Initial commit

This commit is contained in:
John Wang 2023-05-15 08:51:32 +08:00
commit db896255d6
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#!/usr/bin/env bash
set -eo pipefail
SHA=$(git rev-parse HEAD)
REPO_NAME=langgenius/dify
API_REPO_NAME="${REPO_NAME}-api"
if [[ "${GITHUB_EVENT_NAME}" == "pull_request" ]]; then
REFSPEC=$(echo "${GITHUB_HEAD_REF}" | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
PR_NUM=$(echo "${GITHUB_REF}" | sed 's:refs/pull/::' | sed 's:/merge::')
LATEST_TAG="pr-${PR_NUM}"
CACHE_FROM_TAG="latest"
elif [[ "${GITHUB_EVENT_NAME}" == "release" ]]; then
REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/tags/::' | head -c 40)
LATEST_TAG="${REFSPEC}"
CACHE_FROM_TAG="latest"
else
REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/heads/::' | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
LATEST_TAG="${REFSPEC}"
CACHE_FROM_TAG="${REFSPEC}"
fi
if [[ "${REFSPEC}" == "main" ]]; then
LATEST_TAG="latest"
CACHE_FROM_TAG="latest"
fi
echo "Pulling cache image ${API_REPO_NAME}:${CACHE_FROM_TAG}"
if docker pull "${API_REPO_NAME}:${CACHE_FROM_TAG}"; then
API_CACHE_FROM_SCRIPT="--cache-from ${API_REPO_NAME}:${CACHE_FROM_TAG}"
else
echo "WARNING: Failed to pull ${API_REPO_NAME}:${CACHE_FROM_TAG}, disable build image cache."
API_CACHE_FROM_SCRIPT=""
fi
cat<<EOF
Rolling with tags:
- ${API_REPO_NAME}:${SHA}
- ${API_REPO_NAME}:${REFSPEC}
- ${API_REPO_NAME}:${LATEST_TAG}
EOF
#
# Build image
#
cd api
docker build \
${API_CACHE_FROM_SCRIPT} \
--build-arg COMMIT_SHA=${SHA} \
-t "${API_REPO_NAME}:${SHA}" \
-t "${API_REPO_NAME}:${REFSPEC}" \
-t "${API_REPO_NAME}:${LATEST_TAG}" \
--label "sha=${SHA}" \
--label "built_at=$(date)" \
--label "build_actor=${GITHUB_ACTOR}" \
.
# push
docker push --all-tags "${API_REPO_NAME}"

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name: Build and Push API Image
on:
push:
branches:
- 'main'
- 'deploy/dev'
pull_request:
types: [synchronize, opened, reopened, ready_for_review]
jobs:
build-and-push:
runs-on: ubuntu-latest
if: github.event.pull_request.draft == false
steps:
- name: "Checkout ${{ github.ref }} ( ${{ github.sha }} )"
uses: actions/checkout@v2
with:
persist-credentials: false
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKERHUB_USER }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push Docker image
shell: bash
env:
DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
run: |
/bin/bash .github/workflows/build-api-image.sh
- name: Deploy to server
if: github.ref == 'refs/heads/deploy/dev'
uses: appleboy/ssh-action@v0.1.8
with:
host: ${{ secrets.SSH_HOST }}
username: ${{ secrets.SSH_USER }}
key: ${{ secrets.SSH_PRIVATE_KEY }}
script: |
${{ secrets.SSH_SCRIPT }}

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#!/usr/bin/env bash
set -eo pipefail
SHA=$(git rev-parse HEAD)
REPO_NAME=langgenius/dify
WEB_REPO_NAME="${REPO_NAME}-web"
if [[ "${GITHUB_EVENT_NAME}" == "pull_request" ]]; then
REFSPEC=$(echo "${GITHUB_HEAD_REF}" | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
PR_NUM=$(echo "${GITHUB_REF}" | sed 's:refs/pull/::' | sed 's:/merge::')
LATEST_TAG="pr-${PR_NUM}"
CACHE_FROM_TAG="latest"
elif [[ "${GITHUB_EVENT_NAME}" == "release" ]]; then
REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/tags/::' | head -c 40)
LATEST_TAG="${REFSPEC}"
CACHE_FROM_TAG="latest"
else
REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/heads/::' | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
LATEST_TAG="${REFSPEC}"
CACHE_FROM_TAG="${REFSPEC}"
fi
if [[ "${REFSPEC}" == "main" ]]; then
LATEST_TAG="latest"
CACHE_FROM_TAG="latest"
fi
echo "Pulling cache image ${WEB_REPO_NAME}:${CACHE_FROM_TAG}"
if docker pull "${WEB_REPO_NAME}:${CACHE_FROM_TAG}"; then
WEB_CACHE_FROM_SCRIPT="--cache-from ${WEB_REPO_NAME}:${CACHE_FROM_TAG}"
else
echo "WARNING: Failed to pull ${WEB_REPO_NAME}:${CACHE_FROM_TAG}, disable build image cache."
WEB_CACHE_FROM_SCRIPT=""
fi
cat<<EOF
Rolling with tags:
- ${WEB_REPO_NAME}:${SHA}
- ${WEB_REPO_NAME}:${REFSPEC}
- ${WEB_REPO_NAME}:${LATEST_TAG}
EOF
#
# Build image
#
cd web
docker build \
${WEB_CACHE_FROM_SCRIPT} \
--build-arg COMMIT_SHA=${SHA} \
-t "${WEB_REPO_NAME}:${SHA}" \
-t "${WEB_REPO_NAME}:${REFSPEC}" \
-t "${WEB_REPO_NAME}:${LATEST_TAG}" \
--label "sha=${SHA}" \
--label "built_at=$(date)" \
--label "build_actor=${GITHUB_ACTOR}" \
.
docker push --all-tags "${WEB_REPO_NAME}"

