Compare commits

..

45 Commits

Author SHA1 Message Date
Archer
661ee79943 fix: CQ module output (#445) 2023-10-30 16:45:36 +08:00
Archer
60ee160131 v4.5.2 (#439) 2023-10-30 13:26:42 +08:00
Archer
008d0af010 Quote Modal UI and fix doc (#432) 2023-10-25 20:13:32 +08:00
lizhuang
f2fb0aedfd Update README.md 文档改为https://doc.fastgpt.in网站访问 (#424)
文档改为https://doc.fastgpt.in网站访问
2023-10-24 18:07:30 +08:00
Archer
1dca5edcc6 v4.5.1-3 (#427) 2023-10-24 17:32:36 +08:00
Archer
1942cb0d67 perf: btn color (#423) 2023-10-24 13:19:23 +08:00
Archer
bf6dbfb245 v4.5.1-2 (#421) 2023-10-23 15:05:13 +08:00
Archer
d37433eacd Config file to set doc baseurl (#419) 2023-10-23 08:56:43 +08:00
Archer
a3534407bf v4.5.1 (#417) 2023-10-22 23:54:04 +08:00
Carson Yang
3091a90df6 Update README (#418)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-22 23:47:09 +08:00
Carson Yang
41b8f4443c Docs: update qr for wechat group (#416)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-22 23:28:46 +08:00
Carson Yang
777f089423 Docs: update README (#407)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-18 15:51:51 +08:00
不做了睡大觉
b23e00f3e5 添加Baichuan2-7B-Chat模型接口文件 (#404)
* 更新镜像

* 更新镜像信息

* 更新镜像信息

* Create openai_api.py

* Create requirements.txt
2023-10-18 10:34:22 +08:00
Archer
3b776b6639 v4.5 (#403) 2023-10-17 10:00:32 +08:00
Archer
dd8f2744bf Extraction schema (#398) 2023-10-14 23:02:01 +08:00
左风
7db8d3ea0f Docs: add quick start and video link (#395) 2023-10-13 20:16:18 +08:00
Archer
ad7a17bf40 Optimize the project structure and introduce DDD design (#394) 2023-10-12 17:46:37 +08:00
李启爱
76ac5238b6 Update 447.md (#392) 2023-10-12 14:56:18 +08:00
Carson Yang
add73aa2c5 Docs: use docsearch (#391)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-12 00:04:45 +08:00
Archer
bcf9491999 v4.4.7-2 (#388) 2023-10-11 17:18:43 +08:00
Archer
d0041a98b4 Optimize the file storage structure of the knowledge base (#386) 2023-10-10 22:41:05 +08:00
Carson Yang
29d152784f Docs: delete image cdn for vercel (#385)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-09 15:03:07 +08:00
Carson Yang
cd7214ba8d Docs: update workflow for building docs image (#384)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-09 14:32:47 +08:00
Archer
6a84e73a82 fix: packages (#378) 2023-10-08 09:59:05 +08:00
Archer
98ce5103a0 v4.4.6 (#377) 2023-10-07 18:02:20 +08:00
Carson Yang
c65a36d3ab Docs: hide button for questionnaire on mobile device (#376)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-07 14:57:26 +08:00
Carson Yang
b6e49da288 Docs: update button for questionnaire (#375)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-06 23:52:45 +08:00
Archer
45998f9cf5 README (#372) 2023-10-06 21:19:44 +08:00
Carson Yang
4197f63751 Update README (#371)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-06 14:07:37 +08:00
Carson Yang
ace8134a16 Docs: add Dockerfile for docs (#369)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-06 08:01:16 +08:00
Carson Yang
7f1fecb84e Docs: update theme (#368)
Signed-off-by: Carson Yang <yangchuansheng33@gmail.com>
2023-10-04 22:25:07 +08:00
Archer
bf172fab81 perf: markdown more wrap (#365) 2023-10-02 20:19:09 +08:00
Archer
36f5648cae perf: v4.4.6-1 (#364) 2023-09-28 17:30:05 +08:00
Archer
ab57bfcc4a perf: completions api.fix: new chat question guide (#361) 2023-09-27 12:05:13 +08:00
Archer
11848b8f44 v4.4.5-3 (#357) 2023-09-26 21:17:13 +08:00
epoh
a11e0bd9c3 Update chatglm2.md (#354) 2023-09-26 15:06:38 +08:00
Archer
f6552d0d4f v4.4.5-2 (#355) 2023-09-26 14:31:37 +08:00
epoh
38d4db5d5f Rename requirement.txt to requirements.txt (#352) 2023-09-26 09:38:14 +08:00
Archer
63cd379682 Add share link hook (#351) 2023-09-25 23:12:42 +08:00
Archer
9136c9306a Add OpenAPI docs;Correct the glm document (#346) 2023-09-25 14:28:44 +08:00
Byte Sound
c9db9f33ea Update intro.md (#348)
错别字,市区改为时区
2023-09-25 13:33:30 +08:00
Archer
3d7178d06f monorepo packages (#344) 2023-09-24 18:02:09 +08:00
Archer
a4ff5a3f73 perf: api key (#342) 2023-09-23 20:28:03 +08:00
Archer
814c5b3d3c Add bill of training and rate of file upload (#339) 2023-09-21 21:02:44 +08:00
Chen X
e7e0677291 Docs:add-workflow-case-全能助手 (#334) 2023-09-21 15:57:42 +08:00
858 changed files with 33751 additions and 49149 deletions

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@@ -0,0 +1,98 @@
name: Build FastGPT docs images and copy image to docker hub
on:
workflow_dispatch:
push:
paths:
- 'docSite/**'
branches:
- 'main'
tags:
- 'v*.*.*'
jobs:
build-fastgpt-docs-images:
runs-on: ubuntu-20.04
steps:
- name: Checkout
uses: actions/checkout@v3
with:
fetch-depth: 1
- name: Set up QEMU (optional)
uses: docker/setup-qemu-action@v2
- name: Set up Docker Buildx
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:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Set DOCKER_REPO_TAGGED based on branch or tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt-docs:latest" >> $GITHUB_ENV
else
echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt-docs:${{ 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: |
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=Apache" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${DOCKER_REPO_TAGGED} \
-f docSite/Dockerfile \
.
push-to-docker-hub:
needs: build-fastgpt-docs-images
runs-on: ubuntu-20.04
if: github.repository == 'labring/FastGPT'
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKER_HUB_NAME }}
password: ${{ secrets.DOCKER_HUB_PASSWORD }}
- name: Set DOCKER_REPO_TAGGED based on branch or tag
run: |
if [[ "${{ github.ref_name }}" == "main" ]]; then
echo "IMAGE_TAG=latest" >> $GITHUB_ENV
else
echo "IMAGE_TAG=${{ github.ref_name }}" >> $GITHUB_ENV
fi
- name: Pull image from GitHub Container Registry
run: docker pull ghcr.io/${{ github.repository_owner }}/fastgpt-docs:${{env.IMAGE_TAG}}
- name: Tag image with Docker Hub repository name and version tag
run: docker tag ghcr.io/${{ github.repository_owner }}/fastgpt-docs:${{env.IMAGE_TAG}} ${{ secrets.DOCKER_IMAGE_NAME }}:${{env.IMAGE_TAG}}
- name: Push image to Docker Hub
run: docker push ${{ secrets.DOCKER_IMAGE_NAME }}:${{env.IMAGE_TAG}}
update-docs-image:
needs: build-fastgpt-docs-images
runs-on: ubuntu-20.04
if: github.repository == 'labring/FastGPT'
steps:
- name: Checkout code
uses: actions/checkout@v3
- uses: actions-hub/kubectl@master
env:
KUBE_CONFIG: ${{ secrets.KUBE_CONFIG }}
with:
args: rollout restart deployment fastgpt-docs

View File

@@ -1,9 +1,10 @@
name: Build fastgpt images and copy image to docker hub
name: Build FastGPT images and copy image to docker hub
on:
workflow_dispatch:
push:
paths:
- 'client/**'
- 'projects/app/**'
- 'packages/**'
branches:
- 'main'
tags:
@@ -49,12 +50,12 @@ jobs:
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" \
--label "org.opencontainers.image.licenses=Apache" \
--push \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
@@ -64,6 +65,7 @@ jobs:
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
@@ -87,6 +89,7 @@ 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

55
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@@ -0,0 +1,55 @@
name: Preview FastGPT images
on:
pull_request_target:
paths:
- 'projects/app/**'
- 'packages/**'
branches:
- 'main'
workflow_dispatch:
jobs:
build-fastgpt-images:
runs-on: ubuntu-20.04
steps:
- 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
- name: Set up Docker Buildx
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:
registry: ghcr.io
username: ${{ github.repository_owner }}
password: ${{ secrets.GH_PAT }}
- name: Set DOCKER_REPO_TAGGED based on branch or tag
run: |
echo "DOCKER_REPO_TAGGED=ghcr.io/${{ github.repository_owner }}/fastgpt-pr:${{ github.event.pull_request.number }}" >> $GITHUB_ENV
- name: Build image for PR
env:
DOCKER_REPO_TAGGED: ${{ env.DOCKER_REPO_TAGGED }}
run: |
docker buildx build \
--build-arg name=app \
--label "org.opencontainers.image.source= https://github.com/ ${{ github.repository_owner }}/FastGPT" \
--label "org.opencontainers.image.description=fastgpt-pr image" \
--label "org.opencontainers.image.licenses=Apache" \
--cache-from=type=local,src=/tmp/.buildx-cache \
--cache-to=type=local,dest=/tmp/.buildx-cache \
-t ${DOCKER_REPO_TAGGED} \
-f Dockerfile \
.

4
.gitignore vendored
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@@ -33,4 +33,6 @@ dist/
# hugo
**/.hugo_build.lock
docSite/public/
docSite/public/
docSite/resources/_gen/
docSite/.vercel

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@@ -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",

68
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@@ -0,0 +1,68 @@
# Install dependencies only when needed
FROM node:18.15-alpine AS deps
# Check https://github.com/nodejs/docker-node/tree/b4117f9333da4138b03a546ec926ef50a31506c3#nodealpine to understand why libc6-compat might be needed.
RUN apk add --no-cache libc6-compat && npm install -g pnpm
WORKDIR /app
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
RUN [ -f pnpm-lock.yaml ] || (echo "Lockfile not found." && exit 1)
RUN pnpm install
# Rebuild the source code only when needed
FROM node:18.15-alpine AS builder
WORKDIR /app
ARG name
# copy common node_modules and one project node_modules
COPY package.json pnpm-workspace.yaml ./
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
# 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=$name run build
FROM node:18.15-alpine AS runner
WORKDIR /app
ARG name
# create user and use it
RUN addgroup --system --gid 1001 nodejs
RUN adduser --system --uid 1001 nextjs
RUN sed -i 's/https/http/' /etc/apk/repositories
RUN apk add curl \
&& apk add ca-certificates \
&& update-ca-certificates
# copy running files
COPY --from=builder /app/projects/$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}"]

View File

@@ -4,25 +4,39 @@
# FastGPT
<p align="center">
<a href="./README_en.md">English</a> |
<a href="./README.md">简体中文</a>
</p>
FastGPT 是一个基于 LLM 大语言模型的知识库问答系统,提供开箱即用的数据处理、模型调用等能力。同时可以通过 Flow 可视化进行工作流编排,从而实现复杂的问答场景!
</div>
<p align="center">
<a href="https://fastgpt.run/">线上体验</a>
·
<a href="https://doc.fastgpt.run/docs/intro">相关文档</a>
·
<a href="https://doc.fastgpt.run/docs/development">本地开发</a>
·
<a href="https://github.com/labring/FastGPT#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">相关项目</a>
<a href="https://fastgpt.run/">
<img height="21" src="https://img.shields.io/badge/在线使用-d4eaf7?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
</a>
<a href="https://doc.fastgpt.run/docs/intro">
<img height="21" src="https://img.shields.io/badge/相关文档-7d09f1?style=flat-square" alt="document">
</a>
<a href="https://doc.fastgpt.run/docs/development">
<img height="21" src="https://img.shields.io/badge/本地开发-%23d4eaf7?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
</a>
<a href="/#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">
<img height="21" src="https://img.shields.io/badge/相关项目-7d09f1?style=flat-square" alt="project">
</a>
<a href="https://github.com/labring/FastGPT/blob/main/LICENSE">
<img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
</a>
</p>
https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409bd33f6d4
## 🛸 在线体验
## 🛸 在线使用
[fastgpt.run](https://fastgpt.run/)(服务器在新加坡,部分地区可能无法直连)
- 🌐 国内版:[ai.fastgpt.in](https://ai.fastgpt.in/)
- 🌍 海外版:[fastgpt.run](https://fastgpt.run/)
| | |
| ---------------------------------- | ---------------------------------- |
@@ -33,21 +47,21 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
1. 强大的可视化编排,轻松构建 AI 应用
- [x] 提供简易模式,无需操作编排
- [x] 用户对话前引导, 全局字符串变量
- [x] 用户对话前引导全局字符串变量
- [x] 知识库搜索
- [x] 多 LLM 模型对话
- [x] 文本内容提取成结构化数据
- [x] HTTP 扩展
- [ ] 嵌入 Laf实现在线编写 HTTP 模块
- [ ] 连续对话引导
- [x] 对话下一步指引
- [ ] 对话多路线选择
- [x] 源文件引用追踪
- [ ] 自定义文件阅读器
- [x] 模块封装,实现多级复用
2. 丰富的知识库预处理
- [x] 多库复用,混用
- [x] chunk 记录修改和删除
- [x] 支持 手动输入, 直接分段, QA 拆分导入
- [x] 支持 url 读取、 CSV 批量导入
- [x] 支持手动输入直接分段QA 拆分导入
- [x] 支持 url 读取、CSV 批量导入
- [x] 支持知识库单独设置向量模型
- [x] 源文件存储
- [ ] 文件学习 Agent
@@ -55,9 +69,9 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
- [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
- [x] 完整上下文呈现
- [ ] 完整模块中间值呈现
- [x] 完整模块中间值呈现
4. OpenAPI
- [x] completions 接口对齐 GPT 接口
- [x] completions 接口 (对齐 GPT 接口)
- [ ] 知识库 CRUD
5. 运营功能
- [x] 免登录分享窗口
@@ -66,7 +80,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
## 👨‍💻 开发
项目技术栈: NextJs + TS + ChakraUI + Mongo + PostgresVector 插件
项目技术栈NextJs + TS + ChakraUI + Mongo + Postgres (Vector 插件)
- **⚡ 快速部署**
@@ -76,12 +90,13 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
由于需要部署数据库,部署完后需要等待 2~4 分钟才能正常访问。默认用了最低配置,首次访问时会有些慢。
* [快开始本地开发](https://doc.fastgpt.run/docs/development/intro/)
* [部署 FastGPT](https://doc.fastgpt.run/docs/installation)
* [系统配置文件说明](https://doc.fastgpt.run/docs/development/configuration/)
* [多模型配置](https://doc.fastgpt.run/docs/installation/one-api/)
* [版本升级](https://doc.fastgpt.run/docs/installation/upgrading)
* [API 文档](https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh?pre_pathname=%2Fdrive%2Fhome%2F)
* [快开始本地开发](https://doc.fastgpt.in/docs/development/intro/)
* [部署 FastGPT](https://doc.fastgpt.in/docs/installation)
* [系统配置文件说明](https://doc.fastgpt.in/docs/development/configuration/)
* [多模型配置](https://doc.fastgpt.in/docs/installation/one-api/)
* [版本更新/升级介绍](https://doc.fastgpt.in/docs/installation/upgrading)
* [OpenAPI API 文档](https://doc.fastgpt.in/docs/development/openapi/)
* [知识库结构详解](https://doc.fastgpt.in/docs/use-cases/datasetengine/)
## 🏘️ 社区交流群
@@ -89,23 +104,22 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
![](https://otnvvf-imgs.oss.laf.run/wx300.jpg)
## 👀 其他
- [FastGPT 常见问题](https://kjqvjse66l.feishu.cn/docx/HtrgdT0pkonP4kxGx8qcu6XDnGh)
- [docker 部署教程视频](https://www.bilibili.com/video/BV1jo4y147fT/)
- [FastGPT 知识库演示](https://www.bilibili.com/video/BV1Wo4y1p7i1/)
## 💪 相关项目
- [Laf: 3 分钟快速接入三方应用](https://github.com/labring/laf)
- [Sealos: 快速部署集群应用](https://github.com/labring/sealos)
- [One API: 多模型管理,支持 Azure、文心一言等](https://github.com/songquanpeng/one-api)
- [TuShan: 5 分钟搭建后台管理系统](https://github.com/msgbyte/tushan)
- [Laf3 分钟快速接入三方应用](https://github.com/labring/laf)
- [Sealos快速部署集群应用](https://github.com/labring/sealos)
- [One API多模型管理,支持 Azure、文心一言等](https://github.com/songquanpeng/one-api)
- [TuShan5 分钟搭建后台管理系统](https://github.com/msgbyte/tushan)
## 👀 其他
- [保姆级 FastGPT 教程](https://www.bilibili.com/video/BV1n34y1A7Bo/?spm_id_from=333.999.0.0)
- [接入飞书](https://www.bilibili.com/video/BV1Su4y1r7R3/?spm_id_from=333.999.0.0)
- [接入企微](https://www.bilibili.com/video/BV1Tp4y1n72T/?spm_id_from=333.999.0.0)
## 🤝 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
@@ -115,7 +129,7 @@ https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409b
本仓库遵循 [FastGPT Open Source License](./LICENSE) 开源协议。
1. 允许作为后台服务直接商用,但不允许直接使用 saas 服务商用
2. 需保留相关版权信息。
1. 允许作为后台服务直接商用,但不允许提供 SaaS 服务。
2. 未经商业授权,任何形式的商用服务均需保留相关版权信息。
3. 完整请查看 [FastGPT Open Source License](./LICENSE)
4. 联系方式yujinlong@sealos.io, [点击查看定价策略](https://fael3z0zfze.feishu.cn/docx/F155dbirfo8vDDx2WgWc6extnwf)
4. 联系方式yujinlong@sealos.io[点击查看商业版定价策略](https://doc.fastgpt.run/docs/commercial)

View File

@@ -1,25 +1,39 @@
<div align="center">
<a href="https://fastgpt.run/"><img src="/.github/imgs/logo.svg" width="120" height="120" alt="fastgpt logo"></a>
# FastGPT
FastGPT is a knowledge-based question answering system built on the LLM. It offers out-of-the-box data processing and model invocation capabilities. Moreover, it allows for workflow orchestration through Flow visualization, thereby enabling complex question and answer scenarios!
<p align="center">
<a href="./README_en.md">English</a> |
<a href="./README.md">简体中文</a>
</p>
FastGPT is a knowledge-based Q&A system built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization!
</div>
<p align="center">
<a href="https://fastgpt.run/">Online</a>
·
<a href="https://doc.fastgpt.run/docs/intro">Document</a>
·
<a href="https://doc.fastgpt.run/docs/development">Development</a>
·
<a href="https://doc.fastgpt.run/docs/installation">Deploy</a>
·
<a href="#powered-by">Power By</a>
<a href="https://fastgpt.run/">
<img height="21" src="https://img.shields.io/badge/在线使用-d4eaf7?style=flat-square&logo=spoj&logoColor=7d09f1" alt="cloud">
</a>
<a href="https://doc.fastgpt.run/docs/intro">
<img height="21" src="https://img.shields.io/badge/相关文档-7d09f1?style=flat-square" alt="document">
</a>
<a href="https://doc.fastgpt.run/docs/development">
<img height="21" src="https://img.shields.io/badge/本地开发-%23d4eaf7?style=flat-square&logo=xcode&logoColor=7d09f1" alt="development">
</a>
<a href="/#-%E7%9B%B8%E5%85%B3%E9%A1%B9%E7%9B%AE">
<img height="21" src="https://img.shields.io/badge/相关项目-7d09f1?style=flat-square" alt="project">
</a>
<a href="https://github.com/labring/FastGPT/blob/main/LICENSE">
<img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
</a>
</p>
## 🛸 Online
https://github.com/labring/FastGPT/assets/15308462/7d3a38df-eb0e-4388-9250-2409bd33f6d4
## 🛸 Use Cloud Services
[fastgpt.run](https://fastgpt.run/)
| | |
@@ -29,35 +43,34 @@ FastGPT is a knowledge-based question answering system built on the LLM. It offe
## 💡 Features
1. Powerful visual orchestration for easy AI application building
1. Powerful visual workflows: Effortlessly craft AI applications
- [x] Provides a simple mode without the need for orchestration operations
- [x] Simple mode on deck - no need for manual arrangement
- [x] User dialogue pre-guidance
- [x] Global variables
- [x] Knowledge base search
- [x] Multi-LLM model dialogue
- [x] Extraction of text content into structured data
- [x] HTTP extension
- [ ] Sandbox JS runtime module
- [ ] Continuous dialogue guidance
- [ ] Dialogue multi-path selection
- [ ] Source file reference tracking
- [x] Dialogue via multiple LLM models
- [x] Text magic - convert to structured data
- [x] Extend with HTTP
- [ ] Embed Laf for on-the-fly HTTP module crafting
- [x] Directions for the next dialogue steps
- [x] Tracking source file references
- [ ] Custom file reader
- [ ] Modules are packaged into plug-ins to achieve reuse
2. Rich knowledge base preprocessing
2. Extensive knowledge base preprocessing
- [x] Multiple library reuse and mixing
- [x] Chunk record modification and deletion
- [x] Supports direct segment import
- [x] Supports QA split import
- [x] Supports manual input content
- [ ] Supports URL import reading
- [x] Supports batch import of Q&A pairs in CSV format
- [ ] Supports separate vector model settings for knowledge bases
- [ ] Source file storage
- [x] Reuse and mix multiple knowledge bases
- [x] Track chunk modifications and deletions
- [x] Supports manual entries, direct segmentation, and QA split imports
- [x] Supports URL fetching and batch CSV imports
- [x] Supports Set unique vector models for knowledge bases
- [x] Store original files
- [ ] File learning Agent
3. Multiple effect testing channels
- [x] Knowledge base single point search testing
- [x] Single-point knowledge base search test
- [x] Feedback references and ability to modify and delete during dialogue
- [x] Complete context presentation
- [ ] Complete module intermediate value presentation
@@ -77,11 +90,17 @@ FastGPT is a knowledge-based question answering system built on the LLM. It offe
Project tech stack: NextJs + TS + ChakraUI + Mongo + Postgres (Vector plugin)
- **⚡ Deployment**
[![](https://cdn.jsdelivr.us/gh/labring-actions/templates@main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-fastdeploy%3FtemplateName%3Dfastgpt)
Give it a 2-4 minute wait after deployment as it sets up the database. Initially, it might be a tad slow since we're using the basic settings.
- [Getting Started with Local Development](https://doc.fastgpt.run/docs/development)
- [Deploying FastGPT](https://doc.fastgpt.run/docs/installation)
- [System Configuration File Explanation](https://doc.fastgpt.run/docs/installation/reference)
- [Multi-model Configuration](https://doc.fastgpt.run/docs/installation/reference/models)
- [V3 Upgrade V4 Initialization](https://doc.fastgpt.run/docs/installation/upgrading)
- [Guide on System Configs](https://doc.fastgpt.run/docs/installation/reference)
- [Configuring Multiple Models](https://doc.fastgpt.run/docs/installation/reference/models)
- [Version Updates & Upgrades](https://doc.fastgpt.run/docs/installation/upgrading)
<!-- ## :point_right: RoadMap
- [FastGPT RoadMap](https://kjqvjse66l.feishu.cn/docx/RVUxdqE2WolDYyxEKATcM0XXnte) -->

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@@ -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"]

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@@ -1,82 +0,0 @@
{
"FeConfig": {
"show_emptyChat": true,
"show_register": false,
"show_appStore": false,
"show_userDetail": false,
"show_contact": true,
"show_git": true,
"show_doc": true,
"systemTitle": "FastGPT",
"authorText": "Made by FastGPT Team.",
"limit": {
"exportLimitMinutes": 0
},
"scripts": []
},
"SystemParams": {
"vectorMaxProcess": 15,
"qaMaxProcess": 15,
"pgIvfflatProbe": 20
},
"ChatModels": [
{
"model": "gpt-3.5-turbo",
"name": "GPT35-4k",
"contextMaxToken": 4000,
"quoteMaxToken": 2000,
"maxTemperature": 1.2,
"price": 0,
"defaultSystem": ""
},
{
"model": "gpt-3.5-turbo-16k",
"name": "GPT35-16k",
"contextMaxToken": 16000,
"quoteMaxToken": 8000,
"maxTemperature": 1.2,
"price": 0,
"defaultSystem": ""
},
{
"model": "gpt-4",
"name": "GPT4-8k",
"contextMaxToken": 8000,
"quoteMaxToken": 4000,
"maxTemperature": 1.2,
"price": 0,
"defaultSystem": ""
}
],
"VectorModels": [
{
"model": "text-embedding-ada-002",
"name": "Embedding-2",
"price": 0,
"defaultToken": 500,
"maxToken": 3000
}
],
"QAModel": {
"model": "gpt-3.5-turbo-16k",
"name": "GPT35-16k",
"maxToken": 16000,
"price": 0
},
"ExtractModel": {
"model": "gpt-3.5-turbo-16k",
"functionCall": false,
"name": "GPT35-16k",
"maxToken": 16000,
"price": 0,
"prompt": ""
},
"CQModel": {
"model": "gpt-3.5-turbo-16k",
"functionCall": false,
"name": "GPT35-16k",
"maxToken": 16000,
"price": 0,
"prompt": ""
}
}

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@@ -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.

