v4.4.5-3 (#357)

错别字，市区改为时区

.github/ISSUE_TEMPLATE/bugs.md

@@ -4,21 +4,22 @@ about: 详细清晰的描述你遇到的问题
 title: ''
 labels: bug
 assignees: ''
-
 ---
 
 **例行检查**
 
-[//]: # (方框内删除已有的空格，填 x 号)
-+ [ ] 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 
-+ [ ] 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**
+[//]: # '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**
 
 **你的版本**
-+ [ ] 公有云版本
-+ [ ] 私有部署版本
+
+- [ ] 公有云版本
+- [ ] 私有部署版本
 
 **问题描述**
 

.github/ISSUE_TEMPLATE/features.md

@@ -8,13 +8,13 @@ assignees: ''
 
 **例行检查**
 
-[//]: # '方框内删除已有的空格，填 x 号'
+[//]: # '方框内填 x 表示打钩'
 
 - [ ] 我已确认目前没有类似 features
 - [ ] 我已确认我已升级到最新版本
 - [ ] 我已完整查看过项目 README，已确定现有版本无法满足需求
 - [ ] 我理解并愿意跟进此 features，协助测试和提供反馈
-- [ ] 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 features 可能会被无视或直接关闭**
+- [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 features 可能会被无视或直接关闭**
 
 **功能描述**
 

.github/gh-bot.yml

@@ -0,0 +1,30 @@
+version: v1
+debug: true
+action:
+  printConfig: false
+  release:
+    retry: 15s
+    actionName: Release
+    allowOps:
+      - cuisongliu
+bot:
+  prefix: /
+  spe: _
+  allowOps:
+    - sealos-ci-robot
+    - sealos-release-robot
+  email: sealos-ci-robot@sealos.io
+  username: sealos-ci-robot
+repo:
+  org: false
+
+message:
+  success: |
+    🤖 says: Hooray! The action {{.Body}} has been completed successfully. 🎉
+  format_error: |
+    🤖 says: ‼️ There is a formatting issue with the action, kindly verify the action's format.
+  permission_error: |
+    🤖 says: ‼️ The action doesn't have permission to trigger.
+  release_error: |
+    🤖 says: ‼️ Release action failed.
+    Error details: {{.Error}}

.github/workflows/bot-issues-translator.yml

@@ -0,0 +1,19 @@
+name: 'Github Rebot for issues-translator'
+on:
+  issues:
+    types: [ opened ]
+  issue_comment:
+    types: [ created ]
+jobs:
+  translate:
+    permissions:
+      issues: write
+      discussions: write
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - uses: usthe/issues-translate-action@v2.7
+        with:
+          IS_MODIFY_TITLE: true
+          BOT_GITHUB_TOKEN: ${{ secrets.GH_PAT }}
+          CUSTOM_BOT_NOTE: Bot detected the issue body's language is not English, translate it automatically. 👯👭🏻🧑‍🤝‍🧑👫🧑🏿‍🤝‍🧑🏻👩🏾‍🤝‍👨🏿👬🏿

.github/workflows/deploy-docs.yml

@@ -55,8 +55,6 @@ jobs:
       # Step 4 - Builds the site using Hugo
       - name: Build
         run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs && hugo -v --minify
-        env:
-          HUGO_BASEURL: ${{ vars.BASE_URL }}
 
       # Step 5 - Push our generated site to Vercel
       - name: Deploy to Vercel
@@ -69,3 +67,4 @@ jobs:
           github-comment: false
           vercel-args: '--prod --local-config ../vercel.json' # Optional
           working-directory: docSite/public
+

.github/workflows/deploy-preview.yml

@@ -0,0 +1,94 @@
+name: deploy-docs-preview
+
+on:
+  pull_request_target:
+    paths:
+      - 'docSite/**'
+    branches:
+      - 'main'
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains jobs "deploy-production"
+  deploy-preview:
+    # The environment this job references
+    environment:
+      name: Preview
+      url: ${{ steps.vercel-action.outputs.preview-url }}
+
+    # The type of runner that the job will run on
+    runs-on: ubuntu-22.04
+
+    # Job outputs
+    outputs:
+      url: ${{ steps.vercel-action.outputs.preview-url }}
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Step 1 - Checks-out your repository under $GITHUB_WORKSPACE
+      - name: Checkout
+        uses: actions/checkout@v3
+        with:
+          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
+
+      # Step 2 Detect changes to Docs Content
+      - name: Detect changes in doc content
+        uses: dorny/paths-filter@v2
+        id: filter
+        with:
+          filters: |
+            docs:
+              - 'docSite/content/docs/**'
+          base: main
+
+      # Step 3 - Install Hugo (specific version)
+      - name: Install Hugo
+        uses: peaceiris/actions-hugo@v2
+        with:
+          hugo-version: '0.117.0'
+          extended: true
+
+      # Step 4 - Builds the site using Hugo
+      - name: Build
+        run: cd docSite && hugo mod get -u github.com/colinwilson/lotusdocs && hugo -v --minify
+
+      # Step 5 - Push our generated site to Vercel
+      - name: Deploy to Vercel
+        uses: amondnet/vercel-action@v25
+        id: vercel-action
+        with:
+          vercel-token: ${{ secrets.VERCEL_TOKEN }} # Required
+          vercel-org-id: ${{ secrets.VERCEL_ORG_ID }} #Required
+          vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }} #Required
+          github-comment: false
+          vercel-args: '--local-config ../vercel.json' # Optional
+          working-directory: docSite/public
+          alias-domains: | #Optional
+            fastgpt-staging.vercel.app
+  docsOutput:
+    needs: [ deploy-preview ]
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          ref: ${{ github.event.pull_request.head.ref }}
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
+      - name: Write md
+        run: |
+          echo "# 🤖 Generated by deploy action" > report.md
+          echo "[👀 Visit Preview](${{ needs.deploy-preview.outputs.url }})" >> report.md
+          cat report.md
+      - name: Gh Rebot for Sealos
+        uses: labring/gh-rebot@v0.0.6
+        if: ${{ (github.event_name == 'pull_request_target') }}
+        with:
+          version: v0.0.6
+        env:
+          GH_TOKEN: "${{ secrets.GH_PAT }}"
+          SEALOS_TYPE: "pr_comment"
+          SEALOS_FILENAME: "report.md"
+          SEALOS_REPLACE_TAG: "DEFAULT_REPLACE_DEPLOY"

.github/workflows/fastgpt-image.yml

@@ -3,7 +3,7 @@ on:
   workflow_dispatch:
   push:
     paths:
-      - 'client/**'
+      - 'projects/app/**'
     branches:
       - 'main'
     tags:
@@ -25,6 +25,13 @@ jobs:
         uses: docker/setup-buildx-action@v2
         with:
           driver-opts: network=host
+      - name: Cache Docker layers
+        uses: actions/cache@v2
+        with:
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-${{ github.sha }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-
       - name: Login to GitHub Container Registry
         uses: docker/login-action@v2
         with:
@@ -38,24 +45,26 @@ jobs:
           else
             echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt:${{ github.ref_name }}" >> $GITHUB_ENV
           fi
-
       - name: Build and publish image for main branch or tag push event
         env:
           DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
         run: |
-          cd client && \
           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.description=fastgpt image" \
           --label "org.opencontainers.image.licenses=MIT" \
           --push \
+          --cache-from=type=local,src=/tmp/.buildx-cache \
+          --cache-to=type=local,dest=/tmp/.buildx-cache \
           -t ${DOCKER_REPO_TAGGED} \
           -f Dockerfile \
           .
   push-to-docker-hub:
     needs: build-fastgpt-images
     runs-on: ubuntu-20.04
+    if: github.repository == 'labring/FastGPT'
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
@@ -79,6 +88,7 @@ jobs:
         run: docker push ${{ secrets.DOCKER_IMAGE_NAME }}:${{env.IMAGE_TAG}}
   push-to-ali-hub:
     needs: build-fastgpt-images
+    if: github.repository == 'labring/FastGPT'
     runs-on: ubuntu-20.04
     steps:
       - name: Checkout code

.prettierignore

@@ -1,4 +1,5 @@
 dist
 .vscode
 **/.DS_Store
-node_modules
+node_modules
+docSite/

.vscode/settings.json

@@ -1,10 +1,10 @@
 {
     "editor.formatOnSave": true,
     "editor.mouseWheelZoom": true,
-    "typescript.tsdk": "client/node_modules/typescript/lib",
+    "typescript.tsdk": "node_modules/typescript/lib",
     "prettier.prettierPath": "./node_modules/prettier",
     "i18n-ally.localesPaths": [
-        "client/public/locales"
+        "projects/app/public/locales"
     ],
     "i18n-ally.enabledParsers": ["json"],
     "i18n-ally.keystyle": "nested",

Dockerfile

@@ -0,0 +1,72 @@
+# Install dependencies only when needed
+FROM node:current-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
+
+ARG name
+
+# copy packages and one project
+COPY package.json pnpm-lock.yaml pnpm-workspace.yaml ./
+COPY ./packages ./packages
+COPY ./projects/$name/package.json ./projects/$name/package.json
+COPY ./projects/$name/pnpm-lock.yaml ./projects/$name/pnpm-lock.yaml
+
+RUN \
+  [ -f pnpm-lock.yaml ] && pnpm install || \
+  (echo "Lockfile not found." && exit 1)
+
+RUN pnpm prune
+
+# Rebuild the source code only when needed
+FROM node:current-alpine AS builder
+WORKDIR /app
+
+ARG name
+
+# copy common node_modules and one project node_modules
+COPY --from=deps /app/node_modules ./node_modules
+COPY --from=deps /app/packages ./packages
+COPY ./projects/$name ./projects/$name
+COPY --from=deps /app/projects/$name/node_modules ./projects/$name/node_modules
+COPY pnpm-lock.yaml pnpm-workspace.yaml ./
+COPY ./packages ./packages
+
+# Uncomment the following line in case you want to disable telemetry during the build.
+ENV NEXT_TELEMETRY_DISABLED 1
+RUN npm install -g pnpm
+RUN pnpm --filter=$name run build
+
+FROM node:current-alpine AS runner
+WORKDIR /app
+
+ARG name
+
+# create user and use it
+RUN addgroup --system --gid 1001 nodejs
+RUN adduser --system --uid 1001 nextjs
+
+RUN sed -i 's/https/http/' /etc/apk/repositories
+RUN apk add curl \
+  && apk add ca-certificates \
+  && update-ca-certificates
+
+# copy running files
+COPY --from=builder /app/projects/$name/public ./projects/$name/public
+COPY --from=builder /app/projects/$name/next.config.js ./projects/$name/next.config.js
+COPY --from=builder --chown=nextjs:nodejs /app/projects/$name/.next/standalone ./
+COPY --from=builder --chown=nextjs:nodejs /app/projects/$name/.next/static ./projects/$name/.next/static
+# copy package.json to version file
+COPY --from=builder /app/projects/$name/package.json ./package.json 
+
+ENV NODE_ENV production
+ENV NEXT_TELEMETRY_DISABLED 1
+ENV PORT=3000
+
+EXPOSE 3000
+
+USER nextjs
+
+ENV serverPath=./projects/$name/server.js
+
+ENTRYPOINT ["sh","-c","node ${serverPath}"]

README.md

@@ -18,6 +18,8 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
   <a href="https://github.com/labring/FastGPT#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">相关项目</a>
 </p>
 
+https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409bd33f6d4
+
 ## 🛸 在线体验
 
 [fastgpt.run](https://fastgpt.run/)（服务器在新加坡，部分地区可能无法直连）
@@ -31,8 +33,7 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 
 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
-   - [x] 全局变量
+   - [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
@@ -45,13 +46,11 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 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] 支持直接分段导入
-   - [x] 支持 QA 拆分导入
-   - [x] 支持手动输入内容
-   - [x] 支持 url 读取导入
-   - [x] 支持 CSV 批量导入问答对
+   - [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
 3. 多种效果测试渠道
    - [x] 知识库单点搜索测试
    - [x] 对话时反馈引用并可修改与删除
@@ -82,7 +81,7 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 * [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/)
 * [版本升级](https://doc.fastgpt.run/docs/installation/upgrading)
-* [API 文档](https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh?pre_pathname=%2Fdrive%2Fhome%2F)
+* [API 文档](https://doc.fastgpt.run/docs/development/openapi?pre_pathname=%2Fdrive%2Fhome%2F)
 
 ## 🏘️ 社区交流群
 
@@ -94,7 +93,6 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 
 - [FastGPT 常见问题](https://kjqvjse66l.feishu.cn/docx/HtrgdT0pkonP4kxGx8qcu6XDnGh)
 - [docker 部署教程视频](https://www.bilibili.com/video/BV1jo4y147fT/)
-- [公众号接入视频教程](https://www.bilibili.com/video/BV1xh4y1t7fy/)
 - [FastGPT 知识库演示](https://www.bilibili.com/video/BV1Wo4y1p7i1/)
 
 ## 💪 相关项目
@@ -106,6 +104,7 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 
 ## 🤝 第三方生态
 
+- [OnWeChat 个人微信/企微机器人](https://doc.fastgpt.run/docs/use-cases/onwechat/)
 - [luolinAI: 企微机器人，开箱即用](https://github.com/luolin-ai/FastGPT-Enterprise-WeChatbot)
 
 ## 🌟 Star History
@@ -119,4 +118,4 @@ FastGPT 是一个基于 LLM 大语言模型的知识库问答系统，提供开
 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。
 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://fael3z0zfze.feishu.cn/docx/F155dbirfo8vDDx2WgWc6extnwf)
+4. 联系方式：yujinlong@sealos.io, [点击查看定价策略](https://doc.fastgpt.run/docs/commercial)

client/Dockerfile

@@ -1,65 +0,0 @@
-# Install dependencies only when needed
-FROM node:current-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
-
-# Install dependencies based on the preferred package manager
-COPY package.json ./
-COPY pnpm-lock.yaml* ./
-RUN \
-  [ -f pnpm-lock.yaml ] && pnpm fetch || \
-  (echo "Lockfile not found." && exit 1)
-
-# Rebuild the source code only when needed
-FROM node:current-alpine AS builder
-WORKDIR /app
-COPY --from=deps /app/node_modules ./node_modules
-COPY pnpm-lock.yaml* ./
-COPY package.json ./
-COPY . .
-
-# Next.js collects completely anonymous telemetry data about general usage.
-# Learn more here: https://nextjs.org/telemetry
-# Uncomment the following line in case you want to disable telemetry during the build.
-ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN npm install -g pnpm
-RUN \
-  [ -f pnpm-lock.yaml ] && (pnpm --offline install && pnpm run build) || \
-  (echo "Lockfile not found." && exit 1) 
-
-# Production image, copy all the files and run next
-FROM node:current-alpine AS runner
-WORKDIR /app
-
-ENV NODE_ENV production
-# Uncomment the following line in case you want to disable telemetry during runtime.
-ENV NEXT_TELEMETRY_DISABLED 1
-
-RUN addgroup --system --gid 1001 nodejs
-RUN adduser --system --uid 1001 nextjs
-
-RUN sed -i 's/https/http/' /etc/apk/repositories
-RUN apk add curl \
-  && apk add ca-certificates \
-  && update-ca-certificates
-
-# You only need to copy next.config.js if you are NOT using the default configuration
-# COPY --from=builder /app/next.config.js ./
-COPY --from=builder /app/public ./public
-COPY --from=builder /app/package.json ./package.json
-# COPY --from=builder /app/.env* .
-
-# Automatically leverage output traces to reduce image size
-# https://nextjs.org/docs/advanced-features/output-file-tracing
-COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
-COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
-
-USER nextjs
-
-ENV PORT=3000
-
-EXPOSE 3000
-
-CMD ["node", "server.js"]

client/next-env.d.ts

@@ -1,5 +0,0 @@
-/// <reference types="next" />
-/// <reference types="next/image-types/global" />
-
-// NOTE: This file should not be edited
-// see https://nextjs.org/docs/basic-features/typescript for more information.

client/public/docs/versionIntro.md

@@ -1,7 +0,0 @@
-### Fast GPT V4.3
-
-1. 新增 - 知识库源文件存储，可以从引用窗口点击文件名，查看源文件。
-2. 新增 - 用户反馈和管理员标注预期答案，以不断提高模型回复准确率。 该功能为测试版，未来交互可能会有变化，欢迎大家提出宝贵意见。
-3. 优化 - [使用文档](https://doc.fastgpt.run/docs/intro/)
-4. [点击查看高级编排介绍文档](https://doc.fastgpt.run/docs/workflow)
-5. [点击查看商业版](https://fael3z0zfze.feishu.cn/docx/F155dbirfo8vDDx2WgWc6extnwf)

client/src/api/openapi.ts

@@ -1,16 +0,0 @@
-import { GET, POST, DELETE } from './request';
-import { UserOpenApiKey } from '@/types/openapi';
-/**
- * crete a api key
- */
-export const createAOpenApiKey = () => POST<string>('/openapi/postKey');
-
-/**
- * get api keys
- */
-export const getOpenApiKeys = () => GET<UserOpenApiKey[]>('/openapi/getKeys');
-
-/**
- * delete api by id
- */
-export const delOpenApiById = (id: string) => DELETE(`/openapi/delKey?id=${id}`);

client/src/api/plugins/kb.ts

@@ -1,97 +0,0 @@
-import { GET, POST, PUT, DELETE } from '../request';
-import type { DatasetItemType, KbItemType, KbListItemType } from '@/types/plugin';
-import { RequestPaging } from '@/types/index';
-import { TrainingModeEnum } from '@/constants/plugin';
-import {
-  Props as PushDataProps,
-  Response as PushDateResponse
-} from '@/pages/api/openapi/kb/pushData';
-import {
-  Props as SearchTestProps,
-  Response as SearchTestResponse
-} from '@/pages/api/openapi/kb/searchTest';
-import { Response as KbDataItemType } from '@/pages/api/plugins/kb/data/getDataById';
-import { Props as UpdateDataProps } from '@/pages/api/openapi/kb/updateData';
-import type { KbUpdateParams, CreateKbParams } from '../request/kb';
-import { QuoteItemType } from '@/types/chat';
-
-/* knowledge base */
-export const getKbList = () => GET<KbListItemType[]>(`/plugins/kb/list`);
-
-export const getKbById = (id: string) => GET<KbItemType>(`/plugins/kb/detail?id=${id}`);
-
-export const postCreateKb = (data: CreateKbParams) => POST<string>(`/plugins/kb/create`, data);
-
-export const putKbById = (data: KbUpdateParams) => PUT(`/plugins/kb/update`, data);
-
-export const delKbById = (id: string) => DELETE(`/plugins/kb/delete?id=${id}`);
-
-/* kb data */
-type GetKbDataListProps = RequestPaging & {
-  kbId: string;
-  searchText: string;
-};
-export const getKbDataList = (data: GetKbDataListProps) =>
-  POST(`/plugins/kb/data/getDataList`, data);
-
-/**
- * 获取导出数据（不分页）
- */
-export const getExportDataList = (kbId: string) =>
-  GET<[string, string, string][]>(
-    `/plugins/kb/data/exportModelData`,
-    { kbId },
-    {
-      timeout: 600000
-    }
-  );
-
-/**
- * 获取模型正在拆分数据的数量
- */
-export const getTrainingData = (data: { kbId: string; init: boolean }) =>
-  POST<{
-    qaListLen: number;
-    vectorListLen: number;
-  }>(`/plugins/kb/data/getTrainingData`, data);
-
-/* get length of system training queue */
-export const getTrainingQueueLen = () => GET<number>(`/plugins/kb/data/getQueueLen`);
-
-export const getKbDataItemById = (dataId: string) =>
-  GET<QuoteItemType>(`/plugins/kb/data/getDataById`, { dataId });
-
-/**
- * 直接push数据
- */
-export const postKbDataFromList = (data: PushDataProps) =>
-  POST<PushDateResponse>(`/openapi/kb/pushData`, data);
-
-/**
- * insert one data to dataset
- */
-export const insertData2Kb = (data: { kbId: string; data: DatasetItemType }) =>
-  POST<string>(`/plugins/kb/data/insertData`, data);
-
-/**
- * 更新一条数据
- */
-export const putKbDataById = (data: UpdateDataProps) => PUT('/openapi/kb/updateData', data);
-/**
- * 删除一条知识库数据
- */
-export const delOneKbDataByDataId = (dataId: string) =>
-  DELETE(`/openapi/kb/delDataById?dataId=${dataId}`);
-
-/**
- * 拆分数据
- */
-export const postSplitData = (data: {
-  kbId: string;
-  chunks: string[];
-  prompt: string;
-  mode: `${TrainingModeEnum}`;
-}) => POST(`/openapi/text/pushData`, data);
-
-export const searchText = (data: SearchTestProps) =>
-  POST<SearchTestResponse>(`/openapi/kb/searchTest`, data);

