| ![](/imgs/datasetprompt1.png) | ![](/imgs/datasetprompt2.png) |
+| ![](/imgs/datasetprompt3.png) | ![](/imgs/datasetprompt5.png) |
+| ![](/imgs/datasetprompt4.png) | ![](/imgs/datasetprompt6.png) |
+
+#### 严格模板
+
+使用非严格模板，我们随便询问一个不在知识库中的内容，模型通常会根据其自身知识进行回答。
+
+| 非严格模板效果 | 选择严格模板 | 严格模板效果 |
+| --- | --- | --- |
+| ![](/imgs/datasetprompt7.png) | ![](/imgs/datasetprompt8.png) |![](/imgs/datasetprompt9.png) |
+
+#### 提示词设计思路
+
+1. 使用序号进行不同要求描述。
+2. 使用首先、然后、最后等词语进行描述。
+3. 列举不同场景的要求时，尽量完整，不要遗漏。例如：背景知识完全可以回答、背景知识可以回答一部分、背景知识与问题无关，3种场景都说明清楚。
+4. 巧用结构化提示，例如在问答模板中，利用了`instruction`和`output`，清楚的告诉模型，`output`是一个预期的答案。
+5. 标点符号正确且完整。
--- a/docSite/content/docs/use-cases/datasetEngine.md
+++ b/docSite/content/docs/use-cases/datasetEngine.md
@@ -0,0 +1,91 @@
+---
+title: "知识库结构讲解"
+description: "本节会详细介绍 FastGPT 知识库结构设计，理解其 QA 的存储格式和多向量映射，以便更好的构建知识库。这篇介绍主要以使用为主，详细原理不多介绍。"
+icon: "dataset"
+draft: false
+toc: true
+weight: 311
+---
+
+## 理解向量
+
+FastGPT 采用了 RAG 中的 Embedding 方案构建知识库，要使用好 FastGPT 需要简单的理解`Embedding`向量是如何工作的及其特点。
+
+人类的文字、图片、视频等媒介是无法直接被计算机理解的，要想让计算机理解两段文字是否有相似性、相关性，通常需要将它们转成计算机可以理解的语言，向量是其中的一种方式。
+
+向量可以简单理解为一个数字数组，两个向量之间可以通过数学公式得出一个`距离`，距离越小代表两个向量的相似度越大。从而映射到文字、图片、视频等媒介上，可以用来判断两个媒介之间的相似度。向量搜索便是利用了这个原理。
+
+而由于文字是有多种类型，并且拥有成千上万种组合方式，因此在转成向量进行相似度匹配时，很难保障其精确性。在向量方案构建的知识库中，通常使用`topk`召回的方式，也就是查找前`k`个最相似的内容，丢给大模型去做更进一步的`语义判断`、`逻辑推理`和`归纳总结`，从而实现知识库问答。因此，在知识库问答中，向量搜索的环节是最为重要的。
+
+影响向量搜索精度的因素非常多，主要包括：向量模型的质量、数据的质量（长度，完整性，多样性）、检索器的精度（速度与精度之间的取舍）。与数据质量对应的就是检索词的质量。
+
+检索器的精度比较容易解决，向量模型的训练略复杂，因此数据和检索词质量优化成了一个重要的环节。
+
+## FastGPT 中向量的结构设计
+
+FastGPT 采用了 `PostgresSQL` 的 `PG Vector` 插件作为向量检索器，索引为`HNSW`。且`PostgresSQL`仅用于向量检索，`MongoDB`用于其他数据的存取。
+
+在`PostgresSQL`的表中，设置一个 `index` 字段用于存储向量，以及一个`data_id`用于在`MongoDB`中寻找对应的映射值。多个`index`可以对应一组`data_id`，也就是说，一组向量可以对应多组数据。在进行检索时，相同数据会进行合并。
+
+![](/imgs/datasetSetting1.png)
+
+### 多向量的目的和使用方式
+
+在一组向量中，内容的长度和语义的丰富度通常是矛盾的，无法兼得。因此，FastGPT 采用了多向量映射的方式，将一组数据映射到多组向量中，从而保障数据的完整性和语义的丰富度。
+
+你可以为一组较长的文本，添加多组向量，从而在检索时，只要其中一组向量被检索到，该数据也将被召回。
+
+### 提高向量搜索精度的方法
+
+1. 更好分词分段：当一段话的结构和语义是完整的，并且是单一的，精度也会提高。因此，许多系统都会优化分词器，尽可能的保障每组数据的完整性。
+2. 精简`index`的内容，减少向量内容的长度：当`index`的内容更少，更准确时，检索精度自然会提高。但与此同时，会牺牲一定的检索范围，适合答案较为严格的场景。
+3. 丰富`index`的数量，可以为同一个`chunk`内容增加多组`index`。
+4. 优化检索词：在实际使用过程中，用户的问题通常是模糊的或是缺失的，并不一定是完整清晰的问题。因此优化用户的问题（检索词）很大程度上也可以提高精度。
+5. 微调向量模型：由于市面上直接使用的向量模型都是通用型模型，在特定领域的检索精度并不高，因此微调向量模型可以很大程度上提高专业领域的检索效果。
+
+## FastGPT 构建知识库方案
+
+在 FastGPT 中，整个知识库由库、集合和数据 3 部分组成。集合可以简单理解为一个`文件`。一个`库`中可以包含多个`集合`，一个`集合`中可以包含多组`数据`。最小的搜索单位是`库`，也就是说，知识库搜索时，是对整个`库`进行搜索，而集合仅是为了对数据进行分类管理，与搜索效果无关。（起码目前还是）
+
+| 库 | 集合 | 数据 |
+| --- | --- | --- |
+| ![](/imgs/datasetEngine1.png) | ![](/imgs/datasetEngine2.png) | ![](/imgs/datasetEngine3.png) |
+
+### 导入数据方案1 - 直接分段导入
+
+选择文件导入时，可以选择直接分段方案。直接分段会利用`句子分词器`对文本进行一定长度拆分，最终分割中多组的`q`。如果使用了直接分段方案，我们建议在`应用`设置`引用提示词`时，使用`通用模板`即可，无需选择`问答模板`。
+
+| 交互 | 结果 |
+| --- | --- |
+| ![](/imgs/datasetEngine4.png) | ![](/imgs/datasetEngine5.png) |
+
+
+### 导入数据方案2 - QA导入
+
+选择文件导入时，可以选择QA拆分方案。仍然需要使用到`句子分词器`对文本进行拆分，但长度比直接分段大很多。在导入后，会先调用`大模型`对分段进行学习，并给出一些`问题`和`答案`，最终问题和答案会一起被存储到`q`中。注意，新版的 FastGPT 为了提高搜索的范围，不再将问题和答案分别存储到 qa 中。
+
+| 交互 | 结果 |
+| --- | --- |
+| ![](/imgs/datasetEngine6.png) | ![](/imgs/datasetEngine7.png) |
+
+### 导入数据方案3 - 手动录入
+
+在 FastGPT 中，你可以在任何一个`集合`中点击右上角的`插入`手动录入知识点，或者使用`标注`功能手动录入。被搜索的内容为`q`，补充内容(可选)为`a`。
+
+|  |  |  |
+| --- | --- | --- |
+| ![](/imgs/datasetEngine8.png) | ![](/imgs/datasetEngine9.png) | ![](/imgs/datasetEngine10.png) |
+
+### 导入数据方案4 - CSV录入
+
+有些数据较为独特，可能需要单独的进行预处理分割后再导入 FastGPT，此时可以选择 csv 导入，可批量的将处理好的数据导入。
+
+![](/imgs/datasetEngine11.png)
+
+### 导入数据方案5 - API导入
+
+参考[FastGPT OpenAPI使用](/docs/development/openapi/#知识库添加数据)。
+
+## QA的组合与引用提示词构建
+
+参考[引用模板与引用提示词示例](/docs/use-cases/ai_settings/#示例)
--- a/docSite/content/docs/use-cases/kb.md
+++ b/docSite/content/docs/use-cases/kb.md
@@ -73,7 +73,7 @@ weight: 340
 ![手动录入知识库结果](/imgs/9.png)

 导入结果如上图。可以看到，我们均采用的是问答对的格式，而不是粗略的直接导入。目的就是为了模拟用户问题，进一步的提高向量搜索的匹配效果。可以为同一个问题设置多种问法，效果更佳。
-FastGPT 还提供了 openapi 功能，你可以在本地对特殊格式的文件进行处理后，再上传到 FastGPT，具体可以参考：[FastGPT Api Docs](https://doc.fastgpt.run/docs/development/openapi)
+FastGPT 还提供了 openapi 功能，你可以在本地对特殊格式的文件进行处理后，再上传到 FastGPT，具体可以参考：[FastGPT Api Docs](https://doc.fastgpt.in/docs/development/openapi)

 ## 知识库微调和参数调整

--- a/docSite/content/docs/use-cases/openapi.md
+++ b/docSite/content/docs/use-cases/openapi.md
@@ -4,7 +4,7 @@ description: "通过与 OpenAI 兼容的 API 对接第三方应用"
 icon: "model_training"
 draft: false
 toc: true
-weight: 311
+weight: 312
 ---

 ## 获取 API 秘钥
--- a/docSite/content/docs/use-cases/wechat.md
+++ b/docSite/content/docs/use-cases/wechat.md
@@ -0,0 +1,76 @@
+---
+title: " 接入微信和企业微信 "
+description: "FastGPT 接入微信和企业微信 "
+icon: "chat"
