LLM Workflow Generation With Visual Flowchart and Progress Tracking
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Solution Overview
Problem
Current workflow management systems require complex operations, including analyzing problems, designing flows, writing code, and setting up execution environments, leading to inefficiencies and high costs.
Innovation Solution
A task generation method utilizing a large language model to interpret task requirements, generate an executable structural body, and display a task execution flowchart, enabling users to visualize and manage workflows without manual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional workflow management systems are used, then workflows can be executed, but the operations become complex requiring problem analysis, flow design, code writing, and environment setup
Solution Approach 1:
The system enables self-service workflow generation by allowing users to input natural language task requirements, which are then automatically processed by the large language model to generate executable workflow code without requiring users to manually design flows or write code
Solution Approach 2:
The patent replaces manual mechanical operations (problem analysis, flow design, code writing, environment setup) with an automated intelligent system based on large language models that processes natural language inputs and generates executable workflows automatically
2Productivity
If manual workflow design and coding is performed, then customized workflows can be created, but time consumption and costs increase
Solution Approach 1:
The system performs preliminary action by pre-training the large language model on workflow-related data and pre-configuring the execution environment, so that when users submit task requirements, the workflow can be quickly generated and executed without requiring time-consuming manual setup
Solution Approach 2:
The patent uses copying by having the large language model generate workflow code based on learned patterns from training data, allowing common workflow patterns to be rapidly replicated and adapted without manual redesign
3Ease of operation
If complex operations are required for workflow management, then precise control can be achieved, but the system becomes difficult to use and costs increase
Solution Approach 1:
The patent introduces an intermediary layer consisting of the large language model and the workflow execution system, which translates simple natural language inputs into reliable executable workflows, maintaining reliability while simplifying user operations
Data Source
AI summary
A task generation method performed by a computer device includes: obtaining task requirement information inputted based on a task generation interface; calling a large language model, and inputting the task requirement information into the large language model for performing semantic interpretation on the task requirement information and outputting an executable structural body of a target task that matches the task requirement information; performing graphic rendering on the target task based on the executable structural body of the target task, to obtain a task execution flowchart of the target task, and displaying the task execution flowchart in the task generation interface, the target task being formed according to one or more target atomic tasks; and displaying an execution progress viewing link of the target task in the task generation interface, the execution progress viewing link being configured for viewing an execution progress of the target task.