client/src/api/request/kb.d.ts

@@ -1,12 +0,0 @@
-export type KbUpdateParams = {
-  id: string;
-  name: string;
-  tags: string;
-  avatar: string;
-};
-export type CreateKbParams = {
-  name: string;
-  tags: string[];
-  avatar: string;
-  vectorModel: string;
-};

client/src/api/service/plugins.ts

@@ -1,8 +0,0 @@
-import { GET, POST } from './request';
-
-export const textCensor = (data: { text: string }) =>
-  POST<{ code?: number; message: string }>('/plugins/censor/text_baidu', data).then((res) => {
-    if (res?.code === 5000) {
-      return Promise.reject(res.message);
-    }
-  });

client/src/components/APIKeyModal/index.tsx

@@ -1,143 +0,0 @@
-import React, { useState } from 'react';
-import {
-  Box,
-  Button,
-  Flex,
-  ModalFooter,
-  ModalBody,
-  Table,
-  Thead,
-  Tbody,
-  Tr,
-  Th,
-  Td,
-  TableContainer,
-  IconButton
-} from '@chakra-ui/react';
-import { getOpenApiKeys, createAOpenApiKey, delOpenApiById } from '@/api/openapi';
-import { useQuery, useMutation } from '@tanstack/react-query';
-import { useLoading } from '@/hooks/useLoading';
-import dayjs from 'dayjs';
-import { AddIcon, DeleteIcon } from '@chakra-ui/icons';
-import { getErrText, useCopyData } from '@/utils/tools';
-import { useToast } from '@/hooks/useToast';
-import MyIcon from '../Icon';
-import MyModal from '../MyModal';
-
-const APIKeyModal = ({ onClose }: { onClose: () => void }) => {
-  const { Loading } = useLoading();
-  const { toast } = useToast();
-  const {
-    data: apiKeys = [],
-    isLoading: isGetting,
-    refetch
-  } = useQuery(['getOpenApiKeys'], getOpenApiKeys);
-  const [apiKey, setApiKey] = useState('');
-  const { copyData } = useCopyData();
-
-  const { mutate: onclickCreateApiKey, isLoading: isCreating } = useMutation({
-    mutationFn: () => createAOpenApiKey(),
-    onSuccess(res) {
-      setApiKey(res);
-      refetch();
-    },
-    onError(err) {
-      toast({
-        status: 'warning',
-        title: getErrText(err)
-      });
-    }
-  });
-
-  const { mutate: onclickRemove, isLoading: isDeleting } = useMutation({
-    mutationFn: async (id: string) => delOpenApiById(id),
-    onSuccess() {
-      refetch();
-    }
-  });
-
-  return (
-    <MyModal isOpen onClose={onClose} w={'600px'}>
-      <Box py={3} px={5}>
-        <Box fontWeight={'bold'} fontSize={'2xl'}>
-          API 秘钥管理
-        </Box>
-        <Box fontSize={'sm'} color={'myGray.600'}>
-          如果你不想 API 秘钥被滥用，请勿将秘钥直接放置在前端使用~
-        </Box>
-      </Box>
-      <ModalBody minH={'300px'} maxH={['70vh', '500px']} overflow={'overlay'}>
-        <TableContainer mt={2} position={'relative'}>
-          <Table>
-            <Thead>
-              <Tr>
-                <Th>Api Key</Th>
-                <Th>创建时间</Th>
-                <Th>最后一次使用时间</Th>
-                <Th />
-              </Tr>
-            </Thead>
-            <Tbody fontSize={'sm'}>
-              {apiKeys.map(({ id, apiKey, createTime, lastUsedTime }) => (
-                <Tr key={id}>
-                  <Td>{apiKey}</Td>
-                  <Td>{dayjs(createTime).format('YYYY/MM/DD HH:mm:ss')}</Td>
-                  <Td>
-                    {lastUsedTime
-                      ? dayjs(lastUsedTime).format('YYYY/MM/DD HH:mm:ss')
-                      : '没有使用过'}
-                  </Td>
-                  <Td>
-                    <IconButton
-                      icon={<DeleteIcon />}
-                      size={'xs'}
-                      aria-label={'delete'}
-                      variant={'base'}
-                      colorScheme={'gray'}
-                      onClick={() => onclickRemove(id)}
-                    />
-                  </Td>
-                </Tr>
-              ))}
-            </Tbody>
-          </Table>
-        </TableContainer>
-      </ModalBody>
-
-      <ModalFooter>
-        <Button
-          variant="base"
-          leftIcon={<AddIcon color={'myGray.600'} fontSize={'sm'} />}
-          onClick={() => onclickCreateApiKey()}
-        >
-          新建秘钥
-        </Button>
-      </ModalFooter>
-
-      <Loading loading={isGetting || isCreating || isDeleting} fixed={false} />
-      <MyModal isOpen={!!apiKey} w={'400px'} onClose={() => setApiKey('')}>
-        <Box py={3} px={5}>
-          <Box fontWeight={'bold'} fontSize={'2xl'}>
-            新的 API 秘钥
-          </Box>
-          <Box fontSize={'sm'} color={'myGray.600'}>
-            请保管好你的秘钥，秘钥不会再次展示~
-          </Box>
-        </Box>
-        <ModalBody>
-          <Flex bg={'myGray.100'} px={3} py={2} cursor={'pointer'} onClick={() => copyData(apiKey)}>
-            <Box flex={1}>{apiKey}</Box>
-            <MyIcon name={'copy'} w={'16px'}></MyIcon>
-          </Flex>
-        </ModalBody>
-        <ModalFooter>
-          <Button variant="base" onClick={() => setApiKey('')}>
-            好的
-          </Button>
-        </ModalFooter>
-      </MyModal>
-    </MyModal>
-  );
-};
-
-export default APIKeyModal;

client/src/components/ChatBox/WholeResponseModal.tsx

@@ -1,71 +0,0 @@
-import React, { useMemo } from 'react';
-import { Box, ModalBody, useTheme, ModalHeader, Flex } from '@chakra-ui/react';
-import type { ChatHistoryItemResType } from '@/types/chat';
-import { useTranslation } from 'react-i18next';
-
-import MyModal from '../MyModal';
-import MyTooltip from '../MyTooltip';
-import { QuestionOutlineIcon } from '@chakra-ui/icons';
-
-const ResponseModal = ({
-  response,
-  onClose
-}: {
-  response: ChatHistoryItemResType[];
-  onClose: () => void;
-}) => {
-  const { t } = useTranslation();
-  const theme = useTheme();
-
-  const formatResponse = useMemo(
-    () =>
-      response.map((item) => {
-        const copy = { ...item };
-        delete copy.completeMessages;
-        delete copy.quoteList;
-        return copy;
-      }),
-    [response]
-  );
-
-  return (
-    <MyModal
-      isOpen={true}
-      onClose={onClose}
-      h={['90vh', '80vh']}
-      minW={['90vw', '600px']}
-      title={
-        <Flex alignItems={'center'}>
-          {t('chat.Complete Response')}
-          <MyTooltip
-            label={
-              'moduleName: 模型名\nprice: 价格，倍率：100000\nmodel?: 模型名\ntokens?: token 消耗\n\nanswer?: 回答内容\nquestion?: 问题\ntemperature?: 温度\nmaxToken?: 最大 tokens\n\nsimilarity?: 相似度\nlimit?: 单次搜索结果\n\ncqList?: 问题分类列表\ncqResult?: 分类结果\n\nextractDescription?: 内容提取描述\nextractResult?: 提取结果'
-            }
-          >
-            <QuestionOutlineIcon ml={2} />
-          </MyTooltip>
-        </Flex>
-      }
-      isCentered
-    >
-      <ModalBody>
-        {formatResponse.map((item, i) => (
-          <Box
-            key={i}
-            p={2}
-            pt={[0, 2]}
-            borderRadius={'lg'}
-            border={theme.borders.base}
-            _notLast={{ mb: 2 }}
-            position={'relative'}
-            whiteSpace={'pre-wrap'}
-          >
-            {JSON.stringify(item, null, 2)}
-          </Box>
-        ))}
-      </ModalBody>
-    </MyModal>
-  );
-};
-
-export default ResponseModal;

client/src/components/Icon/index.tsx

@@ -1,104 +0,0 @@
-import React from 'react';
-import type { IconProps } from '@chakra-ui/react';
-import { Icon } from '@chakra-ui/react';
-
-const map = {
-  appFill: require('./icons/fill/app.svg').default,
-  appLight: require('./icons/light/app.svg').default,
-  copy: require('./icons/copy.svg').default,
-  chatSend: require('./icons/chatSend.svg').default,
-  delete: require('./icons/delete.svg').default,
-  stop: require('./icons/stop.svg').default,
-  collectionLight: require('./icons/collectionLight.svg').default,
-  collectionSolid: require('./icons/collectionSolid.svg').default,
-  empty: require('./icons/empty.svg').default,
-  back: require('./icons/back.svg').default,
-  backFill: require('./icons/fill/back.svg').default,
-  more: require('./icons/more.svg').default,
-  tabbarChat: require('./icons/phoneTabbar/chat.svg').default,
-  tabbarModel: require('./icons/phoneTabbar/app.svg').default,
-  tabbarMore: require('./icons/phoneTabbar/more.svg').default,
-  tabbarMe: require('./icons/phoneTabbar/me.svg').default,
-  closeSolid: require('./icons/closeSolid.svg').default,
-  wx: require('./icons/wx.svg').default,
-  out: require('./icons/out.svg').default,
-  git: require('./icons/git.svg').default,
-  gitFill: require('./icons/fill/git.svg').default,
-  menu: require('./icons/menu.svg').default,
-  edit: require('./icons/edit.svg').default,
-  inform: require('./icons/inform.svg').default,
-  export: require('./icons/export.svg').default,
-  text: require('./icons/text.svg').default,
-  history: require('./icons/history.svg').default,
-  kbTest: require('./icons/kbTest.svg').default,
-  date: require('./icons/date.svg').default,
-  apikey: require('./icons/apikey.svg').default,
-  save: require('./icons/save.svg').default,
-  minus: require('./icons/minus.svg').default,
-  chat: require('./icons/light/chat.svg').default,
-  chatFill: require('./icons/fill/chat.svg').default,
-  clear: require('./icons/light/clear.svg').default,
-  apiLight: require('./icons/light/appApi.svg').default,
-  overviewLight: require('./icons/light/overview.svg').default,
-  settingLight: require('./icons/light/setting.svg').default,
-  shareLight: require('./icons/light/share.svg').default,
-  dbLight: require('./icons/light/db.svg').default,
-  dbFill: require('./icons/fill/db.svg').default,
-  appStoreLight: require('./icons/light/appStore.svg').default,
-  appStoreFill: require('./icons/fill/appStore.svg').default,
-  meLight: require('./icons/light/me.svg').default,
-  meFill: require('./icons/fill/me.svg').default,
-  welcomeText: require('./icons/modules/welcomeText.svg').default,
-  variable: require('./icons/modules/variable.svg').default,
-  setTop: require('./icons/light/setTop.svg').default,
-  fullScreenLight: require('./icons/light/fullScreen.svg').default,
-  voice: require('./icons/voice.svg').default,
-  html: require('./icons/file/html.svg').default,
-  pdf: require('./icons/file/pdf.svg').default,
-  markdown: require('./icons/file/markdown.svg').default,
-  importLight: require('./icons/light/import.svg').default,
-  manualImport: require('./icons/file/manualImport.svg').default,
-  indexImport: require('./icons/file/indexImport.svg').default,
-  csvImport: require('./icons/file/csv.svg').default,
-  qaImport: require('./icons/file/qaImport.svg').default,
-  uploadFile: require('./icons/file/uploadFile.svg').default,
-  closeLight: require('./icons/light/close.svg').default,
-  customTitle: require('./icons/light/customTitle.svg').default,
-  billRecordLight: require('./icons/light/billRecord.svg').default,
-  informLight: require('./icons/light/inform.svg').default,
-  payRecordLight: require('./icons/light/payRecord.svg').default,
-  loginoutLight: require('./icons/light/loginout.svg').default,
-  chatModelTag: require('./icons/light/chatModelTag.svg').default,
-  language_en: require('./icons/language/en.svg').default,
-  language_zh: require('./icons/language/zh.svg').default,
-  outlink_share: require('./icons/outlink/share.svg').default,
-  outlink_iframe: require('./icons/outlink/iframe.svg').default,
-  addCircle: require('./icons/circle/add.svg').default,
-  playFill: require('./icons/fill/play.svg').default,
-  courseLight: require('./icons/light/course.svg').default,
-  promotionLight: require('./icons/light/promotion.svg').default,
-  logsLight: require('./icons/light/logs.svg').default,
-  badLight: require('./icons/light/bad.svg').default,
-  markLight: require('./icons/light/mark.svg').default
-};
-
-export type IconName = keyof typeof map;
-
-const MyIcon = (
-  { name, w = 'auto', h = 'auto', ...props }: { name: IconName } & IconProps,
-  ref: any
-) => {
-  return map[name] ? (
-    <Icon
-      as={map[name]}
-      w={w}
-      h={h}
-      boxSizing={'content-box'}
-      verticalAlign={'top'}
-      fill={'currentcolor'}
-      {...props}
-    />
-  ) : null;
-};
-
-export default React.forwardRef(MyIcon);

client/src/constants/kb.ts

@@ -1,16 +0,0 @@
-import type { KbItemType } from '@/types/plugin';
-
-export const defaultKbDetail: KbItemType = {
-  _id: '',
-  userId: '',
-  avatar: '/icon/logo.svg',
-  name: '',
-  tags: '',
-  vectorModel: {
-    model: 'text-embedding-ada-002',
-    name: 'Embedding-2',
-    price: 0.2,
-    defaultToken: 500,
-    maxToken: 3000
-  }
-};

client/src/pages/api/admin/initChat.ts

@@ -1,58 +0,0 @@
-// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { authUser } from '@/service/utils/auth';
-import { connectToDatabase, Chat } from '@/service/mongo';
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    await authUser({ req, authRoot: true });
-    await connectToDatabase();
-
-    const { limit = 1000 } = req.body as { limit: number };
-    let skip = 0;
-    const total = await Chat.countDocuments({
-      chatId: { $exists: false }
-    });
-    let promise = Promise.resolve();
-    console.log(total);
-
-    for (let i = 0; i < total; i += limit) {
-      const skipVal = skip;
-      skip += limit;
-      promise = promise
-        .then(() => init(limit, skipVal))
-        .then(() => {
-          console.log(skipVal);
-        });
-    }
-
-    await promise;
-
-    jsonRes(res, {});
-  } catch (error) {
-    jsonRes(res, {
-      code: 500,
-      error
-    });
-  }
-}
-
-async function init(limit: number, skip: number) {
-  // 遍历 app
-  const chats = await Chat.find(
-    {
-      chatId: { $exists: false }
-    },
-    '_id'
-  ).limit(limit);
-
-  await Promise.all(
-    chats.map((chat) =>
-      Chat.findByIdAndUpdate(chat._id, {
-        chatId: String(chat._id),
-        source: 'online'
-      })
-    )
-  );
-}

client/src/pages/api/admin/initChatItem.ts

@@ -1,98 +0,0 @@
-// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { authUser } from '@/service/utils/auth';
-import { connectToDatabase, Chat, ChatItem } from '@/service/mongo';
-import { customAlphabet } from 'nanoid';
-const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 24);
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    await authUser({ req, authRoot: true });
-    await connectToDatabase();
-
-    const { limit = 100 } = req.body as { limit: number };
-    let skip = 0;
-
-    const total = await Chat.countDocuments({
-      content: { $exists: true, $not: { $size: 0 } },
-      isInit: { $ne: true }
-    });
-    const totalChat = await Chat.aggregate([
-      {
-        $project: {
-          contentLength: { $size: '$content' }
-        }
-      },
-      {
-        $group: {
-          _id: null,
-          totalLength: { $sum: '$contentLength' }
-        }
-      }
-    ]);
-
-    console.log('chatLen:', total, totalChat);
-
-    let promise = Promise.resolve();
-
-    for (let i = 0; i < total; i += limit) {
-      const skipVal = skip;
-      skip += limit;
-      promise = promise
-        .then(() => init(limit))
-        .then(() => {
-          console.log(skipVal);
-        });
-    }
-
-    await promise;
-
-    jsonRes(res, {});
-  } catch (error) {
-    jsonRes(res, {
-      code: 500,
-      error
-    });
-  }
-}
-
-async function init(limit: number) {
-  // 遍历 app
-  const chats = await Chat.find(
-    {
-      content: { $exists: true, $not: { $size: 0 } },
-      isInit: { $ne: true }
-    },
-    '_id userId appId chatId content'
-  )
-    .sort({ updateTime: -1 })
-    .limit(limit);
-
-  await Promise.all(
-    chats.map(async (chat) => {
-      const inserts = chat.content
-        .map((item) => ({
-          dataId: nanoid(),
-          chatId: chat.chatId,
-          userId: chat.userId,
-          appId: chat.appId,
-          obj: item.obj,
-          value: item.value,
-          responseData: item.responseData
-        }))
-        .filter((item) => item.chatId && item.userId && item.appId && item.obj && item.value);
-
-      try {
-        await Promise.all(inserts.map((item) => ChatItem.create(item)));
-        await Chat.findByIdAndUpdate(chat._id, {
-          isInit: true
-        });
-      } catch (error) {
-        console.log(error);
-
-        await ChatItem.deleteMany({ chatId: chat.chatId });
-      }
-    })
-  );
-}