+draft: false
+toc: true
+weight: 322
+---
+
+# FastGPT 三分钟接入微信/企业微信
+私人微信和企业微信接入的方式基本一样，不同的地方会刻意指出。   
+[查看视频教程](https://www.bilibili.com/video/BV1cu411F7FN/?spm_id_from=333.1007.top_right_bar_window_history.content.click&vd_source=903c2b09b7412037c2eddc6a8fb9828b)
+## 创建APIKey
+首先找到我们需要接入的应用，然后点击「外部使用」->「API访问」创建一个APIKey并保存。
+
+![](/imgs/wechat1.png)
+
+## 配置微秘书
+
+打开[微秘书](https://wechat.aibotk.com?r=zWLnZK) 注册登陆后找到菜单栏「基础配置」->「智能配置」，按照下图配置。
+
+![](/imgs/wechat2.png)
+
+继续往下看到 `apikey` 和`服务器根地址`，这里`apikey`填写我们在 FastGPT 应用外部访问中创建的 APIkey，服务器根地址填写官方地址或者私有化部署的地址，这里用官方地址示例，注意要添加`/v1`后缀,填写完毕后保存。
+
+![](/imgs/wechat3.png)
+
+## sealos部署服务
+
+[访问sealos](https://cloud.sealos.io/) 登陆进来之后打开「应用管理」-> 「新建应用」。
+- 应用名：称随便填写
+- 镜像名：私人微信填写 aibotk/wechat-assistant 企业微信填写 aibotk/worker-assistant
+- cpu和内存建议 1c1g
+
+![](/imgs/wechat4.png)
+
+往下翻页找到「高级配置」-> 「编辑环境变量」
+
+![](/imgs/wechat5.png)
+
+这里需要填写四个环境变量：   
+AIBOTK_KEY="微秘书 APIKEY"   
+AIBOTK_SECRET="微秘书 APISECRET"   
+WORK_PRO_TOKEN="你申请的企微 token"   （企业微信需要填写，私人微信不需要）
+WECHATY_PUPPET_SERVICE_AUTHORITY=token-service-discovery-test.juzibot.com（企业微信需要填写，私人微信不需要）  
+
+这里最后两个变量只有部署企业微信才需要，私人微信只需要填写前两个即可。
+
+![](/imgs/wechat6.png)
+
+这里环境变量我们介绍下如何填写：
+
+`AIBOTK_KEY` 和 `AIBOTK_SECRET` 我们需要回到[微秘书](https://wechat.aibotk.com?r=zWLnZK)找到「个人中心」,这里的 APIKEY 对应 AIBOTK_KEY ，APISECRET 对应 `AIBOTK_SECRET`。
+
+![](/imgs/wechat7.png)
+
+`WORK_PRO_TOKEN` [点击这里](https://tss.juzibot.com?aff=aibotk)申请 token 然后填入即可。   
+
+`WECHATY_PUPPET_SERVICE_AUTHORITY`的值复制过去就可以。
+
+填写完毕后点右上角「部署」，等待应用状态变为运行中。  
+
+![](/imgs/wechat8.png)
+
+返回[微秘书](https://wechat.aibotk.com?r=zWLnZK) 找到「首页」，扫码登陆需要接入的微信号。
+
+![](/imgs/wechat9.png)
+
+## 测试
+只需要发送信息，或者拉入群聊@登陆的微信就会回复信息啦。
+![](/imgs/wechat10.png)
+
+
+
+
+
--- a/docSite/content/docs/workflow/examples/fixingEvidence.md
+++ b/docSite/content/docs/workflow/examples/fixingEvidence.md
@@ -86,7 +86,7 @@ weight: 142
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -210,14 +210,14 @@ weight: 142
        "type": "target",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
--- a/docSite/content/docs/workflow/examples/google_search.md
+++ b/docSite/content/docs/workflow/examples/google_search.md
@@ -132,7 +132,7 @@ export default async function (ctx: FunctionContext) {
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -179,7 +179,7 @@ export default async function (ctx: FunctionContext) {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -410,14 +410,14 @@ export default async function (ctx: FunctionContext) {
        "key": "quoteQA",
        "type": "target",
        "label": "引用内容",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
--- a/docSite/content/docs/workflow/examples/lab_appointment.md
+++ b/docSite/content/docs/workflow/examples/lab_appointment.md
@@ -131,7 +131,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -174,7 +174,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -413,7 +413,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -527,7 +527,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
  {
    "moduleId": "zltb5l",
    "name": "知识库搜索",
-    "flowType": "kbSearchNode",
+    "flowType": "datasetSearchNode",
    "showStatus": true,
    "position": {
      "x": 1634.995464753433,
@@ -630,7 +630,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
        "label": "引用内容",
        "description": "始终返回数组，如果希望搜索结果为空时执行额外操作，需要用到上面的两个输入以及目标模块的触发器",
        "type": "source",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "targets": [
          {
            "moduleId": "bjfklc",
@@ -729,14 +729,14 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
        "key": "quoteQA",
        "type": "target",
        "label": "引用内容",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": true
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -831,7 +831,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -874,7 +874,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -1006,7 +1006,7 @@ HTTP 模块允许你调用任意 POST 类型的 HTTP 接口，从而实验一些
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
--- a/docSite/content/docs/workflow/examples/op_question.md
+++ b/docSite/content/docs/workflow/examples/op_question.md
@@ -83,7 +83,7 @@ weight: 144
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -97,7 +97,7 @@ weight: 144
  {
    "moduleId": "nkxlso",
    "name": "知识库搜索",
-    "flowType": "kbSearchNode",
+    "flowType": "datasetSearchNode",
    "showStatus": true,
    "position": {
      "x": 1542.6434554710224,
@@ -189,7 +189,7 @@ weight: 144
        "label": "引用内容",
        "description": "始终返回数组，如果希望搜索结果为空时执行额外操作，需要用到上面的两个输入以及目标模块的触发器",
        "type": "source",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "targets": [
          {
