v0.6.0
#3163
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Congratulations! Dify brings us a new era of LLM agents. |
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Can a node (specifically the LLM node) have multiple entry points? I'm thinking of using chatflow to mimic function calling. It works like this:
This is basically a home-brewed agent mode chatbot (because currently dify's agent mode cannot be customized - the prompt is not editable, and the agent always replaces dataset context rather than append to it). |
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Introducing Dify Workflow! 🎉
The much-anticipated workflow feature is here: In a nutshell, workflow provides a visual canvas for defining complex tasks as smaller, manageable steps (nodes). This reduces reliance on prompt engineering and LLM agent capabilities, taking the stability and reproducibility of your LLM applications to the next level by letting you be in control.
There are two Workflow application types with this update:
Workflow App
Targeting Automation and Batch Processing: This is ideal for translation, data analysis, content generation, email automation, and more.
Chatflow App (A Sub-Type of Chatbot)
For Conversational Applications: Suitable for customer service, semantic search, and more conversational apps requiring multi-step logic in crafting the response.
Compared to the regular Workflow app type, Chatflow adds chat-specific features such as conversation history support (Memory), tagged replies, an Answer node type for streaming responses, and support for rich text and images.
For more information, please visit: https://docs.dify.ai/features/workflow/introduce
Other Enhancements:
Optimized UI flow for app creation.
Conversion support from various basic application types to Workflow-based applications.
Dify's official app templates are now available in self-hosted mode.
Support for adding descriptions to applications.
Support for porting applications in and out of Dify with DSL.
Under the hood, we also refactored the underlying execution logic of all app types for cleaner architecture and a tidier repo.
Update Guide
If you need to upgrade from
0.6.0-preview-workflow.1
, you will need to connect to PostgreSQL and execute the following SQL (migration inserted in the main branch) to ensure data integrity.Docker compose deployments:
Get the latest code from the main branch:
Go to the next step and update to the latest image:
cd docker docker compose up -d
We also moved the agent data within the database, Execute the below script to complete the migrate: (NEW)
docker compose exec api flask convert-to-agent-apps
Source Code deployments:
Stop API server, Worker and Web frontend Server.
Get the latest code from the main branch:
Update Python dependencies:
cd api pip install -r requirements.txt
Then, let's run the migration script:
We also moved the agent data within the database, Execute the below script to complete the migrate: (NEW)
Finally, run API server, Worker and Web frontend Server again.
What's Changed
New Contributors
Full Changelog: 0.5.11-fix1...0.6.0
This discussion was created from the release v0.6.0.
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