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name: Build and Push WEB Image
on:
push:
branches:
- 'main'
- 'deploy/dev'
pull_request:
types: [synchronize, opened, reopened, ready_for_review]
jobs:
build-and-push:
runs-on: ubuntu-latest
if: github.event.pull_request.draft == false
steps:
- name: "Checkout ${{ github.ref }} ( ${{ github.sha }} )"
uses: actions/checkout@v2
with:
persist-credentials: false
- name: Login to Docker Hub
uses: docker/login-action@v2
with:
username: ${{ secrets.DOCKERHUB_USER }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Build and push Docker image
shell: bash
env:
DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
run: |
/bin/bash .github/workflows/build-web-image.sh
- name: Deploy to server
if: github.ref == 'refs/heads/deploy/dev'
uses: appleboy/ssh-action@v0.1.8
with:
host: ${{ secrets.SSH_HOST }}
username: ${{ secrets.SSH_USER }}
key: ${{ secrets.SSH_PRIVATE_KEY }}
script: |
${{ secrets.SSH_SCRIPT }}

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import os
import re
from zhon.hanzi import punctuation
def has_chinese_characters(text):
for char in text:
if '\u4e00' <= char <= '\u9fff' or char in punctuation:
return True
return False
def check_file_for_chinese_comments(file_path):
with open(file_path, 'r', encoding='utf-8') as file:
for line_number, line in enumerate(file, start=1):
if has_chinese_characters(line):
print(f"Found Chinese characters in {file_path} on line {line_number}:")
print(line.strip())
return True
return False
def main():
has_chinese = False
excluded_files = ["model_template.py", 'stopwords.py', 'commands.py', 'indexing_runner.py']
for root, _, files in os.walk("."):
for file in files:
if file.endswith(".py") and file not in excluded_files:
file_path = os.path.join(root, file)
if check_file_for_chinese_comments(file_path):
has_chinese = True
if has_chinese:
raise Exception("Found Chinese characters in Python files. Please remove them.")
if __name__ == "__main__":
main()

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name: Check for Chinese comments
on:
push:
branches:
- 'main'
pull_request:
branches:
- main
jobs:
check-chinese-comments:
runs-on: ubuntu-latest
steps:
- name: Check out repository
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.9
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install zhon
- name: Run script to check for Chinese comments
run: |
python .github/workflows/check_no_chinese_comments.py

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name: PEP8 Check
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
pep8:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python 3.10
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install flake8
run: pip install flake8
- name: Run flake8
run: flake8 --ignore=E501 .

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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
.idea/'
.DS_Store
.vscode
# Intellij IDEA Files
.idea/
.ideaDataSources/
api/.env
api/storage/*
docker/volumes/app/storage/privkeys/*
docker/volumes/db/data/*
docker/volumes/redis/data/*
docker/volumes/weaviate/*
sdks/python-client/build
sdks/python-client/dist
sdks/python-client/dify_client.egg-info

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nite-knite
goocarlos
crazywoola
iamjoel
idsong
takatost