            "moduleId": "ol82hp",
@@ -291,14 +291,14 @@ weight: 144
        "type": "target",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": true
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -389,7 +389,7 @@ weight: 144
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -432,7 +432,7 @@ weight: 144
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
--- a/docSite/content/docs/workflow/examples/versatile_assistant.md
+++ b/docSite/content/docs/workflow/examples/versatile_assistant.md
@@ -245,7 +245,7 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -300,7 +300,7 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -427,7 +427,7 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -713,14 +713,14 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "type": "custom",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -871,14 +871,14 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "type": "custom",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -1085,14 +1085,14 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "type": "custom",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -1162,7 +1162,7 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
      {
        "key": "history",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "type": "source",
        "targets": [
          {
@@ -1205,7 +1205,7 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
@@ -1452,14 +1452,14 @@ PS2：配置中的问题分类还包含着“联网搜索”，这个是另一
        "type": "custom",
        "label": "引用内容",
        "description": "对象数组格式，结构：\n [{q:'问题',a:'回答'}]",
-        "valueType": "kb_quote",
+        "valueType": "datasetQuote",
        "connected": false
      },
      {
        "key": "history",
        "type": "target",
        "label": "聊天记录",
-        "valueType": "chat_history",
+        "valueType": "chatHistory",
        "connected": true
      },
      {
--- a/docSite/content/docs/workflow/modules/ai_chat.md
+++ b/docSite/content/docs/workflow/modules/ai_chat.md
@@ -42,17 +42,17 @@ weight: 123

 ```ts
 type DataType = {
-  kb_id?: string;
+  dataset_id?: string;
  id?: string;
  q: string;
  a: string;
  source?: string;
 };
-// 如果是外部引入的内容，尽量不要携带 kb_id 和 id
+// 如果是外部引入的内容，尽量不要携带 dataset_id 和 id
 const quoteList: DataType[] = [
-  { kb_id: '11', id: '222', q: '你还', a: '哈哈', source: '' },
-  { kb_id: '11', id: '333', q: '你还', a: '哈哈', source: '' },
-  { kb_id: '11', id: '444', q: '你还', a: '哈哈', source: '' }
+  { dataset_id: '11', id: '222', q: '你还', a: '哈哈', source: '' },
+  { dataset_id: '11', id: '333', q: '你还', a: '哈哈', source: '' },
+  { dataset_id: '11', id: '444', q: '你还', a: '哈哈', source: '' }
 ];
 ```

--- a/docSite/static/wechat-fastgpt.webp
+++ b/docSite/static/wechat-fastgpt.webp
--- a/files/models/Baichuan2/openai_api.py
+++ b/files/models/Baichuan2/openai_api.py
@@ -0,0 +1,233 @@
+# coding=utf-8
+# Implements API for Baichuan2-7B-Chat in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
+# Usage: python openai_api.py
+
+import gc
+import time
+import torch
+import uvicorn
+from pydantic import BaseModel, Field, validator
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+from typing import Any, Dict, List, Optional, Union
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sse_starlette.sse import ServerSentEvent, EventSourceResponse
+from transformers.generation.utils import GenerationConfig
+import random
+import string
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI): # collects GPU memory
+    yield
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class ModelCard(BaseModel):
+    id: str
+    object: str = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: str = "owner"
+    root: Optional[str] = None
+    parent: Optional[str] = None
+    permission: Optional[list] = None
+
+class ModelList(BaseModel):
+    object: str = "list"
+    data: List[str] = []  # Assuming ModelCard is a string type. Replace with the correct type if not.
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+    @validator('role')
+    def check_role(cls, v):
+        if v not in ["user", "assistant", "system"]:
+            raise ValueError('role must be one of "user", "assistant", "system"')
+        return v
+
+class DeltaMessage(BaseModel):
+    role: Optional[str] = None
+    content: Optional[str] = None
+
+    @validator('role', allow_reuse=True)
+    def check_role(cls, v):
+        if v is not None and v not in ["user", "assistant", "system"]:
+            raise ValueError('role must be one of "user", "assistant", "system"')
+        return v
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    max_length: Optional[int] = 8192  # max_length should be an integer.