12583
client/pnpm-lock.yaml generated

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@@ -1,22 +0,0 @@
### 常见问题
- [**Git 地址**,点击查看项目地址](https://github.com/labring/FastGPT)
- [本地部署 FastGPT](https://doc.fastgpt.run/docs/installation)
- [API 文档](https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh?pre_pathname=%2Fdrive%2Fhome%2F)
- **反馈问卷**: 如果你遇到任何使用问题或有期望的功能,可以[填写该问卷](https://www.wjx.cn/vm/rLIw1uD.aspx#)
- **问题文档**: [先看文档,再提问](https://kjqvjse66l.feishu.cn/docx/HtrgdT0pkonP4kxGx8qcu6XDnGh)
- [点击查看商业版文档](https://fael3z0zfze.feishu.cn/docx/F155dbirfo8vDDx2WgWc6extnwf)
**价格表**
| 计费项 | 价格: 元/ 1K tokens包含上下文|
| --- | --- |
| 知识库 - 索引 | 0.002 |
| FastAI4k - 对话 | 0.015 |
| FastAI16k - 对话 | 0.03 |
| FastAI-Plus - 对话 | 0.45 |
| 文件 QA 拆分 | 0.03 |
**其他问题**
| 交流群 | 小助手 |
| ----------------------- | -------------------- |
| ![](https://otnvvf-imgs.oss.laf.run/wxqun300.jpg) | ![](https://otnvvf-imgs.oss.laf.run/wx300.jpg) |

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@@ -1,9 +0,0 @@
### Fast GPT V4.4.4
1. 去除 - 限定词。目前旧应用仍生效9/25 后全面去除,请及时替换。
2. 新增 - 引用模板/引用提示词设置,可以 DIY 引用内容的格式,从而更好的适配场景。
3. 优化 - 更好的兼容无 system role 的模型。
4. 优化 - icon 和 JS 加载逻辑。
5. [使用文档](https://doc.fastgpt.run/docs/intro/)
6. [点击查看高级编排介绍文档](https://doc.fastgpt.run/docs/workflow)
7. [点击查看商业版](https://doc.fastgpt.run/docs/commercial/)

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@@ -1,26 +0,0 @@
import { KbTypeEnum } from '@/constants/dataset';
import type { RequestPaging } from '@/types';
import { TrainingModeEnum } from '@/constants/plugin';
export type PushDataProps = {
kbId: string;
data: DatasetItemType[];
mode: `${TrainingModeEnum}`;
prompt?: string;
};
export type PushDataResponse = {
insertLen: number;
};
export type UpdateDataPrams = {
dataId: string;
kbId: string;
a?: string;
q?: string;
};
export type GetDatasetDataListProps = RequestPaging & {
kbId: string;
searchText: string;
fileId: string;
};

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@@ -1,72 +0,0 @@
import { GET, POST, PUT, DELETE } from '@/api/request';
import type { DatasetDataItemType } from '@/types/core/dataset/data';
import type {
PushDataProps,
PushDataResponse,
UpdateDataPrams,
GetDatasetDataListProps
} from './data.d';
import { QuoteItemType } from '@/types/chat';
import { getToken } from '@/utils/user';
import download from 'downloadjs';
/* kb data */
export const getDatasetDataList = (data: GetDatasetDataListProps) =>
POST(`/core/dataset/data/getDataList`, data);
/**
* export and download data
*/
export const exportDatasetData = (data: { kbId: string }) =>
fetch(`/api/core/dataset/data/exportAll?kbId=${data.kbId}`, {
method: 'GET',
headers: {
token: getToken()
}
})
.then(async (res) => {
if (!res.ok) {
const data = await res.json();
throw new Error(data?.message || 'Export failed');
}
return res.blob();
})
.then((blob) => download(blob, 'dataset.csv', 'text/csv'));
/**
* 获取模型正在拆分数据的数量
*/
export const getTrainingData = (data: { kbId: string; init: boolean }) =>
POST<{
qaListLen: number;
vectorListLen: number;
}>(`/core/dataset/data/getTrainingData`, data);
/* get length of system training queue */
export const getTrainingQueueLen = () => GET<number>(`/core/dataset/data/getQueueLen`);
export const getDatasetDataItemById = (dataId: string) =>
GET<QuoteItemType>(`/core/dataset/data/getDataById`, { dataId });
/**
* push data to training queue
*/
export const postChunks2Dataset = (data: PushDataProps) =>
POST<PushDataResponse>(`/core/dataset/data/pushData`, data);
/**
* insert one data to dataset (immediately insert)
*/
export const postData2Dataset = (data: { kbId: string; data: DatasetDataItemType }) =>
POST<string>(`/core/dataset/data/insertData`, data);
/**
* 更新一条数据
*/
export const putDatasetDataById = (data: UpdateDataPrams) =>
PUT('/core/dataset/data/updateData', data);
/**
* 删除一条知识库数据
*/
export const delOneDatasetDataById = (dataId: string) =>
DELETE(`/core/dataset/data/delDataById?dataId=${dataId}`);

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@@ -1,8 +0,0 @@
import { RequestPaging } from '../../../types/index';
export type GetFileListProps = RequestPaging & {
kbId: string;
searchText: string;
};
export type UpdateFileProps = { id: string; name?: string; datasetUsed?: boolean };

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@@ -1,16 +0,0 @@
import { GET, POST, PUT, DELETE } from '@/api/request';
import type { DatasetFileItemType } from '@/types/core/dataset/file';
import type { GSFileInfoType } from '@/types/common/file';
import type { GetFileListProps, UpdateFileProps } from './file.d';
export const getDatasetFiles = (data: GetFileListProps) =>
POST<DatasetFileItemType[]>(`/core/dataset/file/list`, data);
export const delDatasetFileById = (params: { fileId: string; kbId: string }) =>
DELETE(`/core/dataset/file/delById`, params);
export const getFileInfoById = (fileId: string) =>
GET<GSFileInfoType>(`/core/dataset/file/detail`, { fileId });
export const delDatasetEmptyFiles = (kbId: string) =>
DELETE(`/core/dataset/file/delEmptyFiles`, { kbId });
export const updateDatasetFile = (data: UpdateFileProps) => PUT(`/core/dataset/file/update`, data);

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@@ -1,34 +0,0 @@
import { KbTypeEnum } from '@/constants/dataset';
import type { RequestPaging } from '@/types';
import { TrainingModeEnum } from '@/constants/plugin';
import type { SearchTestItemType } from '@/types/core/dataset';
export type DatasetUpdateParams = {
id: string;
parentId?: string;
tags?: string;
name?: string;
avatar?: string;
};
export type CreateDatasetParams = {
parentId?: string;
name: string;
tags: string[];
avatar: string;
vectorModel?: string;
type: `${KbTypeEnum}`;
};
export type DatasetUpdateParams = {
id: string;
parentId?: string;
tags?: string;
name?: string;
avatar?: string;
};
export type SearchTestProps = {
kbId: string;
text: string;
};
export type SearchTestResponseType = SearchTestItemType['results'];

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import { GET, POST, PUT, DELETE } from '@/api/request';
import type { DatasetItemType, DatasetsItemType, DatasetPathItemType } from '@/types/core/dataset';
import type {
DatasetUpdateParams,
CreateDatasetParams,
SearchTestProps,
SearchTestResponseType
} from './index.d';
import { KbTypeEnum } from '@/constants/dataset';
export const getDatasets = (data: { parentId?: string; type?: `${KbTypeEnum}` }) =>
GET<DatasetsItemType[]>(`/core/dataset/list`, data);
/**
* get type=dataset list
*/
export const getAllDataset = () => GET<DatasetsItemType[]>(`/core/dataset/allDataset`);
export const getDatasetPaths = (parentId?: string) =>
GET<DatasetPathItemType[]>('/core/dataset/paths', { parentId });
export const getDatasetById = (id: string) => GET<DatasetItemType>(`/core/dataset/detail?id=${id}`);
export const postCreateDataset = (data: CreateDatasetParams) =>
POST<string>(`/core/dataset/create`, data);
export const putDatasetById = (data: DatasetUpdateParams) => PUT(`/core/dataset/update`, data);
export const delDatasetById = (id: string) => DELETE(`/core/dataset/delete?id=${id}`);
export const postSearchText = (data: SearchTestProps) =>
POST<SearchTestResponseType>(`/core/dataset/searchTest`, data);

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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}`);

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import { GET, POST, PUT, DELETE } from '../request';
import type { FetchResultItem } from '@/types/plugin';
export const fetchUrls = (urlList: string[]) =>
POST<FetchResultItem[]>(`/plugins/urlFetch`, { urlList });

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export type AdminUpdateFeedbackParams = {
chatItemId: string;
kbId: string;
dataId: string;
content: string;
};

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import { GET, POST } from '../request';
import { AxiosProgressEvent } from 'axios';
export const uploadImg = (base64Img: string) => POST<string>('/system/uploadImage', { base64Img });
export const postUploadFiles = (
data: FormData,
onUploadProgress: (progressEvent: AxiosProgressEvent) => void
) =>
POST<string[]>('/support/file/upload', data, {
onUploadProgress,
headers: {
'Content-Type': 'multipart/form-data; charset=utf-8'
}
});
export const getFileViewUrl = (fileId: string) => GET<string>('/support/file/readUrl', { fileId });

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import { GET, POST, PUT } from './request';
import type { InitDateResponse } from '@/pages/api/system/getInitData';
export const getInitData = () => GET<InitDateResponse>('/system/getInitData');

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@@ -1,144 +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 } from '@/utils/tools';
import { useCopyData } from '@/hooks/useCopyData';
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
</Box>
<Box fontSize={'sm'} color={'myGray.600'}>
API 使~
</Box>
</Box>
<ModalBody minH={'300px'} maxH={['70vh', '500px']} overflow={'overlay'}>
<TableContainer mt={2} position={'relative'}>
<Table>
<Thead>
<Tr>
<Th>Api Key</Th>
<Th></Th>
<Th>使</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()}
>
</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'}>
API
</Box>
<Box fontSize={'sm'} color={'myGray.600'}>
~
</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('')}>
</Button>
</ModalFooter>
</MyModal>
</MyModal>
);
};
export default APIKeyModal;

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@@ -1,151 +0,0 @@
import React, { useCallback, useMemo, useState } from 'react';
import { ModalBody, Box, useTheme } from '@chakra-ui/react';
import { getDatasetDataItemById } from '@/api/core/dataset/data';
import { useLoading } from '@/hooks/useLoading';
import { useToast } from '@/hooks/useToast';
import { getErrText } from '@/utils/tools';
import { QuoteItemType } from '@/types/chat';
import MyIcon from '@/components/Icon';
import InputDataModal, { RawFileText } from '@/pages/kb/detail/components/InputDataModal';
import MyModal from '../MyModal';
import type { PgDataItemType } from '@/types/core/dataset/data';
import { useRouter } from 'next/router';
type SearchType = PgDataItemType & {
kb_id?: string;
};
const QuoteModal = ({
onUpdateQuote,
rawSearch = [],
onClose
}: {
onUpdateQuote: (quoteId: string, sourceText?: string) => Promise<void>;
rawSearch: SearchType[];
onClose: () => void;
}) => {
const theme = useTheme();
const router = useRouter();
const { toast } = useToast();
const { setIsLoading, Loading } = useLoading();
const [editDataItem, setEditDataItem] = useState<QuoteItemType>();
const isShare = useMemo(() => router.pathname === '/chat/share', [router.pathname]);
/**
* click edit, get new kbDataItem
*/
const onclickEdit = useCallback(
async (item: SearchType) => {
if (!item.id) return;
try {
setIsLoading(true);
const data = await getDatasetDataItemById(item.id);
if (!data) {
onUpdateQuote(item.id, '已删除');
throw new Error('该数据已被删除');
}
setEditDataItem(data);
} catch (err) {
toast({
status: 'warning',
title: getErrText(err)
});
}
setIsLoading(false);
},
[setIsLoading, toast, onUpdateQuote]
);
return (
<>
<MyModal
isOpen={true}
onClose={onClose}
h={['90vh', '80vh']}
isCentered
minW={['90vw', '600px']}
title={
<>
({rawSearch.length})
<Box fontSize={['xs', 'sm']} fontWeight={'normal'}>
注意: 修改知识库内容成功后
</Box>
</>
}
>
<ModalBody
pt={0}
whiteSpace={'pre-wrap'}
textAlign={'justify'}
wordBreak={'break-all'}
fontSize={'sm'}
>
{rawSearch.map((item, i) => (
<Box
key={i}
flex={'1 0 0'}
p={2}
borderRadius={'lg'}
border={theme.borders.base}
_notLast={{ mb: 2 }}
position={'relative'}
_hover={{ '& .edit': { display: 'flex' } }}
overflow={'hidden'}
>
{item.source && !isShare && (
<RawFileText filename={item.source} fileId={item.file_id} />
)}
<Box>{item.q}</Box>
<Box>{item.a}</Box>
{item.id && !isShare && (
<Box
className="edit"
display={'none'}
position={'absolute'}
right={0}
top={0}
bottom={0}
w={'40px'}
bg={'rgba(255,255,255,0.9)'}
alignItems={'center'}
justifyContent={'center'}
boxShadow={'-10px 0 10px rgba(255,255,255,1)'}
>
<MyIcon
name={'edit'}
w={'18px'}
h={'18px'}
cursor={'pointer'}
color={'myGray.600'}
_hover={{
color: 'myBlue.700'
}}
onClick={() => onclickEdit(item)}
/>
</Box>
)}
</Box>
))}
</ModalBody>
<Loading fixed={false} />
</MyModal>
{editDataItem && (
<InputDataModal
onClose={() => setEditDataItem(undefined)}
onSuccess={() => onUpdateQuote(editDataItem.id)}
onDelete={() => onUpdateQuote(editDataItem.id, '已删除')}
kbId={editDataItem.kb_id}
defaultValues={{
...editDataItem,
dataId: editDataItem.id
}}
/>
)}
</>
);
};
export default QuoteModal;

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@@ -1,123 +0,0 @@
import React, { useCallback, useMemo, useState } from 'react';
import { ChatHistoryItemResType, ChatItemType, QuoteItemType } from '@/types/chat';
import { Flex, BoxProps, useDisclosure } from '@chakra-ui/react';
import { useTranslation } from 'react-i18next';
import { useGlobalStore } from '@/store/global';
import dynamic from 'next/dynamic';
import Tag from '../Tag';
import MyTooltip from '../MyTooltip';
import { FlowModuleTypeEnum } from '@/constants/flow';
const QuoteModal = dynamic(() => import('./QuoteModal'), { ssr: false });
const ContextModal = dynamic(() => import('./ContextModal'), { ssr: false });
const WholeResponseModal = dynamic(() => import('./WholeResponseModal'), { ssr: false });
const ResponseTags = ({
chatId,
contentId,
responseData = []
}: {
chatId?: string;
contentId?: string;
responseData?: ChatHistoryItemResType[];
}) => {
const { isPc } = useGlobalStore();
const { t } = useTranslation();
const [quoteModalData, setQuoteModalData] = useState<QuoteItemType[]>();
const [contextModalData, setContextModalData] = useState<ChatItemType[]>();
const {
isOpen: isOpenWholeModal,
onOpen: onOpenWholeModal,
onClose: onCloseWholeModal
} = useDisclosure();
const {
chatAccount,
quoteList = [],
historyPreview = [],
runningTime = 0
} = useMemo(() => {
const chatData = responseData.find((item) => item.moduleType === FlowModuleTypeEnum.chatNode);
return {
chatAccount: responseData.filter((item) => item.moduleType === FlowModuleTypeEnum.chatNode)
.length,
quoteList: chatData?.quoteList,
historyPreview: chatData?.historyPreview,
runningTime: responseData.reduce((sum, item) => sum + (item.runningTime || 0), 0).toFixed(2)
};
}, [responseData]);
const updateQuote = useCallback(async (quoteId: string, sourceText?: string) => {}, []);
const TagStyles: BoxProps = {
mr: 2,
bg: 'transparent'
};
return responseData.length === 0 ? null : (
<Flex alignItems={'center'} mt={2} flexWrap={'wrap'}>
{chatAccount === 1 && (
<>
{quoteList.length > 0 && (
<MyTooltip label="查看引用">
<Tag
colorSchema="blue"
cursor={'pointer'}
{...TagStyles}
onClick={() => setQuoteModalData(quoteList)}
>
{quoteList.length}
</Tag>
</MyTooltip>
)}
{historyPreview.length > 0 && (
<MyTooltip label={'点击查看完整对话记录'}>
<Tag
colorSchema="green"
cursor={'pointer'}
{...TagStyles}
onClick={() => setContextModalData(historyPreview)}
>
{historyPreview.length}
</Tag>
</MyTooltip>
)}
</>
)}
{chatAccount > 1 && (
<Tag colorSchema="blue" {...TagStyles}>
AI
</Tag>
)}
{isPc && runningTime > 0 && (
<MyTooltip label={'模块运行时间和'}>
<Tag colorSchema="purple" cursor={'default'} {...TagStyles}>
{runningTime}s
</Tag>
</MyTooltip>
)}
<MyTooltip label={'点击查看完整响应'}>
<Tag colorSchema="gray" cursor={'pointer'} {...TagStyles} onClick={onOpenWholeModal}>
{t('chat.Complete Response')}
</Tag>
</MyTooltip>
{!!quoteModalData && (
<QuoteModal
rawSearch={quoteModalData}
onUpdateQuote={updateQuote}
onClose={() => setQuoteModalData(undefined)}
/>
)}
{!!contextModalData && (
<ContextModal context={contextModalData} onClose={() => setContextModalData(undefined)} />
)}
{isOpenWholeModal && (
<WholeResponseModal response={responseData} onClose={onCloseWholeModal} />
)}
</Flex>
);
};
export default ResponseTags;

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@@ -1,114 +0,0 @@
import React, { useState } from 'react';
import { ModalBody, useTheme, ModalFooter, Button, Box, Card, Flex, Grid } from '@chakra-ui/react';
import { useTranslation } from 'next-i18next';
import { useToast } from '@/hooks/useToast';
import Avatar from '../Avatar';
import MyIcon from '@/components/Icon';
import { KbTypeEnum } from '@/constants/dataset';
import DatasetSelectModal, { useDatasetSelect } from '@/components/core/dataset/SelectModal';
const SelectDataset = ({
isOpen,
onSuccess,
onClose
}: {
isOpen: boolean;
onSuccess: (kbId: string) => void;
onClose: () => void;
}) => {
const { t } = useTranslation();
const theme = useTheme();
const { toast } = useToast();
const [selectedId, setSelectedId] = useState<string>();
const { paths, parentId, setParentId, datasets } = useDatasetSelect();
return (
<DatasetSelectModal
isOpen={isOpen}
paths={paths}
onClose={onClose}
parentId={parentId}
setParentId={setParentId}
tips={t('chat.Select Mark Kb Desc')}
>
<ModalBody flex={['1 0 0', '0 0 auto']} maxH={'80vh'} overflowY={'auto'}>
<Grid
gridTemplateColumns={['repeat(1,1fr)', 'repeat(2,1fr)', 'repeat(3,1fr)']}
gridGap={3}
userSelect={'none'}
>
{datasets.map((item) =>
(() => {
const selected = selectedId === item._id;
return (
<Card
key={item._id}
p={3}
border={theme.borders.base}
boxShadow={'sm'}
h={'80px'}
cursor={'pointer'}
_hover={{
boxShadow: 'md'
}}
{...(selected
? {
bg: 'myBlue.300'
}
: {})}
onClick={() => {
if (item.type === KbTypeEnum.folder) {
setParentId(item._id);
} else {
setSelectedId(item._id);
}
}}
>
<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>
);
})()
)}
</Grid>
{datasets.length === 0 && (
<Flex mt={5} flexDirection={'column'} alignItems={'center'}>
<MyIcon name="empty" w={'48px'} h={'48px'} color={'transparent'} />
<Box mt={2} color={'myGray.500'}>
西~
</Box>
</Flex>
)}
</ModalBody>
<ModalFooter>
<Button variant={'base'} mr={2} onClick={onClose}>
{t('Cancel')}
</Button>
<Button
onClick={() => {
if (!selectedId) {
return toast({
status: 'warning',
title: t('Select value is empty')
});
}
onSuccess(selectedId);
}}
>
{t('Confirm')}
</Button>
</ModalFooter>
</DatasetSelectModal>
);
};
export default SelectDataset;

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@@ -1,29 +0,0 @@
import { SystemInputEnum } from '@/constants/app';
import { FlowModuleTypeEnum } from '@/constants/flow';
import { getChatModel } from '@/service/utils/data';
import { AppModuleItemType, VariableItemType } from '@/types/app';
export const getSpecialModule = (modules: AppModuleItemType[]) => {
const welcomeText: string =
modules
.find((item) => item.flowType === FlowModuleTypeEnum.userGuide)
?.inputs?.find((item) => item.key === SystemInputEnum.welcomeText)?.value || '';
const variableModules: VariableItemType[] =
modules
.find((item) => item.flowType === FlowModuleTypeEnum.variable)
?.inputs.find((item) => item.key === SystemInputEnum.variables)?.value || [];
return {
welcomeText,
variableModules
};
};
export const getChatModelNameList = (modules: AppModuleItemType[]): string[] => {
const chatModules = modules.filter((item) => item.flowType === FlowModuleTypeEnum.chatNode);
return chatModules
.map(
(item) => getChatModel(item.inputs.find((input) => input.key === 'model')?.value)?.name || ''
)
.filter((item) => item);
};

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.datePicker {
--rdp-background-color: #d6e8ff;
--rdp-accent-color: #0000ff;
}

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<svg viewBox="0 0 24 24">
<path d="M8.3 5.7a1 1 0 011.4-1.4l7.71 7.7-7.7 7.7a1 1 0 11-1.42-1.4l6.3-6.3-6.3-6.3z" fill-rule="nonzero"></path>
</svg>

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import type { DatasetItemType } from '@/types/core/dataset';
export const defaultKbDetail: DatasetItemType = {
_id: '',
userId: '',
avatar: '/icon/logo.svg',
name: '',
tags: '',
vectorModel: {
model: 'text-embedding-ada-002',
name: 'Embedding-2',
price: 0.2,
defaultToken: 500,
maxToken: 3000
}
};
export enum KbTypeEnum {
folder = 'folder',
dataset = 'dataset'
}
export enum FileStatusEnum {
embedding = 'embedding',
ready = 'ready'
}
export const KbTypeMap = {
[KbTypeEnum.folder]: {
name: 'folder'
},
[KbTypeEnum.dataset]: {
name: 'dataset'
}
};
export const FolderAvatarSrc = '/imgs/files/folder.svg';
export const OtherFileId = 'other';

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@@ -1,84 +0,0 @@
import type { BoxProps } from '@chakra-ui/react';
export enum FlowInputItemTypeEnum {
systemInput = 'systemInput', // history, userChatInput, variableInput
input = 'input',
textarea = 'textarea',
numberInput = 'numberInput',
select = 'select',
slider = 'slider',
custom = 'custom',
target = 'target',
none = 'none',
hidden = 'hidden'
}
export enum FlowOutputItemTypeEnum {
answer = 'answer',
source = 'source',
none = 'none',
hidden = 'hidden'
}
export enum FlowModuleTypeEnum {
empty = 'empty',
variable = 'variable',
userGuide = 'userGuide',
questionInput = 'questionInput',
historyNode = 'historyNode',
chatNode = 'chatNode',
kbSearchNode = 'kbSearchNode',
tfSwitchNode = 'tfSwitchNode',
answerNode = 'answerNode',
classifyQuestion = 'classifyQuestion',
contentExtract = 'contentExtract',
httpRequest = 'httpRequest'
}
export enum SpecialInputKeyEnum {
'answerText' = 'text',
'agents' = 'agents' // cq agent key
}
export enum FlowValueTypeEnum {
'string' = 'string',
'number' = 'number',
'boolean' = 'boolean',
'chatHistory' = 'chat_history',
'kbQuote' = 'kb_quote',
'any' = 'any'
}
export const FlowValueTypeStyle: Record<`${FlowValueTypeEnum}`, BoxProps> = {
[FlowValueTypeEnum.string]: {
background: '#36ADEF'
},
[FlowValueTypeEnum.number]: {
background: '#FB7C3C'
},
[FlowValueTypeEnum.boolean]: {
background: '#E7D118'
},
[FlowValueTypeEnum.chatHistory]: {
background: '#00A9A6'
},
[FlowValueTypeEnum.kbQuote]: {
background: '#A558C9'
},
[FlowValueTypeEnum.any]: {
background: '#9CA2A8'
}
};
export const initModuleType: Record<string, boolean> = {
[FlowModuleTypeEnum.historyNode]: true,
[FlowModuleTypeEnum.questionInput]: true
};
export const edgeOptions = {
style: {
strokeWidth: 1.5,
stroke: '#5A646Es'
}
};
export const connectionLineStyle = { strokeWidth: 1.5, stroke: '#5A646Es' };

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import { FlowInputItemType } from '@/types/flow';
import { SystemInputEnum } from '../app';
import { FlowInputItemTypeEnum, FlowValueTypeEnum } from './index';
export const Input_Template_TFSwitch: FlowInputItemType = {
key: SystemInputEnum.switch,
type: FlowInputItemTypeEnum.target,
label: '触发器',
valueType: FlowValueTypeEnum.any
};
export const Input_Template_History: FlowInputItemType = {
key: SystemInputEnum.history,
type: FlowInputItemTypeEnum.target,
label: '聊天记录',
valueType: FlowValueTypeEnum.chatHistory
};
export const Input_Template_UserChatInput: FlowInputItemType = {
key: SystemInputEnum.userChatInput,
type: FlowInputItemTypeEnum.target,
label: '用户问题',
required: true,
valueType: FlowValueTypeEnum.string
};

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@@ -1,27 +0,0 @@
import type { AppSchema } from '@/types/mongoSchema';
import type { OutLinkEditType } from '@/types/support/outLink';
export const defaultApp: AppSchema = {
_id: '',
userId: 'userId',
name: '模型加载中',
type: 'basic',
avatar: '/icon/logo.svg',
intro: '',
updateTime: Date.now(),
share: {
isShare: false,
isShareDetail: false,
collection: 0
},
modules: []
};
export const defaultOutLinkForm: OutLinkEditType = {
name: '',
responseDetail: false,
limit: {
QPM: 100,
credit: -1
}
};

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@@ -1,10 +0,0 @@
export enum TrainingModeEnum {
'qa' = 'qa',
'index' = 'index'
}
export const TrainingTypeMap = {
[TrainingModeEnum.qa]: 'qa',
[TrainingModeEnum.index]: 'index'
};
export const PgDatasetTableName = 'modeldata';

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@@ -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'
})
)
);
}

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@@ -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 });
}
})
);
}