client/src/pages/api/admin/initv4.ts

@@ -1,446 +0,0 @@
-// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { authUser } from '@/service/utils/auth';
-import { connectToDatabase, App } from '@/service/mongo';
-import { FlowModuleTypeEnum, SpecialInputKeyEnum } from '@/constants/flow';
-import { TaskResponseKeyEnum } from '@/constants/chat';
-import { FlowInputItemType } from '@/types/flow';
-
-const chatModelInput = ({
-  model,
-  temperature,
-  maxToken,
-  systemPrompt,
-  limitPrompt,
-  kbList
-}: {
-  model: string;
-  temperature: number;
-  maxToken: number;
-  systemPrompt: string;
-  limitPrompt: string;
-  kbList: { kbId: string }[];
-}): FlowInputItemType[] => [
-  {
-    key: 'model',
-    value: model,
-    type: 'custom',
-    label: '对话模型',
-    connected: true
-  },
-  {
-    key: 'temperature',
-    value: temperature,
-    label: '温度',
-    type: 'slider',
-    connected: true
-  },
-  {
-    key: 'maxToken',
-    value: maxToken,
-    type: 'custom',
-    label: '回复上限',
-    connected: true
-  },
-  {
-    key: 'systemPrompt',
-    value: systemPrompt,
-    type: 'textarea',
-    label: '系统提示词',
-    connected: true
-  },
-  {
-    key: 'limitPrompt',
-    label: '限定词',
-    type: 'textarea',
-    value: limitPrompt,
-    connected: true
-  },
-  {
-    key: 'switch',
-    type: 'target',
-    label: '触发器',
-    connected: kbList.length > 0
-  },
-  {
-    key: 'quoteQA',
-    type: 'target',
-    label: '引用内容',
-    connected: kbList.length > 0
-  },
-  {
-    key: 'history',
-    type: 'target',
-    label: '聊天记录',
-    connected: true
-  },
-  {
-    key: 'userChatInput',
-    type: 'target',
-    label: '用户问题',
-    connected: true
-  }
-];
-const chatTemplate = ({
-  model,
-  temperature,
-  maxToken,
-  systemPrompt,
-  limitPrompt
-}: {
-  model: string;
-  temperature: number;
-  maxToken: number;
-  systemPrompt: string;
-  limitPrompt: string;
-}) => {
-  return [
-    {
-      flowType: FlowModuleTypeEnum.questionInput,
-      inputs: [
-        {
-          key: 'userChatInput',
-          connected: true
-        }
-      ],
-      outputs: [
-        {
-          key: 'userChatInput',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'userChatInput'
-            }
-          ]
-        }
-      ],
-      position: {
-        x: 464.32198615344566,
-        y: 1602.2698463081606
-      },
-      moduleId: 'userChatInput'
-    },
-    {
-      flowType: FlowModuleTypeEnum.historyNode,
-      inputs: [
-        {
-          key: 'maxContext',
-          value: 10,
-          connected: true
-        },
-        {
-          key: 'history',
-          connected: true
-        }
-      ],
-      outputs: [
-        {
-          key: 'history',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'history'
-            }
-          ]
-        }
-      ],
-      position: {
-        x: 452.5466249541586,
-        y: 1276.3930310334215
-      },
-      moduleId: 'history'
-    },
-    {
-      flowType: FlowModuleTypeEnum.chatNode,
-      inputs: chatModelInput({
-        model,
-        temperature,
-        maxToken,
-        systemPrompt,
-        limitPrompt,
-        kbList: []
-      }),
-      outputs: [
-        {
-          key: TaskResponseKeyEnum.answerText,
-          targets: []
-        }
-      ],
-      position: {
-        x: 981.9682828103937,
-        y: 890.014595014464
-      },
-      moduleId: 'chatModule'
-    }
-  ];
-};
-const kbTemplate = ({
-  model,
-  temperature,
-  maxToken,
-  systemPrompt,
-  limitPrompt,
-  kbList = [],
-  searchSimilarity,
-  searchLimit,
-  searchEmptyText
-}: {
-  model: string;
-  temperature: number;
-  maxToken: number;
-  systemPrompt: string;
-  limitPrompt: string;
-  kbList: { kbId: string }[];
-  searchSimilarity: number;
-  searchLimit: number;
-  searchEmptyText: string;
-}) => {
-  return [
-    {
-      flowType: FlowModuleTypeEnum.questionInput,
-      inputs: [
-        {
-          key: 'userChatInput',
-          connected: true
-        }
-      ],
-      outputs: [
-        {
-          key: 'userChatInput',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'userChatInput'
-            },
-            {
-              moduleId: 'kbSearch',
-              key: 'userChatInput'
-            }
-          ]
-        }
-      ],
-      position: {
-        x: 464.32198615344566,
-        y: 1602.2698463081606
-      },
-      moduleId: 'userChatInput'
-    },
-    {
-      flowType: FlowModuleTypeEnum.historyNode,
-      inputs: [
-        {
-          key: 'maxContext',
-          value: 10,
-          connected: true
-        },
-        {
-          key: 'history',
-          connected: true
-        }
-      ],
-      outputs: [
-        {
-          key: 'history',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'history'
-            }
-          ]
-        }
-      ],
-      position: {
-        x: 452.5466249541586,
-        y: 1276.3930310334215
-      },
-      moduleId: 'history'
-    },
-    {
-      flowType: FlowModuleTypeEnum.kbSearchNode,
-      inputs: [
-        {
-          key: 'kbList',
-          value: kbList,
-          connected: true
-        },
-        {
-          key: 'similarity',
-          value: searchSimilarity,
-          connected: true
-        },
-        {
-          key: 'limit',
-          value: searchLimit,
-          connected: true
-        },
-        {
-          key: 'switch',
-          connected: false
-        },
-        {
-          key: 'userChatInput',
-          connected: true
-        }
-      ],
-      outputs: [
-        {
-          key: 'isEmpty',
-          targets: searchEmptyText
-            ? [
-                {
-                  moduleId: 'emptyText',
-                  key: 'switch'
-                }
-              ]
-            : [
-                {
-                  moduleId: 'chatModule',
-                  key: 'switch'
-                }
-              ]
-        },
-        {
-          key: 'unEmpty',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'switch'
-            }
-          ]
-        },
-        {
-          key: 'quoteQA',
-          targets: [
-            {
-              moduleId: 'chatModule',
-              key: 'quoteQA'
-            }
-          ]
-        }
-      ],
-      position: {
-        x: 956.0838440206068,
-        y: 887.462827870246
-      },
-      moduleId: 'kbSearch'
-    },
-    ...(searchEmptyText
-      ? [
-          {
-            flowType: FlowModuleTypeEnum.answerNode,
-            inputs: [
-              {
-                key: 'switch',
-                connected: true
-              },
-              {
-                key: SpecialInputKeyEnum.answerText,
-                value: searchEmptyText,
-                connected: true
-              }
-            ],
-            outputs: [],
-            position: {
-              x: 1553.5815811529146,
-              y: 637.8753731306779
-            },
-            moduleId: 'emptyText'
-          }
-        ]
-      : []),
-    {
-      flowType: FlowModuleTypeEnum.chatNode,
-      inputs: chatModelInput({ model, temperature, maxToken, systemPrompt, limitPrompt, kbList }),
-      outputs: [
-        {
-          key: TaskResponseKeyEnum.answerText,
-          targets: []
-        }
-      ],
-      position: {
-        x: 1551.71405495818,
-        y: 977.4911578918461
-      },
-      moduleId: 'chatModule'
-    }
-  ];
-};
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    await authUser({ req, authRoot: true });
-    await connectToDatabase();
-
-    const { limit = 1000 } = req.body as { limit: number };
-    let skip = 0;
-    const total = await App.countDocuments();
-    let promise = Promise.resolve();
-    console.log(total);
-
-    for (let i = 0; i < total; i += limit) {
-      const skipVal = skip;
-      skip += limit;
-      promise = promise
-        .then(() => init(limit, skipVal))
-        .then(() => {
-          console.log(skipVal);
-        });
-    }
-
-    await promise;
-
-    jsonRes(res, {});
-  } catch (error) {
-    jsonRes(res, {
-      code: 500,
-      error
-    });
-  }
-}
-
-async function init(limit: number, skip: number) {
-  // 遍历 app
-  const apps = await App.find(
-    {
-      chat: { $ne: null },
-      modules: { $exists: false }
-      // userId: '63f9a14228d2a688d8dc9e1b'
-    },
-    '_id chat'
-  ).limit(limit);
-
-  return Promise.all(
-    apps.map(async (app) => {
-      if (!app.chat) return app;
-      const modules = (() => {
-        if (app.chat.relatedKbs.length === 0) {
-          return chatTemplate({
-            model: app.chat.chatModel,
-            temperature: app.chat.temperature,
-            maxToken: app.chat.maxToken,
-            systemPrompt: app.chat.systemPrompt,
-            limitPrompt: app.chat.limitPrompt
-          });
-        } else {
-          return kbTemplate({
-            model: app.chat.chatModel,
-            temperature: app.chat.temperature,
-            maxToken: app.chat.maxToken,
-            systemPrompt: app.chat.systemPrompt,
-            limitPrompt: app.chat.limitPrompt,
-            kbList: app.chat.relatedKbs.map((id) => ({ kbId: id })),
-            searchEmptyText: app.chat.searchEmptyText,
-            searchLimit: app.chat.searchLimit,
-            searchSimilarity: app.chat.searchSimilarity
-          });
-        }
-      })();
-
-      await App.findByIdAndUpdate(app.id, {
-        modules
-      });
-      return modules;
-    })
-  );
-}

client/src/pages/api/openapi/getKeys.ts

@@ -1,37 +0,0 @@
-// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { connectToDatabase, OpenApi } from '@/service/mongo';
-import { authUser } from '@/service/utils/auth';
-import { UserOpenApiKey } from '@/types/openapi';
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    const { userId } = await authUser({ req, authToken: true });
-
-    await connectToDatabase();
-
-    const findResponse = await OpenApi.find({ userId }).sort({ _id: -1 });
-
-    // jus save four data
-    const apiKeys = findResponse.map<UserOpenApiKey>(
-      ({ _id, apiKey, createTime, lastUsedTime }) => {
-        return {
-          id: _id,
-          apiKey: `******${apiKey.substring(apiKey.length - 4)}`,
-          createTime,
-          lastUsedTime
-        };
-      }
-    );
-
-    jsonRes(res, {
-      data: apiKeys
-    });
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-}

client/src/pages/api/openapi/text/gptMessagesSlice.ts

@@ -1,61 +0,0 @@
-// Next.js API route support: https://nextjs.org/docs/api-routes/introduction
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { authUser } from '@/service/utils/auth';
-import type { ChatItemType } from '@/types/chat';
-import { countOpenAIToken } from '@/utils/plugin/openai';
-
-type Props = {
-  messages: ChatItemType[];
-  model: string;
-  maxLen: number;
-};
-type Response = ChatItemType[];
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  try {
-    await authUser({ req });
-
-    const { messages, model, maxLen } = req.body as Props;
-
-    if (!Array.isArray(messages) || !model || !maxLen) {
-      throw new Error('params is error');
-    }
-
-    return jsonRes<Response>(res, {
-      data: gpt_chatItemTokenSlice({
-        messages,
-        maxToken: maxLen
-      })
-    });
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-}
-
-export function gpt_chatItemTokenSlice({
-  messages,
-  maxToken
-}: {
-  messages: ChatItemType[];
-  maxToken: number;
-}) {
-  let result: ChatItemType[] = [];
-
-  for (let i = 0; i < messages.length; i++) {
-    const msgs = [...result, messages[i]];
-
-    const tokens = countOpenAIToken({ messages: msgs });
-
-    if (tokens < maxToken) {
-      result = msgs;
-    } else {
-      break;
-    }
-  }
-
-  return result.length === 0 && messages[0] ? [messages[0]] : result;
-}

client/src/pages/api/plugins/kb/data/exportModelData.ts

@@ -1,84 +0,0 @@
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { connectToDatabase, User } from '@/service/mongo';
-import { authUser } from '@/service/utils/auth';
-import { PgClient } from '@/service/pg';
-import { PgTrainingTableName } from '@/constants/plugin';
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
-  try {
-    let { kbId } = req.query as {
-      kbId: string;
-    };
-
-    if (!kbId) {
-      throw new Error('缺少参数');
-    }
-
-    await connectToDatabase();
-
-    // 凭证校验
-    const { userId } = await authUser({ req, authToken: true });
-
-    const thirtyMinutesAgo = new Date(Date.now() - 30 * 60 * 1000);
-
-    // auth export times
-    const authTimes = await User.findOne(
-      {
-        _id: userId,
-        $or: [
-          { 'limit.exportKbTime': { $exists: false } },
-          { 'limit.exportKbTime': { $lte: thirtyMinutesAgo } }
-        ]
-      },
-      '_id limit'
-    );
-
-    if (!authTimes) {
-      throw new Error('上次导出未到半小时，每半小时仅可导出一次。');
-    }
-
-    // 统计数据
-    const count = await PgClient.count(PgTrainingTableName, {
-      where: [['kb_id', kbId], 'AND', ['user_id', userId]]
-    });
-    // 从 pg 中获取所有数据
-    const pgData = await PgClient.select<{ q: string; a: string; source: string }>(
-      PgTrainingTableName,
-      {
-        where: [['kb_id', kbId], 'AND', ['user_id', userId]],
-        fields: ['q', 'a', 'source'],
-        order: [{ field: 'id', mode: 'DESC' }],
-        limit: count
-      }
-    );
-
-    const data: [string, string, string][] = pgData.rows.map((item) => [
-      item.q.replace(/\n/g, '\\n'),
-      item.a.replace(/\n/g, '\\n'),
-      item.source
-    ]);
-
-    // update export time
-    await User.findByIdAndUpdate(userId, {
-      'limit.exportKbTime': new Date()
-    });
-
-    jsonRes(res, {
-      data
-    });
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-}
-
-export const config = {
-  api: {
-    bodyParser: {
-      sizeLimit: '100mb'
-    }
-  }
-};

client/src/pages/api/plugins/kb/data/insertData.ts

@@ -1,88 +0,0 @@
-import type { NextApiRequest, NextApiResponse } from 'next';
-import { jsonRes } from '@/service/response';
-import { connectToDatabase, KB } from '@/service/mongo';
-import { authKb, authUser } from '@/service/utils/auth';
-import { withNextCors } from '@/service/utils/tools';
-import { PgTrainingTableName } from '@/constants/plugin';
-import { insertKbItem, PgClient } from '@/service/pg';
-import { modelToolMap } from '@/utils/plugin';
-import { getVectorModel } from '@/service/utils/data';
-import { getVector } from '@/pages/api/openapi/plugin/vector';
-import { DatasetItemType } from '@/types/plugin';
-
-export type Props = {
-  kbId: string;
-  data: DatasetItemType;
-};
-
-export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
-  try {
-    await connectToDatabase();
-
-    const { kbId, data = { q: '', a: '' } } = req.body as Props;
-
-    if (!kbId || !data?.q) {
-      throw new Error('缺少参数');
-    }
-
-    // 凭证校验
-    const { userId } = await authUser({ req });
-
-    // auth kb
-    const kb = await authKb({ kbId, userId });
-
-    const q = data?.q?.replace(/\\n/g, '\n').trim().replace(/'/g, '"');
-    const a = data?.a?.replace(/\\n/g, '\n').trim().replace(/'/g, '"');
-
-    // token check
-    const token = modelToolMap.countTokens({
-      messages: [{ obj: 'System', value: q }]
-    });
-
-    if (token > getVectorModel(kb.vectorModel).maxToken) {
-      throw new Error('Over Tokens');
-    }
-
-    const { rows: existsRows } = await PgClient.query(`
-    SELECT COUNT(*) > 0 AS exists
-    FROM  ${PgTrainingTableName} 
-    WHERE md5(q)=md5('${q}') AND md5(a)=md5('${a}') AND user_id='${userId}' AND kb_id='${kbId}'
-  `);
-    const exists = existsRows[0]?.exists || false;
-
-    if (exists) {
-      throw new Error('已经存在完全一致的数据');
-    }
-
-    const { vectors } = await getVector({
-      model: kb.vectorModel,
-      input: [q],
-      userId
-    });
-
-    const response = await insertKbItem({
-      userId,
-      kbId,
-      data: [
-        {
-          q,
-          a,
-          source: data.source,
-          vector: vectors[0]
-        }
-      ]
-    });
-
-    // @ts-ignore
-    const id = response?.rows?.[0]?.id || '';
-
-    jsonRes(res, {
-      data: id
-    });
-  } catch (err) {
-    jsonRes(res, {
-      code: 500,
-      error: err
-    });
-  }
-});

client/src/pages/app/detail/components/API.tsx

@@ -1,92 +0,0 @@
-import React, { useEffect, useState } from 'react';
-import { Box, Divider, Flex, useTheme, Button, Skeleton, useDisclosure } from '@chakra-ui/react';
-import { useCopyData } from '@/utils/tools';
-import dynamic from 'next/dynamic';
-import MyIcon from '@/components/Icon';
-import { useGlobalStore } from '@/store/global';
-
-const APIKeyModal = dynamic(() => import('@/components/APIKeyModal'), {
-  ssr: false
-});
-
-const API = ({ appId }: { appId: string }) => {
-  const theme = useTheme();
-  const { copyData } = useCopyData();
-  const [baseUrl, setBaseUrl] = useState('https://fastgpt.run/api/openapi');
-  const {
-    isOpen: isOpenAPIModal,
-    onOpen: onOpenAPIModal,
-    onClose: onCloseAPIModal
-  } = useDisclosure();
-  const [isLoaded, setIsLoaded] = useState(false);
-
-  const { isPc } = useGlobalStore();
-
-  useEffect(() => {
-    setBaseUrl(`${location.origin}/api/openapi`);
-  }, []);
-
-  return (
-    <Flex flexDirection={'column'} pt={[0, 5]} h={'100%'}>
-      <Flex px={5} alignItems={'center'}>
-        <Box flex={1}>
-          AppId:
-          <Box
-            as={'span'}
-            ml={2}
-            fontWeight={'bold'}
-            cursor={'pointer'}
-            onClick={() => copyData(appId, '已复制 AppId')}
-          >
-            {appId}
-          </Box>
-        </Box>
-        {isPc && (
-          <>
-            <Flex
-              bg={'myWhite.600'}
-              py={2}
-              px={4}
-              borderRadius={'md'}
-              cursor={'pointer'}
-              onClick={() => copyData(baseUrl, '已复制 API 地址')}
-            >
-              <Box border={theme.borders.md} px={2} borderRadius={'md'} fontSize={'sm'}>
-                API服务器
-              </Box>
-              <Box ml={2} color={'myGray.900'} fontSize={['sm', 'md']}>
-                {baseUrl}
-              </Box>
-            </Flex>
-            <Button
-              ml={3}
-              leftIcon={<MyIcon name={'apikey'} w={'16px'} color={''} />}
-              variant={'base'}
-              onClick={onOpenAPIModal}
-            >
-              API 秘钥
-            </Button>
-          </>
-        )}
-      </Flex>
-      <Divider mt={3} />
-      <Box flex={'1 0 0'} h={0}>
-        <Skeleton h="100%" isLoaded={isLoaded} fadeDuration={2}>
-          <iframe
-            style={{
-              width: '100%',
-              height: '100%'
-            }}
-            src="https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh"
-            frameBorder="0"
-            onLoad={() => setIsLoaded(true)}
-            onError={() => setIsLoaded(true)}
-          />
-        </Skeleton>
-      </Box>
-      {isOpenAPIModal && <APIKeyModal onClose={onCloseAPIModal} />}
-    </Flex>
-  );
-};
-
-export default API;

client/src/pages/app/detail/components/AdEdit/components/Nodes/NodeUserGuide.tsx

@@ -1,59 +0,0 @@
-import React, { useMemo } from 'react';
-import { NodeProps } from 'reactflow';
-import { Box, Flex, Textarea } from '@chakra-ui/react';
-import { QuestionOutlineIcon } from '@chakra-ui/icons';
-import NodeCard from '../modules/NodeCard';
-import { FlowModuleItemType } from '@/types/flow';
-import Container from '../modules/Container';
-import { SystemInputEnum } from '@/constants/app';
-import MyIcon from '@/components/Icon';
-import MyTooltip from '@/components/MyTooltip';
-import { welcomeTextTip } from '@/constants/flow/ModuleTemplate';
-
-const NodeUserGuide = ({ data }: NodeProps<FlowModuleItemType>) => {
-  const { inputs, moduleId, onChangeNode } = data;
-  const welcomeText = useMemo(
-    () => inputs.find((item) => item.key === SystemInputEnum.welcomeText),
-    [inputs]
-  );
-
-  return (
-    <>
-      <NodeCard minW={'300px'} {...data}>
-        <Container borderTop={'2px solid'} borderTopColor={'myGray.200'}>
-          <>
-            <Flex mb={1} alignItems={'center'}>
-              <MyIcon name={'welcomeText'} mr={2} w={'16px'} color={'#E74694'} />
-              <Box>开场白</Box>
-              <MyTooltip label={welcomeTextTip} forceShow>
-                <QuestionOutlineIcon display={['none', 'inline']} ml={1} />
-              </MyTooltip>
-            </Flex>
-            {welcomeText && (
-              <Textarea
-                className="nodrag"
-                rows={6}
-                resize={'both'}
-                defaultValue={welcomeText.value}
-                bg={'myWhite.500'}
-                placeholder={welcomeTextTip}
-                onChange={(e) => {
-                  onChangeNode({
-                    moduleId,
-                    key: SystemInputEnum.welcomeText,
-                    type: 'inputs',
-                    value: {
-                      ...welcomeText,
-                      value: e.target.value
-                    }
-                  });
-                }}
-              />
-            )}
-          </>
-        </Container>
-      </NodeCard>
-    </>
-  );
-};
-export default React.memo(NodeUserGuide);