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# Contributing
Thanks for your interest in [Dify](https://dify.ai) and for wanting to contribute! Before you begin, read the
[code of conduct](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md) and check out the
[existing issues](https://github.com/langgenius/langgenius-gateway/issues).
This document describes how to set up your development environment to build and test [Dify](https://dify.ai).
### Install dependencies
You need to install and configure the following dependencies on your machine to build [Dify](https://dify.ai):
- [Git](http://git-scm.com/)
- [Docker](https://www.docker.com/)
- [Docker Compose](https://docs.docker.com/compose/install/)
- [Node.js v18.x (LTS)](http://nodejs.org)
- [npm](https://www.npmjs.com/) version 8.x.x or [Yarn](https://yarnpkg.com/)
- [Python](https://www.python.org/) version 3.10.x
## Local development
To set up a working development environment, just fork the project git repository and install the backend and frontend dependencies using the proper package manager and create run the docker-compose stack.
### Fork the repository
you need to fork the [repository](https://github.com/langgenius/langgenius-gateway).
### Clone the repo
Clone your GitHub forked repository:
```
git clone git@github.com:<github_username>/langgenius-gateway.git
```
### Install backend
To learn how to install the backend application, please refer to the [Backend README](api/README.md).
### Install frontend
To learn how to install the frontend application, please refer to the [Frontend README](web/README.md).
### Visit dify in your browser
Finally, you can now visit [http://localhost:3000](http://localhost:3000) to view the [Dify](https://dify.ai) in local environment.
## Create a pull request
After making your changes, open a pull request (PR). Once you submit your pull request, others from the Dify team/community will review it with you.
Did you have an issue, like a merge conflict, or don't know how to open a pull request? Check out [GitHub's pull request tutorial](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests) on how to resolve merge conflicts and other issues. Once your PR has been merged, you will be proudly listed as a contributor in the [contributor chart](https://github.com/langgenius/langgenius-gateway/graphs/contributors).
## Community channels
Stuck somewhere? Have any questions? Join the [Discord Community Server](https://discord.gg/AhzKf7dNgk). We are here to help!

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# 贡献
感谢您对 [Dify](https://dify.ai) 的兴趣,并希望您能够做出贡献!在开始之前,请先阅读[行为准则](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md)并查看[现有问题](https://github.com/langgenius/dify/issues)。
本文档介绍了如何设置开发环境以构建和测试 [Dify](https://dify.ai)。
### 安装依赖项
您需要在计算机上安装和配置以下依赖项才能构建 [Dify](https://dify.ai):
- [Git](http://git-scm.com/)
- [Docker](https://www.docker.com/)
- [Docker Compose](https://docs.docker.com/compose/install/)
- [Node.js v18.x (LTS)](http://nodejs.org)
- [npm](https://www.npmjs.com/) 版本 8.x.x 或 [Yarn](https://yarnpkg.com/)
- [Python](https://www.python.org/) 版本 3.10.x
## 本地开发
要设置一个可工作的开发环境,只需 fork 项目的 git 存储库,并使用适当的软件包管理器安装后端和前端依赖项,然后创建并运行 docker-compose 堆栈。
### Fork存储库
您需要 fork [存储库](https://github.com/langgenius/dify)。
### 克隆存储库
克隆您在 GitHub 上 fork 的存储库:
```
git clone git@github.com:<github_username>/dify.git
```
### 安装后端
要了解如何安装后端应用程序,请参阅[后端 README](api/README.md)。
### 安装前端
要了解如何安装前端应用程序,请参阅[前端 README](web/README.md)。
### 在浏览器中访问 Dify
最后,您现在可以访问 [http://localhost:3000](http://localhost:3000) 在本地环境中查看 [Dify](https://dify.ai)。
## 创建拉取请求
在进行更改后,打开一个拉取请求(PR)。提交拉取请求后,Dify 团队/社区的其他人将与您一起审查它。
如果遇到问题,比如合并冲突或不知道如何打开拉取请求,请查看 GitHub 的[拉取请求教程](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests),了解如何解决合并冲突和其他问题。一旦您的 PR 被合并,您将自豪地被列为[贡献者表](https://github.com/langgenius/dify/graphs/contributors)中的一员。
## 社区渠道
遇到困难了吗?有任何问题吗? 加入 [Discord Community Server](https://discord.gg/AhzKf7dNgk),我们将为您提供帮助。