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@@ -1,27 +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, OutLink } from '@/service/mongo';
import { OutLinkTypeEnum } from '@/constants/chat';
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
try {
await authUser({ req, authRoot: true });
await connectToDatabase();
await OutLink.updateMany(
{},
{
$set: { type: OutLinkTypeEnum.share }
}
);
jsonRes(res, {});
} catch (error) {
jsonRes(res, {
code: 500,
error
});
}
}

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@@ -1,35 +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 { PgClient } from '@/service/pg';
import { PgDatasetTableName } from '@/constants/plugin';
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
try {
await authUser({ req, authRoot: true });
const { rowCount } = await PgClient.query(`SELECT 1
FROM information_schema.columns
WHERE table_schema = 'public'
AND table_name = '${PgDatasetTableName}'
AND column_name = 'file_id'`);
if (rowCount > 0) {
return jsonRes(res, {
data: '已经存在file_id字段'
});
}
jsonRes(res, {
data: await PgClient.query(
`ALTER TABLE ${PgDatasetTableName} ADD COLUMN file_id VARCHAR(100)`
)
});
} catch (error) {
jsonRes(res, {
code: 500,
error
});
}
}

View File

@@ -1,44 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, Collection, App } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
/* 模型收藏切换 */
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const { appId } = req.query as { appId: string };
if (!appId) {
throw new Error('缺少参数');
}
// 凭证校验
const { userId } = await authUser({ req, authToken: true });
await connectToDatabase();
const collectionRecord = await Collection.findOne({
userId,
modelId: appId
});
if (collectionRecord) {
await Collection.findByIdAndRemove(collectionRecord._id);
} else {
await Collection.create({
userId,
modelId: appId
});
}
await App.findByIdAndUpdate(appId, {
'share.collection': await Collection.countDocuments({ modelId: appId })
});
jsonRes(res);
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

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@@ -1,106 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, App } from '@/service/mongo';
import type { PagingData } from '@/types';
import type { ShareAppItem } from '@/types/app';
import { authUser } from '@/service/utils/auth';
import { Types } from 'mongoose';
/* 获取模型列表 */
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const {
searchText = '',
pageNum = 1,
pageSize = 20
} = req.body as { searchText: string; pageNum: number; pageSize: number };
await connectToDatabase();
const { userId } = await authUser({ req, authToken: true });
const regex = new RegExp(searchText, 'i');
const where = {
$and: [
{ 'share.isShare': true },
{
$or: [{ name: { $regex: regex } }, { intro: { $regex: regex } }]
}
]
};
const pipeline = [
{
$match: where
},
{
$lookup: {
from: 'collections',
let: { modelId: '$_id' },
pipeline: [
{
$match: {
$expr: {
$and: [
{ $eq: ['$modelId', '$$modelId'] },
{
$eq: ['$userId', userId ? new Types.ObjectId(userId) : new Types.ObjectId()]
}
]
}
}
}
],
as: 'collections'
}
},
{
$project: {
_id: 1,
avatar: { $ifNull: ['$avatar', '/icon/logo.svg'] },
name: 1,
userId: 1,
intro: 1,
share: 1,
isCollection: {
$cond: {
if: { $gt: [{ $size: '$collections' }, 0] },
then: true,
else: false
}
}
}
},
{
$sort: { 'share.topNum': -1, 'share.collection': -1 }
},
{
$skip: (pageNum - 1) * pageSize
},
{
$limit: pageSize
}
];
// 获取被分享的模型
const [models, total] = await Promise.all([
// @ts-ignore
App.aggregate(pipeline),
App.countDocuments(where)
]);
jsonRes<PagingData<ShareAppItem>>(res, {
data: {
pageNum,
pageSize,
data: models,
total
}
});
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

View File

@@ -1,47 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { PgClient } from '@/service/pg';
import { PgDatasetTableName } from '@/constants/plugin';
import type { PgDataItemType } from '@/types/core/dataset/data';
export type Response = {
id: string;
q: string;
a: string;
source: string;
};
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
let { dataId } = req.query as {
dataId: string;
};
if (!dataId) {
throw new Error('缺少参数');
}
// 凭证校验
const { userId } = await authUser({ req, authToken: true });
await connectToDatabase();
const where: any = [['user_id', userId], 'AND', ['id', dataId]];
const searchRes = await PgClient.select<PgDataItemType>(PgDatasetTableName, {
fields: ['kb_id', 'id', 'q', 'a', 'source', 'file_id'],
where,
limit: 1
});
jsonRes(res, {
data: searchRes.rows[0]
});
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

View File

@@ -1,52 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, TrainingData } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { TrainingModeEnum } from '@/constants/plugin';
import { Types } from 'mongoose';
import { startQueue } from '@/service/utils/tools';
/* 拆分数据成QA */
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
try {
const { kbId, init = false } = req.body as { kbId: string; init: boolean };
if (!kbId) {
throw new Error('参数错误');
}
await connectToDatabase();
const { userId } = await authUser({ req, authToken: true });
// split queue data
const result = await TrainingData.aggregate([
{
$match: {
userId: new Types.ObjectId(userId),
kbId: new Types.ObjectId(kbId)
}
},
{
$group: {
_id: '$mode',
count: { $sum: 1 }
}
}
]);
jsonRes(res, {
data: {
qaListLen: result.find((item) => item._id === TrainingModeEnum.qa)?.count || 0,
vectorListLen: result.find((item) => item._id === TrainingModeEnum.index)?.count || 0
}
});
if (init) {
startQueue();
}
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

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@@ -1,86 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase } from '@/service/mongo';
import { authKb, authUser } from '@/service/utils/auth';
import { withNextCors } from '@/service/utils/tools';
import { PgDatasetTableName } from '@/constants/plugin';
import { insertData2Dataset, PgClient } from '@/service/pg';
import { getVectorModel } from '@/service/utils/data';
import { getVector } from '@/pages/api/openapi/plugin/vector';
import { DatasetDataItemType } from '@/types/core/dataset/data';
import { countPromptTokens } from '@/utils/common/tiktoken';
export type Props = {
kbId: string;
data: DatasetDataItemType;
};
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 = countPromptTokens(q, 'system');
if (token > getVectorModel(kb.vectorModel).maxToken) {
throw new Error('Over Tokens');
}
const { rows: existsRows } = await PgClient.query(`
SELECT COUNT(*) > 0 AS exists
FROM ${PgDatasetTableName}
WHERE md5(q)=md5('${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 insertData2Dataset({
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
});
}
});

View File

@@ -1,168 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, TrainingData, KB } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { authKb } from '@/service/utils/auth';
import { withNextCors } from '@/service/utils/tools';
import { PgDatasetTableName, TrainingModeEnum } from '@/constants/plugin';
import { startQueue } from '@/service/utils/tools';
import { PgClient } from '@/service/pg';
import { getVectorModel } from '@/service/utils/data';
import { DatasetDataItemType } from '@/types/core/dataset/data';
import { countPromptTokens } from '@/utils/common/tiktoken';
import type { PushDataProps, PushDataResponse } from '@/api/core/dataset/data.d';
const modeMap = {
[TrainingModeEnum.index]: true,
[TrainingModeEnum.qa]: true
};
export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const { kbId, data, mode = TrainingModeEnum.index, prompt } = req.body as PushDataProps;
if (!kbId || !Array.isArray(data)) {
throw new Error('KbId or data is empty');
}
if (modeMap[mode] === undefined) {
throw new Error('Mode is error');
}
if (data.length > 500) {
throw new Error('Data is too long, max 500');
}
await connectToDatabase();
// 凭证校验
const { userId } = await authUser({ req });
jsonRes<PushDataResponse>(res, {
data: await pushDataToKb({
kbId,
data,
userId,
mode,
prompt
})
});
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
});
export async function pushDataToKb({
userId,
kbId,
data,
mode,
prompt
}: { userId: string } & PushDataProps): Promise<PushDataResponse> {
const [kb, vectorModel] = await Promise.all([
authKb({
userId,
kbId
}),
(async () => {
if (mode === TrainingModeEnum.index) {
const vectorModel = (await KB.findById(kbId, 'vectorModel'))?.vectorModel;
return getVectorModel(vectorModel || global.vectorModels[0].model);
}
return global.vectorModels[0];
})()
]);
const modeMaxToken = {
[TrainingModeEnum.index]: vectorModel.maxToken * 1.5,
[TrainingModeEnum.qa]: global.qaModel.maxToken * 0.8
};
// 过滤重复的 qa 内容
const set = new Set();
const filterData: DatasetDataItemType[] = [];
data.forEach((item) => {
if (!item.q) return;
const text = item.q + item.a;
// count q token
const token = countPromptTokens(item.q, 'system');
if (token > modeMaxToken[mode]) {
return;
}
if (!set.has(text)) {
filterData.push(item);
set.add(text);
}
});
// 数据库去重
const insertData = (
await Promise.allSettled(
filterData.map(async (data) => {
let { q, a } = data;
if (mode !== TrainingModeEnum.index) {
return Promise.resolve(data);
}
if (!q) {
return Promise.reject('q为空');
}
q = q.replace(/\\n/g, '\n').trim().replace(/'/g, '"');
a = a.replace(/\\n/g, '\n').trim().replace(/'/g, '"');
// Exactly the same data, not push
try {
const { rows } = await PgClient.query(`
SELECT COUNT(*) > 0 AS exists
FROM ${PgDatasetTableName}
WHERE md5(q)=md5('${q}') AND md5(a)=md5('${a}') AND user_id='${userId}' AND kb_id='${kbId}'
`);
const exists = rows[0]?.exists || false;
if (exists) {
return Promise.reject('已经存在');
}
} catch (error) {
console.log(error);
}
return Promise.resolve(data);
})
)
)
.filter((item) => item.status === 'fulfilled')
.map<DatasetDataItemType>((item: any) => item.value);
// 插入记录
const insertRes = await TrainingData.insertMany(
insertData.map((item) => ({
...item,
userId,
kbId,
mode,
prompt,
vectorModel: vectorModel.model
}))
);
insertRes.length > 0 && startQueue();
return {
insertLen: insertRes.length
};
}
export const config = {
api: {
responseLimit: '12mb'
}
};

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@@ -1,64 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { authUser } from '@/service/utils/auth';
import { PgClient } from '@/service/pg';
import { withNextCors } from '@/service/utils/tools';
import { KB, connectToDatabase } from '@/service/mongo';
import { getVector } from '@/pages/api/openapi/plugin/vector';
import { PgDatasetTableName } from '@/constants/plugin';
import type { UpdateDataPrams } from '@/api/core/dataset/data.d';
export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const { dataId, a = '', q = '', kbId } = req.body as UpdateDataPrams;
if (!dataId) {
throw new Error('缺少参数');
}
await connectToDatabase();
// auth user and get kb
const [{ userId }, kb] = await Promise.all([
authUser({ req }),
KB.findById(kbId, 'vectorModel')
]);
if (!kb) {
throw new Error("Can't find database");
}
// get vector
const { vectors = [] } = await (async () => {
if (q) {
return getVector({
userId,
input: [q],
model: kb.vectorModel
});
}
return { vectors: [[]] };
})();
// 更新 pg 内容.仅修改a不需要更新向量。
await PgClient.update(PgDatasetTableName, {
where: [['id', dataId], 'AND', ['user_id', userId]],
values: [
{ key: 'a', value: a.replace(/'/g, '"') },
...(q
? [
{ key: 'q', value: q.replace(/'/g, '"') },
{ key: 'vector', value: `[${vectors[0]}]` }
]
: [])
]
});
jsonRes(res);
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
});

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@@ -1,62 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, TrainingData } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { GridFSStorage } from '@/service/lib/gridfs';
import { PgClient } from '@/service/pg';
import { PgDatasetTableName } from '@/constants/plugin';
import { Types } from 'mongoose';
import { OtherFileId } from '@/constants/dataset';
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
await connectToDatabase();
const { fileId, kbId } = req.query as { fileId: string; kbId: string };
if (!fileId || !kbId) {
throw new Error('fileId and kbId is required');
}
// 凭证校验
const { userId } = await authUser({ req, authToken: true });
// other data. Delete only vector data
if (fileId === OtherFileId) {
await PgClient.delete(PgDatasetTableName, {
where: [
['user_id', userId],
'AND',
['kb_id', kbId],
"AND (file_id IS NULL OR file_id = '')"
]
});
} else {
// auth file
const gridFs = new GridFSStorage('dataset', userId);
const bucket = gridFs.GridFSBucket();
await gridFs.findAndAuthFile(fileId);
// delete all pg data
await PgClient.delete(PgDatasetTableName, {
where: [['user_id', userId], 'AND', ['kb_id', kbId], 'AND', ['file_id', fileId]]
});
// delete all training data
await TrainingData.deleteMany({
userId,
file_id: fileId
});
// delete file
await bucket.delete(new Types.ObjectId(fileId));
}
jsonRes(res);
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

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@@ -1,43 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { GridFSStorage } from '@/service/lib/gridfs';
import { OtherFileId } from '@/constants/dataset';
import type { GSFileInfoType } from '@/types/common/file';
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
await connectToDatabase();
const { fileId } = req.query as { kbId: string; fileId: string };
// 凭证校验
const { userId } = await authUser({ req, authToken: true });
if (fileId === OtherFileId) {
return jsonRes<GSFileInfoType>(res, {
data: {
id: OtherFileId,
size: 0,
filename: 'kb.Other Data',
uploadDate: new Date(),
encoding: '',
contentType: ''
}
});
}
const gridFs = new GridFSStorage('dataset', userId);
const file = await gridFs.findAndAuthFile(fileId);
jsonRes<GSFileInfoType>(res, {
data: file
});
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

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@@ -1,112 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { connectToDatabase, TrainingData } from '@/service/mongo';
import { authUser } from '@/service/utils/auth';
import { GridFSStorage } from '@/service/lib/gridfs';
import { PgClient } from '@/service/pg';
import { PgDatasetTableName } from '@/constants/plugin';
import { FileStatusEnum, OtherFileId } from '@/constants/dataset';
export default async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
await connectToDatabase();
let {
pageNum = 1,
pageSize = 10,
kbId,
searchText = ''
} = req.body as { pageNum: number; pageSize: number; kbId: string; searchText: string };
searchText = searchText?.replace(/'/g, '');
// 凭证校验
const { userId } = await authUser({ req, authToken: true });
// find files
const gridFs = new GridFSStorage('dataset', userId);
const collection = gridFs.Collection();
const mongoWhere = {
['metadata.kbId']: kbId,
['metadata.userId']: userId,
['metadata.datasetUsed']: true,
...(searchText && { filename: { $regex: searchText } })
};
const [files, total] = await Promise.all([
collection
.find(mongoWhere, {
projection: {
_id: 1,
filename: 1,
uploadDate: 1,
length: 1
}
})
.skip((pageNum - 1) * pageSize)
.limit(pageSize)
.sort({ uploadDate: -1 })
.toArray(),
collection.countDocuments(mongoWhere)
]);
async function GetOtherData() {
return {
id: OtherFileId,
size: 0,
filename: 'kb.Other Data',
uploadTime: new Date(),
status: (await TrainingData.findOne({ userId, kbId, file_id: '' }))
? FileStatusEnum.embedding
: FileStatusEnum.ready,
chunkLength: await PgClient.count(PgDatasetTableName, {
fields: ['id'],
where: [
['user_id', userId],
'AND',
['kb_id', kbId],
"AND (file_id IS NULL OR file_id = '')"
]
})
};
}
const data = await Promise.all([
GetOtherData(),
...files.map(async (file) => {
return {
id: String(file._id),
size: file.length,
filename: file.filename,
uploadTime: file.uploadDate,
status: (await TrainingData.findOne({ userId, kbId, file_id: file._id }))
? FileStatusEnum.embedding
: FileStatusEnum.ready,
chunkLength: await PgClient.count(PgDatasetTableName, {
fields: ['id'],
where: [
['user_id', userId],
'AND',
['kb_id', kbId],
'AND',
['file_id', String(file._id)]
]
})
};
})
]);
jsonRes(res, {
data: {
pageNum,
pageSize,
data: data.flat(),
total
}
});
} catch (err) {
jsonRes(res, {
code: 500,
error: err
});
}
}

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@@ -1,56 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { authUser } from '@/service/utils/auth';
import { PgClient } from '@/service/pg';
import { withNextCors } from '@/service/utils/tools';
import { getVector } from '../../openapi/plugin/vector';
import { PgDatasetTableName } from '@/constants/plugin';
import { KB } from '@/service/mongo';
import type { SearchTestProps, SearchTestResponseType } from '@/api/core/dataset/index.d';
export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const { kbId, text } = req.body as SearchTestProps;
if (!kbId || !text) {
throw new Error('缺少参数');
}
// 凭证校验
const [{ userId }, kb] = await Promise.all([
authUser({ req }),
KB.findById(kbId, 'vectorModel')
]);
if (!userId || !kb) {
throw new Error('缺少用户ID');
}
const { vectors } = await getVector({
model: kb.vectorModel,
userId,
input: [text]
});
const response: any = await PgClient.query(
`BEGIN;
SET LOCAL ivfflat.probes = ${global.systemEnv.pgIvfflatProbe || 10};
select id, q, a, source, file_id, (vector <#> '[${
vectors[0]
}]') * -1 AS score from ${PgDatasetTableName} where kb_id='${kbId}' AND user_id='${userId}' order by vector <#> '[${
vectors[0]
}]' limit 12;
COMMIT;`
);
jsonRes<SearchTestResponseType>(res, {
data: response?.[2]?.rows || []
});
} catch (err) {
console.log(err);
jsonRes(res, {
code: 500,
error: err
});
}
});

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@@ -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
});
}
}

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@@ -1,99 +0,0 @@
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { authBalanceByUid, authUser } from '@/service/utils/auth';
import { withNextCors } from '@/service/utils/tools';
import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
import { pushGenerateVectorBill } from '@/service/events/pushBill';
type Props = {
model: string;
input: string[];
};
type Response = {
tokenLen: number;
vectors: number[][];
};
export default withNextCors(async function handler(req: NextApiRequest, res: NextApiResponse<any>) {
try {
const { userId } = await authUser({ req });
let { input, model } = req.query as Props;
if (!Array.isArray(input)) {
throw new Error('缺少参数');
}
jsonRes<Response>(res, {
data: await getVector({ userId, input, model })
});
} catch (err) {
console.log(err);
jsonRes(res, {
code: 500,
error: err
});
}
});
export async function getVector({
model = 'text-embedding-ada-002',
userId,
input
}: { userId?: string } & Props) {
userId && (await authBalanceByUid(userId));
for (let i = 0; i < input.length; i++) {
if (!input[i]) {
return Promise.reject({
code: 500,
message: '向量生成模块输入内容为空'
});
}
}
// 获取 chatAPI
const chatAPI = getAIChatApi();
// 把输入的内容转成向量
const result = await chatAPI
.createEmbedding(
{
model,
input
},
{
timeout: 60000,
...axiosConfig()
}
)
.then(async (res) => {
if (!res.data?.data?.[0]?.embedding) {
console.log(res.data);
// @ts-ignore
return Promise.reject(res.data?.err?.message || 'Embedding API Error');
}
return {
tokenLen: res.data.usage.total_tokens || 0,
vectors: await Promise.all(res.data.data.map((item) => unityDimensional(item.embedding)))
};
});
userId &&
pushGenerateVectorBill({
userId,
tokenLen: result.tokenLen,
model
});
return result;
}
function unityDimensional(vector: number[]) {
if (vector.length > 1536) return Promise.reject('向量维度不能超过 1536');
let resultVector = vector;
const vectorLen = vector.length;
const zeroVector = new Array(1536 - vectorLen).fill(0);
return resultVector.concat(zeroVector);
}

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@@ -1,165 +0,0 @@
import type { FeConfigsType, SystemEnvType } from '@/types';
import type { NextApiRequest, NextApiResponse } from 'next';
import { jsonRes } from '@/service/response';
import { readFileSync } from 'fs';
import {
type QAModelItemType,
type ChatModelItemType,
type VectorModelItemType,
FunctionModelItemType
} from '@/types/model';
export type InitDateResponse = {
chatModels: ChatModelItemType[];
qaModel: QAModelItemType;
vectorModels: VectorModelItemType[];
feConfigs: FeConfigsType;
systemVersion: string;
};
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
if (!global.feConfigs) {
await getInitConfig();
}
jsonRes<InitDateResponse>(res, {
data: {
feConfigs: global.feConfigs,
chatModels: global.chatModels,
qaModel: global.qaModel,
vectorModels: global.vectorModels,
systemVersion: global.systemVersion || '0.0.0'
}
});
}
const defaultSystemEnv: SystemEnvType = {
vectorMaxProcess: 15,
qaMaxProcess: 15,
pgIvfflatProbe: 20
};
const defaultFeConfigs: FeConfigsType = {
show_emptyChat: true,
show_register: false,
show_appStore: false,
show_userDetail: false,
show_contact: true,
show_git: true,
show_doc: true,
systemTitle: 'FastGPT',
authorText: 'Made by FastGPT Team.',
limit: {
exportLimitMinutes: 0
},
scripts: []
};
const defaultChatModels = [
{
model: 'gpt-3.5-turbo',
name: 'GPT35-4k',
contextMaxToken: 4000,
quoteMaxToken: 2400,
maxTemperature: 1.2,
price: 0
},
{
model: 'gpt-3.5-turbo-16k',
name: 'GPT35-16k',
contextMaxToken: 16000,
quoteMaxToken: 8000,
maxTemperature: 1.2,
price: 0
},
{
model: 'gpt-4',
name: 'GPT4-8k',
contextMaxToken: 8000,
quoteMaxToken: 4000,
maxTemperature: 1.2,
price: 0
}
];
const defaultQAModel = {
model: 'gpt-3.5-turbo-16k',
name: 'GPT35-16k',
maxToken: 16000,
price: 0
};
const defaultExtractModel: FunctionModelItemType = {
model: 'gpt-3.5-turbo-16k',
name: 'GPT35-16k',
maxToken: 16000,
price: 0,
prompt: '',
functionCall: true
};
const defaultCQModel: FunctionModelItemType = {
model: 'gpt-3.5-turbo-16k',
name: 'GPT35-16k',
maxToken: 16000,
price: 0,
prompt: '',
functionCall: true
};
const defaultVectorModels: VectorModelItemType[] = [
{
model: 'text-embedding-ada-002',
name: 'Embedding-2',
price: 0,
defaultToken: 500,
maxToken: 3000
}
];
export async function getInitConfig() {
try {
if (global.feConfigs) return;
getSystemVersion();
const filename =
process.env.NODE_ENV === 'development' ? 'data/config.local.json' : '/app/data/config.json';
const res = JSON.parse(readFileSync(filename, 'utf-8'));
console.log(`System Version: ${global.systemVersion}`);
console.log(res);
global.systemEnv = res.SystemParams
? { ...defaultSystemEnv, ...res.SystemParams }
: defaultSystemEnv;
global.feConfigs = res.FeConfig ? { ...defaultFeConfigs, ...res.FeConfig } : defaultFeConfigs;
global.chatModels = res.ChatModels || defaultChatModels;
global.qaModel = res.QAModel || defaultQAModel;
global.extractModel = res.ExtractModel || defaultExtractModel;
global.cqModel = res.CQModel || defaultCQModel;
global.vectorModels = res.VectorModels || defaultVectorModels;
} catch (error) {
setDefaultData();
console.log('get init config error, set default', error);
}
}
export function setDefaultData() {
global.systemEnv = defaultSystemEnv;
global.feConfigs = defaultFeConfigs;
global.chatModels = defaultChatModels;
global.qaModel = defaultQAModel;
global.vectorModels = defaultVectorModels;
}
export function getSystemVersion() {
try {
if (process.env.NODE_ENV === 'development') {
global.systemVersion = process.env.npm_package_version || '0.0.0';
return;
}
const packageJson = JSON.parse(readFileSync('/app/package.json', 'utf-8'));
global.systemVersion = packageJson?.version;
} catch (error) {
console.log(error);
global.systemVersion = '0.0.0';
}
}

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@@ -1,112 +0,0 @@
import React from 'react';
import MyModal from '@/components/MyModal';
import { useTranslation } from 'react-i18next';
import { EditFormType } from '@/utils/app';
import { useForm } from 'react-hook-form';
import {
Box,
BoxProps,
Button,
Flex,
Link,
ModalBody,
ModalFooter,
Textarea
} from '@chakra-ui/react';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import { defaultQuotePrompt, defaultQuoteTemplate } from '@/prompts/core/AIChat';
import { feConfigs } from '@/store/static';
const AIChatSettingsModal = ({
onClose,
onSuccess,
defaultData
}: {
onClose: () => void;
onSuccess: (e: EditFormType['chatModel']) => void;
defaultData: EditFormType['chatModel'];
}) => {
const { t } = useTranslation();
const { register, handleSubmit } = useForm({
defaultValues: defaultData
});
const LabelStyles: BoxProps = {
fontWeight: 'bold',
mb: 1,
fontSize: ['sm', 'md']
};
return (
<MyModal
isOpen
title={
<Flex alignItems={'flex-end'}>
{t('app.Quote Prompt Settings')}
{feConfigs?.show_doc && (
<Link
href={'https://doc.fastgpt.run/docs/use-cases/prompt/'}
target={'_blank'}
ml={1}
textDecoration={'underline'}
fontWeight={'normal'}
fontSize={'md'}
>
</Link>
)}
</Flex>
}
w={'700px'}
>
<ModalBody>
<Box>
<Box {...LabelStyles}>
<MyTooltip
label={t('template.Quote Content Tip', { default: defaultQuoteTemplate })}
forceShow
>
<QuestionOutlineIcon display={['none', 'inline']} ml={1} />
</MyTooltip>
</Box>
<Textarea
rows={4}
placeholder={t('template.Quote Content Tip', { default: defaultQuoteTemplate }) || ''}
borderColor={'myGray.100'}
{...register('quoteTemplate')}
/>
</Box>
<Box mt={4}>
<Box {...LabelStyles}>
<MyTooltip
label={t('template.Quote Prompt Tip', { default: defaultQuotePrompt })}
forceShow
>
<QuestionOutlineIcon display={['none', 'inline']} ml={1} />
</MyTooltip>
</Box>
<Textarea
rows={6}
placeholder={t('template.Quote Prompt Tip', { default: defaultQuotePrompt }) || ''}
borderColor={'myGray.100'}
{...register('quotePrompt')}
/>
</Box>
</ModalBody>
<ModalFooter>
<Button variant={'base'} onClick={onClose}>
{t('Cancel')}
</Button>
<Button ml={4} onClick={handleSubmit(onSuccess)}>
{t('Confirm')}
</Button>
</ModalFooter>
</MyModal>
);
};
export default AIChatSettingsModal;