+    stream: Optional[bool] = False
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: str
+
+    @validator('finish_reason')
+    def check_finish_reason(cls, v):
+        if v not in ["stop", "length"]:
+            raise ValueError('finish_reason must be one of "stop" or "length"')
+        return v
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+     index: int
+     delta: DeltaMessage
+     finish_reason: Optional[str]
+
+     @validator('finish_reason', allow_reuse=True)
+     def check_finish_reason(cls, v):
+         if v is not None and v not in ["stop", "length"]:
+             raise ValueError('finish_reason must be one of "stop" or "length"')
+         return v
+
+class ChatCompletionResponse(BaseModel):
+     id:str 
+     object:str 
+     
+     @validator('object')
+     def check_object(cls,v): 
+         if v not in ["chat.completion","chat.completion.chunk"]: 
+             raise ValueError("object must be one of 'chat.completion' or 'chat.completion.chunk'")
+         return v
+     
+     created :Optional[int]=Field(default_factory=lambda:int(time.time()))
+     model:str 
+     choices :List[Union[ChatCompletionResponseChoice,ChatCompletionResponseStreamChoice]]
+
+
+def generate_id():
+    possible_characters = string.ascii_letters + string.digits
+    random_string = ''.join(random.choices(possible_characters, k=29))
+    return 'chatcmpl-' + random_string
+    
+
+@app.get("/v1/models", response_model=ModelList)
+async def list_models():
+    global model_args
+    model_card = ModelCard(id="gpt-3.5-turbo")
+    return ModelList(data=[model_card])
+
+
+@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
+async def create_chat_completion(request: ChatCompletionRequest):
+    global model, tokenizer
+    if request.messages[-1].role != "user":
+        raise HTTPException(status_code=400, detail="Invalid request")
+    query = request.messages[-1].content
+    prev_messages = request.messages[:-1]
+    if len(prev_messages) > 0 and prev_messages[0].role == "system":
+        query = prev_messages.pop(0).content + query
+    messages = []
+    for message in prev_messages:
+        messages.append({"role": message.role, "content": message.content})
+    
+    messages.append({"role": "user", "content": query})
+    
+    if request.stream:
+        generate = predict(messages, request.model)
+        return EventSourceResponse(generate, media_type="text/event-stream")
+    
+    response = '本接口不支持非stream模式'
+    choice_data = ChatCompletionResponseChoice(
+        index=0,
+        message=ChatMessage(role="assistant", content=response),
+        finish_reason="stop"
+    )
+    id='chatcmpl-7QyqpwdfhqwajicIEznoc6Q47XAyW'
+
+    return ChatCompletionResponse(id=id,model=request.model, choices=[choice_data], object="chat.completion")
+
+
+async def predict(messages: List[List[str]], model_id: str):
+    global model, tokenizer
+    id = generate_id()
+    created = int(time.time())
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0,
+        delta=DeltaMessage(role="assistant",content=""),
+        finish_reason=None
+    )
+    chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+
+    current_length = 0
+
+    for new_response in model.chat(tokenizer, messages, stream=True):
+        if len(new_response) == current_length:
+            continue
+
+        new_text = new_response[current_length:]
+        current_length = len(new_response)
+
+        choice_data = ChatCompletionResponseStreamChoice(
+            index=0,
+            delta=DeltaMessage(content=new_text),
+            finish_reason=None
+        )
+        chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+        yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+
+
+    choice_data = ChatCompletionResponseStreamChoice(
+        index=0,
+        delta=DeltaMessage(),
+        finish_reason="stop"
+    )
+    chunk = ChatCompletionResponse(id=id,object="chat.completion.chunk",created=created,model=model_id, choices=[choice_data])
+    yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
+    yield '[DONE]'
+
+
+def load_models():
+    print("本次加载的大语言模型为: Baichuan-13B-Chat")
+    tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", use_fast=False, trust_remote_code=True)
+    # model = AutoModelForCausalLM.from_pretrained("Baichuan2-13B-Chat", torch_dtype=torch.float32, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", torch_dtype=torch.float16, trust_remote_code=True)
+    model = model.cuda() 
+    model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat") 
+    return tokenizer, model
+
+if __name__ == "__main__":
+    tokenizer, model = load_models()
+    uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)
+
+    while True:
+        try:
+            # 在这里执行您的程序逻辑
+
+            # 检查显存使用情况，如果超过阈值（例如90%），则触发垃圾回收
+            if torch.cuda.is_available():
+                gpu_memory_usage = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
+                if gpu_memory_usage > 0.9:
+                    gc.collect()
+                    torch.cuda.empty_cache()
+        except RuntimeError as e:
+            if "out of memory" in str(e):
+                print("显存不足，正在重启程序...")
+                gc.collect()
+                torch.cuda.empty_cache()
+                time.sleep(5) # 等待一段时间以确保显存已释放
+                tokenizer, model = load_models()
+            else:
+                raise e
+
+    
--- a/files/models/Baichuan2/requirements.txt
+++ b/files/models/Baichuan2/requirements.txt
@@ -0,0 +1,14 @@
+protobuf
+transformers==4.30.2
+cpm_kernels
+torch>=2.0
+gradio
+mdtex2html
+sentencepiece
+accelerate
+sse-starlette
+fastapi==0.99.1
+pydantic==1.10.7
+uvicorn==0.21.1
+xformers
+bitsandbytes
--- a/package.json
+++ b/package.json
@@ -4,20 +4,28 @@
  "private": true,
  "scripts": {