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# Dify Open Source License
The Dify project uses a combination of the Apache License 2.0, MIT License, and an additional agreement to protect against direct competition with Dify Cloud services.
As a contributor, you should agree that your contributed code:
a. Might be subject to a more permissive open source license in the future.
b. Can be used for commercial purposes, such as Dify's cloud business.
The following components are open source under the MIT license, allowing you to build and develop applications based on them:
- WebApp elements, e.g., web/app/components/share
- Derived WebApp Template projects
The remaining parts of the project are open source under the Apache License 2.0.
With the Apache License 2.0, MIT License, and this supplementary agreement, anyone can freely use, modify, and distribute Dify, provided that:
- If you use Dify solely as a backend service for other applications, no authorization is needed for commercial or closed source purposes.
- If you wish to use Dify for commercial and closed source SaaS services similar to Dify Cloud, please contact us for authorization.
The interactive design of this product is protected by appearance patent.
© 2023 LangGenius, Inc.
----------
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
----------
The MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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![](./images/describe-en.png)
<p align="center">
<a href="./README.md">English</a> |
<a href="./README_CN.md">简体中文</a>
</p>
[Website](http://dify.ai) • [Docs](https://docs.dify.ai) • [Twitter](https://twitter.com/dify_ai)
**Dify** is an easy-to-use LLMOps platform designed to empower more people to create sustainable, AI-native applications. With visual orchestration for various application types, Dify offers out-of-the-box, ready-to-use applications that can also serve as Backend-as-a-Service APIs. Unify your development process with one API for plugins and datasets integration, and streamline your operations using a single interface for prompt engineering, visual analytics, and continuous improvement.
Applications created with Dify include:
Out-of-the-box web sites supporting form mode and chat conversation mode
A single API encompassing plugin capabilities, context enhancement, and more, saving you backend coding effort
Visual data analysis, log review, and annotation for applications
Dify is compatible with Langchain, meaning we'll gradually support multiple LLMs, currently supported:
- GPT 3 (text-davinci-003)
- GPT 3.5 Turbo(ChatGPT)
- GPT-4
## Use Cloud Services
Visit [Dify.ai](http://dify.ai)
## Install the Community Edition
### System Requirements
Before installing Dify, make sure your machine meets the following minimum system requirements:
- CPU >= 1 Core
- RAM >= 4GB
### Quick Start
The easiest way to start the Dify server is to run our [docker-compose.yml](docker/docker-compose.yaml) file. Before running the installation command, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
```bash
cd docker
docker-compose up -d
```
After running, you can access the Dify console in your browser at [http://localhost](http://localhost) and start the initialization operation.
### Configuration
If you need to customize the configuration, please refer to the comments in our [docker-compose.yml](docker/docker-compose.yaml) file and manually set the environment configuration. After making the changes, please run 'docker-compose up -d' again.
## Roadmap
Features under development:
- **Datasets**, supporting more datasets, e.g. syncing content from Notion or webpages
We will support more datasets, including text, webpages, and even Notion content. Users can build AI applications based on their own data sources.
- **Plugins**, introducing ChatGPT Plugin-standard plugins for applications, or using Dify-produced plugins
We will release plugins complying with ChatGPT standard, or Dify's own plugins to enable more capabilities in applications.
- **Open-source models**, e.g. adopting Llama as a model provider or for further fine-tuning
We will work with excellent open-source models like Llama, by providing them as model options in our platform, or using them for further fine-tuning.
## Q&A
**Q: What can I do with Dify?**
A: Dify is a simple yet powerful LLM development and operations tool. You can use it to build commercial-grade applications, personal assistants. If you want to develop your own applications, LangDifyGenius can save you backend work in integrating with OpenAI and offer visual operations capabilities, allowing you to continuously improve and train your GPT model.
**Q: How do I use Dify to "train" my own model?**
A: A valuable application consists of Prompt Engineering, context enhancement, and Fine-tuning. We've created a hybrid programming approach combining Prompts with programming languages (similar to a template engine), making it easy to accomplish long-text embedding or capturing subtitles from a user-input Youtube video - all of which will be submitted as context for LLMs to process. We place great emphasis on application operability, with data generated by users during App usage available for analysis, annotation, and continuous training. Without the right tools, these steps can be time-consuming.
**Q: What do I need to prepare if I want to create my own application?**
A: We assume you already have an OpenAI API Key; if not, please register for one. If you already have some content that can serve as training context, that's great!
**Q: What interface languages are available?**
A: English and Chinese are currently supported, and you can contribute language packs to us.
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
## Contact Us
If you have any questions, suggestions, or partnership inquiries, feel free to contact us through the following channels:
- Submit an Issue or PR on our GitHub Repo
- Join the discussion in our [Discord](https://discord.gg/AhzKf7dNgk) Community
- Send an email to hello@dify.ai
We're eager to assist you and together create more fun and useful AI applications!
## Contributing
To ensure proper review, all code contributions - including those from contributors with direct commit access - must be submitted via pull requests and approved by the core development team prior to being merged.
We welcome all pull requests! If you'd like to help, check out the [Contribution Guide](CONTRIBUTING.md) for more information on how to get started.
## Security
To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
## Citation
This software uses the following open-source software:
- Chase, H. (2022). LangChain [Computer software]. https://github.com/hwchase17/langchain
- Liu, J. (2022). LlamaIndex [Computer software]. doi: 10.5281/zenodo.1234.
For more information, please refer to the official website or license text of the respective software.
## License
This repository is available under the [Dify Open Source License](LICENSE).