client/src/pages/app/detail/components/KBSelectModal.tsx

@@ -1,242 +0,0 @@
-import React, { useState } from 'react';
-import {
-  Card,
-  Flex,
-  Box,
-  Button,
-  ModalBody,
-  ModalHeader,
-  ModalFooter,
-  useTheme,
-  Textarea
-} from '@chakra-ui/react';
-import Avatar from '@/components/Avatar';
-import { KbListItemType } from '@/types/plugin';
-import { useForm } from 'react-hook-form';
-import { QuestionOutlineIcon } from '@chakra-ui/icons';
-import type { SelectedKbType } from '@/types/plugin';
-import { useGlobalStore } from '@/store/global';
-import { useToast } from '@/hooks/useToast';
-import MySlider from '@/components/Slider';
-import MyTooltip from '@/components/MyTooltip';
-import MyModal from '@/components/MyModal';
-import MyIcon from '@/components/Icon';
-
-export type KbParamsType = {
-  searchSimilarity: number;
-  searchLimit: number;
-  searchEmptyText: string;
-};
-
-export const KBSelectModal = ({
-  kbList,
-  activeKbs = [],
-  onChange,
-  onClose
-}: {
-  kbList: KbListItemType[];
-  activeKbs: SelectedKbType;
-  onChange: (e: SelectedKbType) => void;
-  onClose: () => void;
-}) => {
-  const theme = useTheme();
-  const [selectedKbList, setSelectedKbList] = useState<SelectedKbType>(activeKbs);
-  const { isPc } = useGlobalStore();
-  const { toast } = useToast();
-
-  return (
-    <MyModal
-      isOpen={true}
-      isCentered={!isPc}
-      maxW={['90vw', '800px']}
-      w={'800px'}
-      onClose={onClose}
-    >
-      <Flex flexDirection={'column'} h={['90vh', 'auto']}>
-        <ModalHeader>
-          <Box>关联的知识库({selectedKbList.length})</Box>
-          <Box fontSize={'sm'} color={'myGray.500'} fontWeight={'normal'}>
-            仅能选择同一个索引模型的知识库
-          </Box>
-        </ModalHeader>
-
-        <ModalBody
-          flex={['1 0 0', '0 0 auto']}
-          maxH={'80vh'}
-          overflowY={'auto'}
-          display={'grid'}
-          gridTemplateColumns={['repeat(1,1fr)', 'repeat(2,1fr)', 'repeat(3,1fr)']}
-          gridGap={3}
-          userSelect={'none'}
-        >
-          {kbList.map((item) =>
-            (() => {
-              const selected = !!selectedKbList.find((kb) => kb.kbId === item._id);
-              const active = !!activeKbs.find((kb) => kb.kbId === item._id);
-              return (
-                <Card
-                  key={item._id}
-                  p={3}
-                  border={theme.borders.base}
-                  boxShadow={'sm'}
-                  h={'80px'}
-                  cursor={'pointer'}
-                  order={active ? 0 : 1}
-                  _hover={{
-                    boxShadow: 'md'
-                  }}
-                  {...(selected
-                    ? {
-                        bg: 'myBlue.300'
-                      }
-                    : {})}
-                  onClick={() => {
-                    if (selected) {
-                      setSelectedKbList((state) => state.filter((kb) => kb.kbId !== item._id));
-                    } else {
-                      const vectorModel = selectedKbList[0]?.vectorModel?.model;
-
-                      if (vectorModel && vectorModel !== item.vectorModel.model) {
-                        return toast({
-                          status: 'warning',
-                          title: '仅能选择同一个索引模型的知识库'
-                        });
-                      }
-                      setSelectedKbList((state) => [
-                        ...state,
-                        { kbId: item._id, vectorModel: item.vectorModel }
-                      ]);
-                    }
-                  }}
-                >
-                  <Flex alignItems={'center'} h={'38px'}>
-                    <Avatar src={item.avatar} w={['24px', '28px', '32px']}></Avatar>
-                    <Box ml={3} fontWeight={'bold'} fontSize={['md', 'lg', 'xl']}>
-                      {item.name}
-                    </Box>
-                  </Flex>
-                  <Flex justifyContent={'flex-end'} alignItems={'center'} fontSize={'sm'}>
-                    <MyIcon mr={1} name="kbTest" w={'12px'} />
-                    <Box color={'myGray.500'}>{item.vectorModel.name}</Box>
-                  </Flex>
-                </Card>
-              );
-            })()
-          )}
-        </ModalBody>
-
-        <ModalFooter>
-          <Button
-            onClick={() => {
-              onClose();
-              onChange(selectedKbList);
-            }}
-          >
-            完成
-          </Button>
-        </ModalFooter>
-      </Flex>
-    </MyModal>
-  );
-};
-
-export const KbParamsModal = ({
-  searchEmptyText,
-  searchLimit,
-  searchSimilarity,
-  onClose,
-  onChange
-}: KbParamsType & { onClose: () => void; onChange: (e: KbParamsType) => void }) => {
-  const [refresh, setRefresh] = useState(false);
-  const { register, setValue, getValues, handleSubmit } = useForm<KbParamsType>({
-    defaultValues: {
-      searchEmptyText,
-      searchLimit,
-      searchSimilarity
-    }
-  });
-
-  return (
-    <MyModal isOpen={true} onClose={onClose} title={'搜索参数调整'} minW={['90vw', '600px']}>
-      <Flex flexDirection={'column'}>
-        <ModalBody>
-          <Box display={['block', 'flex']} py={5} pt={[0, 5]}>
-            <Box flex={'0 0 100px'} mb={[8, 0]}>
-              相似度
-              <MyTooltip
-                label={'不同索引模型的相似度有区别，请通过搜索测试来选择合适的数值'}
-                forceShow
-              >
-                <QuestionOutlineIcon ml={1} />
-              </MyTooltip>
-            </Box>
-            <MySlider
-              markList={[
-                { label: '0', value: 0 },
-                { label: '1', value: 1 }
-              ]}
-              min={0}
-              max={1}
-              step={0.01}
-              value={getValues('searchSimilarity')}
-              onChange={(val) => {
-                setValue('searchSimilarity', val);
-                setRefresh(!refresh);
-              }}
-            />
-          </Box>
-          <Box display={['block', 'flex']} py={8}>
-            <Box flex={'0 0 100px'} mb={[8, 0]}>
-              单次搜索数量
-            </Box>
-            <Box flex={1}>
-              <MySlider
-                markList={[
-                  { label: '1', value: 1 },
-                  { label: '20', value: 20 }
-                ]}
-                min={1}
-                max={20}
-                value={getValues('searchLimit')}
-                onChange={(val) => {
-                  setValue('searchLimit', val);
-                  setRefresh(!refresh);
-                }}
-              />
-            </Box>
-          </Box>
-          <Box display={['block', 'flex']} pt={3}>
-            <Box flex={'0 0 100px'} mb={[2, 0]}>
-              空搜索回复
-            </Box>
-            <Box flex={1}>
-              <Textarea
-                rows={5}
-                maxLength={500}
-                placeholder={
-                  '若填写该内容，没有搜索到对应内容时，将直接回复填写的内容。\n为了连贯上下文，FastGPT 会取部分上一个聊天的搜索记录作为补充，因此在连续对话时，该功能可能会失效。'
-                }
-                {...register('searchEmptyText')}
-              ></Textarea>
-            </Box>
-          </Box>
-        </ModalBody>
-        <ModalFooter>
-          <Button variant={'base'} mr={3} onClick={onClose}>
-            取消
-          </Button>
-          <Button
-            onClick={() => {
-              onClose();
-              handleSubmit(onChange)();
-            }}
-          >
-            完成
-          </Button>
-        </ModalFooter>
-      </Flex>
-    </MyModal>
-  );
-};
-
-export default KBSelectModal;

client/src/pages/app/detail/components/OutLink.tsx

@@ -1,263 +0,0 @@
-import React, { useState } from 'react';
-import {
-  Flex,
-  Box,
-  Button,
-  TableContainer,
-  Table,
-  Thead,
-  Tr,
-  Th,
-  Td,
-  Tbody,
-  useDisclosure,
-  ModalFooter,
-  ModalBody,
-  FormControl,
-  Input,
-  useTheme
-} from '@chakra-ui/react';
-import { QuestionOutlineIcon } from '@chakra-ui/icons';
-import MyIcon from '@/components/Icon';
-import { useLoading } from '@/hooks/useLoading';
-import { useQuery } from '@tanstack/react-query';
-import { getShareChatList, delShareChatById, createShareChat } from '@/api/chat';
-import { formatTimeToChatTime, useCopyData } from '@/utils/tools';
-import { useForm } from 'react-hook-form';
-import { defaultShareChat } from '@/constants/model';
-import type { ShareChatEditType } from '@/types/app';
-import { useRequest } from '@/hooks/useRequest';
-import { formatPrice } from '@/utils/user';
-import MyTooltip from '@/components/MyTooltip';
-import MyModal from '@/components/MyModal';
-import MyRadio from '@/components/Radio';
-
-const Share = ({ appId }: { appId: string }) => {
-  const { Loading, setIsLoading } = useLoading();
-  const { copyData } = useCopyData();
-  const {
-    isOpen: isOpenCreateShareChat,
-    onOpen: onOpenCreateShareChat,
-    onClose: onCloseCreateShareChat
-  } = useDisclosure();
-  const {
-    register: registerShareChat,
-    getValues: getShareChatValues,
-    setValue: setShareChatValues,
-    handleSubmit: submitShareChat,
-    reset: resetShareChat
-  } = useForm({
-    defaultValues: defaultShareChat
-  });
-
-  const {
-    isFetching,
-    data: shareChatList = [],
-    refetch: refetchShareChatList
-  } = useQuery(['initShareChatList', appId], () => getShareChatList(appId));
-
-  const { mutate: onclickCreateShareChat, isLoading: creating } = useRequest({
-    mutationFn: async (e: ShareChatEditType) =>
-      createShareChat({
-        ...e,
-        appId
-      }),
-    errorToast: '创建分享链接异常',
-    onSuccess(id) {
-      onCloseCreateShareChat();
-      refetchShareChatList();
-      const url = `${location.origin}/chat/share?shareId=${id}`;
-      copyData(url, '创建成功。已复制分享地址，可直接分享使用');
-      resetShareChat(defaultShareChat);
-    }
-  });
-
-  return (
-    <Box position={'relative'} pt={[3, 5, 8]} px={[5, 8]} minH={'50vh'}>
-      <Flex justifyContent={'space-between'}>
-        <Box fontWeight={'bold'}>
-          免登录窗口
-          <MyTooltip
-            forceShow
-            label="可以直接分享该模型给其他用户去进行对话，对方无需登录即可直接进行对话。注意，这个功能会消耗你账号的tokens。请保管好链接和密码。"
-          >
-            <QuestionOutlineIcon ml={1} />
-          </MyTooltip>
-        </Box>
-        <Button
-          variant={'base'}
-          colorScheme={'myBlue'}
-          size={['sm', 'md']}
-          {...(shareChatList.length >= 10
-            ? {
-                isDisabled: true,
-                title: '最多创建10组'
-              }
-            : {})}
-          onClick={onOpenCreateShareChat}
-        >
-          创建新链接
-        </Button>
-      </Flex>
-      <TableContainer mt={3}>
-        <Table variant={'simple'} w={'100%'} overflowX={'auto'}>
-          <Thead>
-            <Tr>
-              <Th>名称</Th>
-              <Th>金额消耗</Th>
-              <Th>最后使用时间</Th>
-              <Th>操作</Th>
-            </Tr>
-          </Thead>
-          <Tbody>
-            {shareChatList.map((item) => (
-              <Tr key={item._id}>
-                <Td>{item.name}</Td>
-                <Td>{formatPrice(item.total)}元</Td>
-                <Td>{item.lastTime ? formatTimeToChatTime(item.lastTime) : '未使用'}</Td>
-                <Td display={'flex'} alignItems={'center'}>
-                  <MyTooltip label={'嵌入网页'}>
-                    <MyIcon
-                      mr={4}
-                      name="apiLight"
-                      w={'14px'}
-                      cursor={'pointer'}
-                      _hover={{ color: 'myBlue.600' }}
-                      onClick={() => {
-                        const url = `${location.origin}/chat/share?shareId=${item.shareId}`;
-                        const src = `${location.origin}/js/iframe.js`;
-                        const script = `<script src="${src}" id="fastgpt-iframe" data-src="${url}" data-color="#4e83fd"></script>`;
-                        copyData(script, '已复制嵌入 Script，可在应用 HTML 底部嵌入', 3000);
-                      }}
-                    />
-                  </MyTooltip>
-                  <MyTooltip label={'复制分享链接'}>
-                    <MyIcon
-                      mr={4}
-                      name="copy"
-                      w={'14px'}
-                      cursor={'pointer'}
-                      _hover={{ color: 'myBlue.600' }}
-                      onClick={() => {
-                        const url = `${location.origin}/chat/share?shareId=${item.shareId}`;
-                        copyData(url, '已复制分享链接，可直接分享使用');
-                      }}
-                    />
-                  </MyTooltip>
-                  <MyTooltip label={'删除链接'}>
-                    <MyIcon
-                      name="delete"
-                      w={'14px'}
-                      cursor={'pointer'}
-                      _hover={{ color: 'red' }}
-                      onClick={async () => {
-                        setIsLoading(true);
-                        try {
-                          await delShareChatById(item._id);
-                          refetchShareChatList();
-                        } catch (error) {
-                          console.log(error);
-                        }
-                        setIsLoading(false);
-                      }}
-                    />
-                  </MyTooltip>
-                </Td>
-              </Tr>
-            ))}
-          </Tbody>
-        </Table>
-      </TableContainer>
-      {shareChatList.length === 0 && !isFetching && (
-        <Flex h={'100%'} flexDirection={'column'} alignItems={'center'} pt={'10vh'}>
-          <MyIcon name="empty" w={'48px'} h={'48px'} color={'transparent'} />
-          <Box mt={2} color={'myGray.500'}>
-            没有创建分享链接
-          </Box>
-        </Flex>
-      )}
-      {/* create shareChat modal */}
-      <MyModal
-        isOpen={isOpenCreateShareChat}
-        onClose={onCloseCreateShareChat}
-        title={'创建免登录窗口'}
-      >
-        <ModalBody>
-          <FormControl>
-            <Flex alignItems={'center'}>
-              <Box flex={'0 0 60px'} w={0}>
-                名称:
-              </Box>
-              <Input
-                placeholder="记录名字，仅用于展示"
-                maxLength={20}
-                {...registerShareChat('name', {
-                  required: '记录名称不能为空'
-                })}
-              />
-            </Flex>
-          </FormControl>
-        </ModalBody>
-
-        <ModalFooter>
-          <Button variant={'base'} mr={3} onClick={onCloseCreateShareChat}>
-            取消
-          </Button>
-
-          <Button
-            isLoading={creating}
-            onClick={submitShareChat((data) => onclickCreateShareChat(data))}
-          >
-            确认
-          </Button>
-        </ModalFooter>
-      </MyModal>
-      <Loading loading={isFetching} fixed={false} />
-    </Box>
-  );
-};
-
-enum LinkTypeEnum {
-  share = 'share',
-  iframe = 'iframe'
-}
-
-const OutLink = ({ appId }: { appId: string }) => {
-  const theme = useTheme();
-
-  const [linkType, setLinkType] = useState<`${LinkTypeEnum}`>(LinkTypeEnum.share);
-
-  return (
-    <Box pt={[1, 5]}>
-      <Box fontWeight={'bold'} fontSize={['md', 'xl']} mb={2} px={[4, 8]}>
-        外部使用途径
-      </Box>
-      <Box pb={[5, 7]} px={[4, 8]} borderBottom={theme.borders.base}>
-        <MyRadio
-          gridTemplateColumns={['repeat(1,1fr)', 'repeat(auto-fill, minmax(0, 360px))']}
-          iconSize={'20px'}
-          list={[
-            {
-              icon: 'outlink_share',
-              title: '免登录窗口',
-              desc: '分享链接给其他用户，无需登录即可直接进行使用',
-              value: LinkTypeEnum.share
-            }
-            // {
-            //   icon: 'outlink_iframe',
-            //   title: '网页嵌入',
-            //   desc: '嵌入到已有网页中，右下角会生成对话按键',
-            //   value: LinkTypeEnum.iframe
-            // }
-          ]}
-          value={linkType}
-          onChange={(e) => setLinkType(e as `${LinkTypeEnum}`)}
-        />
-      </Box>
-
-      {linkType === LinkTypeEnum.share && <Share appId={appId} />}
-    </Box>
-  );
-};
-
-export default OutLink;

client/src/pages/index.tsx

@@ -1,53 +0,0 @@
-import React from 'react';
-import { Box } from '@chakra-ui/react';
-import { feConfigs } from '@/store/static';
-import { serviceSideProps } from '@/utils/i18n';
-import { useRouter } from 'next/router';
-
-import Navbar from './components/Navbar';
-import Hero from './components/Hero';
-import Ability from './components/Ability';
-import Choice from './components/Choice';
-import Footer from './components/Footer';
-import Loading from '@/components/Loading';
-
-const Home = ({ homeUrl = '/' }: { homeUrl: string }) => {
-  const router = useRouter();
-
-  if (homeUrl !== '/') {
-    router.replace(homeUrl);
-  }
-
-  return homeUrl === '/' ? (
-    <Box id="home" bg={'myWhite.600'} h={'100vh'} overflowY={'auto'} overflowX={'hidden'}>
-      <Box position={'fixed'} zIndex={10} top={0} left={0} right={0}>
-        <Navbar />
-      </Box>
-      <Box maxW={'1200px'} pt={'70px'} m={'auto'}>
-        <Hero />
-        <Ability />
-        <Box my={[4, 6]}>
-          <Choice />
-        </Box>
-      </Box>
-      {feConfigs?.show_git && (
-        <Box bg={'white'}>
-          <Footer />
-        </Box>
-      )}
-    </Box>
-  ) : (
-    <Loading />
-  );
-};
-
-export async function getServerSideProps(content: any) {
-  return {
-    props: {
-      ...(await serviceSideProps(content)),
-      homeUrl: process.env.HOME_URL || '/'
-    }
-  };
-}
-
-export default Home;