    "prepare": "husky install",
-    "format": "prettier --config \"./.prettierrc.js\" --write \"./**/src/**/*.{ts,tsx,scss}\""
+    "format-code": "prettier --config \"./.prettierrc.js\" --write \"./**/src/**/*.{ts,tsx,scss}\"",
+    "format-doc": "zhlint --dir ./docSite *.md --fix"
  },
  "devDependencies": {
+    "@types/multer": "^1.4.10",
    "husky": "^8.0.3",
+    "i18next": "^22.5.1",
    "lint-staged": "^13.2.1",
+    "next-i18next": "^13.3.0",
    "prettier": "^3.0.3",
-    "i18next": "^23.2.11",
-    "react-i18next": "^13.0.2",
-    "next-i18next": "^14.0.0"
+    "react-i18next": "^12.3.1",
+    "zhlint": "^0.7.1"
  },
  "lint-staged": {
-    "./**/**/*.{ts,tsx,scss}": "npm run format"
+    "./**/**/*.{ts,tsx,scss}": "npm run format-code",
+    "./**/**/*.md": "npm run format-doc"
  },
  "engines": {
    "node": ">=18.0.0"
+  },
+  "dependencies": {
+    "multer": "1.4.5-lts.1",
+    "openai": "4.16.1"
  }
 }
--- a/packages/common/bill/constants.ts
+++ b/packages/common/bill/constants.ts
@@ -1 +0,0 @@
-export const PRICE_SCALE = 100000;
--- a/packages/common/package.json
+++ b/packages/common/package.json
@@ -1,15 +0,0 @@
-{
-  "name": "@fastgpt/common",
-  "version": "1.0.0",
-  "dependencies": {
-    "mongoose": "^7.0.2",
-    "winston": "^3.10.0",
-    "winston-mongodb": "^5.1.1",
-    "axios": "^1.5.1",
-    "nextjs-cors": "^2.1.2",
-    "next": "13.5.2"
-  },
-  "devDependencies": {
-    "@types/node": "^20.8.5"
-  }
-}
--- a/packages/common/tools/file.ts
+++ b/packages/common/tools/file.ts
@@ -1,23 +0,0 @@
-import { strIsLink } from './str';
-
-export const fileImgs = [
-  { suffix: 'pdf', src: '/imgs/files/pdf.svg' },
-  { suffix: 'csv', src: '/imgs/files/csv.svg' },
-  { suffix: '(doc|docs)', src: '/imgs/files/doc.svg' },
-  { suffix: 'txt', src: '/imgs/files/txt.svg' },
-  { suffix: 'md', src: '/imgs/files/markdown.svg' },
-  { suffix: '.', src: '/imgs/files/file.svg' }
-];
-
-export function getFileIcon(name = '') {
-  return fileImgs.find((item) => new RegExp(item.suffix, 'gi').test(name))?.src;
-}
-export function getSpecialFileIcon(name = '') {
-  if (name === 'manual') {
-    return '/imgs/files/manual.svg';
-  } else if (name === 'mark') {
-    return '/imgs/files/mark.svg';
-  } else if (strIsLink(name)) {
-    return '/imgs/files/link.svg';
-  }
-}
--- a/packages/common/tools/stream.ts
+++ b/packages/common/tools/stream.ts
@@ -1,39 +0,0 @@
-import type { NextApiResponse } from 'next';
-
-export function responseWriteController({
-  res,
-  readStream
-}: {
-  res: NextApiResponse;
-  readStream: any;
-}) {
-  res.on('drain', () => {
-    readStream.resume();
-  });
-
-  return (text: string | Buffer) => {
-    const writeResult = res.write(text);
-    if (!writeResult) {
-      readStream.pause();
-    }
-  };
-}
-
-export function responseWrite({
-  res,
-  write,
-  event,
-  data
-}: {
-  res?: NextApiResponse;
-  write?: (text: string) => void;
-  event?: string;
-  data: string;
-}) {
-  const Write = write || res?.write;
-
-  if (!Write) return;
-
-  event && Write(`event: ${event}\n`);
-  Write(`data: ${data}\n\n`);
-}
--- a/packages/common/type/chat.d.ts
+++ b/packages/common/type/chat.d.ts
--- a/packages/core/ai/type.d.ts
+++ b/packages/core/ai/type.d.ts
@@ -1,12 +0,0 @@
-import OpenAI from 'openai';
-export type ChatCompletionRequestMessage = OpenAI.Chat.CreateChatCompletionRequestMessage;
-export type ChatCompletion = OpenAI.Chat.ChatCompletion;
-export type CreateChatCompletionRequest = OpenAI.Chat.ChatCompletionCreateParams;
-
-export type StreamChatType = Stream<OpenAI.Chat.ChatCompletionChunk>;
-
-export type PromptTemplateItem = {
-  title: string;
-  desc: string;
-  value: string;
-};
--- a/packages/core/chat/type.d.ts
+++ b/packages/core/chat/type.d.ts
--- a/packages/core/dataset/constant.ts
+++ b/packages/core/dataset/constant.ts
@@ -1,36 +0,0 @@
-export enum DatasetTypeEnum {
-  folder = 'folder',
-  dataset = 'dataset'
-}
-
-export const DatasetTypeMap = {
-  [DatasetTypeEnum.folder]: {
-    name: 'folder'
-  },
-  [DatasetTypeEnum.dataset]: {
-    name: 'dataset'
-  }
-};
-
-export enum FileStatusEnum {
-  embedding = 'embedding',
-  ready = 'ready'
-}
-
-export enum DatasetSpecialIdEnum {
-  manual = 'manual',
-  mark = 'mark'
-}
-export const datasetSpecialIdMap = {
-  [DatasetSpecialIdEnum.manual]: {
-    name: 'kb.Manual Data',
-    sourceName: 'kb.Manual Input'
-  },
-  [DatasetSpecialIdEnum.mark]: {
-    name: 'kb.Mark Data',
-    sourceName: 'kb.Manual Mark'
-  }
-};
-export const datasetSpecialIds: string[] = [DatasetSpecialIdEnum.manual, DatasetSpecialIdEnum.mark];
-
-export const FolderAvatarSrc = '/imgs/files/folder.svg';
--- a/packages/core/dataset/schema.ts
+++ b/packages/core/dataset/schema.ts
@@ -1,46 +0,0 @@
-import { connectionMongo, type Model } from '@fastgpt/common/mongo';
-const { Schema, model, models } = connectionMongo;
-import { DatasetSchemaType } from './type';
-import { DatasetTypeMap } from './constant';
-
-const DatasetSchema = new Schema({
-  parentId: {
-    type: Schema.Types.ObjectId,
-    ref: 'kb',
-    default: null
-  },
-  userId: {
-    type: Schema.Types.ObjectId,
-    ref: 'user',
-    required: true
-  },
-  updateTime: {
-    type: Date,
-    default: () => new Date()
-  },
-  avatar: {
-    type: String,
-    default: '/icon/logo.svg'
-  },
-  name: {
-    type: String,
-    required: true
-  },
-  vectorModel: {
-    type: String,
-    required: true,
-    default: 'text-embedding-ada-002'
-  },
-  type: {
-    type: String,
-    enum: Object.keys(DatasetTypeMap),
-    required: true,
-    default: 'dataset'
-  },
-  tags: {
-    type: [String],
-    default: []
-  }
-});
-
-export const MongoDataset: Model<DatasetSchemaType> = models['kb'] || model('kb', DatasetSchema);
--- a/packages/core/dataset/type.d.ts
+++ b/packages/core/dataset/type.d.ts