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![](./images/describe-cn.jpg)
<p align="center">
<a href="./README.md">English</a> |
<a href="./README_CN.md">简体中文</a>
</p>
[官方网站](http://dify.ai) • [文档](https://docs.dify.ai/v/zh-hans) • [Twitter](https://twitter.com/dify_ai)
**Dify** 是一个易用的 LLMOps 平台,旨在让更多人可以创建可持续运营的原生 AI 应用。Dify 提供多种类型应用的可视化编排,应用可开箱即用,也能以“后端即服务”的 API 提供服务。
通过 Dify 创建的应用包含了:
- 开箱即用的的 Web 站点,支持表单模式和聊天对话模式
- 一套 API 即可包含插件、上下文增强等能力,替你省下了后端代码的编写工作
- 可视化的对应用进行数据分析,查阅日志或进行标注
Dify 兼容 Langchain,这意味着我们将逐步支持多种 LLMs ,目前已支持:
- GPT 3 (text-davinci-003)
- GPT 3.5 Turbo(ChatGPT)
- GPT-4
## 使用云服务
访问 [Dify.ai](http://cloud.dify.ai)
## 安装社区版
### 系统要求
在安装 Dify 之前,请确保您的机器满足以下最低系统要求:
- CPU >= 1 Core
- RAM >= 4GB
### 快速启动
启动 Dify 服务器的最简单方法是运行我们的 [docker-compose.yml](docker/docker-compose.yaml) 文件。在运行安装命令之前,请确保您的机器上安装了 [Docker](https://docs.docker.com/get-docker/) 和 [Docker Compose](https://docs.docker.com/compose/install/):
```bash
cd docker
docker-compose up -d
```
运行后,可以在浏览器上访问 [http://localhost](http://localhost) 进入 Dify 控制台,并开始初始化操作。
### 配置
需要自定义配置,请参考我们的 [docker-compose.yml](docker/docker-compose.yaml) 文件中的注释,并手动设置环境配置,修改完毕后,请再次执行 `docker-compose up -d`。
## Roadmap
我们正在开发中的功能:
- **数据集**,支持更多的数据集,例如同步 Notion 或网页的内容
我们将支持更多的数据集,包括文本、网页,甚至 Notion 内容。用户可以根据自己的数据源构建 AI 应用程序。
- **插件**,推出符合 ChatGPT 标准的插件,或使用 Dify 产生的插件
我们将发布符合 ChatGPT 标准的插件,或者 Dify 自己的插件,以在应用程序中启用更多功能。
- **开源模型**,例如采用 Llama 作为模型提供者,或进行进一步的微调
我们将与优秀的开源模型如 Llama 合作,通过在我们的平台中提供它们作为模型选项,或使用它们进行进一步的微调。
## Q&A
**Q: 我能用 Dify 做什么?**
A: Dify 是一个简单且能力丰富的 LLM 开发和运营工具。你可以用它搭建商用级应用,个人助理。如果你想自己开发应用,Dify 也能为你省下接入 OpenAI 的后端工作,使用我们逐步提供的可视化运营能力,你可以持续的改进和训练你的 GPT 模型。
**Q: 如何使用 Dify “训练”自己的模型?**
A: 一个有价值的应用由 Prompt Engineering、上下文增强和 Fine-tune 三个环节组成。我们创造了一种 Prompt 结合编程语言的 Hybrid 编程方式(类似一个模版引擎),你可以轻松的完成长文本嵌入,或抓取用户输入的一个 Youtube 视频的字幕——这些都将作为上下文提交给 LLMs 进行计算。我们十分注重应用的可运营性,你的用户在使用 App 期间产生的数据,可进行分析、标记和持续训练。以上环节如果没有好的工具支持,可能会消耗你大量的时间。
**Q: 如果要创建一个自己的应用,我需要准备什么?**
A: 我们假定你已经有了 OpenAI API Key,如果没有请去注册一个。如果你已经有了一些内容可以作为训练上下文,就太好了。
**Q: 提供哪些界面语言?**
A: 现已支持英文与中文,你可以为我们贡献语言包。
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
## 联系我们
如果您有任何问题、建议或合作意向,欢迎通过以下方式联系我们:
- 在我们的 [GitHub Repo](https://github.com/langgenius/dify) 上提交 Issue 或 PR
- 在我们的 [Discord 社区](https://discord.gg/AhzKf7dNgk) 上加入讨论
- 发送邮件至 hello@dify.ai
## 贡献代码
为了确保正确审查,所有代码贡献 - 包括来自具有直接提交更改权限的贡献者 - 都必须提交 PR 请求并在合并分支之前得到核心开发人员的批准。
我们欢迎所有人提交 PR!如果您愿意提供帮助,可以在 [贡献指南](CONTRIBUTING_CN.md) 中了解有关如何为项目做出贡献的更多信息。
## 安全
为了保护您的隐私,请避免在 GitHub 上发布安全问题。发送问题至 security@dify.ai,我们将为您做更细致的解答。
## Citation
本软件使用了以下开源软件:
- Chase, H. (2022). LangChain [Computer software]. https://github.com/hwchase17/langchain
- Liu, J. (2022). LlamaIndex [Computer software]. doi: 10.5281/zenodo.1234.
更多信息,请参考相应软件的官方网站或许可证文本。
## License
本仓库遵循 [Dify Open Source License](LICENSE) 开源协议。

