fastworkflow 1.2.1

A framework for rapidly building large-scale, deterministic, interactive workflows with a fault-tolerant, conversational UX

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:
  • A custom-built intent detection pipeline for fault-tolerant, self-correcting 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.

Getting started

• Clone the repo
  • Use WSL if you are on Windows
• Create an env folder with a .env file inside and add the following entries
  • DSPY_LM_MODEL: the model to use for the DSPy API
  • SPEEDDICT_FOLDERNAME: the folder where the workflow definitions are stored
• export the OPENAI_API_KEY as an environment variable
  • Note: If you use a different model, specify the model path in the DSPY_LM_MODEL environment variable (The app uses Litellm as the LLM wrapper)
• Train then run the sample workflow
  • Hint: review the .vscode/launch.json file for training/running the sample workflow

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