@@ -1,13 +0,0 @@
-import { DatasetTypeEnum } from './constant';
-
-export type DatasetSchemaType = {
-  _id: string;
-  userId: string;
-  parentId: string;
-  updateTime: Date;
-  avatar: string;
-  name: string;
-  vectorModel: string;
-  tags: string[];
-  type: `${DatasetTypeEnum}`;
-};
--- a/packages/core/dataset/utils.ts
+++ b/packages/core/dataset/utils.ts
@@ -1,8 +0,0 @@
-import { datasetSpecialIds } from './constant';
-import { strIsLink } from '@fastgpt/common/tools/str';
-
-export function isSpecialFileId(id: string) {
-  if (datasetSpecialIds.includes(id)) return true;
-  if (strIsLink(id)) return true;
-  return false;
-}
--- a/packages/core/package.json
+++ b/packages/core/package.json
@@ -1,14 +0,0 @@
-{
-  "name": "@fastgpt/core",
-  "version": "1.0.0",
-  "dependencies": {
-    "@fastgpt/common": "workspace:*",
-    "@fastgpt/support": "workspace:*",
-    "encoding": "^0.1.13",
-    "openai": "^4.12.1",
-    "tunnel": "^0.0.6"
-  },
-  "devDependencies": {
-    "@types/tunnel": "^0.0.4"
-  }
-}
--- a/packages/global/common/error/code/app.ts
+++ b/packages/global/common/error/code/app.ts
@@ -0,0 +1,28 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 502000 */
+export enum AppErrEnum {
+  unExist = 'unExist',
+  unAuthApp = 'unAuthApp'
+}
+const appErrList = [
+  {
+    statusText: AppErrEnum.unExist,
+    message: '应用不存在'
+  },
+  {
+    statusText: AppErrEnum.unAuthApp,
+    message: '无权操作该应用'
+  }
+];
+export default appErrList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 502000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${AppErrEnum}`>);
--- a/packages/global/common/error/code/chat.ts
+++ b/packages/global/common/error/code/chat.ts
@@ -0,0 +1,23 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 504000 */
+export enum ChatErrEnum {
+  unAuthChat = 'unAuthChat'
+}
+const errList = [
+  {
+    statusText: ChatErrEnum.unAuthChat,
+    message: '无权操作该对话记录'
+  }
+];
+export default errList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 504000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${ChatErrEnum}`>);
--- a/packages/global/common/error/code/dataset.ts
+++ b/packages/global/common/error/code/dataset.ts
@@ -0,0 +1,43 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 501000 */
+export enum DatasetErrEnum {
+  unAuthDataset = 'unAuthDataset',
+  unCreateCollection = 'unCreateCollection',
+  unAuthDatasetCollection = 'unAuthDatasetCollection',
+  unAuthDatasetData = 'unAuthDatasetData',
+  unAuthDatasetFile = 'unAuthDatasetFile'
+}
+const datasetErr = [
+  {
+    statusText: DatasetErrEnum.unAuthDataset,
+    message: '无权操作该知识库'
+  },
+  {
+    statusText: DatasetErrEnum.unAuthDatasetCollection,
+    message: '无权操作该数据集'
+  },
+  {
+    statusText: DatasetErrEnum.unAuthDatasetData,
+    message: '无权操作该数据'
+  },
+  {
+    statusText: DatasetErrEnum.unAuthDatasetFile,
+    message: '无权操作该文件'
+  },
+  {
+    statusText: DatasetErrEnum.unCreateCollection,
+    message: '无权创建数据集'
+  }
+];
+export default datasetErr.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 501000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${DatasetErrEnum}`>);
--- a/packages/global/common/error/code/openapi.ts
+++ b/packages/global/common/error/code/openapi.ts
@@ -0,0 +1,28 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 506000 */
+export enum OpenApiErrEnum {
+  unExist = 'unExist',
+  unAuth = 'unAuth'
+}
+const errList = [
+  {
+    statusText: OpenApiErrEnum.unExist,
+    message: 'Api Key 不存在'
+  },
+  {
+    statusText: OpenApiErrEnum.unAuth,
+    message: '无权操作该 Api Key'
+  }
+];
+export default errList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 506000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${OpenApiErrEnum}`>);
--- a/packages/global/common/error/code/outLink.ts
+++ b/packages/global/common/error/code/outLink.ts
@@ -0,0 +1,34 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 505000 */
+export enum OutLinkErrEnum {
+  unExist = 'unExist',
+  unAuthLink = 'unAuthLink',
+  linkUnInvalid = 'linkUnInvalid'
+}
+const errList = [
+  {
+    statusText: OutLinkErrEnum.unExist,
+    message: '分享链接不存在'
+  },
+  {
+    statusText: OutLinkErrEnum.unAuthLink,
+    message: '分享链接无效'
+  },
+  {
+    code: 501,
+    statusText: OutLinkErrEnum.linkUnInvalid,
+    message: '分享链接无效'
+  }
+];
+export default errList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: cur?.code || 505000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${OutLinkErrEnum}`>);
--- a/packages/global/common/error/code/plugin.ts
+++ b/packages/global/common/error/code/plugin.ts
@@ -0,0 +1,28 @@
+import { ErrType } from '../errorCode';
+
+/* dataset: 507000 */
+export enum PluginErrEnum {
+  unExist = 'unExist',
+  unAuth = 'unAuth'
+}
+const errList = [
+  {
+    statusText: PluginErrEnum.unExist,
+    message: '插件不存在'
+  },
+  {
+    statusText: PluginErrEnum.unAuth,
+    message: '无权操作该插件'
+  }
+];
+export default errList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 507000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${PluginErrEnum}`>);
--- a/packages/global/common/error/code/team.ts
+++ b/packages/global/common/error/code/team.ts
@@ -0,0 +1,22 @@
+import { ErrType } from '../errorCode';
+
+/* team: 500000 */
+export enum TeamErrEnum {
+  teamOverSize = 'teamOverSize',
+  unAuthTeam = 'unAuthTeam'
+}
+const teamErr = [
+  { statusText: TeamErrEnum.teamOverSize, message: 'error.team.overSize' },
+  { statusText: TeamErrEnum.unAuthTeam, message: '无权操作该团队' }
+];
+export default teamErr.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 500000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${TeamErrEnum}`>);
--- a/packages/global/common/error/code/user.ts
+++ b/packages/global/common/error/code/user.ts
@@ -0,0 +1,26 @@
+import { ErrType } from '../errorCode';
+