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@@ -1,96 +0,0 @@
import React, { useEffect, useState } from 'react';
import { Box, Divider, Flex, useTheme, Button, Skeleton, useDisclosure } from '@chakra-ui/react';
import { useCopyData } from '@/hooks/useCopyData';
import dynamic from 'next/dynamic';
import MyIcon from '@/components/Icon';
import { useGlobalStore } from '@/store/global';
import { feConfigs } from '@/store/static';
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
</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={
feConfigs?.openAPIUrl ||
'https://kjqvjse66l.feishu.cn/docx/DmLedTWtUoNGX8xui9ocdUEjnNh'
}
frameBorder="0"
onLoad={() => setIsLoaded(true)}
onError={() => setIsLoaded(true)}
/>
</Skeleton>
</Box>
{isOpenAPIModal && <APIKeyModal onClose={onCloseAPIModal} />}
</Flex>
);
};
export default API;

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@@ -1,138 +0,0 @@
import React from 'react';
import { NodeProps } from 'reactflow';
import { Box, Input, Button, Flex } from '@chakra-ui/react';
import NodeCard from '../modules/NodeCard';
import { FlowModuleItemType } from '@/types/flow';
import Divider from '../modules/Divider';
import Container from '../modules/Container';
import RenderInput from '../render/RenderInput';
import type { ClassifyQuestionAgentItemType } from '@/types/app';
import { customAlphabet } from 'nanoid';
const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 4);
import MyIcon from '@/components/Icon';
import { FlowOutputItemTypeEnum, FlowValueTypeEnum, SpecialInputKeyEnum } from '@/constants/flow';
import SourceHandle from '../render/SourceHandle';
const NodeCQNode = ({ data }: NodeProps<FlowModuleItemType>) => {
const { moduleId, inputs, outputs, onChangeNode } = data;
return (
<NodeCard minW={'400px'} {...data}>
<Divider text="Input" />
<Container>
<RenderInput
moduleId={moduleId}
onChangeNode={onChangeNode}
flowInputList={inputs}
CustomComponent={{
[SpecialInputKeyEnum.agents]: ({
key: agentKey,
value: agents = [],
...props
}: {
key: string;
value?: ClassifyQuestionAgentItemType[];
}) => (
<Box>
{agents.map((item, i) => (
<Flex key={item.key} mb={4} alignItems={'center'}>
<MyIcon
mr={2}
name={'minus'}
w={'14px'}
cursor={'pointer'}
color={'myGray.600'}
_hover={{ color: 'myGray.900' }}
onClick={() => {
const newInputValue = agents.filter((input) => input.key !== item.key);
const newOutputVal = outputs.filter((output) => output.key !== item.key);
onChangeNode({
moduleId,
type: 'inputs',
key: agentKey,
value: {
...props,
key: agentKey,
value: newInputValue
}
});
onChangeNode({
moduleId,
type: 'outputs',
key: '',
value: newOutputVal
});
}}
/>
<Box flex={1}>
<Box flex={1}>{i + 1}</Box>
<Box position={'relative'}>
<Input
mt={1}
defaultValue={item.value}
onChange={(e) => {
const newVal = agents.map((val) =>
val.key === item.key
? {
...val,
value: e.target.value
}
: val
);
onChangeNode({
moduleId,
type: 'inputs',
key: agentKey,
value: {
...props,
key: agentKey,
value: newVal
}
});
}}
/>
<SourceHandle handleKey={item.key} valueType={FlowValueTypeEnum.boolean} />
</Box>
</Box>
</Flex>
))}
<Button
onClick={() => {
const key = nanoid();
const newInputValue = agents.concat({ value: '', key });
const newOutputValue = outputs.concat({
key,
label: '',
type: FlowOutputItemTypeEnum.hidden,
targets: []
});
onChangeNode({
moduleId,
type: 'inputs',
key: agentKey,
value: {
...props,
key: agentKey,
value: newInputValue
}
});
onChangeNode({
moduleId,
type: 'outputs',
key: agentKey,
value: newOutputValue
});
}}
>
</Button>
</Box>
)
}}
/>
</Container>
</NodeCard>
);
};
export default React.memo(NodeCQNode);

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@@ -1,173 +0,0 @@
import React, { useMemo } from 'react';
import { NodeProps } from 'reactflow';
import NodeCard from '../modules/NodeCard';
import { FlowModuleItemType } from '@/types/flow';
import Divider from '../modules/Divider';
import Container from '../modules/Container';
import RenderInput, { Label } from '../render/RenderInput';
import RenderOutput from '../render/RenderOutput';
import { FlowOutputItemTypeEnum } from '@/constants/flow';
import MySelect from '@/components/Select';
import { chatModelList } from '@/store/static';
import MySlider from '@/components/Slider';
import { Box, Button, Flex, useDisclosure } from '@chakra-ui/react';
import { formatPrice } from '@/utils/user';
import MyIcon from '@/components/Icon';
import dynamic from 'next/dynamic';
import { AIChatProps } from '@/types/core/aiChat';
const AIChatSettingsModal = dynamic(() => import('../../../AIChatSettingsModal'));
const NodeChat = ({ data }: NodeProps<FlowModuleItemType>) => {
const { moduleId, inputs, outputs, onChangeNode } = data;
const chatModulesData = useMemo(() => {
const obj: Record<string, any> = {};
inputs.forEach((item) => {
obj[item.key] = item.value;
});
return obj as AIChatProps;
}, [inputs]);
const {
isOpen: isOpenAIChatSetting,
onOpen: onOpenAIChatSetting,
onClose: onCloseAIChatSetting
} = useDisclosure();
return (
<NodeCard minW={'400px'} {...data}>
<Divider text="Input" />
<Container>
<RenderInput
moduleId={moduleId}
onChangeNode={onChangeNode}
flowInputList={inputs}
CustomComponent={{
model: (inputItem) => {
const list = chatModelList.map((item) => {
const priceStr = `(${formatPrice(item.price, 1000)}元/1k Tokens)`;
return {
value: item.model,
label: `${item.name}${priceStr}`
};
});
return (
<MySelect
width={'100%'}
value={inputItem.value}
list={list}
onchange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: inputItem.key,
value: {
...inputItem,
value: e
}
});
// update max tokens
const model =
chatModelList.find((item) => item.model === e) || chatModelList[0];
if (!model) return;
onChangeNode({
moduleId,
type: 'inputs',
key: 'maxToken',
value: {
...inputs.find((input) => input.key === 'maxToken'),
markList: [
{ label: '100', value: 100 },
{ label: `${model.contextMaxToken}`, value: model.contextMaxToken }
],
max: model.contextMaxToken,
value: model.contextMaxToken / 2
}
});
}}
/>
);
},
maxToken: (inputItem) => {
const model = inputs.find((item) => item.key === 'model')?.value;
const modelData = chatModelList.find((item) => item.model === model);
const maxToken = modelData ? modelData.contextMaxToken : 4000;
const markList = [
{ label: '100', value: 100 },
{ label: `${maxToken}`, value: maxToken }
];
return (
<Box pt={5} pb={4} px={2}>
<MySlider
markList={markList}
width={'100%'}
min={inputItem.min || 100}
max={maxToken}
step={inputItem.step || 1}
value={inputItem.value}
onChange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: inputItem.key,
value: {
...inputItem,
value: e
}
});
}}
/>
</Box>
);
},
quoteQA: (inputItem) => {
return (
<Button
variant={'base'}
leftIcon={<MyIcon name={'settingLight'} w={'14px'} />}
onClick={onOpenAIChatSetting}
>
</Button>
);
}
}}
/>
</Container>
<Divider text="Output" />
<Container>
<RenderOutput onChangeNode={onChangeNode} moduleId={moduleId} flowOutputList={outputs} />
</Container>
{isOpenAIChatSetting && (
<AIChatSettingsModal
onClose={onCloseAIChatSetting}
onSuccess={(e) => {
for (let key in e) {
const item = inputs.find((input) => input.key === key);
if (!item) continue;
onChangeNode({
moduleId,
type: 'inputs',
key,
value: {
...item,
// @ts-ignore
value: e[key]
}
});
}
onCloseAIChatSetting();
}}
defaultData={chatModulesData}
/>
)}
</NodeCard>
);
};
export default React.memo(NodeChat);

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@@ -1,108 +0,0 @@
import React, { useMemo } from 'react';
import { NodeProps } from 'reactflow';
import { FlowModuleItemType } from '@/types/flow';
import { Flex, Box, Button, useTheme, useDisclosure, Grid } from '@chakra-ui/react';
import { useDatasetStore } from '@/store/dataset';
import { useQuery } from '@tanstack/react-query';
import NodeCard from '../modules/NodeCard';
import Divider from '../modules/Divider';
import Container from '../modules/Container';
import RenderInput from '../render/RenderInput';
import RenderOutput from '../render/RenderOutput';
import { DatasetSelectModal } from '../../../DatasetSelectModal';
import type { SelectedDatasetType } from '@/types/core/dataset';
import Avatar from '@/components/Avatar';
const KBSelect = ({
activeKbs = [],
onChange
}: {
activeKbs: SelectedDatasetType;
onChange: (e: SelectedDatasetType) => void;
}) => {
const theme = useTheme();
const { allDatasets, loadAllDatasets } = useDatasetStore();
const {
isOpen: isOpenKbSelect,
onOpen: onOpenKbSelect,
onClose: onCloseKbSelect
} = useDisclosure();
const showKbList = useMemo(
() => allDatasets.filter((item) => activeKbs.find((kb) => kb.kbId === item._id)),
[allDatasets, activeKbs]
);
useQuery(['loadAllDatasets'], loadAllDatasets);
return (
<>
<Grid gridTemplateColumns={'1fr 1fr'} gridGap={4}>
<Button h={'36px'} onClick={onOpenKbSelect}>
</Button>
{showKbList.map((item) => (
<Flex
key={item._id}
alignItems={'center'}
h={'36px'}
border={theme.borders.base}
px={2}
borderRadius={'md'}
>
<Avatar src={item.avatar} w={'24px'}></Avatar>
<Box ml={3} fontWeight={'bold'} fontSize={['md', 'lg', 'xl']}>
{item.name}
</Box>
</Flex>
))}
</Grid>
<DatasetSelectModal
isOpen={isOpenKbSelect}
activeKbs={activeKbs}
onChange={onChange}
onClose={onCloseKbSelect}
/>
</>
);
};
const NodeKbSearch = ({ data }: NodeProps<FlowModuleItemType>) => {
const { moduleId, inputs, outputs, onChangeNode } = data;
return (
<NodeCard minW={'400px'} {...data}>
<Divider text="Input" />
<Container>
<RenderInput
moduleId={moduleId}
onChangeNode={onChangeNode}
flowInputList={inputs}
CustomComponent={{
kbList: ({ key, value, ...props }) => (
<KBSelect
activeKbs={value}
onChange={(e) => {
onChangeNode({
moduleId,
key,
type: 'inputs',
value: {
...props,
key,
value: e
}
});
}}
/>
)
}}
/>
</Container>
<Divider text="Output" />
<Container>
<RenderOutput onChangeNode={onChangeNode} moduleId={moduleId} flowOutputList={outputs} />
</Container>
</NodeCard>
);
};
export default React.memo(NodeKbSearch);

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@@ -1,26 +0,0 @@
import React from 'react';
import { NodeProps } from 'reactflow';
import { Box } from '@chakra-ui/react';
import NodeCard from '../modules/NodeCard';
import { FlowModuleItemType } from '@/types/flow';
import Container from '../modules/Container';
import { SystemInputEnum } from '@/constants/app';
import { FlowValueTypeEnum } from '@/constants/flow';
import SourceHandle from '../render/SourceHandle';
const QuestionInputNode = ({ data }: NodeProps<FlowModuleItemType>) => {
return (
<NodeCard minW={'240px'} {...data}>
<Container borderTop={'2px solid'} borderTopColor={'myGray.200'} textAlign={'end'}>
<Box position={'relative'}>
<SourceHandle
handleKey={SystemInputEnum.userChatInput}
valueType={FlowValueTypeEnum.string}
/>
</Box>
</Container>
</NodeCard>
);
};
export default React.memo(QuestionInputNode);

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@@ -1,78 +0,0 @@
import React from 'react';
import { Handle, Position, NodeProps } from 'reactflow';
import { Flex, Box } from '@chakra-ui/react';
import NodeCard from '../modules/NodeCard';
import { SystemInputEnum } from '@/constants/app';
import { FlowModuleItemType } from '@/types/flow';
import Divider from '../modules/Divider';
import Container from '../modules/Container';
import Label from '../modules/Label';
const NodeTFSwitch = ({ data }: NodeProps<FlowModuleItemType>) => {
return (
<NodeCard minW={'220px'} {...data}>
<Divider text="输入输出" />
<Container h={'100px'} py={0} px={0} display={'flex'} alignItems={'center'}>
<Box flex={1} pl={'12px'}>
<Label
required
description="接收到 false、0、null、undefined或空字符串时执行 False反之执行 True"
>
</Label>
<Handle
style={{
top: '50%',
left: '0',
transform: 'translate(-50%,-50%)',
width: '12px',
height: '12px',
background: '#9CA2A8'
}}
id={SystemInputEnum.switch}
type="target"
position={Position.Left}
onConnect={(params) => console.log('input onConnect', params)}
/>
</Box>
<Box flex={1} pr={'12px'}>
<Flex alignItems={'center'} justifyContent={'flex-end'} mb={'26px'} position={'relative'}>
<Label>True</Label>
<Handle
style={{
top: '0',
right: '-12px',
transform: 'translate(50%,5px)',
width: '12px',
height: '12px',
background: '#9CA2A8'
}}
id={'true'}
type="source"
position={Position.Right}
onConnect={(params) => console.log('handle onConnect', params)}
/>
</Flex>
<Flex alignItems={'center'} justifyContent={'flex-end'} position={'relative'}>
<Label>False</Label>
<Handle
style={{
bottom: '0',
right: '-12px',
transform: 'translate(50%,-5px)',
width: '12px',
height: '12px',
background: '#9CA2A8'
}}
id={'false'}
type="source"
position={Position.Right}
onConnect={(params) => console.log('handle onConnect', params)}
/>
</Flex>
</Box>
</Container>
</NodeCard>
);
};
export default React.memo(NodeTFSwitch);

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@@ -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></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);

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@@ -1,126 +0,0 @@
import React, { useMemo } from 'react';
import { Box, Flex } from '@chakra-ui/react';
import { ModuleTemplates } from '@/constants/flow/ModuleTemplate';
import { FlowModuleItemType, FlowModuleTemplateType } from '@/types/flow';
import type { Node, XYPosition } from 'reactflow';
import { useGlobalStore } from '@/store/global';
import Avatar from '@/components/Avatar';
import { FlowModuleTypeEnum } from '@/constants/flow';
const ModuleTemplateList = ({
nodes,
isOpen,
onAddNode,
onClose
}: {
nodes?: Node<FlowModuleItemType>[];
isOpen: boolean;
onAddNode: (e: { template: FlowModuleTemplateType; position: XYPosition }) => void;
onClose: () => void;
}) => {
const { isPc } = useGlobalStore();
const filterTemplates = useMemo(() => {
const guideModulesIndex = ModuleTemplates.findIndex((item) => item.label === '引导模块');
const guideModule: {
label: string;
list: FlowModuleTemplateType[];
} = JSON.parse(JSON.stringify(ModuleTemplates[guideModulesIndex]));
if (nodes?.find((item) => item.type === FlowModuleTypeEnum.userGuide)) {
const index = guideModule.list.findIndex(
(item) => item.flowType === FlowModuleTypeEnum.userGuide
);
guideModule.list.splice(index, 1);
}
if (nodes?.find((item) => item.type === FlowModuleTypeEnum.variable)) {
const index = guideModule.list.findIndex(
(item) => item.flowType === FlowModuleTypeEnum.variable
);
guideModule.list.splice(index, 1);
}
return [
...ModuleTemplates.slice(0, guideModulesIndex),
guideModule,
...ModuleTemplates.slice(guideModulesIndex + 1)
];
}, [nodes]);
return (
<>
<Box
zIndex={2}
display={isOpen ? 'block' : 'none'}
position={'absolute'}
top={0}
left={0}
bottom={0}
w={'360px'}
onClick={onClose}
/>
<Flex
zIndex={3}
flexDirection={'column'}
position={'absolute'}
top={'65px'}
left={0}
pb={4}
h={isOpen ? 'calc(100% - 100px)' : '0'}
w={isOpen ? ['100%', '360px'] : '0'}
bg={'white'}
boxShadow={'3px 0 20px rgba(0,0,0,0.2)'}
borderRadius={'20px'}
overflow={'hidden'}
transition={'.2s ease'}
userSelect={'none'}
>
<Box w={['100%', '330px']} py={4} px={5} fontSize={'xl'} fontWeight={'bold'}>
</Box>
<Box flex={'1 0 0'} overflow={'overlay'}>
<Box w={['100%', '330px']} mx={'auto'}>
{filterTemplates.map((item) =>
item.list.map((item) => (
<Flex
key={item.name}
alignItems={'center'}
p={5}
cursor={'pointer'}
_hover={{ bg: 'myWhite.600' }}
borderRadius={'md'}
draggable
onDragEnd={(e) => {
if (e.clientX < 360) return;
onAddNode({
template: item,
position: { x: e.clientX, y: e.clientY }
});
}}
onClick={(e) => {
if (isPc) return;
onClose();
onAddNode({
template: item,
position: { x: e.clientX, y: e.clientY }
});
}}
>
<Avatar src={item.logo} w={'34px'} objectFit={'contain'} borderRadius={'0'} />
<Box ml={5} flex={'1 0 0'}>
<Box color={'black'}>{item.name}</Box>
<Box color={'myGray.500'} fontSize={'sm'}>
{item.intro}
</Box>
</Box>
</Flex>
))
)}
</Box>
</Box>
</Flex>
</>
);
};
export default ModuleTemplateList;

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@@ -1,30 +0,0 @@
import React from 'react';
import { Box } from '@chakra-ui/react';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import MyTooltip from '@/components/MyTooltip';
const Label = ({
required = false,
children,
description
}: {
required?: boolean;
children: React.ReactNode | string;
description?: string;
}) => (
<Box as={'label'} display={'inline-block'} position={'relative'}>
{children}
{required && (
<Box position={'absolute'} top={'-2px'} right={'-10px'} color={'red.500'} fontWeight={'bold'}>
*
</Box>
)}
{description && (
<MyTooltip label={description} forceShow>
<QuestionOutlineIcon display={['none', 'inline']} fontSize={'12px'} mb={1} ml={1} />
</MyTooltip>
)}
</Box>
);
export default React.memo(Label);

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@@ -1,128 +0,0 @@
import React, { useMemo } from 'react';
import { Box, Flex, useTheme, Menu, MenuButton, MenuList, MenuItem } from '@chakra-ui/react';
import MyIcon from '@/components/Icon';
import Avatar from '@/components/Avatar';
import type { FlowModuleItemType } from '@/types/flow';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import { useTranslation } from 'react-i18next';
import { useEditTitle } from '@/hooks/useEditTitle';
import { useToast } from '@/hooks/useToast';
type Props = FlowModuleItemType & {
children?: React.ReactNode | React.ReactNode[] | string;
minW?: string | number;
};
const NodeCard = (props: Props) => {
const {
children,
logo = '/icon/logo.svg',
name = '未知模块',
description,
minW = '300px',
onCopyNode,
onDelNode,
onChangeNode,
moduleId
} = props;
const { t } = useTranslation();
const theme = useTheme();
const { toast } = useToast();
// custom title edit
const { onOpenModal, EditModal: EditTitleModal } = useEditTitle({
title: t('common.Custom Title'),
placeholder: t('app.module.Custom Title Tip') || ''
});
const menuList = useMemo(
() => [
{
icon: 'edit',
label: t('common.Rename'),
onClick: () =>
onOpenModal({
defaultVal: name,
onSuccess: (e) => {
if (!e) {
return toast({
title: t('app.modules.Title is required'),
status: 'warning'
});
}
onChangeNode({
moduleId,
type: 'attr',
key: 'name',
value: e
});
}
})
},
{
icon: 'copy',
label: t('common.Copy'),
onClick: () => onCopyNode(moduleId)
},
{
icon: 'delete',
label: t('common.Delete'),
onClick: () => onDelNode(moduleId)
},
{
icon: 'back',
label: t('common.Cancel'),
onClick: () => {}
}
],
[moduleId, onCopyNode, onDelNode, t]
);
return (
<Box minW={minW} bg={'white'} border={theme.borders.md} borderRadius={'md'} boxShadow={'sm'}>
<Flex className="custom-drag-handle" px={4} py={3} alignItems={'center'}>
<Avatar src={logo} borderRadius={'md'} objectFit={'contain'} w={'30px'} h={'30px'} />
<Box ml={3} fontSize={'lg'} color={'myGray.600'}>
{name}
</Box>
{description && (
<MyTooltip label={description} forceShow>
<QuestionOutlineIcon
display={['none', 'inline']}
transform={'translateY(1px)'}
ml={1}
/>
</MyTooltip>
)}
<Box flex={1} />
<Menu autoSelect={false} isLazy>
<MenuButton
className={'nodrag'}
_hover={{ bg: 'myWhite.600' }}
cursor={'pointer'}
borderRadius={'md'}
onClick={(e) => {
e.stopPropagation();
}}
>
<MyIcon name={'more'} w={'14px'} p={2} />
</MenuButton>
<MenuList color={'myGray.700'} minW={`120px !important`} zIndex={10}>
{menuList.map((item) => (
<MenuItem key={item.label} onClick={item.onClick} py={[2, 3]}>
<MyIcon name={item.icon as any} w={['14px', '16px']} />
<Box ml={[1, 2]}>{item.label}</Box>
</MenuItem>
))}
</MenuList>
</Menu>
</Flex>
{children}
<EditTitleModal />
</Box>
);
};
export default React.memo(NodeCard);

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@@ -1,115 +0,0 @@
import React, { useMemo, useState } from 'react';
import {
Box,
Button,
ModalHeader,
ModalFooter,
ModalBody,
Flex,
Switch,
Input,
FormControl
} from '@chakra-ui/react';
import { useForm } from 'react-hook-form';
import { customAlphabet } from 'nanoid';
const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 6);
import MyModal from '@/components/MyModal';
import Avatar from '@/components/Avatar';
import MyTooltip from '@/components/MyTooltip';
import { FlowInputItemTypeEnum, FlowValueTypeEnum } from '@/constants/flow';
import { useTranslation } from 'react-i18next';
import MySelect from '@/components/Select';
import { FlowInputItemType } from '@/types/flow';
const typeSelectList = [
{
label: '字符串',
value: FlowValueTypeEnum.string
},
{
label: '数字',
value: FlowValueTypeEnum.number
},
{
label: '布尔',
value: FlowValueTypeEnum.boolean
},
{
label: '任意',
value: FlowValueTypeEnum.any
}
];
const SetInputFieldModal = ({
defaultField = {
label: '',
key: '',
type: FlowInputItemTypeEnum.target,
valueType: FlowValueTypeEnum.string,
description: '',
required: false
},
onClose,
onSubmit
}: {
defaultField?: FlowInputItemType;
onClose: () => void;
onSubmit: (data: FlowInputItemType) => void;
}) => {
const { t } = useTranslation();
const { register, getValues, setValue, handleSubmit } = useForm<FlowInputItemType>({
defaultValues: defaultField
});
const [refresh, setRefresh] = useState(false);
return (
<MyModal isOpen={true} onClose={onClose}>
<ModalHeader display={'flex'} alignItems={'center'}>
<Avatar src={'/imgs/module/extract.png'} mr={2} w={'20px'} objectFit={'cover'} />
{t('app.Input Field Settings')}
</ModalHeader>
<ModalBody>
<Flex alignItems={'center'}>
<Box flex={'0 0 70px'}></Box>
<Switch {...register('required')} />
</Flex>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}></Box>
<MySelect
w={'288px'}
list={typeSelectList}
value={getValues('valueType')}
onchange={(e: any) => {
setValue('valueType', e);
setRefresh(!refresh);
}}
/>
</Flex>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}></Box>
<Input
placeholder="预约字段/sql语句……"
{...register('label', { required: '字段名不能为空' })}
/>
</Flex>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}> key</Box>
<Input
placeholder="appointment/sql"
{...register('key', { required: '字段 key 不能为空' })}
/>
</Flex>
</ModalBody>
<ModalFooter>
<Button variant={'base'} mr={3} onClick={onClose}>
</Button>
<Button onClick={handleSubmit(onSubmit)}></Button>
</ModalFooter>
</MyModal>
);
};
export default React.memo(SetInputFieldModal);