client/src/pages/kb/detail/index.tsx

@@ -1,194 +0,0 @@
-import React, { useCallback, useRef } from 'react';
-import { useRouter } from 'next/router';
-import { Box, Flex, IconButton, useTheme } from '@chakra-ui/react';
-import { useToast } from '@/hooks/useToast';
-import { useForm } from 'react-hook-form';
-import { useQuery } from '@tanstack/react-query';
-import { useUserStore } from '@/store/user';
-import { KbItemType } from '@/types/plugin';
-import { getErrText } from '@/utils/tools';
-import { useGlobalStore } from '@/store/global';
-import { type ComponentRef } from './components/Info';
-import Tabs from '@/components/Tabs';
-import dynamic from 'next/dynamic';
-import DataCard from './components/DataCard';
-import MyIcon from '@/components/Icon';
-import SideTabs from '@/components/SideTabs';
-import PageContainer from '@/components/PageContainer';
-import Avatar from '@/components/Avatar';
-import Info from './components/Info';
-import { serviceSideProps } from '@/utils/i18n';
-import { useTranslation } from 'react-i18next';
-import { getTrainingQueueLen } from '@/api/plugins/kb';
-import MyTooltip from '@/components/MyTooltip';
-import { QuestionOutlineIcon } from '@chakra-ui/icons';
-import { feConfigs } from '@/store/static';
-
-const ImportData = dynamic(() => import('./components/Import'), {
-  ssr: false
-});
-const Test = dynamic(() => import('./components/Test'), {
-  ssr: false
-});
-
-enum TabEnum {
-  data = 'data',
-  import = 'import',
-  test = 'test',
-  info = 'info'
-}
-
-const Detail = ({ kbId, currentTab }: { kbId: string; currentTab: `${TabEnum}` }) => {
-  const InfoRef = useRef<ComponentRef>(null);
-  const theme = useTheme();
-  const { t } = useTranslation();
-  const { toast } = useToast();
-  const router = useRouter();
-  const { isPc } = useGlobalStore();
-  const { kbDetail, getKbDetail } = useUserStore();
-
-  const tabList = useRef([
-    { label: '数据集', id: TabEnum.data, icon: 'overviewLight' },
-    { label: '导入数据', id: TabEnum.import, icon: 'importLight' },
-    { label: '搜索测试', id: TabEnum.test, icon: 'kbTest' },
-    { label: '配置', id: TabEnum.info, icon: 'settingLight' }
-  ]);
-
-  const setCurrentTab = useCallback(
-    (tab: `${TabEnum}`) => {
-      router.replace({
-        query: {
-          kbId,
-          currentTab: tab
-        }
-      });
-    },
-    [kbId, router]
-  );
-
-  const form = useForm<KbItemType>({
-    defaultValues: kbDetail
-  });
-
-  useQuery([kbId], () => getKbDetail(kbId), {
-    onSuccess(res) {
-      form.reset(res);
-      InfoRef.current?.initInput(res.tags);
-    },
-    onError(err: any) {
-      router.replace(`/kb/list`);
-      toast({
-        title: getErrText(err, '获取知识库异常'),
-        status: 'error'
-      });
-    }
-  });
-
-  const { data: trainingQueueLen = 0 } = useQuery(['getTrainingQueueLen'], getTrainingQueueLen, {
-    refetchInterval: 5000
-  });
-
-  return (
-    <PageContainer>
-      <Box display={['block', 'flex']} h={'100%'} pt={[4, 0]}>
-        {isPc ? (
-          <Flex
-            flexDirection={'column'}
-            p={4}
-            h={'100%'}
-            flex={'0 0 200px'}
-            borderRight={theme.borders.base}
-          >
-            <Flex mb={4} alignItems={'center'}>
-              <Avatar src={kbDetail.avatar} w={'34px'} borderRadius={'lg'} />
-              <Box ml={2} fontWeight={'bold'}>
-                {kbDetail.name}
-              </Box>
-            </Flex>
-            <SideTabs
-              flex={1}
-              mx={'auto'}
-              mt={2}
-              w={'100%'}
-              list={tabList.current}
-              activeId={currentTab}
-              onChange={(e: any) => {
-                setCurrentTab(e);
-              }}
-            />
-            <Box textAlign={'center'}>
-              <Flex justifyContent={'center'} alignItems={'center'}>
-                <MyIcon mr={1} name="overviewLight" w={'16px'} color={'green.500'} />
-                <Box>{t('dataset.System Data Queue')}</Box>
-                <MyTooltip
-                  label={t('dataset.Queue Desc', { title: feConfigs?.systemTitle })}
-                  placement={'top'}
-                >
-                  <QuestionOutlineIcon ml={1} w={'16px'} />
-                </MyTooltip>
-              </Flex>
-              <Box mt={1} fontWeight={'bold'}>
-                {trainingQueueLen}
-              </Box>
-            </Box>
-            <Flex
-              alignItems={'center'}
-              cursor={'pointer'}
-              py={2}
-              px={3}
-              borderRadius={'md'}
-              _hover={{ bg: 'myGray.100' }}
-              onClick={() => router.replace('/kb/list')}
-            >
-              <IconButton
-                mr={3}
-                icon={<MyIcon name={'backFill'} w={'18px'} color={'myBlue.600'} />}
-                bg={'white'}
-                boxShadow={'1px 1px 9px rgba(0,0,0,0.15)'}
-                h={'28px'}
-                size={'sm'}
-                borderRadius={'50%'}
-                aria-label={''}
-              />
-              全部知识库
-            </Flex>
-          </Flex>
-        ) : (
-          <Box mb={3}>
-            <Tabs
-              m={'auto'}
-              w={'260px'}
-              size={isPc ? 'md' : 'sm'}
-              list={tabList.current.map((item) => ({
-                id: item.id,
-                label: item.label
-              }))}
-              activeId={currentTab}
-              onChange={(e: any) => setCurrentTab(e)}
-            />
-          </Box>
-        )}
-
-        {!!kbDetail._id && (
-          <Box flex={'1 0 0'} h={'100%'} pb={[4, 0]}>
-            {currentTab === TabEnum.data && <DataCard kbId={kbId} />}
-            {currentTab === TabEnum.import && <ImportData kbId={kbId} />}
-            {currentTab === TabEnum.test && <Test kbId={kbId} />}
-            {currentTab === TabEnum.info && <Info ref={InfoRef} kbId={kbId} form={form} />}
-          </Box>
-        )}
-      </Box>
-    </PageContainer>
-  );
-};
-
-export async function getServerSideProps(context: any) {
-  const currentTab = context?.query?.currentTab || TabEnum.data;
-  const kbId = context?.query?.kbId;
-
-  return {
-    props: { currentTab, kbId, ...(await serviceSideProps(context)) }
-  };
-}
-
-export default React.memo(Detail);

client/src/pages/kb/list/index.tsx

@@ -1,171 +0,0 @@
-import React, { useCallback } from 'react';
-import {
-  Box,
-  Card,
-  Flex,
-  Grid,
-  useTheme,
-  Button,
-  IconButton,
-  useDisclosure
-} from '@chakra-ui/react';
-import { useRouter } from 'next/router';
-import { useUserStore } from '@/store/user';
-import PageContainer from '@/components/PageContainer';
-import { useConfirm } from '@/hooks/useConfirm';
-import { AddIcon } from '@chakra-ui/icons';
-import { useQuery } from '@tanstack/react-query';
-import { useToast } from '@/hooks/useToast';
-import { delKbById } from '@/api/plugins/kb';
-import Avatar from '@/components/Avatar';
-import MyIcon from '@/components/Icon';
-import Tag from '@/components/Tag';
-import { serviceSideProps } from '@/utils/i18n';
-import dynamic from 'next/dynamic';
-
-const CreateModal = dynamic(() => import('./component/CreateModal'), { ssr: false });
-
-const Kb = () => {
-  const theme = useTheme();
-  const router = useRouter();
-  const { toast } = useToast();
-  const { openConfirm, ConfirmModal } = useConfirm({
-    title: '删除提示',
-    content: '确认删除该知识库？知识库相关的文件、记录将永久删除，无法恢复！'
-  });
-  const { myKbList, loadKbList, setKbList } = useUserStore();
-
-  const {
-    isOpen: isOpenCreateModal,
-    onOpen: onOpenCreateModal,
-    onClose: onCloseCreateModal
-  } = useDisclosure();
-
-  const { refetch } = useQuery(['loadKbList'], () => loadKbList());
-
-  /* 点击删除 */
-  const onclickDelKb = useCallback(
-    async (id: string) => {
-      try {
-        delKbById(id);
-        toast({
-          title: '删除成功',
-          status: 'success'
-        });
-        setKbList(myKbList.filter((item) => item._id !== id));
-      } catch (err: any) {
-        toast({
-          title: err?.message || '删除失败',
-          status: 'error'
-        });
-      }
-    },
-    [toast, setKbList, myKbList]
-  );
-
-  return (
-    <PageContainer>
-      <Flex pt={3} px={5} alignItems={'center'}>
-        <Box flex={1} className="textlg" letterSpacing={1} fontSize={'24px'} fontWeight={'bold'}>
-          我的知识库
-        </Box>
-        <Button leftIcon={<AddIcon />} variant={'base'} onClick={onOpenCreateModal}>
-          新建
-        </Button>
-      </Flex>
-      <Grid
-        p={5}
-        gridTemplateColumns={['1fr', 'repeat(3,1fr)', 'repeat(4,1fr)', 'repeat(5,1fr)']}
-        gridGap={5}
-      >
-        {myKbList.map((kb) => (
-          <Card
-            display={'flex'}
-            flexDirection={'column'}
-            key={kb._id}
-            py={4}
-            px={5}
-            cursor={'pointer'}
-            h={'140px'}
-            border={theme.borders.md}
-            boxShadow={'none'}
-            userSelect={'none'}
-            position={'relative'}
-            _hover={{
-              boxShadow: '1px 1px 10px rgba(0,0,0,0.2)',
-              borderColor: 'transparent',
-              '& .delete': {
-                display: 'block'
-              }
-            }}
-            onClick={() =>
-              router.push({
-                pathname: '/kb/detail',
-                query: {
-                  kbId: kb._id
-                }
-              })
-            }
-          >
-            <Flex alignItems={'center'} h={'38px'}>
-              <Avatar src={kb.avatar} borderRadius={'lg'} w={'28px'} />
-              <Box ml={3}>{kb.name}</Box>
-              <IconButton
-                className="delete"
-                position={'absolute'}
-                top={4}
-                right={4}
-                size={'sm'}
-                icon={<MyIcon name={'delete'} w={'14px'} />}
-                variant={'base'}
-                borderRadius={'md'}
-                aria-label={'delete'}
-                display={['', 'none']}
-                _hover={{
-                  bg: 'red.100'
-                }}
-                onClick={(e) => {
-                  e.stopPropagation();
-                  openConfirm(() => onclickDelKb(kb._id))();
-                }}
-              />
-            </Flex>
-            <Box flex={'1 0 0'} overflow={'hidden'} pt={2}>
-              <Flex>
-                {kb.tags.map((tag, i) => (
-                  <Tag key={i} mr={2} mb={2}>
-                    {tag}
-                  </Tag>
-                ))}
-              </Flex>
-            </Box>
-            <Flex justifyContent={'flex-end'} alignItems={'center'} fontSize={'sm'}>
-              <MyIcon mr={1} name="kbTest" w={'12px'} />
-              <Box color={'myGray.500'}>{kb.vectorModel.name}</Box>
-            </Flex>
-          </Card>
-        ))}
-      </Grid>
-      {myKbList.length === 0 && (
-        <Flex mt={'35vh'} flexDirection={'column'} alignItems={'center'}>
-          <MyIcon name="empty" w={'48px'} h={'48px'} color={'transparent'} />
-          <Box mt={2} color={'myGray.500'}>
-            还没有知识库，快去创建一个吧！
-          </Box>
-        </Flex>
-      )}
-      <ConfirmModal />
-      {isOpenCreateModal && <CreateModal onClose={onCloseCreateModal} />}
-    </PageContainer>
-  );
-};
-
-export async function getServerSideProps(content: any) {
-  return {
-    props: {
-      ...(await serviceSideProps(content))
-    }
-  };
-}
-
-export default Kb;

client/src/service/events/pushBill.ts

@@ -1,175 +0,0 @@
-import { connectToDatabase, Bill, User, OutLink } from '../mongo';
-import { BillSourceEnum } from '@/constants/user';
-import { getModel } from '../utils/data';
-import { ChatHistoryItemResType } from '@/types/chat';
-import { formatPrice } from '@/utils/user';
-import { addLog } from '../utils/tools';
-
-export const pushTaskBill = async ({
-  appName,
-  appId,
-  userId,
-  source,
-  shareId,
-  response
-}: {
-  appName: string;
-  appId: string;
-  userId: string;
-  source: `${BillSourceEnum}`;
-  shareId?: string;
-  response: ChatHistoryItemResType[];
-}) => {
-  const total = response.reduce((sum, item) => sum + item.price, 0);
-
-  await Promise.allSettled([
-    Bill.create({
-      userId,
-      appName,
-      appId,
-      total,
-      source,
-      list: response.map((item) => ({
-        moduleName: item.moduleName,
-        amount: item.price || 0,
-        model: item.model,
-        tokenLen: item.tokens
-      }))
-    }),
-    User.findByIdAndUpdate(userId, {
-      $inc: { balance: -total }
-    }),
-    ...(shareId
-      ? [
-          updateShareChatBill({
-            shareId,
-            total
-          })
-        ]
-      : [])
-  ]);
-
-  addLog.info(`finish completions`, {
-    source,
-    userId,
-    price: formatPrice(total)
-  });
-};
-
-export const updateShareChatBill = async ({
-  shareId,
-  total
-}: {
-  shareId: string;
-  total: number;
-}) => {
-  try {
-    await OutLink.findOneAndUpdate(
-      { shareId },
-      {
-        $inc: { total },
-        lastTime: new Date()
-      }
-    );
-  } catch (err) {
-    addLog.error('update shareChat error', err);
-  }
-};
-
-export const pushQABill = async ({
-  userId,
-  totalTokens,
-  appName
-}: {
-  userId: string;
-  totalTokens: number;
-  appName: string;
-}) => {
-  addLog.info('splitData generate success', { totalTokens });
-
-  let billId;
-
-  try {
-    await connectToDatabase();
-
-    // 获取模型单价格, 都是用 gpt35 拆分
-    const unitPrice = global.qaModel.price || 3;
-    // 计算价格
-    const total = unitPrice * totalTokens;
-
-    // 插入 Bill 记录
-    const res = await Bill.create({
-      userId,
-      appName,
-      tokenLen: totalTokens,
-      total
-    });
-    billId = res._id;
-
-    // 账号扣费
-    await User.findByIdAndUpdate(userId, {
-      $inc: { balance: -total }
-    });
-  } catch (err) {
-    addLog.error('Create completions bill error', err);
-    billId && Bill.findByIdAndDelete(billId);
-  }
-};
-
-export const pushGenerateVectorBill = async ({
-  userId,
-  tokenLen,
-  model
-}: {
-  userId: string;
-  tokenLen: number;
-  model: string;
-}) => {
-  let billId;
-
-  try {
-    await connectToDatabase();
-
-    try {
-      // 计算价格. 至少为1
-      const vectorModel =
-        global.vectorModels.find((item) => item.model === model) || global.vectorModels[0];
-      const unitPrice = vectorModel.price || 0.2;
-      let total = unitPrice * tokenLen;
-      total = total > 1 ? total : 1;
-
-      // 插入 Bill 记录
-      const res = await Bill.create({
-        userId,
-        model: vectorModel.model,
-        appName: '索引生成',
-        total,
-        list: [
-          {
-            moduleName: '索引生成',
-            amount: total,
-            model: vectorModel.model,
-            tokenLen
-          }
-        ]
-      });
-      billId = res._id;
-
-      // 账号扣费
-      await User.findByIdAndUpdate(userId, {
-        $inc: { balance: -total }
-      });
-    } catch (err) {
-      addLog.error('Create generateVector bill error', err);
-      billId && Bill.findByIdAndDelete(billId);
-    }
-  } catch (error) {
-    console.log(error);
-  }
-};
-
-export const countModelPrice = ({ model, tokens }: { model: string; tokens: number }) => {
-  const modelData = getModel(model);
-  if (!modelData) return 0;
-  return modelData.price * tokens;
-};

client/src/service/models/openapi.ts

@@ -1,23 +0,0 @@
-import { Schema, model, models, Model } from 'mongoose';
-import { OpenApiSchema } from '@/types/mongoSchema';
-
-const OpenApiSchema = new Schema({
-  userId: {
-    type: Schema.Types.ObjectId,
-    ref: 'user',
-    required: true
-  },
-  apiKey: {
-    type: String,
-    required: true
-  },
-  createTime: {
-    type: Date,
-    default: () => new Date()
-  },
-  lastUsedTime: {
-    type: Date
-  }
-});
-
-export const OpenApi: Model<OpenApiSchema> = models['openapi'] || model('openapi', OpenApiSchema);

client/src/service/moduleDispatch/agent/classifyQuestion.ts

@@ -1,107 +0,0 @@
-import { adaptChatItem_openAI } from '@/utils/plugin/openai';
-import { ChatContextFilter } from '@/service/utils/chat/index';
-import type { ChatHistoryItemResType, ChatItemType } from '@/types/chat';
-import { ChatModuleEnum, ChatRoleEnum, TaskResponseKeyEnum } from '@/constants/chat';
-import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
-import type { ClassifyQuestionAgentItemType } from '@/types/app';
-import { countModelPrice } from '@/service/events/pushBill';
-import { UserModelSchema } from '@/types/mongoSchema';
-import { getModel } from '@/service/utils/data';
-import { SystemInputEnum } from '@/constants/app';
-import { SpecialInputKeyEnum } from '@/constants/flow';
-
-export type CQProps = {
-  systemPrompt?: string;
-  history?: ChatItemType[];
-  [SystemInputEnum.userChatInput]: string;
-  userOpenaiAccount: UserModelSchema['openaiAccount'];
-  [SpecialInputKeyEnum.agents]: ClassifyQuestionAgentItemType[];
-};
-export type CQResponse = {
-  [TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
-  [key: string]: any;
-};
-
-const agentModel = 'gpt-3.5-turbo';
-const agentFunName = 'agent_user_question';
-const maxTokens = 3000;
-
-/* request openai chat */
-export const dispatchClassifyQuestion = async (props: Record<string, any>): Promise<CQResponse> => {
-  const { agents, systemPrompt, history = [], userChatInput, userOpenaiAccount } = props as CQProps;
-
-  if (!userChatInput) {
-    return Promise.reject('Input is empty');
-  }
-
-  const messages: ChatItemType[] = [
-    ...(systemPrompt
-      ? [
-          {
-            obj: ChatRoleEnum.System,
-            value: systemPrompt
-          }
-        ]
-      : []),
-    ...history,
-    {
-      obj: ChatRoleEnum.Human,
-      value: userChatInput
-    }
-  ];
-  const filterMessages = ChatContextFilter({
-    model: agentModel,
-    prompts: messages,
-    maxTokens
-  });
-  const adaptMessages = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
-
-  //   function body
-  const agentFunction = {
-    name: agentFunName,
-    description: '判断用户问题的类型属于哪方面，返回对应的枚举字段',
-    parameters: {
-      type: 'object',
-      properties: {
-        type: {
-          type: 'string',
-          description: agents.map((item) => `${item.value}，返回：'${item.key}'`).join('；'),
-          enum: agents.map((item) => item.key)
-        }
-      },
-      required: ['type']
-    }
-  };
-  const chatAPI = getAIChatApi(userOpenaiAccount);
-
-  const response = await chatAPI.createChatCompletion(
-    {
-      model: agentModel,
-      temperature: 0,
-      messages: [...adaptMessages],
-      function_call: { name: agentFunName },
-      functions: [agentFunction]
-    },
-    {
-      ...axiosConfig(userOpenaiAccount)
-    }
-  );
-
-  const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
-
-  const tokens = response.data.usage?.total_tokens || 0;
-
-  const result = agents.find((item) => item.key === arg?.type) || agents[0];
-
-  return {
-    [result.key]: 1,
-    [TaskResponseKeyEnum.responseData]: {
-      moduleName: ChatModuleEnum.CQ,
-      price: userOpenaiAccount?.key ? 0 : countModelPrice({ model: agentModel, tokens }),
-      model: getModel(agentModel)?.name || agentModel,
-      tokens,
-      cqList: agents,
-      cqResult: result.value
-    }
-  };
-};

client/src/service/moduleDispatch/agent/extract.ts

@@ -1,131 +0,0 @@
-import { adaptChatItem_openAI } from '@/utils/plugin/openai';
-import { ChatContextFilter } from '@/service/utils/chat/index';
-import type { ChatHistoryItemResType, ChatItemType } from '@/types/chat';
-import { ChatModuleEnum, ChatRoleEnum, TaskResponseKeyEnum } from '@/constants/chat';
-import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
-import type { ContextExtractAgentItemType } from '@/types/app';
-import { ContextExtractEnum } from '@/constants/flow/flowField';
-import { countModelPrice } from '@/service/events/pushBill';
-import { UserModelSchema } from '@/types/mongoSchema';
-import { getModel } from '@/service/utils/data';
-
-export type Props = {
-  userOpenaiAccount: UserModelSchema['openaiAccount'];
-  history?: ChatItemType[];
-  [ContextExtractEnum.content]: string;
-  [ContextExtractEnum.extractKeys]: ContextExtractAgentItemType[];
-  [ContextExtractEnum.description]: string;
-};
-export type Response = {
-  [ContextExtractEnum.success]?: boolean;
-  [ContextExtractEnum.failed]?: boolean;
-  [ContextExtractEnum.fields]: string;
-  [TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
-};
-
-const agentModel = 'gpt-3.5-turbo';
-const agentFunName = 'agent_extract_data';
-const maxTokens = 4000;
-
-export async function dispatchContentExtract({
-  userOpenaiAccount,
-  content,
-  extractKeys,
-  history = [],
-  description
-}: Props): Promise<Response> {
-  if (!content) {
-    return Promise.reject('Input is empty');
-  }
-  const messages: ChatItemType[] = [
-    ...history,
-    {
-      obj: ChatRoleEnum.Human,
-      value: content
-    }
-  ];
-  const filterMessages = ChatContextFilter({
-    // @ts-ignore
-    model: agentModel,
-    prompts: messages,
-    maxTokens
-  });
-  const adaptMessages = adaptChatItem_openAI({ messages: filterMessages, reserveId: false });
-
-  const properties: Record<
-    string,
-    {
-      type: string;
-      description: string;
-    }
-  > = {};
-  extractKeys.forEach((item) => {
-    properties[item.key] = {
-      type: 'string',
-      description: item.desc
-    };
-  });
-
-  // function body
-  const agentFunction = {
-    name: agentFunName,
-    description: `${description}\n如果内容不存在，返回空字符串。`,
-    parameters: {
-      type: 'object',
-      properties,
-      required: extractKeys.filter((item) => item.required).map((item) => item.key)
-    }
-  };
-
-  const chatAPI = getAIChatApi(userOpenaiAccount);
-
-  const response = await chatAPI.createChatCompletion(
-    {
-      model: agentModel,
-      temperature: 0,
-      messages: [...adaptMessages],
-      function_call: { name: agentFunName },
-      functions: [agentFunction]
-    },
-    {
-      ...axiosConfig(userOpenaiAccount)
-    }
-  );
-
-  const arg: Record<string, any> = (() => {
-    try {
-      return JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '{}');
-    } catch (error) {
-      return {};
-    }
-  })();
-
-  // auth fields
-  let success = !extractKeys.find((item) => !arg[item.key]);
-  // auth empty value
-  if (success) {
-    for (const key in arg) {
-      if (arg[key] === '') {
-        success = false;
-        break;
-      }
-    }
-  }
-
-  const tokens = response.data.usage?.total_tokens || 0;
-
-  return {
-    [ContextExtractEnum.success]: success ? true : undefined,
-    [ContextExtractEnum.failed]: success ? undefined : true,
-    [ContextExtractEnum.fields]: JSON.stringify(arg),
-    ...arg,
-    [TaskResponseKeyEnum.responseData]: {
-      moduleName: ChatModuleEnum.Extract,
-      price: userOpenaiAccount?.key ? 0 : countModelPrice({ model: agentModel, tokens }),
-      model: getModel(agentModel)?.name || agentModel,
-      tokens,
-      extractDescription: description,
-      extractResult: arg
-    }
-  };
-}

