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:
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.
Getting started
- Clone the repo
- Use WSL if you are on Windows
- Create a .env file in the passwords folder and add below keys if required
- LITELLM_API_KEY_SYNDATA_GEN
- LITELLM_API_KEY_PARAM_EXTRACTION
- LITELLM_API_KEY_RESPONSE_GEN
- LITELLM_API_KEY_AGENT
- Train fastworkflow, then train the sample workflow, finally run the sample workflow agent or assistant
- Hint: review the .vscode/launch.json file for training/running the sample workflow
Future Roadmap
- AI enabled python applications
- Tools to enable rapid application development - declarative/imperative/visual
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