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@@ -1,105 +0,0 @@
import React, { useMemo, useState } from 'react';
import {
Box,
Button,
ModalHeader,
ModalFooter,
ModalBody,
Flex,
Switch,
Input,
FormControl
} from '@chakra-ui/react';
import type { ContextExtractAgentItemType, HttpFieldItemType } from '@/types/app';
import { useForm } from 'react-hook-form';
import { customAlphabet } from 'nanoid';
const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 6);
import MyModal from '@/components/MyModal';
import Avatar from '@/components/Avatar';
import MyTooltip from '@/components/MyTooltip';
import { FlowOutputItemTypeEnum, FlowValueTypeEnum, FlowValueTypeStyle } from '@/constants/flow';
import { useTranslation } from 'react-i18next';
import MySelect from '@/components/Select';
import { FlowOutputItemType } from '@/types/flow';
const typeSelectList = [
{
label: '字符串',
value: FlowValueTypeEnum.string
},
{
label: '数字',
value: FlowValueTypeEnum.number
},
{
label: '布尔',
value: FlowValueTypeEnum.boolean
},
{
label: '任意',
value: FlowValueTypeEnum.any
}
];
const SetInputFieldModal = ({
defaultField,
onClose,
onSubmit
}: {
defaultField: FlowOutputItemType;
onClose: () => void;
onSubmit: (data: FlowOutputItemType) => void;
}) => {
const { t } = useTranslation();
const { register, getValues, setValue, handleSubmit } = useForm<FlowOutputItemType>({
defaultValues: defaultField
});
const [refresh, setRefresh] = useState(false);
return (
<MyModal isOpen={true} onClose={onClose}>
<ModalHeader display={'flex'} alignItems={'center'}>
<Avatar src={'/imgs/module/extract.png'} mr={2} w={'20px'} objectFit={'cover'} />
{t('app.Output Field Settings')}
</ModalHeader>
<ModalBody>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}></Box>
<MySelect
w={'288px'}
list={typeSelectList}
value={getValues('valueType')}
onchange={(e: any) => {
setValue('valueType', e);
setRefresh(!refresh);
}}
/>
</Flex>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}></Box>
<Input
placeholder="预约字段/sql语句……"
{...register('label', { required: '字段名不能为空' })}
/>
</Flex>
<Flex mt={5} alignItems={'center'}>
<Box flex={'0 0 70px'}> key</Box>
<Input
placeholder="appointment/sql"
{...register('key', { required: '字段 key 不能为空' })}
/>
</Flex>
</ModalBody>
<ModalFooter>
<Button variant={'base'} mr={3} onClick={onClose}>
</Button>
<Button onClick={handleSubmit(onSubmit)}></Button>
</ModalFooter>
</MyModal>
);
};
export default React.memo(SetInputFieldModal);

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@@ -1,272 +0,0 @@
import React, { useState } from 'react';
import type { FlowInputItemType, FlowModuleItemType } from '@/types/flow';
import {
Box,
Textarea,
Input,
NumberInput,
NumberInputField,
NumberInputStepper,
NumberIncrementStepper,
NumberDecrementStepper,
Flex
} from '@chakra-ui/react';
import { FlowInputItemTypeEnum } from '@/constants/flow';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import dynamic from 'next/dynamic';
import MySelect from '@/components/Select';
import MySlider from '@/components/Slider';
import MyTooltip from '@/components/MyTooltip';
import TargetHandle from './TargetHandle';
import MyIcon from '@/components/Icon';
const SetInputFieldModal = dynamic(() => import('../modules/SetInputFieldModal'));
export const Label = ({
moduleId,
inputKey,
onChangeNode,
...item
}: FlowInputItemType & {
moduleId: string;
inputKey: string;
onChangeNode: FlowModuleItemType['onChangeNode'];
}) => {
const { required = false, description, edit, label, type, valueType } = item;
const [editField, setEditField] = useState<FlowInputItemType>();
return (
<Flex className="nodrag" cursor={'default'} alignItems={'center'} position={'relative'}>
<Box position={'relative'}>
{label}
{description && (
<MyTooltip label={description} forceShow>
<QuestionOutlineIcon display={['none', 'inline']} ml={1} />
</MyTooltip>
)}
{required && (
<Box
position={'absolute'}
top={'-2px'}
right={'-8px'}
color={'red.500'}
fontWeight={'bold'}
>
*
</Box>
)}
</Box>
{(type === FlowInputItemTypeEnum.target || valueType) && (
<TargetHandle handleKey={inputKey} valueType={valueType} />
)}
{edit && (
<>
<MyIcon
name={'settingLight'}
w={'14px'}
cursor={'pointer'}
ml={3}
_hover={{ color: 'myBlue.600' }}
onClick={() =>
setEditField({
...item,
key: inputKey
})
}
/>
<MyIcon
className="delete"
name={'delete'}
w={'14px'}
cursor={'pointer'}
ml={2}
_hover={{ color: 'red.500' }}
onClick={() => {
onChangeNode({
moduleId,
type: 'delInput',
key: inputKey,
value: ''
});
}}
/>
</>
)}
{!!editField && (
<SetInputFieldModal
defaultField={editField}
onClose={() => setEditField(undefined)}
onSubmit={(data) => {
// same key
if (editField.key === data.key) {
onChangeNode({
moduleId,
type: 'inputs',
key: inputKey,
value: data
});
} else {
// diff key. del and add
onChangeNode({
moduleId,
type: 'addInput',
key: data.key,
value: data
});
setTimeout(() => {
onChangeNode({
moduleId,
type: 'delInput',
key: editField.key,
value: ''
});
});
}
setEditField(undefined);
}}
/>
)}
</Flex>
);
};
const RenderInput = ({
flowInputList,
moduleId,
CustomComponent = {},
onChangeNode
}: {
flowInputList: FlowInputItemType[];
moduleId: string;
CustomComponent?: Record<string, (e: FlowInputItemType) => React.ReactNode>;
onChangeNode: FlowModuleItemType['onChangeNode'];
}) => {
return (
<>
{flowInputList.map(
(item) =>
item.type !== FlowInputItemTypeEnum.hidden && (
<Box key={item.key} _notLast={{ mb: 7 }} position={'relative'}>
{!!item.label && (
<Label
moduleId={moduleId}
onChangeNode={onChangeNode}
inputKey={item.key}
{...item}
/>
)}
<Box mt={2} className={'nodrag'}>
{item.type === FlowInputItemTypeEnum.numberInput && (
<NumberInput
defaultValue={item.value}
min={item.min}
max={item.max}
onChange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: item.key,
value: {
...item,
value: Number(e)
}
});
}}
>
<NumberInputField />
<NumberInputStepper>
<NumberIncrementStepper />
<NumberDecrementStepper />
</NumberInputStepper>
</NumberInput>
)}
{item.type === FlowInputItemTypeEnum.input && (
<Input
placeholder={item.placeholder}
defaultValue={item.value}
onChange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: item.key,
value: {
...item,
value: e.target.value
}
});
}}
/>
)}
{item.type === FlowInputItemTypeEnum.textarea && (
<Textarea
rows={5}
placeholder={item.placeholder}
resize={'both'}
defaultValue={item.value}
onChange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: item.key,
value: {
...item,
value: e.target.value
}
});
}}
/>
)}
{item.type === FlowInputItemTypeEnum.select && (
<MySelect
width={'100%'}
value={item.value}
list={item.list || []}
onchange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: item.key,
value: {
...item,
value: e
}
});
}}
/>
)}
{item.type === FlowInputItemTypeEnum.slider && (
<Box pt={5} pb={4} px={2}>
<MySlider
markList={item.markList}
width={'100%'}
min={item.min || 0}
max={item.max}
step={item.step || 1}
value={item.value}
onChange={(e) => {
onChangeNode({
moduleId,
type: 'inputs',
key: item.key,
value: {
...item,
value: e
}
});
}}
/>
</Box>
)}
{item.type === FlowInputItemTypeEnum.custom && CustomComponent[item.key] && (
<>{CustomComponent[item.key]({ ...item })}</>
)}
</Box>
</Box>
)
)}
</>
);
};
export default React.memo(RenderInput);

View File

@@ -1,5 +0,0 @@
.panel {
.react-flow__panel {
display: none;
}
}

View File

@@ -1,613 +0,0 @@
import React, { useCallback, useEffect, useRef, useState } from 'react';
import ReactFlow, {
Background,
Controls,
ReactFlowProvider,
addEdge,
useNodesState,
useEdgesState,
XYPosition,
Connection,
useViewport
} from 'reactflow';
import { Box, Flex, IconButton, useTheme, useDisclosure } from '@chakra-ui/react';
import { SmallCloseIcon } from '@chakra-ui/icons';
import {
edgeOptions,
connectionLineStyle,
FlowModuleTypeEnum,
FlowInputItemTypeEnum,
FlowValueTypeEnum
} from '@/constants/flow';
import { appModule2FlowNode, appModule2FlowEdge } from '@/utils/adapt';
import {
FlowModuleItemType,
FlowModuleTemplateType,
FlowOutputTargetItemType,
type FlowModuleItemChangeProps
} from '@/types/flow';
import { AppModuleItemType } from '@/types/app';
import { customAlphabet } from 'nanoid';
import { useRequest } from '@/hooks/useRequest';
import type { AppSchema } from '@/types/mongoSchema';
import { useUserStore } from '@/store/user';
import { useToast } from '@/hooks/useToast';
import { useTranslation } from 'next-i18next';
import { useCopyData } from '@/hooks/useCopyData';
import dynamic from 'next/dynamic';
import MyIcon from '@/components/Icon';
import ButtonEdge from './components/modules/ButtonEdge';
import MyTooltip from '@/components/MyTooltip';
import TemplateList from './components/TemplateList';
import ChatTest, { type ChatTestComponentRef } from './components/ChatTest';
const ImportSettings = dynamic(() => import('./components/ImportSettings'), {
ssr: false
});
const NodeChat = dynamic(() => import('./components/Nodes/NodeChat'), {
ssr: false
});
const NodeKbSearch = dynamic(() => import('./components/Nodes/NodeKbSearch'), {
ssr: false
});
const NodeHistory = dynamic(() => import('./components/Nodes/NodeHistory'), {
ssr: false
});
const NodeTFSwitch = dynamic(() => import('./components/Nodes/NodeTFSwitch'), {
ssr: false
});
const NodeAnswer = dynamic(() => import('./components/Nodes/NodeAnswer'), {
ssr: false
});
const NodeQuestionInput = dynamic(() => import('./components/Nodes/NodeQuestionInput'), {
ssr: false
});
const NodeCQNode = dynamic(() => import('./components/Nodes/NodeCQNode'), {
ssr: false
});
const NodeVariable = dynamic(() => import('./components/Nodes/NodeVariable'), {
ssr: false
});
const NodeUserGuide = dynamic(() => import('./components/Nodes/NodeUserGuide'), {
ssr: false
});
const NodeExtract = dynamic(() => import('./components/Nodes/NodeExtract'), {
ssr: false
});
const NodeHttp = dynamic(() => import('./components/Nodes/NodeHttp'), {
ssr: false
});
import 'reactflow/dist/style.css';
import styles from './index.module.scss';
import { AppTypeEnum } from '@/constants/app';
const nanoid = customAlphabet('abcdefghijklmnopqrstuvwxyz1234567890', 6);
const nodeTypes = {
[FlowModuleTypeEnum.userGuide]: NodeUserGuide,
[FlowModuleTypeEnum.variable]: NodeVariable,
[FlowModuleTypeEnum.questionInput]: NodeQuestionInput,
[FlowModuleTypeEnum.historyNode]: NodeHistory,
[FlowModuleTypeEnum.chatNode]: NodeChat,
[FlowModuleTypeEnum.kbSearchNode]: NodeKbSearch,
[FlowModuleTypeEnum.tfSwitchNode]: NodeTFSwitch,
[FlowModuleTypeEnum.answerNode]: NodeAnswer,
[FlowModuleTypeEnum.classifyQuestion]: NodeCQNode,
[FlowModuleTypeEnum.contentExtract]: NodeExtract,
[FlowModuleTypeEnum.httpRequest]: NodeHttp
// [FlowModuleTypeEnum.empty]: EmptyModule
};
const edgeTypes = {
buttonedge: ButtonEdge
};
type Props = { app: AppSchema; onCloseSettings: () => void };
const AppEdit = ({ app, onCloseSettings }: Props) => {
const theme = useTheme();
const { toast } = useToast();
const { t } = useTranslation();
const { copyData } = useCopyData();
const reactFlowWrapper = useRef<HTMLDivElement>(null);
const ChatTestRef = useRef<ChatTestComponentRef>(null);
const { updateAppDetail } = useUserStore();
const { x, y, zoom } = useViewport();
const [nodes, setNodes, onNodesChange] = useNodesState<FlowModuleItemType>([]);
const [edges, setEdges, onEdgesChange] = useEdgesState([]);
const {
isOpen: isOpenTemplate,
onOpen: onOpenTemplate,
onClose: onCloseTemplate
} = useDisclosure();
const { isOpen: isOpenImport, onOpen: onOpenImport, onClose: onCloseImport } = useDisclosure();
const [testModules, setTestModules] = useState<AppModuleItemType[]>();
const onFixView = useCallback(() => {
const btn = document.querySelector('.react-flow__controls-fitview') as HTMLButtonElement;
setTimeout(() => {
btn && btn.click();
}, 100);
}, []);
const onAddNode = useCallback(
({ template, position }: { template: FlowModuleTemplateType; position: XYPosition }) => {
if (!reactFlowWrapper.current) return;
const reactFlowBounds = reactFlowWrapper.current.getBoundingClientRect();
const mouseX = (position.x - reactFlowBounds.left - x) / zoom - 100;
const mouseY = (position.y - reactFlowBounds.top - y) / zoom;
setNodes((state) =>
state.concat(
appModule2FlowNode({
item: {
...template,
moduleId: nanoid(),
position: { x: mouseX, y: mouseY }
},
onChangeNode,
onDelNode,
onDelEdge,
onCopyNode,
onCollectionNode
})
)
);
},
[x, zoom, y]
);
const onDelNode = useCallback(
(nodeId: string) => {
setNodes((state) => state.filter((item) => item.id !== nodeId));
setEdges((state) => state.filter((edge) => edge.source !== nodeId && edge.target !== nodeId));
},
[setEdges, setNodes]
);
const onDelEdge = useCallback(
({
moduleId,
sourceHandle,
targetHandle
}: {
moduleId: string;
sourceHandle?: string;
targetHandle?: string;
}) => {
if (!sourceHandle && !targetHandle) return;
setEdges((state) =>
state.filter((edge) => {
if (edge.source === moduleId && edge.sourceHandle === sourceHandle) return false;
if (edge.target === moduleId && edge.targetHandle === targetHandle) return false;
return true;
})
);
},
[setEdges]
);
const onCopyNode = useCallback(
(nodeId: string) => {
setNodes((nodes) => {
const node = nodes.find((node) => node.id === nodeId);
if (!node) return nodes;
const template = {
logo: node.data.logo,
name: node.data.name,
intro: node.data.intro,
description: node.data.description,
flowType: node.data.flowType,
inputs: node.data.inputs,
outputs: node.data.outputs,
showStatus: node.data.showStatus
};
return nodes.concat(
appModule2FlowNode({
item: {
...template,
moduleId: nanoid(),
position: { x: node.position.x + 200, y: node.position.y + 50 }
},
onChangeNode,
onDelNode,
onDelEdge,
onCopyNode,
onCollectionNode
})
);
});
},
[setNodes]
);
const onCollectionNode = useCallback(
(nodeId: string) => {
console.log(nodes.find((node) => node.id === nodeId));
},
[nodes]
);
const flow2AppModules = useCallback(() => {
const modules: AppModuleItemType[] = nodes.map((item) => ({
moduleId: item.data.moduleId,
name: item.data.name,
flowType: item.data.flowType,
showStatus: item.data.showStatus,
position: item.position,
inputs: item.data.inputs.map((item) => ({
...item,
connected: item.connected ?? item.type !== FlowInputItemTypeEnum.target
})),
outputs: item.data.outputs.map((item) => ({
...item,
targets: [] as FlowOutputTargetItemType[]
}))
}));
// update inputs and outputs
modules.forEach((module) => {
module.inputs.forEach((input) => {
input.connected =
input.connected ||
!!edges.find(
(edge) => edge.target === module.moduleId && edge.targetHandle === input.key
);
});
module.outputs.forEach((output) => {
output.targets = edges
.filter(
(edge) =>
edge.source === module.moduleId &&
edge.sourceHandle === output.key &&
edge.targetHandle
)
.map((edge) => ({
moduleId: edge.target,
key: edge.targetHandle || ''
}));
});
});
return modules;
}, [edges, nodes]);
const onChangeNode = useCallback(
({ moduleId, key, type = 'inputs', value }: FlowModuleItemChangeProps) => {
setNodes((nodes) =>
nodes.map((node) => {
if (node.id !== moduleId) return node;
const updateObj: Record<string, any> = {};
if (type === 'inputs') {
updateObj.inputs = node.data.inputs.map((item) => (item.key === key ? value : item));
} else if (type === 'addInput') {
const input = node.data.inputs.find((input) => input.key === value.key);
if (input) {
toast({
status: 'warning',
title: 'key 重复'
});
updateObj.inputs = node.data.inputs;
} else {
updateObj.inputs = node.data.inputs.concat(value);
}
} else if (type === 'delInput') {
onDelEdge({ moduleId, targetHandle: key });
updateObj.inputs = node.data.inputs.filter((item) => item.key !== key);
} else if (type === 'attr') {
updateObj[key] = value;
} else if (type === 'outputs') {
// del output connect
const delOutputs = node.data.outputs.filter(
(item) => !value.find((output: FlowOutputTargetItemType) => output.key === item.key)
);
delOutputs.forEach((output) => {
onDelEdge({ moduleId, sourceHandle: output.key });
});
updateObj.outputs = value;
}
return {
...node,
data: {
...node.data,
...updateObj
}
};
})
);
},
[]
);
const onDelConnect = useCallback((id: string) => {
setEdges((state) => state.filter((item) => item.id !== id));
}, []);
const onConnect = useCallback(
({ connect }: { connect: Connection }) => {
const source = nodes.find((node) => node.id === connect.source)?.data;
const sourceType = (() => {
if (source?.flowType === FlowModuleTypeEnum.classifyQuestion) {
return FlowValueTypeEnum.boolean;
}
return source?.outputs.find((output) => output.key === connect.sourceHandle)?.valueType;
})();
const targetType = nodes
.find((node) => node.id === connect.target)
?.data?.inputs.find((input) => input.key === connect.targetHandle)?.valueType;
if (!sourceType || !targetType) {
return toast({
status: 'warning',
title: t('app.Connection is invalid')
});
}
if (
sourceType !== FlowValueTypeEnum.any &&
targetType !== FlowValueTypeEnum.any &&
sourceType !== targetType
) {
return toast({
status: 'warning',
title: t('app.Connection type is different')
});
}
setEdges((state) =>
addEdge(
{
...connect,
type: 'buttonedge',
animated: true,
data: {
onDelete: onDelConnect
}
},
state
)
);
},
[nodes]
);
const { mutate: onclickSave, isLoading } = useRequest({
mutationFn: () => {
return updateAppDetail(app._id, {
modules: flow2AppModules(),
type: AppTypeEnum.advanced
});
},
successToast: '保存配置成功',
errorToast: '保存配置异常',
onSuccess() {
ChatTestRef.current?.resetChatTest();
}
});
const initData = useCallback(
(modules: AppModuleItemType[]) => {
const edges = appModule2FlowEdge({
modules,
onDelete: onDelConnect
});
setEdges(edges);
setNodes(
modules.map((item) =>
appModule2FlowNode({
item,
onChangeNode,
onDelNode,
onDelEdge,
onCopyNode,
onCollectionNode
})
)
);
onFixView();
},
[
onDelConnect,
setEdges,
setNodes,
onFixView,
onChangeNode,
onDelNode,
onDelEdge,
onCopyNode,
onCollectionNode
]
);
useEffect(() => {
initData(JSON.parse(JSON.stringify(app.modules)));
}, [app.modules]);
return (
<>
{/* header */}
<Flex
py={3}
px={[2, 5, 8]}
borderBottom={theme.borders.base}
alignItems={'center'}
userSelect={'none'}
>
<MyTooltip label={'返回'} offset={[10, 10]}>
<IconButton
size={'sm'}
icon={<MyIcon name={'back'} w={'14px'} />}
borderRadius={'md'}
borderColor={'myGray.300'}
variant={'base'}
aria-label={''}
onClick={() => {
onCloseSettings();
onFixView();
}}
/>
</MyTooltip>
<Box ml={[3, 6]} fontSize={['md', '2xl']} flex={1}>
{app.name}
</Box>
<MyTooltip label={t('app.Import Configs')}>
<IconButton
mr={[3, 6]}
icon={<MyIcon name={'importLight'} w={['14px', '16px']} />}
borderRadius={'lg'}
variant={'base'}
aria-label={'save'}
onClick={onOpenImport}
/>
</MyTooltip>
<MyTooltip label={t('app.Export Configs')}>
<IconButton
mr={[3, 6]}
icon={<MyIcon name={'export'} w={['14px', '16px']} />}
borderRadius={'lg'}
variant={'base'}
aria-label={'save'}
onClick={() =>
copyData(
JSON.stringify(flow2AppModules(), null, 2),
t('app.Export Config Successful')
)
}
/>
</MyTooltip>
{testModules ? (
<IconButton
mr={[3, 6]}
icon={<SmallCloseIcon fontSize={'25px'} />}
variant={'base'}
color={'myGray.600'}
borderRadius={'lg'}
aria-label={''}
onClick={() => setTestModules(undefined)}
/>
) : (
<MyTooltip label={'测试对话'}>
<IconButton
mr={[3, 6]}
icon={<MyIcon name={'chat'} w={['14px', '16px']} />}
borderRadius={'lg'}
aria-label={'save'}
variant={'base'}
onClick={() => {
setTestModules(flow2AppModules());
}}
/>
</MyTooltip>
)}
<MyTooltip label={'保存配置'}>
<IconButton
icon={<MyIcon name={'save'} w={['14px', '16px']} />}
borderRadius={'lg'}
isLoading={isLoading}
aria-label={'save'}
onClick={onclickSave}
/>
</MyTooltip>
</Flex>
<Box
minH={'400px'}
flex={'1 0 0'}
w={'100%'}
h={0}
position={'relative'}
onContextMenu={(e) => {
e.preventDefault();
return false;
}}
>
{/* open module template */}
<IconButton
position={'absolute'}
top={5}
left={5}
w={'38px'}
h={'38px'}
borderRadius={'50%'}
icon={<SmallCloseIcon fontSize={'26px'} />}
transform={isOpenTemplate ? '' : 'rotate(135deg)'}
transition={'0.2s ease'}
aria-label={''}
zIndex={1}
boxShadow={'2px 2px 6px #85b1ff'}
onClick={() => {
isOpenTemplate ? onCloseTemplate() : onOpenTemplate();
}}
/>
<ReactFlow
ref={reactFlowWrapper}
className={styles.panel}
fitView
nodes={nodes}
edges={edges}
minZoom={0.1}
maxZoom={1.5}
defaultEdgeOptions={edgeOptions}
connectionLineStyle={connectionLineStyle}
nodeTypes={nodeTypes}
edgeTypes={edgeTypes}
onNodesChange={onNodesChange}
onEdgesChange={onEdgesChange}
onConnect={(connect) => {
connect.sourceHandle &&
connect.targetHandle &&
onConnect({
connect
});
}}
>
<Background />
<Controls position={'bottom-right'} style={{ display: 'flex' }} showInteractive={false} />
</ReactFlow>
<TemplateList
isOpen={isOpenTemplate}
nodes={nodes}
onAddNode={onAddNode}
onClose={onCloseTemplate}
/>
<ChatTest
ref={ChatTestRef}
modules={testModules}
app={app}
onClose={() => setTestModules(undefined)}
/>
</Box>
{isOpenImport && (
<ImportSettings
onClose={onCloseImport}
onSuccess={(data) => {
setEdges([]);
setNodes([]);
setTimeout(() => {
initData(data);
}, 10);
}}
/>
)}
</>
);
};
const Flow = (data: Props) => (
<Box h={'100%'} position={'fixed'} zIndex={999} top={0} left={0} right={0} bottom={0}>
<ReactFlowProvider>
<Flex h={'100%'} flexDirection={'column'} bg={'#fff'}>
{!!data.app._id && <AppEdit {...data} />}
</Flex>
</ReactFlowProvider>
</Box>
);
export default React.memo(Flow);

View File

@@ -1,300 +0,0 @@
import React, { useMemo, useState } from 'react';
import {
Card,
Flex,
Box,
Button,
ModalBody,
ModalFooter,
useTheme,
Textarea,
Grid,
Divider
} from '@chakra-ui/react';
import Avatar from '@/components/Avatar';
import { useForm } from 'react-hook-form';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import type { SelectedDatasetType } from '@/types/core/dataset';
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';
import { KbTypeEnum } from '@/constants/dataset';
import { useTranslation } from 'react-i18next';
import { useQuery } from '@tanstack/react-query';
import { useDatasetStore } from '@/store/dataset';
import { feConfigs } from '@/store/static';
import DatasetSelectContainer, { useDatasetSelect } from '@/components/core/dataset/SelectModal';
export type KbParamsType = {
searchSimilarity: number;
searchLimit: number;
searchEmptyText: string;
};
export const DatasetSelectModal = ({
isOpen,
activeKbs = [],
onChange,
onClose
}: {
isOpen: boolean;
activeKbs: SelectedDatasetType;
onChange: (e: SelectedDatasetType) => void;
onClose: () => void;
}) => {
const { t } = useTranslation();
const theme = useTheme();
const [selectedKbList, setSelectedKbList] = useState<SelectedDatasetType>(activeKbs);
const { toast } = useToast();
const { paths, parentId, setParentId, datasets } = useDatasetSelect();
const { allDatasets, loadAllDatasets } = useDatasetStore();
useQuery(['loadAllDatasets'], loadAllDatasets);
const filterKbList = useMemo(() => {
return {
selected: allDatasets.filter((item) => selectedKbList.find((kb) => kb.kbId === item._id)),
unSelected: datasets.filter((item) => !selectedKbList.find((kb) => kb.kbId === item._id))
};
}, [datasets, allDatasets, selectedKbList]);
return (
<DatasetSelectContainer
isOpen={isOpen}
paths={paths}
parentId={parentId}
setParentId={setParentId}
tips={'仅能选择同一个索引模型的知识库'}
onClose={onClose}
>
<ModalBody flex={['1 0 0', '0 0 auto']} maxH={'80vh'} overflowY={'auto'} userSelect={'none'}>
<Grid gridTemplateColumns={['repeat(1,1fr)', 'repeat(2,1fr)', 'repeat(3,1fr)']} gridGap={3}>
{filterKbList.selected.map((item) =>
(() => {
return (
<Card
key={item._id}
p={3}
border={theme.borders.base}
boxShadow={'sm'}
bg={'myBlue.300'}
>
<Flex alignItems={'center'} h={'38px'}>
<Avatar src={item.avatar} w={['24px', '28px']}></Avatar>
<Box flex={'1 0 0'} mx={3}>
{item.name}
</Box>
<MyIcon
name={'delete'}
w={'14px'}
cursor={'pointer'}
_hover={{ color: 'red.500' }}
onClick={() => {
setSelectedKbList((state) => state.filter((kb) => kb.kbId !== item._id));
}}
/>
</Flex>
</Card>
);
})()
)}
</Grid>
{filterKbList.selected.length > 0 && <Divider my={3} />}
<Grid gridTemplateColumns={['repeat(1,1fr)', 'repeat(2,1fr)', 'repeat(3,1fr)']} gridGap={3}>
{filterKbList.unSelected.map((item) =>
(() => {
return (
<MyTooltip
key={item._id}
label={
item.type === KbTypeEnum.dataset
? t('kb.Select Dataset')
: t('kb.Select Folder')
}
>
<Card
p={3}
border={theme.borders.base}
boxShadow={'sm'}
h={'80px'}
cursor={'pointer'}
_hover={{
boxShadow: 'md'
}}
onClick={() => {
if (item.type === KbTypeEnum.folder) {
setParentId(item._id);
} else if (item.type === KbTypeEnum.dataset) {
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']}></Avatar>
<Box
className="textEllipsis"
ml={3}
fontWeight={'bold'}
fontSize={['md', 'lg', 'xl']}
>
{item.name}
</Box>
</Flex>
<Flex justifyContent={'flex-end'} alignItems={'center'} fontSize={'sm'}>
{item.type === KbTypeEnum.folder ? (
<Box color={'myGray.500'}>{t('Folder')}</Box>
) : (
<>
<MyIcon mr={1} name="kbTest" w={'12px'} />
<Box color={'myGray.500'}>{item.vectorModel.name}</Box>
</>
)}
</Flex>
</Card>
</MyTooltip>
);
})()
)}
</Grid>
{filterKbList.unSelected.length === 0 && (
<Flex mt={5} flexDirection={'column'} alignItems={'center'}>
<MyIcon name="empty" w={'48px'} h={'48px'} color={'transparent'} />
<Box mt={2} color={'myGray.500'}>
西~
</Box>
</Flex>
)}
</ModalBody>
<ModalFooter>
<Button
onClick={() => {
// filter out the kb that is not in the kList
const filterKbList = selectedKbList.filter((kb) => {
return allDatasets.find((item) => item._id === kb.kbId);
});
onClose();
onChange(filterKbList);
}}
>
</Button>
</ModalFooter>
</DatasetSelectContainer>
);
};
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]}>
<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]}>
</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]}>
</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${feConfigs?.systemTitle} 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}>
</Button>
<Button
onClick={() => {
onClose();
handleSubmit(onChange)();
}}
>
</Button>
</ModalFooter>
</Flex>
</MyModal>
);
};
export default DatasetSelectModal;