client/src/store/kb.ts

@@ -1,42 +0,0 @@
-import { create } from 'zustand';
-import { devtools, persist } from 'zustand/middleware';
-import { immer } from 'zustand/middleware/immer';
-import { type KbTestItemType } from '@/types/plugin';
-
-type State = {
-  kbTestList: KbTestItemType[];
-  pushKbTestItem: (data: KbTestItemType) => void;
-  delKbTestItemById: (id: string) => void;
-  updateKbItemById: (data: KbTestItemType) => void;
-};
-
-export const useKbStore = create<State>()(
-  devtools(
-    persist(
-      immer((set, get) => ({
-        kbTestList: [],
-        pushKbTestItem(data) {
-          set((state) => {
-            state.kbTestList = [data, ...state.kbTestList].slice(0, 500);
-          });
-        },
-        delKbTestItemById(id) {
-          set((state) => {
-            state.kbTestList = state.kbTestList.filter((item) => item.id !== id);
-          });
-        },
-        updateKbItemById(data: KbTestItemType) {
-          set((state) => {
-            state.kbTestList = state.kbTestList.map((item) => (item.id === data.id ? data : item));
-          });
-        }
-      })),
-      {
-        name: 'kbStore',
-        partialize: (state) => ({
-          kbTestList: state.kbTestList
-        })
-      }
-    )
-  )
-);

client/src/types/openapi.d.ts

@@ -1,6 +0,0 @@
-export interface UserOpenApiKey {
-  id: string;
-  apiKey: string;
-  createTime: Date;
-  lastUsedTime?: Date;
-}

client/src/types/plugin.d.ts

@@ -1,44 +0,0 @@
-import { VectorModelItemType } from './model';
-import type { kbSchema } from './mongoSchema';
-
-export type SelectedKbType = { kbId: string; vectorModel: VectorModelItemType }[];
-
-export type KbListItemType = {
-  _id: string;
-  avatar: string;
-  name: string;
-  tags: string[];
-  vectorModel: VectorModelItemType;
-};
-/* kb type */
-export interface KbItemType {
-  _id: string;
-  avatar: string;
-  name: string;
-  userId: string;
-  vectorModel: VectorModelItemType;
-  tags: string;
-}
-
-export type DatasetItemType = {
-  q: string; // 提问词
-  a: string; // 原文
-  source?: string;
-  file_id?: string;
-};
-export type KbDataItemType = DatasetItemType & {
-  id: string;
-};
-
-export type KbTestItemType = {
-  id: string;
-  kbId: string;
-  text: string;
-  time: Date;
-  results: (KbDataItemType & { score: number })[];
-};
-
-export type FetchResultItem = {
-  url: string;
-  content: string;
-};

client/src/utils/i18n.ts

@@ -1,25 +0,0 @@
-import { serverSideTranslations } from 'next-i18next/serverSideTranslations';
-import Cookies from 'js-cookie';
-
-export const LANG_KEY = 'NEXT_LOCALE_LANG';
-export enum LangEnum {
-  'zh' = 'zh',
-  'en' = 'en'
-}
-
-export const setLangStore = (value: `${LangEnum}`) => {
-  return Cookies.set(LANG_KEY, value, { expires: 7, sameSite: 'None', secure: true });
-};
-
-export const getLangStore = () => {
-  return (Cookies.get(LANG_KEY) as `${LangEnum}`) || LangEnum.zh;
-};
-
-export const serviceSideProps = (content: any) => {
-  return serverSideTranslations(
-    content.req.cookies[LANG_KEY] || 'en',
-    undefined,
-    null,
-    content.locales
-  );
-};

client/src/utils/plugin/index.ts

@@ -1,8 +0,0 @@
-import { countOpenAIToken, openAiSliceTextByToken } from './openai';
-import { gpt_chatItemTokenSlice } from '@/pages/api/openapi/text/gptMessagesSlice';
-
-export const modelToolMap = {
-  countTokens: countOpenAIToken,
-  sliceText: openAiSliceTextByToken,
-  tokenSlice: gpt_chatItemTokenSlice
-};

client/src/utils/plugin/openai.ts

@@ -1,86 +0,0 @@
-import { encoding_for_model } from '@dqbd/tiktoken';
-import type { ChatItemType } from '@/types/chat';
-import { ChatRoleEnum } from '@/constants/chat';
-import { ChatCompletionRequestMessageRoleEnum } from 'openai';
-import axios from 'axios';
-import type { MessageItemType } from '@/pages/api/openapi/v1/chat/completions';
-
-export const getOpenAiEncMap = () => {
-  if (typeof window !== 'undefined' && window.OpenAiEncMap) {
-    return window.OpenAiEncMap;
-  }
-  if (typeof global !== 'undefined' && global.OpenAiEncMap) {
-    return global.OpenAiEncMap;
-  }
-  const enc = encoding_for_model('gpt-3.5-turbo', {
-    '<|im_start|>': 100264,
-    '<|im_end|>': 100265,
-    '<|im_sep|>': 100266
-  });
-
-  if (typeof window !== 'undefined') {
-    window.OpenAiEncMap = enc;
-  }
-  if (typeof global !== 'undefined') {
-    global.OpenAiEncMap = enc;
-  }
-
-  return enc;
-};
-
-export const adaptChatItem_openAI = ({
-  messages,
-  reserveId
-}: {
-  messages: ChatItemType[];
-  reserveId: boolean;
-}): MessageItemType[] => {
-  const map = {
-    [ChatRoleEnum.AI]: ChatCompletionRequestMessageRoleEnum.Assistant,
-    [ChatRoleEnum.Human]: ChatCompletionRequestMessageRoleEnum.User,
-    [ChatRoleEnum.System]: ChatCompletionRequestMessageRoleEnum.System
-  };
-  return messages.map((item) => ({
-    ...(reserveId && { dataId: item.dataId }),
-    role: map[item.obj] || ChatCompletionRequestMessageRoleEnum.System,
-    content: item.value || ''
-  }));
-};
-
-export function countOpenAIToken({ messages }: { messages: ChatItemType[] }) {
-  const adaptMessages = adaptChatItem_openAI({ messages, reserveId: true });
-  const token = adaptMessages.reduce((sum, item) => {
-    const text = `${item.role}\n${item.content}`;
-    const enc = getOpenAiEncMap();
-    const encodeText = enc.encode(text);
-    const tokens = encodeText.length + 3; // 补充估算值
-    return sum + tokens;
-  }, 0);
-
-  return token;
-}
-
-export const openAiSliceTextByToken = ({ text, length }: { text: string; length: number }) => {
-  const enc = getOpenAiEncMap();
-  const encodeText = enc.encode(text);
-  const decoder = new TextDecoder();
-  return decoder.decode(enc.decode(encodeText.slice(0, length)));
-};
-
-export const authOpenAiKey = async (key: string) => {
-  return axios
-    .get('https://ccdbwscohpmu.cloud.sealos.io/openai/v1/dashboard/billing/subscription', {
-      headers: {
-        Authorization: `Bearer ${key}`
-      }
-    })
-    .then((res) => {
-      if (!res.data.access_until) {
-        return Promise.resolve('OpenAI Key 可能无效');
-      }
-    })
-    .catch((err) => {
-      console.log(err);
-      return Promise.reject(err?.response?.data?.error?.message || 'OpenAI Key 可能无效');
-    });
-};

client/src/utils/user.ts

@@ -1,26 +0,0 @@
-import { PRICE_SCALE } from '@/constants/common';
-import { loginOut } from '@/api/user';
-
-const tokenKey = 'token';
-export const clearToken = () => {
-  try {
-    loginOut();
-    localStorage.removeItem(tokenKey);
-  } catch (error) {
-    error;
-  }
-};
-
-export const setToken = (token: string) => {
-  localStorage.setItem(tokenKey, token);
-};
-export const getToken = () => {
-  return localStorage.getItem(tokenKey) || '';
-};
-
-/**
- * 把数据库读取到的price，转化成元
- */
-export const formatPrice = (val = 0, multiple = 1) => {
-  return Number(((val / PRICE_SCALE) * multiple).toFixed(10));
-};

docSite/assets/docs/scss/custom/pages/_custom.scss

@@ -0,0 +1,99 @@
+.docs-content .main-content img, .docs-content .main-content  svg:not(.gitinfo svg):not(a svg) {
+    max-width: 80% !important;
+    height: auto;
+    display: block !important;
+    margin: 0 auto !important;
+    border-radius: .25rem;
+}
+
+div.code-toolbar {
+    padding-top: 1.95rem !important;
+}
+
+.docs-content .main-content pre code::before {
+    background: #fc625d;
+    border-radius: 50%;
+    box-shadow: 20px 0 #fdbc40, 40px 0 #35cd4b;
+    content: ' ';
+    height: 12px;
+    left: 12px;
+    margin-top: -21px;
+    position: absolute;
+    width: 12px;
+    z-index: 1;
+}
+
+li p {
+    margin-top: 1rem !important;
+    margin-bottom: 1rem;
+}
+
+footer {
+	height: 118px !important;
+}
+
+/*
+footer a:hover {
+    text-decoration: none !important;
+}
+*/
+
+.medium-zoom-overlay,
+.medium-zoom-image--opened {
+  z-index: 1999;
+}
+
+/* 徽章样式 */
+.github-badge {
+	display: inline-block;
+	border-radius: 4px;
+	text-shadow: none;
+	font-size: 12px;
+	color: #fff;
+	line-height: 15px;
+    margin-bottom: 5px;
+    margin-top: 5px;
+}
+.github-badge .badge-subject {
+	display: inline-block;
+	background-color: #4D4D4D;
+	padding: 4px 4px 4px 6px;
+	border-top-left-radius: 4px;
+	border-bottom-left-radius: 4px;
+}
+.github-badge .badge-value {
+	display: inline-block;
+	padding: 4px 6px 4px 4px;
+	border-top-right-radius: 4px;
+	border-bottom-right-radius: 4px;
+}
+.github-badge .bg-brightgreen {
+	background-color: #4DC820 !important;
+}
+.github-badge .bg-orange {
+	background-color: #FFA500 !important;
+}
+.github-badge .bg-yellow {
+	background-color: #D8B024 !important;
+}
+.github-badge .bg-blueviolet {
+	background-color: #8833D7 !important;
+}
+.github-badge .bg-pink {
+	background-color: #F26BAE !important;
+}
+.github-badge .bg-red {
+	background-color: #e05d44 !important;
+}
+.github-badge .bg-blue {
+	background-color: #007EC6 !important;
+}
+.github-badge .bg-lightgrey {
+	background-color: #9F9F9F !important;
+}
+.github-badge .bg-grey, .github-badge .bg-gray {
+	background-color: #555 !important;
+}
+.github-badge .bg-lightgrey, .github-badge .bg-lightgray {
+	background-color: #9f9f9f !important;
+}

docSite/assets/docs/scss/style.scss

@@ -0,0 +1,77 @@
+/* Template Name: Lotus Docs
+   Author: Colin Wilson
+   E-mail: colin@aigis.uk
+   Created: October 2022
+   Version: 1.2.0
+   File Description: Main CSS file for Lotus Docs
+*/
+
+// Custom Font Variables
+$font-family-secondary:  {{ .Site.Params.secondary_font | default "-apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Helvetica Neue', 'Ubuntu'" }};
+$font-family-sans-serif: {{ .Site.Params.sans_serif_font | default "-apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Helvetica Neue', 'Ubuntu'" }};
+$font-family-monospace:  {{ .Site.Params.mono_font | default "SFMono-Regular, Menlo, Monaco, Consolas, 'Liberation Mono', 'Courier New', monospace" }};
+
+// Code Padding Variables
+$code-block-padding-top: {{ if eq .Site.Params.docs.prism true -}}0{{ else }}1.25rem 0 0 0{{ end }};
+
+// Icon Fonts
+@import "custom/plugins/icons/google-material";
+
+// Core files
+@import "../../scss/bootstrap/functions";
+@import "../../scss/bootstrap/variables";
+@import {{ printf "'%s%s'" "custom/colors/" (.Site.Params.docs.themeColor | default "blue") }}; // current theme color
+@import "../../scss/bootstrap/mixins";
+@import "../../scss/bootstrap/bootstrap";
+@import "variables";
+
+{{ if and (.Site.Params.docsearch.appID) (.Site.Params.docsearch.apiKey) -}}
+@import "custom/plugins/docsearch/style";
+{{ end }}
+
+// Structure
+@import "custom/structure/general";
+@import "custom/structure/content";
+@import "custom/structure/sidebar";
+@import "custom/structure/doc-nav";
+@import "custom/structure/toc";
+@import "custom/structure/footer";
+
+// Components
+@import "custom/components/buttons";
+@import "custom/components/modal";
+@import "custom/components/breadcrumb";
+@import "custom/components/badge";
+@import "custom/components/backgrounds";
+@import "custom/components/alerts";
+@import "custom/components/card";
+@import "custom/components/forms";
+@import "custom/components/table";
+@import "custom/components/tabs";
+@import "custom/components/tooltip";
+
+// Pages
+@import "custom/pages/features";
+@import "custom/pages/helper";
+
+// Plugins
+
+// Prism / Chroma
+{{- if eq .Site.Params.docs.prism true }}
+@import {{ printf "'%s%s'" "custom/plugins/prism/themes/" (.Site.Params.docs.prismTheme | default "lotusdocs") }}; // current prism theme
+@import "custom/plugins/prism/prism";
+{{- else }}
+@import "custom/plugins/chroma/default";
+{{- end -}}
+
+// FlexSearch
+{{ if or (not (isset .Site.Params.flexsearch "enabled")) (eq .Site.Params.flexsearch.enabled true) -}}@import "custom/plugins/flexsearch/flexsearch";{{ end }}
+
+// Feedback Widget
+{{ if .Site.Params.feedback.enabled | default false -}}@import "custom/plugins/feedback/feedback";{{ end}}
+
+// Mermaid
+@import "custom/plugins/mermaid/mermaid";
+
+// change
+@import "custom/pages/custom";

docSite/assets/images/logos/mark.svg

@@ -1,4 +1,4 @@
-<svg width="32" height="32" viewBox="0 0 1041 1348" fill="none" xmlns="http://www.w3.org/2000/svg">
+<svg width="26" height="26" viewBox="0 0 1041 1348" fill="none" xmlns="http://www.w3.org/2000/svg">
 <path d="M340.837 0.33933L681.068 0.338989V0.455643C684.032 0.378397 686.999 0.339702 689.967 0.339702C735.961 0.3397 781.504 9.62899 823.997 27.6772C866.49 45.7254 905.099 72.1791 937.622 105.528C970.144 138.877 995.942 178.467 1013.54 222.04C1031.14 265.612 1040.2 312.312 1040.2 359.474L340.836 359.474L340.836 1347.84C296.157 1347.84 251.914 1338.55 210.636 1320.49C169.357 1302.43 131.85 1275.95 100.257 1242.58C68.6636 1209.21 43.6023 1169.59 26.5041 1125.99C11.3834 1087.43 2.75216 1046.42 0.957956 1004.81H0.605869L0.605897 368.098H0.70363C0.105752 341.831 2.23741 315.443 7.14306 289.411C20.2709 219.745 52.6748 155.754 100.257 105.528C147.839 55.3017 208.462 21.0975 274.461 7.24017C296.426 2.62833 318.657 0.339101 340.837 0.33933Z" fill="url(#paint0_linear_1172_228)"/>
 <path d="M633.639 904.645H513.029V576.37H635.422V576.377C678.161 576.607 720.454 585.093 759.951 601.37C799.997 617.874 836.384 642.064 867.033 672.559C897.683 703.054 921.996 739.257 938.583 779.101C955.171 818.944 963.709 861.648 963.709 904.775H633.639V904.645Z" fill="url(#paint1_linear_1172_228)"/>
 <defs>

docSite/content/docs/commercial.md

@@ -0,0 +1,89 @@
+---
+title: '商业版'
+description: 'FastGPT 商业版相关说明'
+icon: 'shopping_cart'
+draft: false
+toc: true
+weight: 20
+---
+
+## FastGPT 线上服务
+
+[按线上标准计费](/docs/pricing)即可。 地址: https://fastgpt.run
+
+## 商业版
+
+商业版最终交付版本功能与 https://fastgpt.run 完全一致，品牌内容可自定义。
+
+{{% alert icon="🤖" context="warning" %}}
+商业版与开源版功能差异：（目前计划）
+
+1. 自定义 title 和 logo
+2. 用户注册，支付 （已有微信扫码支付，后续会补充支付方式）
+3. API 访问限制，可配置：额度、过期时间
+4. 团队空间 （计划）
+5. 完善的 OpenAPI（计划）
+6. 高级编排额外插件（计划）
+7. 后台管理系统   
+  a. 查询：用户、支付、应用、知识库  
+  b. 变更：用户  
+  c. 新增：用户  
+{{% /alert %}}
+
+### 商业版定价
+
+#### 交付费用
+
++ 使用 [Sealos 公有云](https://sealos.io)部署：1万元/年/套 （无部署费用。赠送 8000 sealos 公有云额度，可用于 FastGPT 或其他云资源）。
++ 渠道商使用 Sealos 部署：返现 20% 成交额。
++ 私有服务器部署：2万元/年/套（如需部署支持，按技术服务费计算）
++ 渠道商私有服务器部署：1.3万元/年/套（渠道商合同单独约谈，累计 5 套以上可签）
+
+#### 用户注册数量费用（按注册量算，不计量分享和 API）
+
+{{< table "table-hover table-striped-columns" >}}
+| 最大用户数量 | 费用 元/年 |
+| ------------ | ---------- |
+| 100          | 0          |
+| 5000         | 5000       |
+| 2万          | 18000      |
+| 5万          | 35000      |
+| 20万         | 100000     |
+{{< /table >}}
+
+#### 总费用
+
+总费用 = 商业版功能费用 + 用户数量费用
+
+## 技术支持
+
+### 应用定制
+
+根据需求，定制实现某个需求的编排功能，最终会交付一个应用编排。可根据实际情况商讨。
+
+### 技术服务费（定开、部署、维护等）
+
+2000元/人/天
+
+### 更新费用
+
+大部分更新，重新拉镜像就可以了，不需要执行额外操作。
+
+复杂更新可参考文档自行更新；或付费支持，标准与技术服务费一致。
+
+## 联系方式
+
+微信: allence1004
+邮箱: yujinlong@sealos.io
+
+## QA
+
+1. 如何交付？
+   
+   完整版应用 = 开源版镜像 + 商业版镜像
+
+   我们会提供一个商业版镜像给你使用，该镜像需要一个 license 启动，license 有效期为 1 年。此外，还会提供一个简单的后台管理系统（目前只设置了简单的查询功能）
+
+2. 二次开发如何操作？
+   
+   可自行修改开源版代码进行二次开发，不支持修改商业版镜像。

docSite/content/docs/community/_index.md

@@ -0,0 +1,8 @@
+---
+weight: 1100
+title: '社区'
+description: '社区相关内容'
+icon: 'forum'
+draft: false
+images: []
+---

docSite/content/docs/community/open-source.md

@@ -0,0 +1,24 @@
+---
+title: '开源协议'
+description: ' FastGPT 开源许可证'
+icon: 'verified_user'
+draft: false
+toc: true
+weight: 1120
+---
+
+FastGPT 项目在 Apache License 2.0 许可下开源，同时包含以下附加条件：
+
++ FastGPT 允许被用于商业化，例如作为其他应用的“后端即服务”使用，或者作为应用开发平台提供给企业。然而，当满足以下条件时，必须联系作者获得商业许可：
+   
+   + 多租户 SaaS 服务：除非获得 FastGPT 的明确书面授权，否则不得使用 fastgpt.run 的源码来运营与 fastgpt.run 服务版类似的多租户 SaaS 服务。
+   + LOGO 及版权信息：在使用 FastGPT 的过程中，不得移除或修改 FastGPT 控制台内的 LOGO 或版权信息。
+
+   请通过电子邮件 yujinlong@sealos.io 联系我们咨询许可事宜。
+
++ 作为贡献者，你必须同意将你贡献的代码用于以下用途：
+  
+   + 生产者有权将开源协议调整为更严格或更宽松的形式。
+   + 可用于商业目的，例如 FastGPT 的云服务。 
+
+除此之外，所有其他权利和限制均遵循 Apache License 2.0。如果你需要更多详细信息，可以参考 Apache License 2.0 的完整版本。本产品的交互设计受到外观专利保护。© 2023 Sealos.

