A framework for rapidly building large-scale, deterministic, interactive workflows with a fault-tolerant, conversational UX
Project description
fastWorkflow
A framework for rapidly building large-scale, deterministic, interactive workflows with a fault-tolerant, conversational UX and AI-powered recommendations.
- Built on the principle on "Convention over configuration", ALA Ruby on Rails
- Uses:
- Semantic Router for fast command routing
- Pydantic and DSPy for parameter extraction and response generation
Concepts
- Workflows are defined as a directory hierarchy of workitem types
- Workitems can be ordered
- Min/max constraints can be defined for the number of child workitems (one, unlimited, min/max)
- Workflows can delegate to other workflows
- Commands are exposed for each workitem type
- Commands may be specific to one workitem type or inheritable by child workitem types (base commands)
- Users are guided through the workflow but have complete control over navigation
- Workflow navigation and command execution are exposed via a chat interface
- Special constrained workflows are used to handle routing and parameter extraction errors
- AI-powered recommendations after every command interaction
- Recommendations are generated AFTER a command has been processed. The user has complete control over the workflow and discretion over whether to follow a recommendation or take a different action.
Future Roadmap
- Training pipeline for prompt-tuning/fine-tuning the models - routing, parameter extraction, response generation, and recommendations
- Connectors to email, slack, databases etc.
- A chat assistant for generating workflow application code
- A chat assistant for generating natural language to SQL mappings
- An AI engine to guide users at every step of the workflow with command recommendations
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