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.env
storage/privkeys/*

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# Server Edition
EDITION=SELF_HOSTED
# Your App secret key will be used for securely signing the session cookie
# Make sure you are changing this key for your deployment with a strong key.
# You can generate a strong key using `openssl rand -base64 42`.
# Alternatively you can set it with `SECRET_KEY` environment variable.
SECRET_KEY=
# Console API base URL
CONSOLE_URL=http://127.0.0.1:5001
# Service API base URL
API_URL=http://127.0.0.1:5001
# Web APP base URL
APP_URL=http://127.0.0.1:5001
# celery configuration
CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
# redis configuration
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=difyai123456
REDIS_DB=0
# PostgreSQL database configuration
DB_USERNAME=postgres
DB_PASSWORD=difyai123456
DB_HOST=localhost
DB_PORT=5432
DB_DATABASE=dify
# Storage configuration
# use for store upload files, private keys...
# storage type: local, s3
STORAGE_TYPE=local
STORAGE_LOCAL_PATH=storage
S3_ENDPOINT=https://your-bucket-name.storage.s3.clooudflare.com
S3_BUCKET_NAME=your-bucket-name
S3_ACCESS_KEY=your-access-key
S3_SECRET_KEY=your-secret-key
S3_REGION=your-region
# CORS configuration
WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
CONSOLE_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
# Cookie configuration
COOKIE_HTTPONLY=true
COOKIE_SAMESITE=None
COOKIE_SECURE=true
# Session configuration
SESSION_PERMANENT=true
SESSION_USE_SIGNER=true
## support redis, sqlalchemy
SESSION_TYPE=redis
# session redis configuration
SESSION_REDIS_HOST=localhost
SESSION_REDIS_PORT=6379
SESSION_REDIS_PASSWORD=difyai123456
SESSION_REDIS_DB=2
# Vector database configuration, support: weaviate, qdrant
VECTOR_STORE=weaviate
# Weaviate configuration
WEAVIATE_ENDPOINT=http://localhost:8080
WEAVIATE_API_KEY=WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih
WEAVIATE_GRPC_ENABLED=false
# Qdrant configuration, use `path:` prefix for local mode or `https://your-qdrant-cluster-url.qdrant.io` for remote mode
QDRANT_URL=path:storage/qdrant
QDRANT_API_KEY=your-qdrant-api-key
# Sentry configuration
SENTRY_DSN=
# DEBUG
DEBUG=false
SQLALCHEMY_ECHO=false

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FROM langgenius/base:1.0.0-bullseye-slim as langgenius-api
LABEL maintainer="takatost@gmail.com"
ENV FLASK_APP app.py
ENV EDITION SELF_HOSTED
ENV DEPLOY_ENV PRODUCTION
ENV CONSOLE_URL http://127.0.0.1:5001
ENV API_URL http://127.0.0.1:5001
ENV APP_URL http://127.0.0.1:5001
EXPOSE 5001
WORKDIR /app/api
COPY requirements.txt /app/api/requirements.txt
RUN pip install -r requirements.txt
COPY . /app/api/
COPY docker/entrypoint.sh /entrypoint.sh
RUN chmod +x /entrypoint.sh
ARG COMMIT_SHA
ENV COMMIT_SHA ${COMMIT_SHA}
ENTRYPOINT ["/entrypoint.sh"]