docSite/content/docs/community/support.md

@@ -0,0 +1,16 @@
+---
+title: '加入社区'
+description: ' 加入 FastGPT 开发者社区和我们一起成长'
+icon: 'forum'
+draft: false
+toc: true
+weight: 1110
+---
+
+FastGPT 是一个由用户和贡献者参与推动的开源项目，如果您对产品使用存在疑问和建议，可尝试以下方式寻求支持。我们的团队与社区会竭尽所能为您提供帮助。
+
++ 📱 扫码加入社区微信交流群👇
+
+   <img width="400px" src="/wechat-fastgpt.webp" />
+
++ 🐞 请将任何 FastGPT 的 Bug、问题和需求提交到 [GitHub Issue](https://github.com/labring/fastgpt/issues/new/choose)。

docSite/content/docs/custom-models/_index.md

@@ -1,5 +1,5 @@
 ---
-weight: 800
+weight: 900
 title: '本地模型使用'
 description: 'FastGPT 对接本地模型'
 icon: 'model_training'

docSite/content/docs/custom-models/chatglm2-m3e.md

@@ -4,21 +4,26 @@ description: ' 将 FastGPT 接入私有化模型 ChatGLM2和m3e-large'
 icon: 'model_training'
 draft: false
 toc: true
-weight: 200
+weight: 930
 ---
 
 ## 前言
 
-FastGPT 默认使用了 openai 的 LLM 模型和向量模型，如果想要私有化部署的话，可以使用 ChatGLM2 和 m3e-large 模型。以下是由用户@不做了睡大觉 提供的接入方法。该镜像直接集成了 M3E-Large 和 ChatGLM2-6B 模型，可以直接使用。
+FastGPT 默认使用了 OpenAI 的 LLM 模型和向量模型，如果想要私有化部署的话，可以使用 ChatGLM2 和 m3e-large 模型。以下是由用户@不做了睡大觉 提供的接入方法。该镜像直接集成了 M3E-Large 和 ChatGLM2-6B 模型，可以直接使用。
 
 ## 部署镜像
 
-镜像名: `stawky/chatglm2-m3e:latest`  
-国内镜像名: `registry.cn-hangzhou.aliyuncs.com/fastgpt/chatglm2-m3e:latest`
-端口号: 6006
-镜像默认 sk-key: `sk-aaabbbcccdddeeefffggghhhiiijjjkkk`
++ 镜像名: `stawky/chatglm2-m3e:latest`  
++ 国内镜像名: `registry.cn-hangzhou.aliyuncs.com/fastgpt_docker/chatglm2-m3e:latest`
++ 端口号: 6006
 
-## 接入 OneAPI
+```
+# 设置安全凭证（即oneapi中的渠道密钥）
+默认值：sk-aaabbbcccdddeeefffggghhhiiijjjkkk
+也可以通过环境变量引入：sk-key。有关docker环境变量引入的方法请自寻教程，此处不再赘述。
+```
+
+## 接入 [One API](/docs/installation/one-api/)
 
 为 chatglm2 和 m3e-large 各添加一个渠道，参数如下：
 
@@ -50,26 +55,25 @@ curl --location --request POST 'https://domain/v1/chat/completions' \
 }'
 ```
 
-Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 OneAPI 填写的自定义模型。
+Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One API 填写的自定义模型。
 
 ## 接入 FastGPT
 
 修改 config.json 配置文件，在 VectorModels 中加入 chatglm2 和 M3E 模型：
 
 ```json
-  "ChatModels": [
-    //已有模型
-    {
-      "model": "chatglm2",
-      "name": "chatglm2",
-      "contextMaxToken": 8000,
-      "quoteMaxToken": 4000,
-      "maxTemperature": 1.2,
-      "price": 0,
-      "defaultSystem": ""
-    }
-  ],
-
+"ChatModels": [
+  //已有模型
+  {
+    "model": "chatglm2",
+    "name": "chatglm2",
+    "contextMaxToken": 8000,
+    "quoteMaxToken": 4000,
+    "maxTemperature": 1.2,
+    "price": 0,
+    "defaultSystem": ""
+  }
+],
 "VectorModels": [
     {
       "model": "text-embedding-ada-002",
@@ -94,20 +98,20 @@ M3E 模型的使用方法如下：
 
 1. 创建知识库时候选择 M3E 模型。
 
-注意，一旦选择后，知识库将无法修改向量模型。
-
-![](/imgs/model-m3e2.png)
+   注意，一旦选择后，知识库将无法修改向量模型。
+   
+   ![](/imgs/model-m3e2.png)
 
 2. 导入数据
 3. 搜索测试
 
-![](/imgs/model-m3e3.png)
+   ![](/imgs/model-m3e3.png)
 
 4. 应用绑定知识库
 
-注意，应用只能绑定同一个向量模型的知识库，不能跨模型绑定。并且，需要注意调整相似度，不同向量模型的相似度（距离）会有所区别，需要自行测试实验。
-
-![](/imgs/model-m3e4.png)
+   注意，应用只能绑定同一个向量模型的知识库，不能跨模型绑定。并且，需要注意调整相似度，不同向量模型的相似度（距离）会有所区别，需要自行测试实验。
+   
+   ![](/imgs/model-m3e4.png)
 
 chatglm2 模型的使用方法如下：
 模型选择 chatglm2 即可

docSite/content/docs/custom-models/chatglm2.md

@@ -4,7 +4,7 @@ description: ' 将 FastGPT 接入私有化模型 ChatGLM2-6B'
 icon: 'model_training'
 draft: false
 toc: true
-weight: 100
+weight: 910
 ---
 
 ## 前言
@@ -27,7 +27,7 @@ ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本，
 
 因此推荐配置如下：
 
-{{< table "table-hover table-striped" >}}
+{{< table "table-hover table-striped-columns" >}}
 | 类型 | 内存 | 显存 | 硬盘空间 | 启动命令 |
 |------|---------|---------|----------|--------------------------|
 | fp16 | >=16GB | >=16GB | >=25GB | python openai_api.py 16 |
@@ -48,8 +48,8 @@ ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本，
 1. 根据上面的环境配置配置好环境，具体教程自行 GPT；
 2. 下载 [python 文件](https://github.com/labring/FastGPT/blob/main/files/models/ChatGLM2/openai_api.py)
 3. 在命令行输入命令 `pip install -r requirments.txt`；
-4. 打开你需要启动的 py 文件，在代码的第 76 行配置 token，这里的 token 只是加一层验证，防止接口被人盗用；
-5. 执行命令 `python openai_api.py 16`。这里的数字根据上面的配置进行选择。
+4. 打开你需要启动的 py 文件，在代码的 `verify_token` 方法中配置 token，这里的 token 只是加一层验证，防止接口被人盗用；
+5. 执行命令 `python openai_api.py --model_name 16`。这里的数字根据上面的配置进行选择。
 
 然后等待模型下载，直到模型加载完毕为止。如果出现报错先问 GPT。
 
@@ -63,12 +63,17 @@ ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本，
 
 **镜像和端口**
 
-镜像名: `stawky/chatglm2:latest`  
-国内镜像名: `registry.cn-hangzhou.aliyuncs.com/fastgpt/chatglm2:latest`
-端口号: 6006
-镜像默认 sk-key: `sk-aaabbbcccdddeeefffggghhhiiijjjkkk`
++ 镜像名: `stawky/chatglm2:latest`  
++ 国内镜像名: `registry.cn-hangzhou.aliyuncs.com/fastgpt_docker/chatglm2:latest`
++ 端口号: 6006
 
-## 接入 OneAPI
+```
+# 设置安全凭证（即oneapi中的渠道密钥）
+默认值：sk-aaabbbcccdddeeefffggghhhiiijjjkkk
+也可以通过环境变量引入：sk-key。有关docker环境变量引入的方法请自寻教程，此处不再赘述。
+```
+
+## 接入 One API
 
 为 chatglm2 添加一个渠道，参数如下：
 
@@ -90,28 +95,29 @@ curl --location --request POST 'https://domain/v1/chat/completions' \
 }'
 ```
 
-Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 OneAPI 填写的自定义模型。
+Authorization 为 sk-aaabbbcccdddeeefffggghhhiiijjjkkk。model 为刚刚在 One API 填写的自定义模型。
 
 ## 接入 FastGPT
 
-修改 config.json 配置文件，在 VectorModels 中加入 chatglm2 和 M3E 模型：
+修改 config.json 配置文件，在 VectorModels 中加入 chatglm2 模型：
 
 ```json
-  "ChatModels": [
-    //已有模型
-    {
-      "model": "chatglm2",
-      "name": "chatglm2",
-      "contextMaxToken": 8000,
-      "quoteMaxToken": 4000,
-      "maxTemperature": 1.2,
-      "price": 0,
-      "defaultSystem": ""
-    }
-  ],
+"ChatModels": [
+  //已有模型
+  {
+    "model": "chatglm2",
+    "name": "chatglm2",
+    "contextMaxToken": 8000,
+    "quoteMaxToken": 4000,
+    "maxTemperature": 1.2,
+    "price": 0,
+    "defaultSystem": ""
+  }
+]
 ```
 
 ## 测试使用
 
 chatglm2 模型的使用方法如下：
+
 模型选择 chatglm2 即可

docSite/content/docs/custom-models/m3e.md

@@ -4,7 +4,7 @@ description: ' 将 FastGPT 接入私有化模型 M3E'
 icon: 'model_training'
 draft: false
 toc: true
-weight: 100
+weight: 920
 ---
 
 ## 前言
@@ -14,10 +14,17 @@ FastGPT 默认使用了 openai 的 embedding 向量模型，如果你想私有
 ## 部署镜像
 
 镜像名: `stawky/m3e-large-api:latest`  
-国内镜像： `registry.cn-guangzhou.aliyuncs.com/kbgpt/m3e-large-api:latest`
+国内镜像： `registry.cn-hangzhou.aliyuncs.com/fastgpt_docker/m3e-large-api:latest`
 端口号: 6008
+环境变量：
 
-## 接入 OneAPI
+```
+# 设置安全凭证（即oneapi中的渠道密钥）
+默认值：sk-aaabbbcccdddeeefffggghhhiiijjjkkk
+也可以通过环境变量引入：sk-key。有关docker环境变量引入的方法请自寻教程，此处不再赘述。
+```
+
+## 接入 One API
 
 添加一个渠道，参数如下：
 
@@ -29,7 +36,7 @@ curl 例子：
 
 ```bash
 curl --location --request POST 'https://domain/v1/embeddings' \
---header 'Authorization: Bearer sk-key' \
+--header 'Authorization: Bearer xxxx' \
 --header 'Content-Type: application/json' \
 --data-raw '{
   "model": "m3e",
@@ -37,7 +44,7 @@ curl --location --request POST 'https://domain/v1/embeddings' \
 }'
 ```
 
-Authorization 为 sk-key。model 为刚刚在 OneAPI 填写的自定义模型。
+Authorization 为 sk-key。model 为刚刚在 One API 填写的自定义模型。
 
 ## 接入 FastGPT
 
@@ -59,24 +66,24 @@ Authorization 为 sk-key。model 为刚刚在 OneAPI 填写的自定义模型。
       "defaultToken": 500,
       "maxToken": 1800
     }
-],
+]
 ```
 
 ## 测试使用
 
 1. 创建知识库时候选择 M3E 模型。
 
-注意，一旦选择后，知识库将无法修改向量模型。
+      注意，一旦选择后，知识库将无法修改向量模型。
 
-![](/imgs/model-m3e2.png)
+      ![](/imgs/model-m3e2.png)
 
 2. 导入数据
 3. 搜索测试
 
-![](/imgs/model-m3e3.png)
+      ![](/imgs/model-m3e3.png)
 
 4. 应用绑定知识库
 
-注意，应用只能绑定同一个向量模型的知识库，不能跨模型绑定。并且，需要注意调整相似度，不同向量模型的相似度（距离）会有所区别，需要自行测试实验。
+      注意，应用只能绑定同一个向量模型的知识库，不能跨模型绑定。并且，需要注意调整相似度，不同向量模型的相似度（距离）会有所区别，需要自行测试实验。
 
-![](/imgs/model-m3e4.png)
+      ![](/imgs/model-m3e4.png)

docSite/content/docs/development/_index.md

@@ -1,5 +1,5 @@
 ---
-weight: 200
+weight: 500
 title: '开发指南'
 description: '本地开发 FastGPT 必看'
 icon: 'code_blocks'

docSite/content/docs/development/configuration.md

@@ -4,10 +4,10 @@ description: 'FastGPT 配置参数介绍'
 icon: 'settings'
 draft: false
 toc: true
-weight: 100
+weight: 520
 ---
 
-由于环境变量不利于配置复杂的内容，新版 FastGPT 采用了 ConfigMap 的形式挂载配置文件，你可以在 `client/data/config.json` 看到默认的配置文件。可以参考 [docker-compose 快速部署](/docs/installation/docker/) 来挂载配置文件。
+由于环境变量不利于配置复杂的内容，新版 FastGPT 采用了 ConfigMap 的形式挂载配置文件，你可以在 `projects/app/data/config.json` 看到默认的配置文件。可以参考 [docker-compose 快速部署](/docs/installation/docker/) 来挂载配置文件。
 
 **开发环境下**，你需要将示例配置文件 `config.json` 复制成 `config.local.json` 文件才会生效。
 
@@ -22,18 +22,6 @@ weight: 100
 这里介绍一些基础的配置字段：
 
 ```json
-// 这个配置会控制前端的一些样式
-"FeConfig": {
-    "show_emptyChat": true, // 对话页面，空内容时，是否展示介绍页
-    "show_register": false, // 是否展示注册按键（包括忘记密码，注册账号和三方登录）
-    "show_appStore": false, // 是否展示应用市场（不过目前权限还没做好，放开也没用）
-    "show_userDetail": false, // 是否展示用户详情（账号余额、OpenAI 绑定）
-    "show_git": true, // 是否展示 Git
-    "systemTitle": "FastGPT", // 系统的 title
-    "authorText": "Made by FastGPT Team.", // 签名
-    "gitLoginKey": "" // Git 登录凭证
-},
-...
 ...
 // 这个配置文件是系统级参数
 "SystemParams": {
@@ -48,23 +36,11 @@ weight: 100
 
 ```json
 {
-  "FeConfig": {
-    "show_emptyChat": true,
-    "show_register": false,
-    "show_appStore": false,
-    "show_userDetail": false,
-    "show_git": true,
-    "systemTitle": "FastGPT",
-    "authorText": "Made by FastGPT Team.",
-    "gitLoginKey": "",
-    "scripts": []
-  },
   "SystemParams": {
     "vectorMaxProcess": 15,
     "qaMaxProcess": 15,
     "pgIvfflatProbe": 20
   },
-  "plugins": {},
   "ChatModels": [
     {
       "model": "gpt-3.5-turbo",
@@ -94,12 +70,6 @@ weight: 100
       "defaultSystem": ""
     }
   ],
-  "QAModel": {
-    "model": "gpt-3.5-turbo-16k",
-    "name": "GPT35-16k",
-    "maxToken": 16000,
-    "price": 0
-  },
   "VectorModels": [
     {
       "model": "text-embedding-ada-002",
@@ -108,6 +78,28 @@ weight: 100
       "defaultToken": 500,
       "maxToken": 3000
     }
-  ]
+  ],
+  "QAModel": {
+    "model": "gpt-3.5-turbo-16k",
+    "name": "GPT35-16k",
+    "maxToken": 0,
+    "price": 0
+  },
+  "ExtractModel": {
+    "model": "gpt-3.5-turbo-16k",
+    "functionCall": true,
+    "name": "GPT35-16k",
+    "maxToken": 0,
+    "price": 0,
+    "prompt": ""
+  },
+  "CQModel": {
+    "model": "gpt-3.5-turbo-16k",
+    "functionCall": true,
+    "name": "GPT35-16k",
+    "maxToken": 0,
+    "price": 0,
+    "prompt": ""
+  }
 }
 ```

docSite/content/docs/development/intro.md

@@ -4,12 +4,18 @@ description: '对 FastGPT 进行开发调试'
 icon: 'developer_guide'
 draft: false
 toc: true
-weight: -10
+weight: 510
 ---
 
 本文档介绍了如何设置开发环境以构建和测试 [FastGPT](https://fastgpt.run)。
 
-### 安装依赖项
+
+## Tips
+
+1. 用户默认的时区为 `Asia/Shanghai`,非 linux 环境时候，获取系统时间会异常，本地开发时候，可以将用户的时区调整成 UTC（+0）。
+
+
+## 前置依赖项
 
 您需要在计算机上安装和配置以下依赖项才能构建 [FastGPT](https://fastgpt.run)：
 
@@ -35,7 +41,9 @@ weight: -10
 git clone git@github.com:<github_username>/FastGPT.git
 ```
 
-client 目录下为 FastGPT 核心代码。NextJS 框架前后端放在一起，API 服务位于 `src/pages/api` 目录内。
+**projects 目录下为 FastGPT 应用代码。NextJS 框架前后端放在一起，API 服务位于 `src/pages/api` 目录内。**
+
+**packages 目录为相关的共用包。**
 
 ### 安装数据库
 
@@ -62,15 +70,15 @@ client 目录下为 FastGPT 核心代码。NextJS 框架前后端放在一起，
 ### 运行
 
 ```bash
-cd client
 pnpm i
+cd projects/app # FastGPT 主程序
 pnpm dev
 ```
 
 ### 镜像打包
 
 ```bash
-docker build -t dockername/fastgpt .
+docker build -t dockername/fastgpt --build-arg name=app .
 ```
 