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@@ -1,7 +0,0 @@
.intro {
display: -webkit-box;
-webkit-line-clamp: 3;
-webkit-box-orient: vertical;
overflow: hidden;
text-overflow: ellipsis;
}

View File

@@ -1,7 +0,0 @@
.intro {
display: -webkit-box;
-webkit-line-clamp: 3;
-webkit-box-orient: vertical;
overflow: hidden;
text-overflow: ellipsis;
}

View File

@@ -1,341 +0,0 @@
import React, { useCallback, useState, useRef, useMemo } from 'react';
import {
Box,
Flex,
TableContainer,
Table,
Thead,
Tr,
Th,
Td,
Tbody,
Image,
MenuButton
} from '@chakra-ui/react';
import { getTrainingData } from '@/api/core/dataset/data';
import { getDatasetFiles, delDatasetFileById, updateDatasetFile } from '@/api/core/dataset/file';
import { useQuery } from '@tanstack/react-query';
import { debounce } from 'lodash';
import { formatFileSize } from '@/utils/tools';
import { useConfirm } from '@/hooks/useConfirm';
import { useTranslation } from 'react-i18next';
import MyIcon from '@/components/Icon';
import MyInput from '@/components/MyInput';
import dayjs from 'dayjs';
import { fileImgs } from '@/constants/common';
import { useRequest } from '@/hooks/useRequest';
import { useLoading } from '@/hooks/useLoading';
import { FileStatusEnum, OtherFileId } from '@/constants/dataset';
import { useRouter } from 'next/router';
import { usePagination } from '@/hooks/usePagination';
import type { DatasetFileItemType } from '@/types/core/dataset/file';
import { useGlobalStore } from '@/store/global';
import MyMenu from '@/components/MyMenu';
import { useEditTitle } from '@/hooks/useEditTitle';
const FileCard = ({ kbId }: { kbId: string }) => {
const BoxRef = useRef<HTMLDivElement>(null);
const lastSearch = useRef('');
const router = useRouter();
const { t } = useTranslation();
const { Loading } = useLoading();
const [searchText, setSearchText] = useState('');
const { setLoading } = useGlobalStore();
const { openConfirm, ConfirmModal } = useConfirm({
content: t('kb.Confirm to delete the file')
});
const {
data: files,
Pagination,
total,
getData,
isLoading,
pageNum,
pageSize
} = usePagination<DatasetFileItemType>({
api: getDatasetFiles,
pageSize: 20,
params: {
kbId,
searchText
},
onChange() {
if (BoxRef.current) {
BoxRef.current.scrollTop = 0;
}
}
});
// change search
const debounceRefetch = useCallback(
debounce(() => {
getData(1);
lastSearch.current = searchText;
}, 300),
[]
);
// add file icon
const formatFiles = useMemo(
() =>
files.map((file) => ({
...file,
icon: fileImgs.find((item) => new RegExp(item.suffix, 'gi').test(file.filename))?.src
})),
[files]
);
const { mutate: onDeleteFile } = useRequest({
mutationFn: (fileId: string) => {
setLoading(true);
return delDatasetFileById({
fileId,
kbId
});
},
onSuccess() {
getData(pageNum);
},
onSettled() {
setLoading(false);
},
successToast: t('common.Delete Success'),
errorToast: t('common.Delete Failed')
});
const { mutate: onUpdateFilename } = useRequest({
mutationFn: (data: { id: string; name: string }) => {
setLoading(true);
return updateDatasetFile(data);
},
onSuccess() {
getData(pageNum);
},
onSettled() {
setLoading(false);
},
successToast: t('common.Delete Success'),
errorToast: t('common.Delete Failed')
});
const { onOpenModal, EditModal: EditTitleModal } = useEditTitle({
title: t('Rename')
});
const statusMap = {
[FileStatusEnum.embedding]: {
color: 'myGray.500',
text: t('file.Embedding')
},
[FileStatusEnum.ready]: {
color: 'green.500',
text: t('file.Ready')
}
};
// training data
const { data: { qaListLen = 0, vectorListLen = 0 } = {}, refetch: refetchTrainingData } =
useQuery(['getModelSplitDataList', kbId], () => getTrainingData({ kbId, init: false }), {
onError(err) {
console.log(err);
}
});
useQuery(
['refetchTrainingData', kbId],
() => Promise.all([refetchTrainingData(), getData(pageNum)]),
{
refetchInterval: 8000,
enabled: qaListLen > 0 || vectorListLen > 0
}
);
return (
<Box ref={BoxRef} py={[1, 5]} h={'100%'} overflow={'overlay'}>
<Flex justifyContent={'space-between'} px={[2, 5]}>
<Box>
<Box fontWeight={'bold'} fontSize={['md', 'lg']} mr={2}>
{t('kb.Files', { total })}
</Box>
<Box as={'span'} fontSize={'sm'}>
{(qaListLen > 0 || vectorListLen > 0) && (
<>
({qaListLen > 0 ? `${qaListLen}条数据正在拆分,` : ''}
{vectorListLen > 0 ? `${vectorListLen}条数据正在生成索引,` : ''}
... )
</>
)}
</Box>
</Box>
<Flex alignItems={'center'}>
<MyInput
leftIcon={
<MyIcon name="searchLight" position={'absolute'} w={'14px'} color={'myGray.500'} />
}
w={['100%', '250px']}
size={['sm', 'md']}
placeholder={t('common.Search') || ''}
value={searchText}
onChange={(e) => {
setSearchText(e.target.value);
debounceRefetch();
}}
onBlur={() => {
if (searchText === lastSearch.current) return;
getData(1);
}}
onKeyDown={(e) => {
if (searchText === lastSearch.current) return;
if (e.key === 'Enter') {
getData(1);
}
}}
/>
</Flex>
</Flex>
<TableContainer mt={[0, 3]} position={'relative'} minH={'70vh'}>
<Table variant={'simple'} fontSize={'sm'}>
<Thead>
<Tr>
<Th>{t('kb.Filename')}</Th>
<Th>{t('kb.Chunk Length')}</Th>
<Th>{t('kb.Upload Time')}</Th>
<Th>{t('kb.File Size')}</Th>
<Th>{t('common.Status')}</Th>
<Th />
</Tr>
</Thead>
<Tbody>
{formatFiles.map((file) => (
<Tr
key={file.id}
_hover={{ bg: 'myWhite.600' }}
cursor={'pointer'}
title={'点击查看数据详情'}
onClick={() =>
router.replace({
query: {
kbId,
fileId: file.id,
currentTab: 'dataCard'
}
})
}
>
<Td>
<Flex alignItems={'center'}>
<Image src={file.icon} w={'16px'} mr={2} alt={''} />
<Box maxW={['300px', '400px']} className="textEllipsis">
{t(file.filename)}
</Box>
</Flex>
</Td>
<Td fontSize={'md'} fontWeight={'bold'}>
{file.chunkLength}
</Td>
<Td>{dayjs(file.uploadTime).format('YYYY/MM/DD HH:mm')}</Td>
<Td>{formatFileSize(file.size)}</Td>
<Td>
<Flex
alignItems={'center'}
_before={{
content: '""',
w: '10px',
h: '10px',
mr: 2,
borderRadius: 'lg',
bg: statusMap[file.status].color
}}
>
{statusMap[file.status].text}
</Flex>
</Td>
<Td onClick={(e) => e.stopPropagation()}>
<MyMenu
width={100}
Button={
<MenuButton
w={'22px'}
h={'22px'}
borderRadius={'md'}
_hover={{
color: 'myBlue.600',
'& .icon': {
bg: 'myGray.100'
}
}}
>
<MyIcon
className="icon"
name={'more'}
h={'16px'}
w={'16px'}
px={1}
py={1}
borderRadius={'md'}
cursor={'pointer'}
/>
</MenuButton>
}
menuList={[
...(file.id !== OtherFileId
? [
{
child: (
<Flex alignItems={'center'}>
<MyIcon name={'edit'} w={'14px'} mr={2} />
{t('Rename')}
</Flex>
),
onClick: () =>
onOpenModal({
defaultVal: file.filename,
onSuccess: (newName) => {
onUpdateFilename({
id: file.id,
name: newName
});
}
})
}
]
: []),
{
child: (
<Flex alignItems={'center'}>
<MyIcon
mr={1}
name={'delete'}
w={'14px'}
_hover={{ color: 'red.600' }}
/>
<Box>{t('common.Delete')}</Box>
</Flex>
),
onClick: () =>
openConfirm(() => {
onDeleteFile(file.id);
})()
}
]}
/>
</Td>
</Tr>
))}
</Tbody>
</Table>
<Loading loading={isLoading && files.length === 0} fixed={false} />
{total > pageSize && (
<Flex mt={2} justifyContent={'center'}>
<Pagination />
</Flex>
)}
</TableContainer>
<ConfirmModal />
<EditTitleModal />
</Box>
);
};
export default React.memo(FileCard);

View File

@@ -1,83 +0,0 @@
import React, { useState } from 'react';
import { Box, type BoxProps, Flex, Textarea, useTheme } from '@chakra-ui/react';
import MyRadio from '@/components/Radio/index';
import dynamic from 'next/dynamic';
import ManualImport from './Import/Manual';
const ChunkImport = dynamic(() => import('./Import/Chunk'), {
ssr: true
});
const QAImport = dynamic(() => import('./Import/QA'), {
ssr: true
});
const CsvImport = dynamic(() => import('./Import/Csv'), {
ssr: true
});
enum ImportTypeEnum {
manual = 'manual',
index = 'index',
qa = 'qa',
csv = 'csv'
}
const ImportData = ({ kbId }: { kbId: string }) => {
const theme = useTheme();
const [importType, setImportType] = useState<`${ImportTypeEnum}`>(ImportTypeEnum.manual);
const TitleStyle: BoxProps = {
fontWeight: 'bold',
fontSize: ['md', 'xl'],
mb: [3, 5]
};
return (
<Flex flexDirection={'column'} h={'100%'} pt={[1, 5]}>
<Box {...TitleStyle} px={[4, 8]}>
</Box>
<Box pb={[5, 7]} px={[4, 8]} borderBottom={theme.borders.base}>
<MyRadio
gridTemplateColumns={['repeat(1,1fr)', 'repeat(2, 350px)']}
list={[
{
icon: 'manualImport',
title: '手动输入',
desc: '手动输入问答对,是最精准的数据',
value: ImportTypeEnum.manual
},
{
icon: 'indexImport',
title: '直接分段',
desc: '选择文本文件,直接将其按分段进行处理',
value: ImportTypeEnum.index
},
{
icon: 'qaImport',
title: 'QA拆分',
desc: '选择文本文件,让大模型自动生成问答对',
value: ImportTypeEnum.qa
},
{
icon: 'csvImport',
title: 'CSV 导入',
desc: '批量导入问答对,是最精准的数据',
value: ImportTypeEnum.csv
}
]}
value={importType}
onChange={(e) => setImportType(e as `${ImportTypeEnum}`)}
/>
</Box>
<Box flex={'1 0 0'} h={0}>
{importType === ImportTypeEnum.manual && <ManualImport kbId={kbId} />}
{importType === ImportTypeEnum.index && <ChunkImport kbId={kbId} />}
{importType === ImportTypeEnum.qa && <QAImport kbId={kbId} />}
{importType === ImportTypeEnum.csv && <CsvImport kbId={kbId} />}
</Box>
</Flex>
);
};
export default ImportData;

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@@ -1,440 +0,0 @@
import React, { useState, useCallback, useMemo } from 'react';
import {
Box,
Flex,
Button,
useTheme,
NumberInput,
NumberInputField,
NumberInputStepper,
NumberIncrementStepper,
NumberDecrementStepper,
Image
} from '@chakra-ui/react';
import { useToast } from '@/hooks/useToast';
import { useConfirm } from '@/hooks/useConfirm';
import { useRouter } from 'next/router';
import { useMutation } from '@tanstack/react-query';
import { splitText2Chunks } from '@/utils/file';
import { getErrText } from '@/utils/tools';
import { formatPrice } from '@/utils/user';
import MyIcon from '@/components/Icon';
import CloseIcon from '@/components/Icon/close';
import DeleteIcon, { hoverDeleteStyles } from '@/components/Icon/delete';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import { TrainingModeEnum } from '@/constants/plugin';
import FileSelect, { type FileItemType } from './FileSelect';
import { useDatasetStore } from '@/store/dataset';
import { updateDatasetFile } from '@/api/core/dataset/file';
import { chunksUpload } from '@/utils/web/core/dataset';
const fileExtension = '.txt, .doc, .docx, .pdf, .md';
const ChunkImport = ({ kbId }: { kbId: string }) => {
const { kbDetail } = useDatasetStore();
const vectorModel = kbDetail.vectorModel;
const unitPrice = vectorModel?.price || 0.2;
const theme = useTheme();
const router = useRouter();
const { toast } = useToast();
const [chunkLen, setChunkLen] = useState(vectorModel?.defaultToken || 300);
const [showRePreview, setShowRePreview] = useState(false);
const [files, setFiles] = useState<FileItemType[]>([]);
const [previewFile, setPreviewFile] = useState<FileItemType>();
const [successChunks, setSuccessChunks] = useState(0);
const totalChunk = useMemo(
() => files.reduce((sum, file) => sum + file.chunks.length, 0),
[files]
);
const emptyFiles = useMemo(() => files.length === 0, [files]);
// price count
const price = useMemo(() => {
return formatPrice(files.reduce((sum, file) => sum + file.tokens, 0) * unitPrice);
}, [files, unitPrice]);
const { openConfirm, ConfirmModal } = useConfirm({
content: `该任务无法终止,需要一定时间生成索引,请确认导入。如果余额不足,未完成的任务会被暂停,充值后可继续进行。`
});
const { mutate: onclickUpload, isLoading: uploading } = useMutation({
mutationFn: async () => {
const chunks = files.map((file) => file.chunks).flat();
// mark the file is used
await Promise.all(
files.map((file) =>
updateDatasetFile({
id: file.id,
datasetUsed: true
})
)
);
// upload data
const { insertLen } = await chunksUpload({
kbId,
chunks,
mode: TrainingModeEnum.index,
onUploading: (insertLen) => {
setSuccessChunks(insertLen);
}
});
toast({
title: `去重后共导入 ${insertLen} 条数据,请耐心等待训练.`,
status: 'success'
});
router.replace({
query: {
kbId,
currentTab: 'dataset'
}
});
},
onError(err) {
toast({
title: getErrText(err, '导入文件失败'),
status: 'error'
});
}
});
const onRePreview = useCallback(async () => {
try {
setFiles((state) =>
state.map((file) => {
const splitRes = splitText2Chunks({
text: file.text,
maxLen: chunkLen
});
return {
...file,
tokens: splitRes.tokens,
chunks: splitRes.chunks.map((chunk) => ({
a: '',
source: file.filename,
file_id: file.id,
q: chunk
}))
};
})
);
setPreviewFile(undefined);
setShowRePreview(false);
} catch (error) {
toast({
status: 'warning',
title: getErrText(error, '文本分段异常')
});
}
}, [chunkLen, toast]);
const filenameStyles = {
className: 'textEllipsis',
maxW: '400px'
};
return (
<Box display={['block', 'flex']} h={['auto', '100%']} overflow={'overlay'}>
<Flex
flexDirection={'column'}
flex={'1 0 0'}
h={'100%'}
minW={['auto', '400px']}
w={['100%', 0]}
p={[4, 8]}
>
<FileSelect
fileExtension={fileExtension}
onPushFiles={(files) => {
setFiles((state) => files.concat(state));
}}
chunkLen={chunkLen}
py={emptyFiles ? '100px' : 5}
/>
{!emptyFiles && (
<>
<Box py={4} px={2} minH={['auto', '100px']} maxH={'400px'} overflow={'auto'}>
{files.map((item) => (
<Flex
key={item.id}
w={'100%'}
_notLast={{ mb: 5 }}
px={5}
py={2}
boxShadow={'1px 1px 5px rgba(0,0,0,0.15)'}
borderRadius={'md'}
cursor={'pointer'}
position={'relative'}
alignItems={'center'}
_hover={{
bg: 'myBlue.100',
'& .delete': {
display: 'block'
}
}}
onClick={() => setPreviewFile(item)}
>
<Image src={item.icon} w={'16px'} alt={''} />
<Box ml={2} flex={'1 0 0'} pr={3} {...filenameStyles}>
{item.filename}
</Box>
<MyIcon
position={'absolute'}
right={3}
className="delete"
name={'delete'}
w={'16px'}
_hover={{ color: 'red.600' }}
display={['block', 'none']}
onClick={(e) => {
e.stopPropagation();
setFiles((state) => state.filter((file) => file.id !== item.id));
}}
/>
</Flex>
))}
</Box>
{/* chunk size */}
<Flex py={5} alignItems={'center'}>
<Box>
<MyTooltip
label={
'按结束标点符号进行分段。前后段落会有 30% 的内容重叠。\n中文文档建议不要超过800英文不要超过1500'
}
forceShow
>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Box>
<Box
flex={1}
css={{
'& > span': {
display: 'block'
}
}}
>
<MyTooltip label={`范围: 100~${kbDetail.vectorModel.maxToken}`}>
<NumberInput
ml={4}
defaultValue={chunkLen}
min={100}
max={kbDetail.vectorModel.maxToken}
step={10}
onChange={(e) => {
setChunkLen(+e);
setShowRePreview(true);
}}
>
<NumberInputField />
<NumberInputStepper>
<NumberIncrementStepper />
<NumberDecrementStepper />
</NumberInputStepper>
</NumberInput>
</MyTooltip>
</Box>
</Flex>
{/* price */}
<Flex py={5} alignItems={'center'}>
<Box>
<MyTooltip
label={`索引生成计费为: ${formatPrice(unitPrice, 1000)}/1k tokens`}
forceShow
>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Box>
<Box ml={4}>{price}</Box>
</Flex>
<Flex mt={3}>
{showRePreview && (
<Button variant={'base'} mr={4} onClick={onRePreview}>
</Button>
)}
<Button isDisabled={uploading} onClick={openConfirm(onclickUpload)}>
{uploading ? (
<Box>{Math.round((successChunks / totalChunk) * 100)}%</Box>
) : (
'确认导入'
)}
</Button>
</Flex>
</>
)}
</Flex>
{!emptyFiles && (
<Box flex={'2 0 0'} w={['100%', 0]} h={'100%'}>
{previewFile ? (
<Box
position={'relative'}
display={['block', 'flex']}
h={'100%'}
flexDirection={'column'}
pt={[4, 8]}
bg={'myWhite.400'}
>
<Box px={[4, 8]} fontSize={['lg', 'xl']} fontWeight={'bold'} {...filenameStyles}>
{previewFile.filename}
</Box>
<CloseIcon
position={'absolute'}
right={[4, 8]}
top={4}
onClick={() => setPreviewFile(undefined)}
/>
<Box
flex={'1 0 0'}
h={['auto', 0]}
overflow={'overlay'}
px={[4, 8]}
my={4}
contentEditable
dangerouslySetInnerHTML={{ __html: previewFile.text }}
fontSize={'sm'}
whiteSpace={'pre-wrap'}
wordBreak={'break-all'}
onBlur={(e) => {
// @ts-ignore
const val = e.target.innerText;
setShowRePreview(true);
setFiles((state) =>
state.map((file) =>
file.id === previewFile.id
? {
...file,
text: val
}
: file
)
);
}}
/>
</Box>
) : (
<Box h={'100%'} pt={[4, 8]} overflow={'overlay'}>
<Flex px={[4, 8]} alignItems={'center'}>
<Box fontSize={['lg', 'xl']} fontWeight={'bold'}>
({totalChunk})
</Box>
{totalChunk > 100 && (
<Box ml={2} fontSize={'sm'} color={'myhGray.500'}>
</Box>
)}
</Flex>
<Box px={[4, 8]} overflow={'overlay'}>
{files.map((file) =>
file.chunks.slice(0, 50).map((chunk, i) => (
<Box
key={i}
py={4}
bg={'myWhite.500'}
my={2}
borderRadius={'md'}
fontSize={'sm'}
_hover={{ ...hoverDeleteStyles }}
>
<Flex mb={1} px={4} userSelect={'none'}>
<Box
flexShrink={0}
px={3}
py={'1px'}
border={theme.borders.base}
borderRadius={'md'}
>
# {i + 1}
</Box>
<Box ml={2} fontSize={'sm'} color={'myhGray.500'} {...filenameStyles}>
{file.filename}
</Box>
<Box flex={1} />
<DeleteIcon
onClick={() => {
setFiles((state) =>
state.map((stateFile) =>
stateFile.id === file.id
? {
...file,
chunks: [
...file.chunks.slice(0, i),
...file.chunks.slice(i + 1)
]
}
: stateFile
)
);
}}
/>
</Flex>
<Box
px={4}
fontSize={'sm'}
whiteSpace={'pre-wrap'}
wordBreak={'break-all'}
contentEditable
dangerouslySetInnerHTML={{ __html: chunk.q }}
onBlur={(e) => {
// @ts-ignore
const val = e.target.innerText;
/* delete file */
if (val === '') {
setFiles((state) =>
state.map((stateFile) =>
stateFile.id === file.id
? {
...file,
chunks: [
...file.chunks.slice(0, i),
...file.chunks.slice(i + 1)
]
}
: stateFile
)
);
} else {
// update file
setFiles((stateFiles) =>
stateFiles.map((stateFile) =>
file.id === stateFile.id
? {
...stateFile,
chunks: stateFile.chunks.map((chunk, index) => ({
...chunk,
q: i === index ? val : chunk.q
}))
}
: stateFile
)
);
}
}}
/>
</Box>
))
)}
</Box>
</Box>
)}
</Box>
)}
<ConfirmModal />
</Box>
);
};
export default ChunkImport;