+/* team: 503000 */
+export enum UserErrEnum {
+  unAuthUser = 'unAuthUser',
+  unAuthRole = 'unAuthRole',
+  binVisitor = 'binVisitor',
+  balanceNotEnough = 'balanceNotEnough'
+}
+const errList = [
+  { statusText: UserErrEnum.unAuthUser, message: '找不到该用户' },
+  { statusText: UserErrEnum.binVisitor, message: '您的身份校验未通过' },
+  { statusText: UserErrEnum.binVisitor, message: '您当前身份为游客，无权操作' },
+  { statusText: UserErrEnum.balanceNotEnough, message: '账号余额不足~' }
+];
+export default errList.reduce((acc, cur, index) => {
+  return {
+    ...acc,
+    [cur.statusText]: {
+      code: 503000 + index,
+      statusText: cur.statusText,
+      message: cur.message,
+      data: null
+    }
+  };
+}, {} as ErrType<`${UserErrEnum}`>);
--- a/packages/global/common/error/errorCode.ts
+++ b/packages/global/common/error/errorCode.ts
@@ -1,3 +1,12 @@
+import appErr from './code/app';
+import chatErr from './code/chat';
+import datasetErr from './code/dataset';
+import openapiErr from './code/openapi';
+import pluginErr from './code/plugin';
+import outLinkErr from './code/outLink';
+import teamErr from './code/team';
+import userErr from './code/user';
+
 export const ERROR_CODE: { [key: number]: string } = {
  400: '请求失败',
  401: '无权访问',
@@ -27,9 +36,19 @@ export enum ERROR_ENUM {
  insufficientQuota = 'insufficientQuota',
  unAuthModel = 'unAuthModel',
  unAuthApiKey = 'unAuthApiKey',
-  unAuthKb = 'unAuthKb',
  unAuthFile = 'unAuthFile'
 }
+
+export type ErrType<T> = Record<
+  string,
+  {
+    code: number;
+    statusText: T;
+    message: string;
+    data: null;
+  }
+>;
+
 export const ERROR_RESPONSE: Record<
  any,
  {
@@ -54,15 +73,10 @@ export const ERROR_RESPONSE: Record<
  [ERROR_ENUM.unAuthModel]: {
    code: 511,
    statusText: ERROR_ENUM.unAuthModel,
-    message: '无权使用该模型',
-    data: null
-  },
-  [ERROR_ENUM.unAuthKb]: {
-    code: 512,
-    statusText: ERROR_ENUM.unAuthKb,
-    message: '无权使用该知识库',
+    message: '无权操作该模型',
    data: null
  },
+
  [ERROR_ENUM.unAuthFile]: {
    code: 513,
    statusText: ERROR_ENUM.unAuthFile,
@@ -74,5 +88,13 @@ export const ERROR_RESPONSE: Record<
    statusText: ERROR_ENUM.unAuthApiKey,
    message: 'Api Key 不合法',
    data: null
-  }
+  },
+  ...appErr,
+  ...chatErr,
+  ...datasetErr,
+  ...openapiErr,
+  ...outLinkErr,
+  ...teamErr,
+  ...userErr,
+  ...pluginErr
 };
--- a/packages/global/common/error/utils.ts
+++ b/packages/global/common/error/utils.ts
@@ -0,0 +1,5 @@
+export const getErrText = (err: any, def = '') => {
+  const msg: string = typeof err === 'string' ? err : err?.message || def || '';
+  msg && console.log('error =>', msg);
+  return msg;
+};
--- a/packages/global/common/file/constants.ts
+++ b/packages/global/common/file/constants.ts
@@ -0,0 +1,5 @@
+export enum BucketNameEnum {
+  dataset = 'dataset'
+}
+
+export const FileBaseUrl = '/api/common/file/read';
--- a/packages/global/common/file/icon.ts
+++ b/packages/global/common/file/icon.ts
@@ -0,0 +1,12 @@
+export const fileImgs = [
+  { suffix: 'pdf', src: '/imgs/files/pdf.svg' },
+  { suffix: 'csv', src: '/imgs/files/csv.svg' },
+  { suffix: '(doc|docs)', src: '/imgs/files/doc.svg' },
+  { suffix: 'txt', src: '/imgs/files/txt.svg' },
+  { suffix: 'md', src: '/imgs/files/markdown.svg' }
+  // { suffix: '.', src: '/imgs/files/file.svg' }
+];
+
+export function getFileIcon(name = '', defaultImg = '/imgs/files/file.svg') {
+  return fileImgs.find((item) => new RegExp(item.suffix, 'gi').test(name))?.src || defaultImg;
+}
--- a/packages/global/common/file/tools.ts
+++ b/packages/global/common/file/tools.ts
@@ -0,0 +1,9 @@
+export const formatFileSize = (bytes: number): string => {
+  if (bytes === 0) return '0 B';
+
+  const k = 1024;
+  const sizes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'];
+  const i = Math.floor(Math.log(bytes) / Math.log(k));
+
+  return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i];
+};
--- a/packages/global/common/file/type.d.ts
+++ b/packages/global/common/file/type.d.ts
@@ -0,0 +1,8 @@
+import { BucketNameEnum } from './constants';
+
+export type FileTokenQuery = {
+  bucketName: `${BucketNameEnum}`;
+  teamId: string;
+  tmbId: string;
+  fileId: string;
+};
--- a/packages/global/common/parentFolder/type.d.ts
+++ b/packages/global/common/parentFolder/type.d.ts
@@ -0,0 +1,4 @@
+export type ParentTreePathItemType = {
+  parentId: string;
+  parentName: string;
+};
--- a/packages/global/common/plugin/types/pluginRes.d.ts
+++ b/packages/global/common/plugin/types/pluginRes.d.ts
--- a/packages/global/common/string/textSplitter.ts
+++ b/packages/global/common/string/textSplitter.ts
@@ -0,0 +1,131 @@
+import { getErrText } from '../error/utils';
+import { countPromptTokens } from './tiktoken';
+
+/**
+ * text split into chunks
+ * maxLen - one chunk len. max: 3500
+ * overlapLen - The size of the before and after Text
+ * maxLen > overlapLen
+ * markdown
+ */
+export const splitText2Chunks = (props: { text: string; maxLen: number; overlapLen?: number }) => {
+  const { text = '', maxLen, overlapLen = Math.floor(maxLen * 0.2) } = props;
+  const tempMarker = 'SPLIT_HERE_SPLIT_HERE';
+
+  const stepReg: Record<number, RegExp> = {
+    0: /^(#\s[^\n]+)\n/gm,
+    1: /^(##\s[^\n]+)\n/gm,
+    2: /^(###\s[^\n]+)\n/gm,
+    3: /^(####\s[^\n]+)\n/gm,
+
+    4: /(\n\n)/g,
+    5: /([\n])/g,
+    6: /[。]|(?!<[^a-zA-Z])\.\s/g,
+    7: /([！？]|!\s|\?\s)/g,
+    8: /([；]|;\s)/g,
+    9: /([，]|,\s)/g
+  };
+
+  const splitTextRecursively = ({
+    text = '',
+    step,
+    lastChunk,
+    overlayChunk
+  }: {
+    text: string;
+    step: number;
+    lastChunk: string;
+    overlayChunk: string;
+  }) => {
+    if (text.length <= maxLen) {
+      return [text];
+    }
+    const reg = stepReg[step];
+    const isMarkdownSplit = step < 4;
+
+    if (!reg) {
+      // use slice-maxLen to split text
