Skip to main content

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:
    • 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 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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fastworkflow-2.7.4.tar.gz (114.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fastworkflow-2.7.4-py3-none-any.whl (140.2 kB view details)

Uploaded Python 3

File details

Details for the file fastworkflow-2.7.4.tar.gz.

File metadata

  • Download URL: fastworkflow-2.7.4.tar.gz
  • Upload date:
  • Size: 114.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.2 Linux/6.6.87.2-microsoft-standard-WSL2

File hashes

Hashes for fastworkflow-2.7.4.tar.gz
Algorithm Hash digest
SHA256 a51a6c3c61c04e98c6b63fc66b28dccbe828f3d8b44b07fb5b9eb51995df01fe
MD5 31faea0cfc653952cf3a2eda1e3fdcc4
BLAKE2b-256 d5367fdb863af35c10a9d2102cb69ddce9bc786c781feed5a500a97870769607

See more details on using hashes here.

File details

Details for the file fastworkflow-2.7.4-py3-none-any.whl.

File metadata

  • Download URL: fastworkflow-2.7.4-py3-none-any.whl
  • Upload date:
  • Size: 140.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.2 Linux/6.6.87.2-microsoft-standard-WSL2

File hashes

Hashes for fastworkflow-2.7.4-py3-none-any.whl
Algorithm Hash digest
SHA256 3e57bc378a5ae97def593dc4087065218c9533fc1fc7d3d92a2720639480669c
MD5 85de9e25a54484e745664cd5644de987
BLAKE2b-256 15aabb0b70f2312dcfe19fc9f225f1e5000fda44f9baec81a35977d7983f4029

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page