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@@ -1,229 +0,0 @@
import React, { useState, useMemo } from 'react';
import { Box, Flex, Button, useTheme, Image } from '@chakra-ui/react';
import { useToast } from '@/hooks/useToast';
import { useConfirm } from '@/hooks/useConfirm';
import { useMutation } from '@tanstack/react-query';
import { getErrText } from '@/utils/tools';
import MyIcon from '@/components/Icon';
import DeleteIcon, { hoverDeleteStyles } from '@/components/Icon/delete';
import { TrainingModeEnum } from '@/constants/plugin';
import FileSelect, { type FileItemType } from './FileSelect';
import { useRouter } from 'next/router';
import { useDatasetStore } from '@/store/dataset';
import { updateDatasetFile } from '@/api/core/dataset/file';
import { chunksUpload } from '@/utils/web/core/dataset';
const fileExtension = '.csv';
const CsvImport = ({ kbId }: { kbId: string }) => {
const { kbDetail } = useDatasetStore();
const maxToken = kbDetail.vectorModel?.maxToken || 2000;
const theme = useTheme();
const router = useRouter();
const { toast } = useToast();
const [files, setFiles] = useState<FileItemType[]>([]);
const [successChunks, setSuccessChunks] = useState(0);
const totalChunk = useMemo(
() => files.reduce((sum, file) => sum + file.chunks.length, 0),
[files]
);
const emptyFiles = useMemo(() => files.length === 0, [files]);
const { openConfirm, ConfirmModal } = useConfirm({
content: `该任务无法终止,需要一定时间生成索引,请确认导入。如果余额不足,未完成的任务会被暂停,充值后可继续进行。`
});
const { mutate: onclickUpload, isLoading: uploading } = useMutation({
mutationFn: async () => {
// mark the file is used
await Promise.all(
files.map((file) =>
updateDatasetFile({
id: file.id,
datasetUsed: true
})
)
);
const chunks = files
.map((file) => file.chunks)
.flat()
.filter((item) => item?.q);
const filterChunks = chunks.filter((item) => item.q.length < maxToken * 1.5);
if (filterChunks.length !== chunks.length) {
toast({
title: `${chunks.length - filterChunks.length}条数据超出长度,已被过滤`,
status: 'info'
});
}
// upload data
const { insertLen } = await chunksUpload({
kbId,
chunks,
mode: TrainingModeEnum.index,
onUploading: (insertLen) => {
setSuccessChunks(insertLen);
}
});
toast({
title: `去重后共导入 ${insertLen} 条数据,请耐心等待训练.`,
status: 'success'
});
router.replace({
query: {
kbId,
currentTab: 'dataset'
}
});
},
onError(err) {
toast({
title: getErrText(err, '导入文件失败'),
status: 'error'
});
}
});
const filenameStyles = {
className: 'textEllipsis',
maxW: '400px'
};
return (
<Box display={['block', 'flex']} h={['auto', '100%']} overflow={'overlay'}>
<Flex
flexDirection={'column'}
flex={'1 0 0'}
h={'100%'}
minW={['auto', '400px']}
w={['100%', 0]}
p={[4, 8]}
>
<FileSelect
fileExtension={fileExtension}
tipText={
'file.If the imported file is garbled, please convert CSV to UTF-8 encoding format'
}
onPushFiles={(files) => setFiles((state) => files.concat(state))}
showUrlFetch={false}
showCreateFile={false}
py={emptyFiles ? '100px' : 5}
isCsv
/>
{!emptyFiles && (
<>
<Box py={4} minH={['auto', '100px']} px={2} maxH={'400px'} overflow={'auto'}>
{files.map((item) => (
<Flex
key={item.id}
w={'100%'}
_notLast={{ mb: 5 }}
px={5}
py={2}
boxShadow={'1px 1px 5px rgba(0,0,0,0.15)'}
borderRadius={'md'}
position={'relative'}
alignItems={'center'}
_hover={{ ...hoverDeleteStyles }}
>
<Image src={'/imgs/files/csv.svg'} w={'16px'} alt={''} />
<Box ml={2} flex={'1 0 0'} pr={3} {...filenameStyles}>
{item.filename}
</Box>
<MyIcon
position={'absolute'}
right={3}
className="delete"
name={'delete'}
w={'16px'}
_hover={{ color: 'red.600' }}
display={['block', 'none']}
onClick={(e) => {
e.stopPropagation();
setFiles((state) => state.filter((file) => file.id !== item.id));
}}
/>
</Flex>
))}
</Box>
<Flex mt={3}>
<Button isDisabled={uploading} onClick={openConfirm(onclickUpload)}>
{uploading ? (
<Box>{Math.round((successChunks / totalChunk) * 100)}%</Box>
) : (
'确认导入'
)}
</Button>
</Flex>
</>
)}
</Flex>
{!emptyFiles && (
<Box flex={'2 0 0'} w={['100%', 0]} h={'100%'} pt={[4, 8]} overflow={'overlay'}>
<Flex px={[4, 8]} alignItems={'center'}>
<Box fontSize={['lg', 'xl']} fontWeight={'bold'}>
({totalChunk})
</Box>
{totalChunk > 100 && (
<Box ml={2} fontSize={'sm'} color={'myhGray.500'}>
</Box>
)}
</Flex>
<Box px={[4, 8]} overflow={'overlay'}>
{files.map((file) =>
file.chunks.slice(0, 100).map((item, i) => (
<Box
key={i}
py={4}
bg={'myWhite.500'}
my={2}
borderRadius={'md'}
fontSize={'sm'}
_hover={{ ...hoverDeleteStyles }}
>
<Flex mb={1} px={4} userSelect={'none'}>
<Box px={3} py={'1px'} border={theme.borders.base} borderRadius={'md'}>
# {i + 1}
</Box>
{item.source && <Box ml={1}>({item.source})</Box>}
<Box flex={1} />
<DeleteIcon
onClick={() => {
setFiles((state) =>
state.map((stateFile) =>
stateFile.id === file.id
? {
...file,
chunks: [...file.chunks.slice(0, i), ...file.chunks.slice(i + 1)]
}
: stateFile
)
);
}}
/>
</Flex>
<Box px={4} fontSize={'sm'} whiteSpace={'pre-wrap'} wordBreak={'break-all'}>
{`${item.q}\n${item.a}`}
</Box>
</Box>
))
)}
</Box>
</Box>
)}
<ConfirmModal />
</Box>
);
};
export default CsvImport;

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@@ -1,122 +0,0 @@
import React, { useState } from 'react';
import { Box, Textarea, Button, Flex } from '@chakra-ui/react';
import { useForm } from 'react-hook-form';
import { useToast } from '@/hooks/useToast';
import { useRequest } from '@/hooks/useRequest';
import { getErrText } from '@/utils/tools';
import { postChunks2Dataset } from '@/api/core/dataset/data';
import { TrainingModeEnum } from '@/constants/plugin';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import { useDatasetStore } from '@/store/dataset';
type ManualFormType = { q: string; a: string };
const ManualImport = ({ kbId }: { kbId: string }) => {
const { kbDetail } = useDatasetStore();
const maxToken = kbDetail.vectorModel?.maxToken || 2000;
const { register, handleSubmit, reset } = useForm({
defaultValues: { q: '', a: '' }
});
const { toast } = useToast();
const [qLen, setQLen] = useState(0);
const { mutate: onImportData, isLoading } = useRequest({
mutationFn: async (e: ManualFormType) => {
if (e.a.length + e.q.length >= 3000) {
toast({
title: '总长度超长了',
status: 'warning'
});
return;
}
try {
const data = {
a: e.a,
q: e.q,
source: '手动录入'
};
const { insertLen } = await postChunks2Dataset({
kbId,
mode: TrainingModeEnum.index,
data: [data]
});
if (insertLen === 0) {
toast({
title: '已存在完全一致的数据',
status: 'warning'
});
} else {
toast({
title: '导入数据成功,需要一段时间训练',
status: 'success'
});
reset({
a: '',
q: ''
});
}
} catch (err: any) {
toast({
title: getErrText(err, '出现了点意外~'),
status: 'error'
});
}
}
});
return (
<Box p={[4, 8]} h={'100%'} overflow={'overlay'}>
<Box display={'flex'} flexDirection={['column', 'row']}>
<Box flex={1} mr={[0, 4]} mb={[4, 0]} h={['50%', '100%']} position={'relative'}>
<Flex>
<Box h={'30px'}>{'匹配的知识点'}</Box>
<MyTooltip label={'被向量化的部分,通常是问题,也可以是一段陈述描述'}>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Flex>
<Textarea
placeholder={`匹配的知识点。这部分内容会被搜索,请把控内容的质量。最多 ${maxToken} 字。`}
maxLength={maxToken}
h={['250px', '500px']}
{...register(`q`, {
required: true,
onChange(e) {
setQLen(e.target.value.length);
}
})}
/>
<Box position={'absolute'} color={'myGray.500'} right={5} bottom={3} zIndex={99}>
{qLen}
</Box>
</Box>
<Box flex={1} h={['50%', '100%']}>
<Flex>
<Box h={'30px'}>{'预期答案'}</Box>
<MyTooltip
label={'匹配的知识点被命中后,这部分内容会随匹配知识点一起注入模型,引导模型回答'}
>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Flex>
<Textarea
placeholder={
'预期答案。这部分内容不会被搜索,但会作为"匹配的知识点"的内容补充,通常是问题的答案。总和最多 3000 字。'
}
h={['250px', '500px']}
maxLength={3000}
{...register('a')}
/>
</Box>
</Box>
<Button mt={5} isLoading={isLoading} onClick={handleSubmit((data) => onImportData(data))}>
</Button>
</Box>
);
};
export default React.memo(ManualImport);

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@@ -1,410 +0,0 @@
import React, { useState, useCallback, useMemo } from 'react';
import { Box, Flex, Button, useTheme, Image, Input } from '@chakra-ui/react';
import { useToast } from '@/hooks/useToast';
import { useConfirm } from '@/hooks/useConfirm';
import { useMutation } from '@tanstack/react-query';
import { splitText2Chunks } from '@/utils/file';
import { getErrText } from '@/utils/tools';
import { formatPrice } from '@/utils/user';
import { qaModel } from '@/store/static';
import MyIcon from '@/components/Icon';
import CloseIcon from '@/components/Icon/close';
import DeleteIcon, { hoverDeleteStyles } from '@/components/Icon/delete';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon, InfoOutlineIcon } from '@chakra-ui/icons';
import { TrainingModeEnum } from '@/constants/plugin';
import FileSelect, { type FileItemType } from './FileSelect';
import { useRouter } from 'next/router';
import { updateDatasetFile } from '@/api/core/dataset/file';
import { Prompt_AgentQA } from '@/prompts/core/agent';
import { replaceVariable } from '@/utils/common/tools/text';
import { chunksUpload } from '@/utils/web/core/dataset';
const fileExtension = '.txt, .doc, .docx, .pdf, .md';
const QAImport = ({ kbId }: { kbId: string }) => {
const unitPrice = qaModel.price || 3;
const chunkLen = qaModel.maxToken * 0.45;
const theme = useTheme();
const router = useRouter();
const { toast } = useToast();
const [files, setFiles] = useState<FileItemType[]>([]);
const [showRePreview, setShowRePreview] = useState(false);
const [previewFile, setPreviewFile] = useState<FileItemType>();
const [successChunks, setSuccessChunks] = useState(0);
const [prompt, setPrompt] = useState('');
const totalChunk = useMemo(
() => files.reduce((sum, file) => sum + file.chunks.length, 0),
[files]
);
const emptyFiles = useMemo(() => files.length === 0, [files]);
// price count
const price = useMemo(() => {
const filesToken = files.reduce((sum, file) => sum + file.tokens, 0);
const promptTokens = files.reduce((sum, file) => sum + file.chunks.length, 0) * 139;
const totalToken = (filesToken + promptTokens) * 2;
return formatPrice(totalToken * unitPrice);
}, [files, unitPrice]);
const { openConfirm, ConfirmModal } = useConfirm({
content: `该任务无法终止!导入后会自动调用大模型生成问答对,会有一些细节丢失,请确认!如果余额不足,未完成的任务会被暂停。`
});
const previewQAPrompt = useMemo(() => {
return replaceVariable(Prompt_AgentQA.prompt, {
theme: prompt || Prompt_AgentQA.defaultTheme
});
}, [prompt]);
const { mutate: onclickUpload, isLoading: uploading } = useMutation({
mutationFn: async () => {
const chunks = files.map((file) => file.chunks).flat();
// mark the file is used
await Promise.all(
files.map((file) =>
updateDatasetFile({
id: file.id,
datasetUsed: true
})
)
);
// upload data
const { insertLen } = await chunksUpload({
kbId,
chunks,
mode: TrainingModeEnum.qa,
prompt: previewQAPrompt,
onUploading: (insertLen) => {
setSuccessChunks(insertLen);
}
});
toast({
title: `共导入 ${insertLen} 条数据,请耐心等待训练.`,
status: 'success'
});
router.replace({
query: {
kbId,
currentTab: 'dataset'
}
});
},
onError(err) {
toast({
title: getErrText(err, '导入文件失败'),
status: 'error'
});
}
});
const onRePreview = useCallback(async () => {
try {
setFiles((state) =>
state.map((file) => {
const splitRes = splitText2Chunks({
text: file.text,
maxLen: chunkLen
});
return {
...file,
tokens: splitRes.tokens,
chunks: splitRes.chunks.map((chunk) => ({
a: '',
source: file.filename,
file_id: file.id,
q: chunk
}))
};
})
);
setPreviewFile(undefined);
setShowRePreview(false);
} catch (error) {
toast({
status: 'warning',
title: getErrText(error, '文本分段异常')
});
}
}, [chunkLen, toast]);
const filenameStyles = {
className: 'textEllipsis',
maxW: '400px'
};
return (
<Box display={['block', 'flex']} h={['auto', '100%']} overflow={'overlay'}>
<Flex
flexDirection={'column'}
flex={'1 0 0'}
h={'100%'}
minW={['auto', '400px']}
w={['100%', 0]}
p={[4, 8]}
>
<FileSelect
fileExtension={fileExtension}
onPushFiles={(files) => {
setFiles((state) => files.concat(state));
}}
chunkLen={chunkLen}
py={emptyFiles ? '100px' : 5}
/>
{!emptyFiles && (
<>
<Box py={4} px={2} minH={['auto', '100px']} maxH={'400px'} overflow={'auto'}>
{files.map((item) => (
<Flex
key={item.id}
w={'100%'}
_notLast={{ mb: 5 }}
px={5}
py={2}
boxShadow={'1px 1px 5px rgba(0,0,0,0.15)'}
borderRadius={'md'}
cursor={'pointer'}
position={'relative'}
alignItems={'center'}
_hover={{
bg: 'myBlue.100',
'& .delete': {
display: 'block'
}
}}
onClick={() => setPreviewFile(item)}
>
<Image src={item.icon} w={'16px'} alt={''} />
<Box ml={2} flex={'1 0 0'} pr={3} {...filenameStyles}>
{item.filename}
</Box>
<MyIcon
position={'absolute'}
right={3}
className="delete"
name={'delete'}
w={'16px'}
_hover={{ color: 'red.600' }}
display={['block', 'none']}
onClick={(e) => {
e.stopPropagation();
setFiles((state) => state.filter((file) => file.id !== item.id));
}}
/>
</Flex>
))}
</Box>
{/* prompt */}
<Box py={5}>
<Box mb={2}>
QA {' '}
<MyTooltip label={previewQAPrompt} forceShow>
<InfoOutlineIcon ml={1} />
</MyTooltip>
</Box>
<Flex alignItems={'center'} fontSize={'sm'}>
<Box mr={2}></Box>
<Input
fontSize={'sm'}
flex={1}
placeholder={Prompt_AgentQA.defaultTheme}
bg={'myWhite.500'}
defaultValue={prompt}
onChange={(e) => setPrompt(e.target.value || '')}
/>
</Flex>
</Box>
{/* price */}
<Flex py={5} alignItems={'center'}>
<Box>
<MyTooltip
label={`索引生成计费为: ${formatPrice(unitPrice, 1000)}/1k tokens`}
forceShow
>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Box>
<Box ml={4}>{price}</Box>
</Flex>
<Flex mt={3}>
{showRePreview && (
<Button variant={'base'} mr={4} onClick={onRePreview}>
</Button>
)}
<Button isDisabled={uploading} onClick={openConfirm(onclickUpload)}>
{uploading ? (
<Box>{Math.round((successChunks / totalChunk) * 100)}%</Box>
) : (
'确认导入'
)}
</Button>
</Flex>
</>
)}
</Flex>
{!emptyFiles && (
<Box flex={'2 0 0'} w={['100%', 0]} h={'100%'}>
{previewFile ? (
<Box
position={'relative'}
display={['block', 'flex']}
h={'100%'}
flexDirection={'column'}
pt={[4, 8]}
bg={'myWhite.400'}
>
<Box px={[4, 8]} fontSize={['lg', 'xl']} fontWeight={'bold'} {...filenameStyles}>
{previewFile.filename}
</Box>
<CloseIcon
position={'absolute'}
right={[4, 8]}
top={4}
onClick={() => setPreviewFile(undefined)}
/>
<Box
flex={'1 0 0'}
h={['auto', 0]}
overflow={'overlay'}
px={[4, 8]}
my={4}
contentEditable
dangerouslySetInnerHTML={{ __html: previewFile.text }}
fontSize={'sm'}
whiteSpace={'pre-wrap'}
wordBreak={'break-all'}
onBlur={(e) => {
// @ts-ignore
const val = e.target.innerText;
setShowRePreview(true);
setFiles((state) =>
state.map((file) =>
file.id === previewFile.id
? {
...file,
text: val
}
: file
)
);
}}
/>
</Box>
) : (
<Box h={'100%'} pt={[4, 8]} overflow={'overlay'}>
<Flex px={[4, 8]} alignItems={'center'}>
<Box fontSize={['lg', 'xl']} fontWeight={'bold'}>
({totalChunk})
</Box>
{totalChunk > 100 && (
<Box ml={2} fontSize={'sm'} color={'myhGray.500'}>
</Box>
)}
</Flex>
<Box px={[4, 8]} overflow={'overlay'}>
{files.map((file) =>
file.chunks.slice(0, 30).map((chunk, i) => (
<Box
key={i}
py={4}
bg={'myWhite.500'}
my={2}
borderRadius={'md'}
fontSize={'sm'}
_hover={{ ...hoverDeleteStyles }}
>
<Flex mb={1} px={4} userSelect={'none'}>
<Box px={3} py={'1px'} border={theme.borders.base} borderRadius={'md'}>
# {i + 1}
</Box>
<Box ml={2} fontSize={'sm'} color={'myhGray.500'} {...filenameStyles}>
{file.filename}
</Box>
<Box flex={1} />
<DeleteIcon
onClick={() => {
setFiles((state) =>
state.map((stateFile) =>
stateFile.id === file.id
? {
...file,
chunks: [
...file.chunks.slice(0, i),
...file.chunks.slice(i + 1)
]
}
: stateFile
)
);
}}
/>
</Flex>
<Box
px={4}
fontSize={'sm'}
whiteSpace={'pre-wrap'}
wordBreak={'break-all'}
contentEditable
dangerouslySetInnerHTML={{ __html: chunk.q }}
onBlur={(e) => {
// @ts-ignore
const val = e.target.innerText;
/* delete file */
if (val === '') {
setFiles((state) =>
state.map((stateFile) =>
stateFile.id === file.id
? {
...file,
chunks: [
...file.chunks.slice(0, i),
...file.chunks.slice(i + 1)
]
}
: stateFile
)
);
} else {
// update file
setFiles((stateFiles) =>
stateFiles.map((stateFile) =>
file.id === stateFile.id
? {
...stateFile,
chunks: stateFile.chunks.map((chunk, index) => ({
...chunk,
q: i === index ? val : chunk.q
}))
}
: stateFile
)
);
}
}}
/>
</Box>
))
)}
</Box>
</Box>
)}
</Box>
)}
<ConfirmModal />
</Box>
);
};
export default QAImport;

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@@ -1,290 +0,0 @@
import React, { useState, useCallback } from 'react';
import { Box, Flex, Button, Textarea, IconButton, BoxProps } from '@chakra-ui/react';
import { useForm } from 'react-hook-form';
import {
postData2Dataset,
putDatasetDataById,
delOneDatasetDataById
} from '@/api/core/dataset/data';
import { useToast } from '@/hooks/useToast';
import { getErrText } from '@/utils/tools';
import MyIcon from '@/components/Icon';
import MyModal from '@/components/MyModal';
import MyTooltip from '@/components/MyTooltip';
import { QuestionOutlineIcon } from '@chakra-ui/icons';
import { useQuery } from '@tanstack/react-query';
import { DatasetDataItemType } from '@/types/core/dataset/data';
import { useTranslation } from 'react-i18next';
import { useDatasetStore } from '@/store/dataset';
import { getFileAndOpen } from '@/utils/web/file';
export type FormData = { dataId?: string } & DatasetDataItemType;
const InputDataModal = ({
onClose,
onSuccess,
onDelete,
kbId,
defaultValues = {
a: '',
q: ''
}
}: {
onClose: () => void;
onSuccess: (data: FormData) => void;
onDelete?: () => void;
kbId: string;
defaultValues?: FormData;
}) => {
const { t } = useTranslation();
const [loading, setLoading] = useState(false);
const { toast } = useToast();
const { kbDetail, getKbDetail } = useDatasetStore();
const { getValues, register, handleSubmit, reset } = useForm<FormData>({
defaultValues
});
const maxToken = kbDetail.vectorModel?.maxToken || 2000;
/**
* 确认导入新数据
*/
const sureImportData = useCallback(
async (e: FormData) => {
if (e.q.length >= maxToken) {
toast({
title: '总长度超长了',
status: 'warning'
});
return;
}
setLoading(true);
try {
const data = {
dataId: '',
a: e.a,
q: e.q,
source: '手动录入'
};
data.dataId = await postData2Dataset({
kbId,
data
});
toast({
title: '导入数据成功,需要一段时间训练',
status: 'success'
});
reset({
a: '',
q: ''
});
onSuccess(data);
} catch (err: any) {
toast({
title: getErrText(err, '出现了点意外~'),
status: 'error'
});
}
setLoading(false);
},
[kbId, maxToken, onSuccess, reset, toast]
);
const updateData = useCallback(
async (e: FormData) => {
if (!e.dataId) return;
if (e.a !== defaultValues.a || e.q !== defaultValues.q) {
setLoading(true);
try {
const data = {
dataId: e.dataId,
kbId,
a: e.a,
q: e.q === defaultValues.q ? '' : e.q
};
await putDatasetDataById(data);
onSuccess(data);
} catch (err) {
toast({
status: 'error',
title: getErrText(err, '更新数据失败')
});
}
setLoading(false);
}
toast({
title: '修改数据成功',
status: 'success'
});
onClose();
},
[defaultValues.a, defaultValues.q, kbId, onClose, onSuccess, toast]
);
useQuery(['getKbDetail'], () => {
if (kbDetail._id === kbId) return null;
return getKbDetail(kbId);
});
return (
<MyModal
isOpen={true}
onClose={onClose}
isCentered
title={defaultValues.dataId ? '变更数据' : '手动导入数据'}
w={'90vw'}
maxW={'90vw'}
h={'90vh'}
>
<Flex flexDirection={'column'} h={'100%'}>
<Box
display={'flex'}
flexDirection={['column', 'row']}
flex={'1 0 0'}
h={['100%', 0]}
overflow={'overlay'}
px={6}
pb={2}
>
<Box flex={1} mr={[0, 4]} mb={[4, 0]} h={['50%', '100%']}>
<Flex>
<Box h={'30px'}>{'匹配的知识点'}</Box>
<MyTooltip label={'被向量化的部分,通常是问题,也可以是一段陈述描述'}>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Flex>
<Textarea
placeholder={`匹配的知识点。这部分内容会被搜索,请把控内容的质量,最多 ${maxToken} 字。`}
maxLength={maxToken}
resize={'none'}
h={'calc(100% - 30px)'}
{...register(`q`, {
required: true
})}
/>
</Box>
<Box flex={1} h={['50%', '100%']}>
<Flex>
<Box h={'30px'}>{'补充内容'}</Box>
<MyTooltip
label={'匹配的知识点被命中后,这部分内容会随匹配知识点一起注入模型,引导模型回答'}
>
<QuestionOutlineIcon ml={1} />
</MyTooltip>
</Flex>
<Textarea
placeholder={
'这部分内容不会被搜索,但会作为"匹配的知识点"的内容补充,通常是问题的答案。'
}
resize={'none'}
h={'calc(100% - 30px)'}
{...register('a')}
/>
</Box>
</Box>
<Flex px={6} pt={['34px', 2]} pb={4} alignItems={'center'} position={'relative'}>
<RawFileText
fileId={getValues('file_id')}
filename={getValues('source')}
position={'absolute'}
left={'50%'}
top={['16px', '50%']}
transform={'translate(-50%,-50%)'}
/>
<Box flex={1}>
{defaultValues.dataId && onDelete && (
<IconButton
variant={'outline'}
icon={<MyIcon name={'delete'} w={'16px'} h={'16px'} />}
aria-label={''}
isLoading={loading}
size={'sm'}
_hover={{
color: 'red.600',
borderColor: 'red.600'
}}
onClick={async () => {
if (!onDelete || !defaultValues.dataId) return;
try {
await delOneDatasetDataById(defaultValues.dataId);
onDelete();
onClose();
toast({
status: 'success',
title: '记录已删除'
});
} catch (error) {
toast({
status: 'warning',
title: getErrText(error)
});
console.log(error);
}
}}
/>
)}
</Box>
<Box>
<Button variant={'base'} mr={3} isLoading={loading} onClick={onClose}>
</Button>
<Button
isLoading={loading}
onClick={handleSubmit(defaultValues.dataId ? updateData : sureImportData)}
>
{defaultValues.dataId ? '确认变更' : '确认导入'}
</Button>
</Box>
</Flex>
</Flex>
</MyModal>
);
};
export default InputDataModal;
interface RawFileTextProps extends BoxProps {
filename?: string;
fileId?: string;
}
export function RawFileText({ fileId, filename = '', ...props }: RawFileTextProps) {
const { t } = useTranslation();
const { toast } = useToast();
return (
<MyTooltip label={fileId ? t('file.Click to view file') || '' : ''} shouldWrapChildren={false}>
<Box
color={'myGray.600'}
display={'inline-block'}
whiteSpace={'nowrap'}
{...(!!fileId
? {
cursor: 'pointer',
textDecoration: 'underline',
onClick: async () => {
try {
await getFileAndOpen(fileId);
} catch (error) {
toast({
title: getErrText(error, '获取文件地址失败'),
status: 'error'
});
}
}
}
: {})}
{...props}
>
{filename}
</Box>
</MyTooltip>
);
}

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@@ -1,5 +0,0 @@
.loginPage {
background: url('/icon/login-bg.svg') no-repeat;
background-size: cover;
user-select: none;
}

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@@ -1,10 +0,0 @@
export const defaultQuoteTemplate = `{instruction:"{{q}}",output:"{{a}}"}`;
export const defaultQuotePrompt = `你的背景知识:
"""
{{quote}}
"""
对话要求:
1. 背景知识是最新的,其中 instruction 是相关介绍output 是预期回答或补充。
2. 使用背景知识回答问题。
3. 背景知识无法满足问题时,你需严谨的回答问题。
我的问题是:"{{question}}"`;

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@@ -1,24 +0,0 @@
import { Schema, model, models, Model } from 'mongoose';
import type { IpLimitSchemaType } from '@/types/common/ipLimit';
const IpLimitSchema = new Schema({
eventId: {
type: String,
required: true
},
ip: {
type: String,
required: true
},
account: {
type: Number,
default: 0
},
lastMinute: {
type: Date,
default: () => new Date()
}
});
export const IpLimit: Model<IpLimitSchemaType> =
models['ip_limit'] || model('ip_limit', IpLimitSchema);

