siliconflow/dify

siliconflow/dify

Description: Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.

License: NOASSERTION

Stars: 0

Forks: 0

Open issues: 0

Created: 2024-09-26T01:59:02Z

Pushed: 2024-09-26T02:47:07Z

Default branch: main

Fork: yes

Parent repository: langgenius/dify

Archived: no

README: !cover-v5-optimized

Dify Cloud · Self-hosting · Documentation · Enterprise inquiry

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.

https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa

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.

!providers-v5

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.

Feature comparison

Feature Dify.AI LangChain Flowise OpenAI Assistants API

Programming Approach API + App-oriented Python Code App-oriented API-oriented

Supported LLMs Rich Variety Rich Variety Rich Variety OpenAI-only

RAG Engine ✅ ✅ ✅ ✅

Agent ✅ ✅ ❌ ✅

Workflow ✅ ❌ ✅ ❌

Observability ✅ ✅ ❌ ❌

Enterprise Features (SSO/Access control) ✅ ❌ ❌ ❌

Local Deployment ✅ ✅ ✅ ❌

Using Dify

• 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](#quick-start). 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](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) 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.

Staying ahead

Star Dify on GitHub and be instantly notified of new releases.

!star-us

Quick start

> Before installing Dify, make sure your machine meets the following minimum system requirements: > >- CPU >= 2 Core >- RAM >= 4GB

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 and Docker Compose are installed on your machine:

cd docker
cp .env.example .env
docker compose up -d

After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.

> If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code

Next steps

If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.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.

• Helm Chart by @LeoQuote
• Helm Chart by @BorisPolonsky
• YAML file by @Winson-030

Using Terraform for Deployment

##### Azure Global Deploy Dify to Azure with a single click using terraform.

• Azure Terraform by @nikawang

Contributing

For those who'd like to contribute code, see our [Contribution…