📌 Introducing Dify Workflow File Upload: Recreate Google NotebookLM Podcast
Dify Cloud · Self-hosting · Documentation · Enterprise inquiry
The quickest way to deploy Dify locally is to run our docker-compose.yml. Follow the instructions to start in 5 minutes.
Before installing Dify, make sure your machine meets the following minimum system requirements:
- CPU >= 2 Core
- RAM >= 4 GiB
- Docker and Docker Compose Installed
Run the following command in your terminal to clone the whole repo.
git clone https://github.com/langgenius/dify.gitAfter cloning,run the following command one by one.
cd dify
cd docker
cp .env.example .env
docker compose up -dAfter running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process. You will be asked to setup an admin account. For more info of quick setup, check here
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production. Here's a list of the core features:
1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
optimized_workflow_intro.mp4
2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.
3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
5. Agent capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
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Cloud
We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan. -
Self-hosting Dify Community Edition
Quickly get Dify running in your environment with this starter guide. Use our documentation for further references and more in-depth instructions. -
Dify for enterprise / organizations
We provide additional enterprise-centric features. Log your questions for us through this chatbot or send us an email to discuss enterprise needs.For startups and small businesses using AWS, check out Dify Premium on AWS Marketplace and deploy it to your own AWS VPC with one-click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
Star Dify on GitHub and be instantly notified of new releases.
Go to quick-start to setup your Dify or setup by source code.
If you forget your admin account, you can refer to this guide to reset the password.
Use docker compose up without "-d" to enable logs printing out in your terminal. This might be useful if you have encountered unknow problems when using Dify.
If you encountered system error and would like to acquire help in Github issues, make sure you always paste logs of the error in the request to accerate the conversation. Go to Community & contact for more information.
Please read the Dify Documentation for detailed how-to-use guidance. Most of the potential problems are explained in the doc.
If you'd like to contribute to Dify or make additional development, refer to our guide to deploying from source code
If you need to customize the configuration, please refer to the comments in our .env.example file and update the corresponding values in your .env file. Additionally, you might need to make adjustments to the docker-compose.yaml file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run docker-compose up -d. You can find the full list of available environment variables here.
If you'd like to configure a highly-available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.
Deploy Dify to Cloud Platform with a single click using terraform
For those who'd like to contribute code, see our Contribution Guide. At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the i18n README for more information, and leave us a comment in the
global-userschannel of our Discord Community Server.
Contributors
- Github Discussion. Best for: sharing feedback and asking questions.
- GitHub Issues. Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide.
- Discord. Best for: sharing your applications and hanging out with the community.
- X(Twitter). Best for: sharing your applications and hanging out with the community.
- Make sure a log, if possible, is attached to an error reported to maximize solution efficiency.
To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to [email protected] and we will provide you with a more detailed answer.
This repository is available under the Dify Open Source License, which is essentially Apache 2.0 with a few additional restrictions.