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# Dify Backend API
## Usage
1. Start the docker-compose stack
The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
```bash
cd ../docker
docker-compose -f docker-compose.middleware.yaml up -d
cd ../api
```
2. Copy `.env.example` to `.env`
3. Generate a `SECRET_KEY` in the `.env` file.
```bash
openssl rand -base64 42
```
4. Install dependencies
```bash
pip install -r requirements.txt
```
5. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
flask db upgrade
```
6. Start backend:
```bash
flask run --host 0.0.0.0 --port=5001 --debug
```
7. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...

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# -*- coding:utf-8 -*-
import os
if not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true':
from gevent import monkey
monkey.patch_all()
import logging
import json
import threading
from flask import Flask, request, Response, session
import flask_login
from flask_cors import CORS
from extensions import ext_session, ext_celery, ext_sentry, ext_redis, ext_login, ext_vector_store, ext_migrate, \
ext_database, ext_storage
from extensions.ext_database import db
from extensions.ext_login import login_manager
# DO NOT REMOVE BELOW
from models import model, account, dataset, web, task
from events import event_handlers
# DO NOT REMOVE ABOVE
import core
from config import Config, CloudEditionConfig
from commands import register_commands
from models.account import TenantAccountJoin
from models.model import Account, EndUser, App
import warnings
warnings.simplefilter("ignore", ResourceWarning)
class DifyApp(Flask):
pass
# -------------
# Configuration
# -------------
config_type = os.getenv('EDITION', default='SELF_HOSTED') # ce edition first
# ----------------------------
# Application Factory Function
# ----------------------------
def create_app(test_config=None) -> Flask:
app = DifyApp(__name__)
if test_config:
app.config.from_object(test_config)
else:
if config_type == "CLOUD":
app.config.from_object(CloudEditionConfig())
else:
app.config.from_object(Config())
app.secret_key = app.config['SECRET_KEY']
logging.basicConfig(level=app.config.get('LOG_LEVEL', 'INFO'))
initialize_extensions(app)
register_blueprints(app)
register_commands(app)
core.init_app(app)
return app
def initialize_extensions(app):
# Since the application instance is now created, pass it to each Flask
# extension instance to bind it to the Flask application instance (app)
ext_database.init_app(app)
ext_migrate.init(app, db)
ext_redis.init_app(app)
ext_vector_store.init_app(app)
ext_storage.init_app(app)
ext_celery.init_app(app)
ext_session.init_app(app)
ext_login.init_app(app)
ext_sentry.init_app(app)
# Flask-Login configuration
@login_manager.user_loader
def load_user(user_id):
"""Load user based on the user_id."""
if request.blueprint == 'console':
# Check if the user_id contains a dot, indicating the old format
if '.' in user_id:
tenant_id, account_id = user_id.split('.')
else:
account_id = user_id
account = db.session.query(Account).filter(Account.id == account_id).first()
if account:
workspace_id = session.get('workspace_id')
if workspace_id:
tenant_account_join = db.session.query(TenantAccountJoin).filter(
TenantAccountJoin.account_id == account.id,
TenantAccountJoin.tenant_id == workspace_id
).first()
if not tenant_account_join:
tenant_account_join = db.session.query(TenantAccountJoin).filter(
TenantAccountJoin.account_id == account.id).first()
if tenant_account_join:
account.current_tenant_id = tenant_account_join.tenant_id
session['workspace_id'] = account.current_tenant_id
else:
account.current_tenant_id = workspace_id
else:
tenant_account_join = db.session.query(TenantAccountJoin).filter(
TenantAccountJoin.account_id == account.id).first()
if tenant_account_join:
account.current_tenant_id = tenant_account_join.tenant_id
session['workspace_id'] = account.current_tenant_id
# Log in the user with the updated user_id
flask_login.login_user(account, remember=True)
return account
else:
return None
@login_manager.unauthorized_handler
def unauthorized_handler():
"""Handle unauthorized requests."""
return Response(json.dumps({
'code': 'unauthorized',
'message': "Unauthorized."
}), status=401, content_type="application/json")
# register blueprint routers
def register_blueprints(app):
from controllers.service_api import bp as service_api_bp
from controllers.web import bp as web_bp
from controllers.console import bp as console_app_bp
app.register_blueprint(service_api_bp)
CORS(web_bp,
resources={
r"/*": {"origins": app.config['WEB_API_CORS_ALLOW_ORIGINS']}},
supports_credentials=True,
allow_headers=['Content-Type', 'Authorization'],
methods=['GET', 'PUT', 'POST', 'DELETE', 'OPTIONS', 'PATCH'],
expose_headers=['X-Version', 'X-Env']
)
app.register_blueprint(web_bp)
CORS(console_app_bp,
resources={
r"/*": {"origins": app.config['CONSOLE_CORS_ALLOW_ORIGINS']}},
supports_credentials=True,
allow_headers=['Content-Type', 'Authorization'],
methods=['GET', 'PUT', 'POST', 'DELETE', 'OPTIONS', 'PATCH'],
expose_headers=['X-Version', 'X-Env']
)
app.register_blueprint(console_app_bp)
# create app
app = create_app()
celery = app.extensions["celery"]
if app.config['TESTING']:
print("App is running in TESTING mode")
@app.after_request
def after_request(response):
"""Add Version headers to the response."""
response.headers.add('X-Version', app.config['CURRENT_VERSION'])
response.headers.add('X-Env', app.config['DEPLOY_ENV'])
return response
@app.route('/health')