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@@ -1,224 +0,0 @@
import { TrainingData } from '@/service/mongo';
import { pushQABill } from '@/service/events/pushBill';
import { pushDataToKb } from '@/pages/api/core/dataset/data/pushData';
import { TrainingModeEnum } from '@/constants/plugin';
import { ERROR_ENUM } from '../errorCode';
import { sendInform } from '@/pages/api/user/inform/send';
import { authBalanceByUid } from '../utils/auth';
import { axiosConfig, getAIChatApi } from '../lib/openai';
import { ChatCompletionRequestMessage } from 'openai';
import { gptMessage2ChatType } from '@/utils/adapt';
import { addLog } from '../utils/tools';
import { splitText2Chunks } from '@/utils/file';
import { countMessagesTokens } from '@/utils/common/tiktoken';
import { replaceVariable } from '@/utils/common/tools/text';
import { Prompt_AgentQA } from '@/prompts/core/agent';
const reduceQueue = () => {
global.qaQueueLen = global.qaQueueLen > 0 ? global.qaQueueLen - 1 : 0;
};
export async function generateQA(): Promise<any> {
if (global.qaQueueLen >= global.systemEnv.qaMaxProcess) return;
global.qaQueueLen++;
let trainingId = '';
let userId = '';
try {
const data = await TrainingData.findOneAndUpdate(
{
mode: TrainingModeEnum.qa,
lockTime: { $lte: new Date(Date.now() - 4 * 60 * 1000) }
},
{
lockTime: new Date()
}
).select({
_id: 1,
userId: 1,
kbId: 1,
prompt: 1,
q: 1,
source: 1,
file_id: 1
});
// task preemption
if (!data) {
reduceQueue();
global.qaQueueLen <= 0 && console.log(`【QA】任务完成`);
return;
}
trainingId = data._id;
userId = String(data.userId);
const kbId = String(data.kbId);
await authBalanceByUid(userId);
const startTime = Date.now();
const chatAPI = getAIChatApi();
// 请求 chatgpt 获取回答
const response = await Promise.all(
[data.q].map((text) => {
const messages: ChatCompletionRequestMessage[] = [
{
role: 'user',
content: data.prompt
? replaceVariable(data.prompt, { text })
: replaceVariable(Prompt_AgentQA.prompt, {
theme: Prompt_AgentQA.defaultTheme,
text
})
}
];
const modelTokenLimit = global.qaModel.maxToken || 16000;
const promptsToken = countMessagesTokens({
messages: gptMessage2ChatType(messages)
});
const maxToken = modelTokenLimit - promptsToken;
return chatAPI
.createChatCompletion(
{
model: global.qaModel.model,
temperature: 0.01,
messages,
stream: false,
max_tokens: maxToken
},
{
timeout: 480000,
...axiosConfig()
}
)
.then((res) => {
const answer = res.data.choices?.[0].message?.content;
const totalTokens = res.data.usage?.total_tokens || 0;
const result = formatSplitText(answer || ''); // 格式化后的QA对
console.log(`split result length: `, result.length);
// 计费
if (result.length > 0) {
pushQABill({
userId: data.userId,
totalTokens,
appName: 'QA 拆分'
});
} else {
addLog.info(`QA result 0:`, { answer });
}
return {
rawContent: answer,
result
};
})
.catch((err) => {
console.log('QA拆分错误');
console.log(err.response?.status, err.response?.statusText, err.response?.data);
return Promise.reject(err);
});
})
);
const responseList = response.map((item) => item.result).flat();
// 创建 向量生成 队列
await pushDataToKb({
kbId,
data: responseList.map((item) => ({
...item,
source: data.source,
file_id: data.file_id
})),
userId,
mode: TrainingModeEnum.index
});
// delete data from training
await TrainingData.findByIdAndDelete(data._id);
console.log('生成QA成功time:', `${(Date.now() - startTime) / 1000}s`);
reduceQueue();
generateQA();
} catch (err: any) {
reduceQueue();
// log
if (err?.response) {
console.log('openai error: 生成QA错误');
console.log(err.response?.status, err.response?.statusText, err.response?.data);
} else {
addLog.error('生成 QA 错误', err);
}
// message error or openai account error
if (err?.message === 'invalid message format') {
await TrainingData.findByIdAndRemove(trainingId);
}
// 账号余额不足,删除任务
if (userId && err === ERROR_ENUM.insufficientQuota) {
sendInform({
type: 'system',
title: 'QA 任务中止',
content:
'由于账号余额不足,索引生成任务中止,重新充值后将会继续。暂停的任务将在 7 天后被删除。',
userId
});
console.log('余额不足,暂停向量生成任务');
await TrainingData.updateMany(
{
userId
},
{
lockTime: new Date('2999/5/5')
}
);
return generateQA();
}
setTimeout(() => {
generateQA();
}, 1000);
}
}
/**
* 检查文本是否按格式返回
*/
function formatSplitText(text: string) {
text = text.replace(/\\n/g, '\n'); // 将换行符替换为空格
const regex = /Q\d+:(\s*)(.*)(\s*)A\d+:(\s*)([\s\S]*?)(?=Q|$)/g; // 匹配Q和A的正则表达式
const matches = text.matchAll(regex); // 获取所有匹配到的结果
const result = []; // 存储最终的结果
for (const match of matches) {
const q = match[2];
const a = match[5];
if (q && a) {
// 如果Q和A都存在就将其添加到结果中
result.push({
q: `${q}\n${a.trim().replace(/\n\s*/g, '\n')}`,
a: ''
});
}
}
// empty result. direct split chunk
if (result.length === 0) {
const splitRes = splitText2Chunks({ text: text, maxLen: 500 });
splitRes.chunks.forEach((item) => {
result.push({
q: item,
a: ''
});
});
}
return result;
}

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@@ -1,179 +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[];
}) => {
try {
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)
});
} catch (error) {
addLog.error(`pushTaskBill error`, error);
}
};
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;
};

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@@ -1,28 +0,0 @@
import { UserModelSchema } from '@/types/mongoSchema';
import { Configuration, OpenAIApi } from 'openai';
export const openaiBaseUrl = process.env.OPENAI_BASE_URL || 'https://api.openai.com/v1';
export const baseUrl = process.env.ONEAPI_URL || openaiBaseUrl;
export const systemAIChatKey = process.env.CHAT_API_KEY || '';
export const getAIChatApi = (props?: UserModelSchema['openaiAccount']) => {
return new OpenAIApi(
new Configuration({
basePath: props?.baseUrl || baseUrl,
apiKey: props?.key || systemAIChatKey
})
);
};
/* openai axios config */
export const axiosConfig = (props?: UserModelSchema['openaiAccount']) => {
return {
baseURL: props?.baseUrl || baseUrl, // 此处仅对非 npm 模块有效
httpsAgent: global.httpsAgent,
headers: {
Authorization: `Bearer ${props?.key || systemAIChatKey}`,
auth: process.env.OPENAI_BASE_URL_AUTH || ''
}
};
};

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@@ -1,18 +0,0 @@
import { Schema, model, models, Model as MongoModel } from 'mongoose';
import { CollectionSchema as CollectionType } from '@/types/mongoSchema';
const CollectionSchema = new Schema({
userId: {
type: Schema.Types.ObjectId,
ref: 'user',
required: true
},
appId: {
type: Schema.Types.ObjectId,
ref: 'model',
required: true
}
});
export const Collection: MongoModel<CollectionType> =
models['collection'] || model('collection', CollectionSchema);

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@@ -1,45 +0,0 @@
import { Schema, model, models, Model } from 'mongoose';
import { kbSchema as SchemaType } from '@/types/mongoSchema';
import { KbTypeMap } from '@/constants/dataset';
const kbSchema = new Schema({
parentId: {
type: Schema.Types.ObjectId,
ref: 'kb',
default: null
},
userId: {
type: Schema.Types.ObjectId,
ref: 'user',
required: true
},
updateTime: {
type: Date,
default: () => new Date()
},
avatar: {
type: String,
default: '/icon/logo.svg'
},
name: {
type: String,
required: true
},
vectorModel: {
type: String,
required: true,
default: 'text-embedding-ada-002'
},
type: {
type: String,
enum: Object.keys(KbTypeMap),
required: true,
default: 'dataset'
},
tags: {
type: [String],
default: []
}
});
export const KB: Model<SchemaType> = models['kb'] || model('kb', kbSchema);

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@@ -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);

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@@ -1,68 +0,0 @@
/* 模型的知识库 */
import { Schema, model, models, Model as MongoModel } from 'mongoose';
import { TrainingDataSchema as TrainingDateType } from '@/types/mongoSchema';
import { TrainingTypeMap } from '@/constants/plugin';
// pgList and vectorList, Only one of them will work
const TrainingDataSchema = new Schema({
userId: {
type: Schema.Types.ObjectId,
ref: 'user',
required: true
},
kbId: {
type: Schema.Types.ObjectId,
ref: 'kb',
required: true
},
expireAt: {
type: Date,
default: () => new Date()
},
lockTime: {
type: Date,
default: () => new Date('2000/1/1')
},
mode: {
type: String,
enum: Object.keys(TrainingTypeMap),
required: true
},
vectorModel: {
type: String,
required: true,
default: 'text-embedding-ada-002'
},
prompt: {
// qa split prompt
type: String,
default: ''
},
q: {
type: String,
default: ''
},
a: {
type: String,
default: ''
},
source: {
type: String,
default: ''
},
file_id: {
type: String,
default: ''
}
});
try {
TrainingDataSchema.index({ lockTime: 1 });
TrainingDataSchema.index({ userId: 1 });
TrainingDataSchema.index({ expireAt: 1 }, { expireAfterSeconds: 7 * 24 * 60 });
} catch (error) {
console.log(error);
}
export const TrainingData: MongoModel<TrainingDateType> =
models['trainingData'] || model('trainingData', TrainingDataSchema);

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@@ -1,171 +0,0 @@
import { adaptChat2GptMessages } from '@/utils/common/adapt/message';
import { ChatContextFilter } from '@/service/common/tiktoken';
import type { ChatHistoryItemResType, ChatItemType } from '@/types/chat';
import { ChatRoleEnum, TaskResponseKeyEnum } from '@/constants/chat';
import { getAIChatApi, axiosConfig } from '@/service/lib/openai';
import type { ClassifyQuestionAgentItemType } from '@/types/app';
import { SystemInputEnum } from '@/constants/app';
import { SpecialInputKeyEnum } from '@/constants/flow';
import { FlowModuleTypeEnum } from '@/constants/flow';
import { ModuleDispatchProps } from '@/types/core/modules';
import { replaceVariable } from '@/utils/common/tools/text';
import { Prompt_CQJson } from '@/prompts/core/agent';
type Props = ModuleDispatchProps<{
systemPrompt?: string;
history?: ChatItemType[];
[SystemInputEnum.userChatInput]: string;
[SpecialInputKeyEnum.agents]: ClassifyQuestionAgentItemType[];
}>;
type CQResponse = {
[TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
[key: string]: any;
};
const agentFunName = 'agent_user_question';
/* request openai chat */
export const dispatchClassifyQuestion = async (props: Props): Promise<CQResponse> => {
const {
moduleName,
userOpenaiAccount,
inputs: { agents, userChatInput }
} = props as Props;
if (!userChatInput) {
return Promise.reject('Input is empty');
}
const cqModel = global.cqModel;
const { arg, tokens } = await (async () => {
if (cqModel.functionCall) {
return functionCall(props);
}
return completions(props);
})();
const result = agents.find((item) => item.key === arg?.type) || agents[0];
return {
[result.key]: 1,
[TaskResponseKeyEnum.responseData]: {
moduleType: FlowModuleTypeEnum.classifyQuestion,
moduleName,
price: userOpenaiAccount?.key ? 0 : cqModel.price * tokens,
model: cqModel.name || '',
tokens,
cqList: agents,
cqResult: result.value
}
};
};
async function functionCall({
userOpenaiAccount,
inputs: { agents, systemPrompt, history = [], userChatInput }
}: Props) {
const cqModel = global.cqModel;
const messages: ChatItemType[] = [
...(systemPrompt
? [
{
obj: ChatRoleEnum.System,
value: systemPrompt
}
]
: []),
...history,
{
obj: ChatRoleEnum.Human,
value: userChatInput
}
];
const filterMessages = ChatContextFilter({
messages,
maxTokens: cqModel.maxToken
});
const adaptMessages = adaptChat2GptMessages({ 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: cqModel.model,
temperature: 0,
messages: [...adaptMessages],
function_call: { name: agentFunName },
functions: [agentFunction]
},
{
...axiosConfig(userOpenaiAccount)
}
);
const arg = JSON.parse(response.data.choices?.[0]?.message?.function_call?.arguments || '');
return {
arg,
tokens: response.data.usage?.total_tokens || 0
};
}
async function completions({
userOpenaiAccount,
inputs: { agents, systemPrompt = '', history = [], userChatInput }
}: Props) {
const extractModel = global.extractModel;
const messages: ChatItemType[] = [
{
obj: ChatRoleEnum.Human,
value: replaceVariable(extractModel.prompt || Prompt_CQJson, {
systemPrompt,
typeList: agents.map((item) => `ID: "${item.key}", 问题类型:${item.value}`).join('\n'),
text: `${history.map((item) => `${item.obj}:${item.value}`).join('\n')}
Human:${userChatInput}`
})
}
];
const chatAPI = getAIChatApi(userOpenaiAccount);
const { data } = await chatAPI.createChatCompletion(
{
model: extractModel.model,
temperature: 0.01,
messages: adaptChat2GptMessages({ messages, reserveId: false }),
stream: false
},
{
timeout: 480000,
...axiosConfig()
}
);
const answer = data.choices?.[0].message?.content || '';
const totalTokens = data.usage?.total_tokens || 0;
const id = agents.find((item) => answer.includes(item.key))?.key || '';
return {
tokens: totalTokens,
arg: { type: id }
};
}

View File

@@ -1,74 +0,0 @@
import { PgClient } from '@/service/pg';
import type { ChatHistoryItemResType } from '@/types/chat';
import { TaskResponseKeyEnum } from '@/constants/chat';
import { getVector } from '@/pages/api/openapi/plugin/vector';
import { countModelPrice } from '@/service/events/pushBill';
import type { SelectedDatasetType } from '@/types/core/dataset';
import type { QuoteItemType } from '@/types/chat';
import { PgDatasetTableName } from '@/constants/plugin';
import { FlowModuleTypeEnum } from '@/constants/flow';
import { ModuleDispatchProps } from '@/types/core/modules';
type KBSearchProps = ModuleDispatchProps<{
kbList: SelectedDatasetType;
similarity: number;
limit: number;
userChatInput: string;
}>;
export type KBSearchResponse = {
[TaskResponseKeyEnum.responseData]: ChatHistoryItemResType;
isEmpty?: boolean;
unEmpty?: boolean;
quoteQA: QuoteItemType[];
};
export async function dispatchKBSearch(props: Record<string, any>): Promise<KBSearchResponse> {
const {
moduleName,
inputs: { kbList = [], similarity = 0.4, limit = 5, userChatInput }
} = props as KBSearchProps;
if (kbList.length === 0) {
return Promise.reject("You didn't choose the knowledge base");
}
if (!userChatInput) {
return Promise.reject('Your input is empty');
}
// get vector
const vectorModel = kbList[0]?.vectorModel || global.vectorModels[0];
const { vectors, tokenLen } = await getVector({
model: vectorModel.model,
input: [userChatInput]
});
// search kb
const res: any = await PgClient.query(
`BEGIN;
SET LOCAL ivfflat.probes = ${global.systemEnv.pgIvfflatProbe || 10};
select kb_id,id,q,a,source,file_id from ${PgDatasetTableName} where kb_id IN (${kbList
.map((item) => `'${item.kbId}'`)
.join(',')}) AND vector <#> '[${vectors[0]}]' < -${similarity} order by vector <#> '[${
vectors[0]
}]' limit ${limit};
COMMIT;`
);
const searchRes: QuoteItemType[] = res?.[2]?.rows || [];
return {
isEmpty: searchRes.length === 0 ? true : undefined,
unEmpty: searchRes.length > 0 ? true : undefined,
quoteQA: searchRes,
responseData: {
moduleType: FlowModuleTypeEnum.kbSearchNode,
moduleName,
price: countModelPrice({ model: vectorModel.model, tokens: tokenLen }),
model: vectorModel.name,
tokens: tokenLen,
similarity,
limit
}
};
}

View File

@@ -1,142 +0,0 @@
import mongoose from 'mongoose';
import tunnel from 'tunnel';
import { startQueue } from './utils/tools';
import { getInitConfig } from '@/pages/api/system/getInitData';
import { User } from './models/user';
import { PRICE_SCALE } from '@/constants/common';
import { initPg } from './pg';
import { createHashPassword } from '@/utils/tools';
import { createLogger, format, transports } from 'winston';
import 'winston-mongodb';
import { getTikTokenEnc } from '@/utils/common/tiktoken';
/**
* connect MongoDB and init data
*/
export async function connectToDatabase(): Promise<void> {
if (global.mongodb) {
return;
}
global.mongodb = 'connecting';
// init global data
global.qaQueueLen = 0;
global.vectorQueueLen = 0;
global.sendInformQueue = [];
global.sendInformQueueLen = 0;
// proxy obj
if (process.env.AXIOS_PROXY_HOST && process.env.AXIOS_PROXY_PORT) {
global.httpsAgent = tunnel.httpsOverHttp({
proxy: {
host: process.env.AXIOS_PROXY_HOST,
port: +process.env.AXIOS_PROXY_PORT
}
});
}
// logger
initLogger();
// init function
getInitConfig();
// init tikToken
getTikTokenEnc();
try {
mongoose.set('strictQuery', true);
global.mongodb = await mongoose.connect(process.env.MONGODB_URI as string, {
bufferCommands: true,
maxConnecting: Number(process.env.DB_MAX_LINK || 5),
maxPoolSize: Number(process.env.DB_MAX_LINK || 5),
minPoolSize: 2
});
await initRootUser();
initPg();
console.log('mongo connected');
} catch (error) {
console.log('error->', 'mongo connect error');
global.mongodb = null;
}
// init function
startQueue();
}
function initLogger() {
global.logger = createLogger({
transports: [
new transports.MongoDB({
db: process.env.MONGODB_URI as string,
collection: 'server_logs',
options: {
useUnifiedTopology: true
},
cappedSize: 500000000,
tryReconnect: true,
metaKey: 'meta',
format: format.combine(format.timestamp(), format.json())
}),
new transports.Console({
format: format.combine(
format.timestamp({ format: 'YYYY-MM-DD HH:mm:ss' }),
format.printf((info) => {
if (info.level === 'error') {
console.log(info.meta);
return `[${info.level.toLocaleUpperCase()}]: ${[info.timestamp]}: ${info.message}`;
}
return `[${info.level.toLocaleUpperCase()}]: ${[info.timestamp]}: ${info.message}${
info.meta ? `: ${JSON.stringify(info.meta)}` : ''
}`;
})
)
})
]
});
}
async function initRootUser() {
try {
const rootUser = await User.findOne({
username: 'root'
});
const psw = process.env.DEFAULT_ROOT_PSW || '123456';
if (rootUser) {
await User.findOneAndUpdate(
{ username: 'root' },
{
password: createHashPassword(psw),
balance: 999999 * PRICE_SCALE
}
);
} else {
await User.create({
username: 'root',
password: createHashPassword(psw),
balance: 999999 * PRICE_SCALE
});
}
console.log(`root user init:`, {
username: 'root',
password: psw
});
} catch (error) {
console.log('init root user error', error);
}
}
export * from './models/chat';
export * from './models/chatItem';
export * from './models/app';
export * from './models/user';
export * from './models/bill';
export * from './models/pay';
export * from './models/trainingData';
export * from './models/openapi';
export * from './models/promotionRecord';
export * from './models/collection';
export * from './models/kb';
export * from './models/inform';
export * from './models/image';
export * from './support/outLink/schema';

View File

@@ -1,79 +0,0 @@
import { PRICE_SCALE } from '@/constants/common';
import { IpLimit } from '@/service/common/ipLimit/schema';
import { authBalanceByUid, AuthUserTypeEnum } from '@/service/utils/auth';
import { OutLinkSchema } from '@/types/support/outLink';
import { OutLink } from './schema';
export async function authOutLinkChat({ shareId, ip }: { shareId: string; ip?: string | null }) {
// get outLink
const outLink = await OutLink.findOne({
shareId
});
if (!outLink) {
return Promise.reject('分享链接无效');
}
const uid = String(outLink.userId);
// authBalance
const user = await authBalanceByUid(uid);
// limit auth
await authOutLinkLimit({ outLink, ip });
return {
user,
userId: String(outLink.userId),
appId: String(outLink.appId),
authType: AuthUserTypeEnum.token,
responseDetail: outLink.responseDetail
};
}
export async function authOutLinkLimit({
outLink,
ip
}: {
outLink: OutLinkSchema;
ip?: string | null;
}) {
if (!ip || !outLink.limit) {
return;
}
if (outLink.limit.expiredTime && outLink.limit.expiredTime.getTime() < Date.now()) {
return Promise.reject('分享链接已过期');
}
if (outLink.limit.credit > -1 && outLink.total > outLink.limit.credit * PRICE_SCALE) {
return Promise.reject('链接超出使用限制');
}
const ipLimit = await IpLimit.findOne({ ip, eventId: outLink._id });
try {
if (!ipLimit) {
await IpLimit.create({
eventId: outLink._id,
ip,
account: outLink.limit.QPM - 1
});
return;
}
// over one minute
const diffTime = Date.now() - ipLimit.lastMinute.getTime();
if (diffTime >= 60 * 1000) {
ipLimit.account = outLink.limit.QPM - 1;
ipLimit.lastMinute = new Date();
return await ipLimit.save();
}
if (ipLimit.account <= 0) {
return Promise.reject(
`每分钟仅能请求 ${outLink.limit.QPM} 次, ${60 - Math.round(diffTime / 1000)}s 后重试~`
);
}
ipLimit.account = ipLimit.account - 1;
await ipLimit.save();
} catch (error) {}
}

View File

@@ -1,210 +0,0 @@
import type { NextApiRequest } from 'next';
import Cookie from 'cookie';
import { App, OpenApi, User, OutLink, KB } from '../mongo';
import type { AppSchema, UserModelSchema } from '@/types/mongoSchema';
import { ERROR_ENUM } from '../errorCode';
import { authJWT } from './tools';
export enum AuthUserTypeEnum {
token = 'token',
root = 'root',
apikey = 'apikey'
}
export const authCookieToken = async (cookie?: string, token?: string): Promise<string> => {
// 获取 cookie
const cookies = Cookie.parse(cookie || '');
const cookieToken = cookies.token || token;
if (!cookieToken) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
return await authJWT(cookieToken);
};
/* auth balance */
export const authBalanceByUid = async (uid: string) => {
const user = await User.findById<UserModelSchema>(
uid,
'_id username balance openaiAccount timezone'
);
if (!user) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
if (user.balance <= 0) {
return Promise.reject(ERROR_ENUM.insufficientQuota);
}
return user;
};
/* uniform auth user */
export const authUser = async ({
req,
authToken = false,
authRoot = false,
authBalance = false
}: {
req: NextApiRequest;
authToken?: boolean;
authRoot?: boolean;
authBalance?: boolean;
}) => {
const parseOpenApiKey = async (apiKey?: string) => {
if (!apiKey) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
try {
const openApi = await OpenApi.findOne({ apiKey });
if (!openApi) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
const userId = String(openApi.userId);
// 更新使用的时间
await OpenApi.findByIdAndUpdate(openApi._id, {
lastUsedTime: new Date()
});
return userId;
} catch (error) {
return Promise.reject(error);
}
};
const parseAuthorization = async (authorization?: string) => {
if (!authorization) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
// Bearer fastgpt-xxxx-appId
const auth = authorization.split(' ')[1];
if (!auth) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
const { apiKey, appId } = await (async () => {
const arr = auth.split('-');
if (arr.length !== 3) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
return {
apiKey: `${arr[0]}-${arr[1]}`,
appId: arr[2]
};
})();
// auth apiKey
const uid = await parseOpenApiKey(apiKey);
return {
uid,
appId
};
};
const parseRootKey = async (rootKey?: string, userId = '') => {
if (!rootKey || !process.env.ROOT_KEY || rootKey !== process.env.ROOT_KEY) {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
return userId;
};
const { cookie, token, apikey, rootkey, userid, authorization } = (req.headers || {}) as {
cookie?: string;
token?: string;
apikey?: string;
rootkey?: string;
userid?: string;
authorization?: string;
};
let uid = '';
let appId = '';
let authType: `${AuthUserTypeEnum}` = AuthUserTypeEnum.token;
if (authToken) {
uid = await authCookieToken(cookie, token);
authType = AuthUserTypeEnum.token;
} else if (authRoot) {
uid = await parseRootKey(rootkey, userid);
authType = AuthUserTypeEnum.root;
} else if (cookie || token) {
uid = await authCookieToken(cookie, token);
authType = AuthUserTypeEnum.token;
} else if (apikey) {
uid = await parseOpenApiKey(apikey);
authType = AuthUserTypeEnum.apikey;
} else if (authorization) {
const authResponse = await parseAuthorization(authorization);
uid = authResponse.uid;
appId = authResponse.appId;
authType = AuthUserTypeEnum.apikey;
} else if (rootkey) {
uid = await parseRootKey(rootkey, userid);
authType = AuthUserTypeEnum.root;
} else {
return Promise.reject(ERROR_ENUM.unAuthorization);
}
// balance check
const user = await (() => {
if (authBalance) {
return authBalanceByUid(uid);
}
})();
return {
userId: String(uid),
appId,
authType,
user
};
};
// 模型使用权校验
export const authApp = async ({
appId,
userId,
authUser = true,
authOwner = true,
reserveDetail = false
}: {
appId: string;
userId: string;
authUser?: boolean;
authOwner?: boolean;
reserveDetail?: boolean; // focus reserve detail
}) => {
// 获取 app 数据
const app = await App.findById<AppSchema>(appId);
if (!app) {
return Promise.reject('App is not exists');
}
/*
Access verification
1. authOwner=true or authUser = true , just owner can use
2. authUser = false and share, anyone can use
*/
if (authOwner || (authUser && !app.share.isShare)) {
if (userId !== String(app.userId)) return Promise.reject(ERROR_ENUM.unAuthModel);
}
return {
app,
showModelDetail: userId === String(app.userId)
};
};
// 知识库操作权限
export const authKb = async ({ kbId, userId }: { kbId: string; userId: string }) => {
const kb = await KB.findOne({
_id: kbId,
userId
});
if (kb) {
return kb;
}
return Promise.reject(ERROR_ENUM.unAuthKb);
};

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