Custom Workflows
Workflows reduce system complexity by breaking down complex tasks into smaller steps (nodes), decreasing reliance on prompt engineering and model inference capabilities.
Last updated
Workflows reduce system complexity by breaking down complex tasks into smaller steps (nodes), decreasing reliance on prompt engineering and model inference capabilities.
Last updated
Aera workflows are divided into two types:
Chatflow: Designed for conversational scenarios such as customer service, semantic search, and other applications that require multi-step logic in response construction.
Workflow: Geared towards automation and batch processing scenarios, ideal for high-quality translation, data analysis, content generation, email automation, and more.
To address the complexity of user intent recognition in natural language input, Chatflow provides question understanding nodes. Compared to Workflow, it adds support for chatbot features such as conversation history (Memory), annotated replies, and Answer nodes.
Workflow, on the other hand, offers a variety of logic nodes for handling complex business logic in automation and batch processing scenarios. These include code nodes, IF/ELSE nodes, template transformations, iteration nodes, and more. Additionally, Workflow provides capabilities for timed and event-triggered actions, enabling the construction of automated processes.
Start by building a workflow from scratch or use system templates to get started.
Familiarise yourself with basic operations such as creating nodes on the canvas, connecting and configuring nodes, debugging workflows, and viewing run history.
Save and publish a workflow.
Run the published application or call the workflow through an API.
Nodes are the key components of a workflow. By connecting nodes with different functionalities, you can execute a series of operations within the workflow. For core workflow nodes, please refer to Block Description.
Variables link the input and output of nodes within a workflow, enabling complex processing logic throughout the process. For more details, please refer to Variables.
Application Scenarios
Chatflow: Designed for conversational scenarios, such as customer service, semantic search, and other applications requiring multi-step logic in response construction.
Workflow: Geared towards automation and batch processing scenarios, suitable for high-quality translation, data analysis, content generation, email automation, and more.
Usage Entry Points
Differences in Available Nodes
The End node is the ending node for Workflow and can only be selected at the end of the process.
The Answer node is specific to Chatflow, used for streaming text output, and can be output at intermediate steps in the process.
Chatflow has built-in chat memory (Memory) for storing and passing multi-turn conversation history, which can be enabled in nodes like LLM and question classifiers. Workflow does not support Memory-related configurations.
Built-in variables for Chatflow’s start node include: sys.query, sys.files, sys.conversation_id, sys.user_id. Built-in variables for Workflow’s start node include: sys.files, sys_id.