 ## 创建拉取请求

docSite/content/docs/development/openApi.md

@@ -0,0 +1,531 @@
+---
+title: 'OpenAPI 使用'
+description: 'FastGPT OpenAPI 文档'
+icon: 'api'
+draft: false
+toc: true
+weight: 512
+---
+
+# 基本配置
+```
+baseUrl: "https://fastgpt.run/api"
+headers: {
+    Authorization: "Bearer apikey"
+}
+```
+
+# 如何获取 API Key
+
+FastGPT 的 API Key 有 2 类，一类是全局通用的 key；一类是携带了 AppId 也就是有应用标记的 key。
+
+|        通用key               |           应用特定 key            |
+| --------------------- | --------------------- |
+| ![](/imgs/fastgpt-api2.png) | ![](/imgs/fastgpt-api.png) |
+
+# 接口
+
+## 发起对话
+
+{{% alert icon="🤖 " context="success" %}}
+该接口 API Key 需使用应用特定的 key，否则会报错。
+{{% /alert %}}
+
+
+对话接口兼容 openai 的接口！如果你有第三方项目，可以直接通过修改 BaseUrl 和 Authorization 来访问 FastGpt 应用。缺点是你无法获取到响应的token值。
+
+请求内容
+- headers.Authorization: Bearer apikey
+- chatId: string | undefined 。
+  -  为 undefined 时（不传入），不使用 FastGpt 提供的上下文功能，完全通过传入的 messages 构建上下文。 不会将你的记录存储到数据库中，你也无法在记录汇总中查阅到。
+  - 为非空字符串时，意味着使用 chatId 进行对话，自动从 FastGpt 数据库取历史记录。并拼接 messages 数组最后一个内容作为完整请求。(自行确保 chatId 唯一，长度不限)
+- messages: 与 openai gpt 接口完全一致。
+- detail: 是否返回详细值(模块状态，响应的完整结果），会通过event进行区分
+- variables: 变量。一个对象，效果同全局变量。
+
+**请求示例：**
+
+```bash
+curl --location --request POST 'https://fastgpt.run/api/openapi/v1/chat/completions' \
+--header 'Authorization: Bearer apikey' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "chatId":"111",
+    "stream":false,
+    "detail": false,
+    "variables": {
+        "cTime": "2022/2/2 22:22"
+    },
+    "messages": [
+        {
+            "content": "导演是谁",
+            "role": "user"
+        }
+    ]
+}'
+```
+
+
+{{< tabs tabTotal="3" >}}
+{{< tab tabName="detail=false 响应" >}}
+{{< markdownify >}}
+
+```bash
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
+
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"电"},"index":0,"finish_reason":null}]}
+
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"影"},"index":0,"finish_reason":null}]}
+
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"《"},"index":0,"finish_reason":null}]}
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="detail=true 响应" >}}
+{{< markdownify >}}
+
+```bash
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"电"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"影"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"《"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"铃"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"芽"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":"。"},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}
+
+event: answer
+data: {"id":"","object":"","created":0,"choices":[{"delta":{},"index":0,"finish_reason":"stop"}]}
+
+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。"}]}]
+
+```
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="stream=false,detail=true 响应" >}}
+{{< markdownify >}}
+
+```json
+{
+    "responseData": [ // 不同模块的响应值, 不同版本具体值可能有差异，可先 log 自行查看最新值。
+        {
+            "moduleName": "KB Search",
+            "price": 1.2000000000000002,
+            "model": "Embedding-2",
+            "tokens": 6,
+            "similarity": 0.61,
+            "limit": 3
+        },
+        {
+            "moduleName": "AI Chat",
+            "price": 454.5,
+            "model": "FastAI-4k",
+            "tokens": 303,
+            "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。"
+                }
+            ]
+        }
+    ],
+    "id": "",
+    "model": "",
+    "usage": {
+        "prompt_tokens": 1,
+        "completion_tokens": 1,
+        "total_tokens": 1
+    },
+    "choices": [
+        {
+            "message": {
+                "role": "assistant",
+                "content": "电影《铃芽之旅》的导演是新海诚。"
+            },
+            "finish_reason": "stop",
+            "index": 0
+        }
+    ]
+}
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+{{< /tabs >}}
+
+## 知识库
+
+{{% alert icon="🤖 " context="success" %}}
+此部分 API 需使用全局通用的 API Key。
+{{% /alert %}}
+
+### 如何获取知识库ID（kbId）
+
+![](/imgs/getKbId.png)
+
+### 知识库添加数据
+
+{{< tabs tabTotal="4" >}}
+{{< tab tabName="请求示例" >}}
+{{< markdownify >}}
+
+```bash
+curl --location --request POST 'https://fastgpt.run/api/core/dataset/data/pushData' \
+--header 'Authorization: Bearer apikey' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "kbId": "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。",
+    "data": [
+        {
+            "a": "test",
+            "q": "1111",
+        },
+        {
+            "a": "test2",
+            "q": "22222"
+        }
+    ]
+}'
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="参数说明" >}}
+{{< markdownify >}}
+
+```json
+{
+    "kbId": "知识库的ID，可以在知识库详情查看。",
+    "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": [
+        {
+            "q": "生成索引的内容，index 模式下最大 tokens 为3000，建议不超过 1000",
+            "a": "预期回答/补充"
+        },
+        {
+            "q": "生成索引的内容，qa 模式下最大 tokens 为10000，建议 8000 左右",
+            "a": "预期回答/补充"
+        }
+    ]
+}
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="响应例子" >}}
+{{< markdownify >}}
+
+```json
+{
+    "code": 200,
+    "statusText": "",
+    "data": {
+        "insertLen": 1 // 最终插入成功的数量，可能因为超出 tokens 或者插入异常，index 可以重复插入，会自动去重
+    }
+}
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="QA Prompt 模板" >}}
+{{< markdownify >}}
+
+{{theme}} 里的内容可以换成数据的主题。默认为：它们可能包含多个主题内容
+
+```
+我会给你一段文本，{{theme}}，学习它们，并整理学习成果，要求为：
+1. 提出最多 25 个问题。
+2. 给出每个问题的答案。
+3. 答案要详细完整，答案可以包含普通文字、链接、代码、表格、公示、媒体链接等 markdown 元素。
+4. 按格式返回多个问题和答案:
+
+Q1: 问题。
+A1: 答案。
+Q2:
+A2:
+……
+
+我的文本："""{{text}}"""
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< /tabs >}}
+
+
+### 搜索测试
+
+{{< tabs tabTotal="2" >}}
+{{< tab tabName="请求示例" >}}
+{{< markdownify >}}
+
+```bash
+curl --location --request POST 'https://fastgpt.run/api/core/dataset/searchTest' \
+--header 'Authorization: Bearer apiKey' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "kbId": "xxxxx",
+    "text": "导演是谁"
+}'
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< tab tabName="响应示例" >}}
+{{< markdownify >}}
+
+返回 top12 结果
+
+```bash
+{
+  "code": 200,
+  "statusText": "",
+  "data": [
+    {
+      "id": "5613327",
+      "q": "该人有获奖情况吗？",
+      "a": "该人获得过2020/07全国大学生服务外包大赛国家一等奖和2021/05国家创新创业计划立项的获奖情况。",
+      "source": "余金隆简历.pdf",
+      "score": 0.41556452839298963
+    },
+    ......
+  ]
+}
+```
+
+{{< /markdownify >}}
+{{< /tab >}}
+
+{{< /tabs >}}
+
+## 订单
+
+### 创建训练订单
+
+**请求示例**
+
+```bash
+curl --location --request POST 'https://fastgpt.run/api/common/bill/createTrainingBill' \
+--header 'Authorization: Bearer {{apikey}}' \
+--header 'Content-Type: application/json' \
+--data-raw ''
+```
+
+**响应结果**
+
+data 为 billId，可用于 api 添加数据时进行账单聚合。
+
+```json
+{
+  "code": 200,
+  "statusText": "",
+  "message": "",
+  "data": "65112ab717c32018f4156361"
+}
+```
+
+## 免登录分享链接校验（内测中）
+
+免登录链接配置中，增加了`凭证校验服务器`后，使用分享链接时会向服务器发起请求，校验链接是否可用，并在每次对话结束后，向服务器发送对话结果。下面以`host`来表示`凭证校验服务器`。服务器接口仅需返回是否校验成功即可，不需要返回其他数据，格式如下：
+
+```json
+{
+    "success": true,
+    "message": "错误提示"
+}
+```
+
+![](/imgs/sharelinkProcess.png)
+
+### 分享链接中增加额外 query
+
+增加一个 query: authToken。例如：
+
+原始的链接：https://fastgpt.run/chat/share?shareId=648aaf5ae121349a16d62192  
+完整链接: https://fastgpt.run/chat/share?shareId=648aaf5ae121349a16d62192&authToken=userid12345
+
+发出校验请求时候，会在`body`中携带 token={{authToken}} 的参数。
+
+### 初始化校验
+
+**FastGPT 发出的请求**
+
+```bash
+curl --location --request POST '{{host}}/shareAuth/init' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "token": "sintdolore"
+}'
+```
+
+### 对话前校验
+
+**FastGPT 发出的请求**
+
+```bash
+curl --location --request POST '{{host}}/shareAuth/start' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "token": "sintdolore",
+    "question": "用户问题",
+}'
+```
+
+### 对话结果上报
+
+**FastGPT 发出的请求**
+
+```bash
+curl --location --request POST '{{host}}/shareAuth/finish' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "token": "sint dolore",
+    "responseData": [
+        {
+            "moduleName": "KB Search",
+            "price": 1.2000000000000002,
+            "model": "Embedding-2",
+            "tokens": 6,
+            "similarity": 0.61,
+            "limit": 3
+        },
+        {
+            "moduleName": "AI Chat",
+            "price": 454.5,
+            "model": "FastAI-4k",
+            "tokens": 303,
+            "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。"
+                }
+            ]
+        }
+    ]
+}'
+```
+
+响应值与 chat 接口相同，增加了一个 token。可以重点关注`responseData`里的值，price 与实际价格的倍率为`100000`。
+
+**此接口无需响应值**
+
+# 使用案例
+
+- [接入 NextWeb/ChatGPT web 等应用](/docs/use-cases/openapi)
+- [接入 onwechat](/docs/use-cases/onwechat)
+- [接入 飞书](/docs/use-cases/feishu)

docSite/content/docs/installation/docker.md

@@ -19,7 +19,7 @@ weight: 720
 
 具体部署方法可参考该项目的 [README](https://github.com/songquanpeng/one-api)，也可以直接通过以下按钮一键部署：
 
-[![](https://cdn.jsdelivr.us/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Done-api)
+[![](https://fastly.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Done-api)
 
 ## 安装 Docker 和 docker-compose
 
@@ -55,11 +55,11 @@ brew install orbstack
 {{< tab tabName="Windows" >}}
 {{< markdownify >}}
 
-> 我们建议将源代码和其他数据绑定到 Linux 容器中时，将其存储在 Linux 文件系统中，而不是 Windows 文件系统中。
->
-> 可以选择直接[使用 WSL 2 后端在 Windows 中安装 Docker Desktop](https://docs.docker.com/desktop/wsl/)。
->
-> 也可以直接[在 WSL 2 中安装命令行版本的 Docker](https://nickjanetakis.com/blog/install-docker-in-wsl-2-without-docker-desktop)。
+我们建议将源代码和其他数据绑定到 Linux 容器中时，将其存储在 Linux 文件系统中，而不是 Windows 文件系统中。
+
+可以选择直接[使用 WSL 2 后端在 Windows 中安装 Docker Desktop](https://docs.docker.com/desktop/wsl/)。
+
+也可以直接[在 WSL 2 中安装命令行版本的 Docker](https://nickjanetakis.com/blog/install-docker-in-wsl-2-without-docker-desktop)。
 
 {{< /markdownify >}}
 {{< /tab >}}
@@ -133,6 +133,7 @@ services:
       - DB_MAX_LINK=5 # database max link
       - TOKEN_KEY=any
       - ROOT_KEY=root_key
+      - FILE_TOKEN_KEY=filetoken
       # mongo 配置，不需要改. 如果连不上，可能需要去掉 ?authSource=admin
       - MONGODB_URI=mongodb://username:password@mongo:27017/fastgpt?authSource=admin
       # pg配置. 不需要改
@@ -158,7 +159,12 @@ docker-compose up -d
 
 ### 如何更新？
 
-执行 `docker-compose up -d` 会自动拉取最新镜像，一般情况下不需要执行额外操作。
+执行下面命令会自动拉取最新镜像，一般情况下不需要执行额外操作。
+
+```bash
+docker-compose pull
+docker-compose up -d
+```
 
 ### 如何自定义配置文件？
 

docSite/content/docs/installation/one-api.md

@@ -1,6 +1,6 @@
 ---
-title: '部署 one-api，实现多模型支持'
-description: '通过接入 one-api 来实现对各种大模型的支持'
+title: '部署 One API，实现多模型支持'
+description: '通过接入 One API 来实现对各种大模型的支持'
 icon: 'Api'
 draft: false
 toc: true
@@ -9,9 +9,9 @@ weight: 730
 
 默认情况下，FastGPT 只配置了 GPT 的 3 个模型，如果你需要接入其他模型，需要进行一些额外配置。
 
-[one-api](https://github.com/songquanpeng/one-api) 是一个 OpenAI 接口管理 & 分发系统，可以通过标准的 OpenAI API 格式访问所有的大模型，开箱即用。
+[One API](https://github.com/songquanpeng/one-api) 是一个 OpenAI 接口管理 & 分发系统，可以通过标准的 OpenAI API 格式访问所有的大模型，开箱即用。
 
-FastGPT 可以通过接入 one-api 来实现对各种大模型的支持。部署方法也很简单。
+FastGPT 可以通过接入 One API 来实现对各种大模型的支持。部署方法也很简单。
 
 ## MySQL 版本
 
@@ -19,7 +19,7 @@ MySQL 版本支持多实例，高并发。
 
 直接点击以下按钮即可一键部署 👇
 
-[![](https://cdn.jsdelivr.us/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Done-api)
+[![](https://fastly.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Done-api)
 
 部署完后会跳转「应用管理」，数据库在另一个应用「数据库」中。需要等待 1~3 分钟数据库运行后才能访问成功。
 
@@ -55,17 +55,17 @@ BATCH_UPDATE_INTERVAL=60
 
 ## 使用步骤
 
-### 1. 登录 one-api
+### 1. 登录 One API
 
-打开 【one-api 应用详情】，找到访问地址：
+打开 【One API 应用详情】，找到访问地址：
 ![step4](/imgs/oneapi-step4.png)
 
-登录 one-api
+登录 One API
 ![step5](/imgs/oneapi-step5.png)
 
 ### 2. 创建渠道和令牌
 
-在 one-api 中添加对应渠道，直接点击 【添加基础模型】，不要遗漏了向量模型
+在 One API 中添加对应渠道，直接点击 【添加基础模型】，不要遗漏了向量模型
 ![step6](/imgs/oneapi-step6.png)
 
 创建一个令牌
@@ -73,12 +73,12 @@ BATCH_UPDATE_INTERVAL=60
 
 ### 3. 修改 FastGPT 的环境变量
 
-有了 one-api 令牌后，FastGPT 可以通过修改 baseurl 和 key 去请求到 one-api，再由 one-api 去请求不同的模型。修改下面两个环境变量：
+有了 One API 令牌后，FastGPT 可以通过修改 baseurl 和 key 去请求到 One API，再由 One API 去请求不同的模型。修改下面两个环境变量：
 
 ```bash
 # 下面的地址是 Sealos 提供的，务必写上 v1， 两个项目都在 sealos 部署时候，https://xxxx.cloud.sealos.io 可以改用内网地址
 OPENAI_BASE_URL=https://xxxx.cloud.sealos.io/v1
-# 下面的 key 是由 one-api 提供的令牌
+# 下面的 key 是由 One API 提供的令牌
 CHAT_API_KEY=sk-xxxxxx
 ```
 
@@ -86,19 +86,19 @@ CHAT_API_KEY=sk-xxxxxx
 
 **以添加文心一言为例:**
 
-### 1. One-API 添加对应模型渠道
+### 1. One API 添加对应模型渠道
 
 ![](/imgs/oneapi-demo1.png)
 
 ### 2. 修改 FastGPT 配置文件
 
-可以在 `/client/src/data/config.json` 里找到配置文件（本地开发需要复制成 config.local.json），配置文件中有一项是对话模型配置：
+可以在 `/projects/app/src/data/config.json` 里找到配置文件（本地开发需要复制成 config.local.json），配置文件中有一项是对话模型配置：
 
 ```json
 "ChatModels": [
     ...
     {
-      "model": "ERNIE-Bot", // 这里的模型需要对应 OneAPI 的模型
+      "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
       "contextMaxToken": 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。
       // 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.

docSite/content/docs/installation/sealos.md

@@ -9,7 +9,7 @@ weight: 710
 
 Sealos 的服务器在国外，不需要额外处理网络问题，无需服务器、无需魔法、无需域名，支持高并发 & 动态伸缩。点击以下按钮即可一键部署 👇
 
-[![](https://cdn.jsdelivr.us/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt)
+[![](https://fastly.jsdelivr.net/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt)
 
 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。
 
@@ -21,4 +21,8 @@ Sealos 的服务器在国外，不需要额外处理网络问题，无需服务
 
 > 用户名：`root`
 > 
-> 密码就是刚刚一键部署时设置的环境变量
+> 密码就是刚刚一键部署时设置的环境变量
+
+## 部署架构图
+
+![](/imgs/sealos-fastgpt.webp)

docSite/content/docs/installation/upgrading/43.md

@@ -13,6 +13,12 @@ weight: 996
 
 1. https://xxxxx/api/admin/initv43
 
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv43' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
 会给 PG 数据库的 modeldata 表插入一个新列 file_id，用于存储文件 ID。
 
 ## 增加环境变量

docSite/content/docs/installation/upgrading/44.md

@@ -0,0 +1,23 @@
+---
+title: '升级到 V4.4'
+description: 'FastGPT 从旧版本升级到 V4.4 操作指南'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 995
+---
+
+## 执行初始化 API
+
+发起 1 个 HTTP 请求（记得携带 `headers.rootkey`，这个值是环境变量里的）
+
+1. https://xxxxx/api/admin/initv44
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv44' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+会给初始化 Mongo 的部分字段。
+

docSite/content/docs/installation/upgrading/441.md

@@ -0,0 +1,23 @@
+---
+title: '升级到 V4.4.1'
+description: 'FastGPT 从旧版本升级到 V4.4.1 操作指南'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 994
+---
+
+## 执行初始化 API
+
+发起 1 个 HTTP 请求（记得携带 `headers.rootkey`，这个值是环境变量里的）
+
+1. https://xxxxx/api/admin/initv441
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv441' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+会给初始化 Mongo 的 dataset.files，将所有数据设置为可用。
+

docSite/content/docs/installation/upgrading/442.md

@@ -0,0 +1,23 @@
+---
+title: '升级到 V4.4.2'
+description: 'FastGPT 从旧版本升级到 V4.4.2 操作指南'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 993
+---
+
+## 执行初始化 API
+
+发起 1 个 HTTP 请求（记得携带 `headers.rootkey`，这个值是环境变量里的）
+
+1. https://xxxxx/api/admin/initv442
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv442' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+会给初始化 Mongo 的 Bill 表的索引，之前过期时间有误。
+

docSite/content/docs/installation/upgrading/445.md

@@ -0,0 +1,31 @@
+---
+title: 'V4.4.5'
+description: 'FastGPT V4.4.5 更新（需执行升级脚本）'
+icon: 'upgrade'
+draft: false
+toc: true
+weight: 992
+---
+
+## 执行初始化 API
+
+发起 1 个 HTTP 请求（记得携带 `headers.rootkey`，这个值是环境变量里的）
+
+1. https://xxxxx/api/admin/initv445
+
+```bash
+curl --location --request POST 'https://{{host}}/api/admin/initv445' \
+--header 'rootkey: {{rootkey}}' \
+--header 'Content-Type: application/json'
+```
+
+初始化了 variable 模块，将其合并到用户引导模块中。
+
+## 功能介绍
+
+### Fast GPT V4.4.5
+
+1. 新增 - 下一步指引选项，可以通过模型生成 3 个预测问题。
+2. 新增 - 分享链接 hook 身份校验。
+3. 新增 - Api Key 使用。增加别名、额度限制和过期时间。自带 appId，无需额外连接。
+4. 优化 - 全局变量与开场白合并成同一模块。

docSite/content/docs/installation/upgrading/_index.md

@@ -1,7 +1,7 @@
 ---
 weight: 760
-title: "版本升级"
-description: "FastGPT 升级指南"
+title: "版本更新/升级操作"
+description: "FastGPT 版本更新介绍及升级操作"
 icon: upgrade
 draft: false
 images: []