def health():
return Response(json.dumps({
'status': 'ok',
'version': app.config['CURRENT_VERSION']
}), status=200, content_type="application/json")
@app.route('/threads')
def threads():
num_threads = threading.active_count()
threads = threading.enumerate()
thread_list = []
for thread in threads:
thread_name = thread.name
thread_id = thread.ident
is_alive = thread.is_alive()
thread_list.append({
'name': thread_name,
'id': thread_id,
'is_alive': is_alive
})
return {
'thread_num': num_threads,
'threads': thread_list
}
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5001)

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import datetime
import json
import random
import string
import click
from libs.password import password_pattern, valid_password, hash_password
from libs.helper import email as email_validate
from extensions.ext_database import db
from models.account import InvitationCode
from models.model import Account, AppModelConfig, ApiToken, Site, App, RecommendedApp
import secrets
import base64
@click.command('reset-password', help='Reset the account password.')
@click.option('--email', prompt=True, help='The email address of the account whose password you need to reset')
@click.option('--new-password', prompt=True, help='the new password.')
@click.option('--password-confirm', prompt=True, help='the new password confirm.')
def reset_password(email, new_password, password_confirm):
if str(new_password).strip() != str(password_confirm).strip():
click.echo(click.style('sorry. The two passwords do not match.', fg='red'))
return
account = db.session.query(Account). \
filter(Account.email == email). \
one_or_none()
if not account:
click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
return
try:
valid_password(new_password)
except:
click.echo(
click.style('sorry. The passwords must match {} '.format(password_pattern), fg='red'))
return
# generate password salt
salt = secrets.token_bytes(16)
base64_salt = base64.b64encode(salt).decode()
# encrypt password with salt
password_hashed = hash_password(new_password, salt)
base64_password_hashed = base64.b64encode(password_hashed).decode()
account.password = base64_password_hashed
account.password_salt = base64_salt
db.session.commit()
click.echo(click.style('Congratulations!, password has been reset.', fg='green'))
@click.command('reset-email', help='Reset the account email.')
@click.option('--email', prompt=True, help='The old email address of the account whose email you need to reset')
@click.option('--new-email', prompt=True, help='the new email.')
@click.option('--email-confirm', prompt=True, help='the new email confirm.')
def reset_email(email, new_email, email_confirm):
if str(new_email).strip() != str(email_confirm).strip():
click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red'))
return
account = db.session.query(Account). \
filter(Account.email == email). \
one_or_none()
if not account:
click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
return
try:
email_validate(new_email)
except:
click.echo(
click.style('sorry. {} is not a valid email. '.format(email), fg='red'))
return
account.email = new_email
db.session.commit()
click.echo(click.style('Congratulations!, email has been reset.', fg='green'))
@click.command('generate-invitation-codes', help='Generate invitation codes.')
@click.option('--batch', help='The batch of invitation codes.')
@click.option('--count', prompt=True, help='Invitation codes count.')
def generate_invitation_codes(batch, count):
if not batch:
now = datetime.datetime.now()
batch = now.strftime('%Y%m%d%H%M%S')
if not count or int(count) <= 0:
click.echo(click.style('sorry. the count must be greater than 0.', fg='red'))
return
count = int(count)
click.echo('Start generate {} invitation codes for batch {}.'.format(count, batch))
codes = ''
for i in range(count):
code = generate_invitation_code()
invitation_code = InvitationCode(
code=code,
batch=batch
)
db.session.add(invitation_code)
click.echo(code)
codes += code + "\n"
db.session.commit()
filename = 'storage/invitation-codes-{}.txt'.format(batch)
with open(filename, 'w') as f:
f.write(codes)
click.echo(click.style(
'Congratulations! Generated {} invitation codes for batch {} and saved to the file \'{}\''.format(count, batch,
filename),
fg='green'))
def generate_invitation_code():
code = generate_upper_string()
while db.session.query(InvitationCode).filter(InvitationCode.code == code).count() > 0:
code = generate_upper_string()
return code
def generate_upper_string():
letters_digits = string.ascii_uppercase + string.digits
result = ""
for i in range(8):
result += random.choice(letters_digits)
return result
@click.command('gen-recommended-apps', help='Number of records to generate')
def generate_recommended_apps():
print('Generating recommended app data...')
apps = App.query.all()
for app in apps:
recommended_app = RecommendedApp(
app_id=app.id,
description={
'en': 'Description for ' + app.name,
'zh': '描述 ' + app.name
},
copyright='Copyright ' + str(random.randint(1990, 2020)),
privacy_policy='https://privacypolicy.example.com',
category=random.choice(['Games', 'News', 'Music', 'Sports']),
position=random.randint(1, 100),
install_count=random.randint(100, 100000)
)
db.session.add(recommended_app)
db.session.commit()
print('Done!')
def register_commands(app):
app.cli.add_command(reset_password)
app.cli.add_command(reset_email)
app.cli.add_command(generate_invitation_codes)
app.cli.add_command(generate_recommended_apps)

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