+      const chunks: string[] = [];
+      let chunk = '';
+      for (let i = 0; i < text.length; i += maxLen - overlapLen) {
+        chunk = text.slice(i, i + maxLen);
+        chunks.push(chunk);
+      }
+      return chunks;
+    }
+
+    // split text by special char
+    const splitTexts = text
+      .replace(reg, isMarkdownSplit ? `${tempMarker}$1` : `$1${tempMarker}`)
+      .split(`${tempMarker}`)
+      .filter((part) => part);
+
+    let chunks: string[] = [];
+    for (let i = 0; i < splitTexts.length; i++) {
+      let text = splitTexts[i];
+      let chunkToken = lastChunk.length;
+      const textToken = text.length;
+
+      // next chunk is too large / new chunk is too large(The current chunk must be smaller than maxLen)
+      if (textToken >= maxLen || chunkToken + textToken > maxLen * 1.4) {
+        // last chunk is too large, push it to chunks, not add to next chunk
+        if (chunkToken > maxLen * 0.7) {
+          chunks.push(lastChunk);
+          lastChunk = '';
+          overlayChunk = '';
+        }
+        // chunk is small, insert to next chunks
+        const innerChunks = splitTextRecursively({
+          text,
+          step: step + 1,
+          lastChunk,
+          overlayChunk
+        });
+        if (innerChunks.length === 0) continue;
+        chunks = chunks.concat(innerChunks);
+        lastChunk = '';
+        overlayChunk = '';
+        continue;
+      }
+
+      // size less than maxLen, push text to last chunk
+      lastChunk += text;
+      chunkToken += textToken; // Definitely less than 1.4 * maxLen
+
+      // size over lapLen, push it to next chunk
+      if (
+        overlapLen !== 0 &&
+        !isMarkdownSplit &&
+        chunkToken >= maxLen - overlapLen &&
+        textToken < overlapLen
+      ) {
+        overlayChunk += text;
+      }
+      if (chunkToken >= maxLen) {
+        chunks.push(lastChunk);
+        lastChunk = overlayChunk;
+        overlayChunk = '';
+      }
+    }
+
+    /* If the last chunk is independent, it needs to be push chunks. */
+    if (lastChunk && chunks[chunks.length - 1] && !chunks[chunks.length - 1].endsWith(lastChunk)) {
+      chunks.push(lastChunk);
+    }
+
+    return chunks;
+  };
+
+  try {
+    const chunks = splitTextRecursively({ text, step: 0, lastChunk: '', overlayChunk: '' });
+
+    const tokens = chunks.reduce((sum, chunk) => sum + countPromptTokens(chunk, 'system'), 0);
+
+    return {
+      chunks,
+      tokens
+    };
+  } catch (err) {
+    throw new Error(getErrText(err));
+  }
+};
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Archer	c5664c7e90	feat: vision model (#489 ) * mongo init * perf: mongo connect * perf: tts perf: whisper and tts peref: tts whisper permission log reabase (#488) * perf: modal * i18n * perf: schema lean * feat: vision model format * perf: tts loading * perf: static data * perf: tts * feat: image * perf: image * perf: upload image and title * perf: image size * doc * perf: color * doc * speaking can not select file * doc	2023-11-18 15:42:35 +08:00
heheer	70f3373246	add image input (#486 ) * add image input * use json	2023-11-17 18:22:29 +08:00
左风	af16817a4a	add wechat (#482 )	2023-11-17 17:15:41 +08:00
Archer	4358b6de4d	Add whisper and tts ui (#484 ) Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>	2023-11-17 00:03:05 +08:00
Archer	f6aea484ce	fix: 46 tmbId empty (#480 ) * mongo init * perf: mongo connect * perf: favicon * fix: member id * 46fix sh * doc	2023-11-16 17:10:04 +08:00
Archer	fbe1d8cfed	Fixed the duplicate data check problem, history filter and add tts stream (#477 )	2023-11-16 16:22:08 +08:00
Archer	16103029f5	doc and config rerank (#475 )	2023-11-16 10:46:47 +08:00
Archer	cd3acb44ab	v4.6-4 (#473 )	2023-11-15 21:35:50 +08:00
Archer	bfd8be5df0	v4.6-3 (#471 )	2023-11-15 11:36:25 +08:00
Carson Yang	592e1a93a2	README: add "back top top" button (#441 ) Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>	2023-11-15 11:30:21 +08:00
heheer	2b8ff7d32c	change default title (#468 )	2023-11-13 22:44:11 +08:00
heheer	4593eef2ff	add “favicon” feconfig (#467 ) * add favicon config * add default	2023-11-13 16:05:05 +08:00
Archer	d91551e6be	v4.6-3 (#463 )	2023-11-10 11:14:08 +08:00
Archer	0a0fe31d3c	V4.6-2 (#460 )	2023-11-09 12:56:16 +08:00
不做了睡大觉	9f889d8806	Create Python API (#457 ) * 更新镜像 * 更新镜像信息 * 更新镜像信息 * Create openai_api.py * Create requirements.txt * Create README.md * 添加python接口 * Delete python directory * Create README.md * Create Python API * 文件结构化 * 文件结构化	2023-11-09 11:52:53 +08:00
Archer	8bb5588305	v4.6 -1 (#459 )	2023-11-09 09:46:57 +08:00
Archer	661ee79943	fix: CQ module output (#445 )	2023-10-30 16:45:36 +08:00
Archer	60ee160131	v4.5.2 (#439 )	2023-10-30 13:26:42 +08:00
Archer	008d0af010	Quote Modal UI and fix doc (#432 )	2023-10-25 20:13:32 +08:00
lizhuang	f2fb0aedfd	Update README.md 文档改为 https://doc.fastgpt.in网站访问 (#424 ) 文档改为https://doc.fastgpt.in网站访问	2023-10-24 18:07:30 +08:00
Archer	1dca5edcc6	v4.5.1-3 (#427 )	2023-10-24 17:32:36 +08:00
Archer	1942cb0d67	perf: btn color (#423 )	2023-10-24 13:19:23 +08:00
Archer	bf6dbfb245	v4.5.1-2 (#421 )	2023-10-23 15:05:13 +08:00
Archer	d37433eacd	Config file to set doc baseurl (#419 )	2023-10-23 08:56:43 +08:00
Archer	a3534407bf	v4.5.1 (#417 )	2023-10-22 23:54:04 +08:00
Carson Yang	3091a90df6	Update README (#418 ) Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>	2023-10-22 23:47:09 +08:00
Carson Yang	41b8f4443c	Docs: update qr for wechat group (#416 ) Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>	2023-10-22 23:28:46 +08:00
Carson Yang	777f089423	Docs: update README (#407 ) Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>	2023-10-18 15:51:51 +08:00
不做了睡大觉	b23e00f3e5	添加Baichuan2-7B-Chat模型接口文件 (#404 ) * 更新镜像 * 更新镜像信息 * 更新镜像信息 * Create openai_api.py * Create requirements.txt	2023-10-18 10:34:22 +08:00