Skip to main content

A library for running AI workflows.

Project description

GenFlow

Description

GenFlow is an innovative AI workflow engine, designed to integrate seamlessly with a node-based web interface. It enables users to orchestrate complex workflows involving various AI models for generating multimedia content, including images, text, audio, and video, all within a single, unified platform.

Features

  • Diverse AI Model Integration: Effortlessly combine a broad spectrum of AI models.
  • Multimedia Content Generation: Create images, text, audio, and video within the same workflow.
  • Flexible Execution Environments: Supports both local and remote execution options.
  • Comprehensive Platform Support: Utilize models from leading platforms like Huggingface, Replicate, and OpenAI.
  • Advanced Database Integration: Leverage support for vector databases to enhance your workflows.
  • ComfyUI Workflows: Incorporate ComfyUI workflows for enhanced user interaction.
  • Retrieval Augmented Generation: Implement RAG for sophisticated data retrieval and generation.
  • Serverless GPU Deployment: Easily deploy workflows on serverless GPU platforms, including modal.com, for scalable processing power.

Installation

GenFlow supports various Python environments, including CPython and PyPy. The installation instructions provided below cater to both environments for maximum compatibility.

For CPython (default Python implementation)

pip install genflow-lib

Usage

To get started with GenFlow:

genflow setup

Execution

  • GenFlow Node Editor: Install the GenFlow Node Editor for an intuitive, graphical interface to design and manage workflows.
  • Command Line Execution: Alternatively, execute workflows directly from the command line for automation and scripting purposes.

Implementing Nodes

New nodes can be added by subclassing GenflowNode.

Node properties are defined using class fields with type annotations.

The node operation is defined in the process method, which takes a context object, allowing I/O amongst other operations.

class MyNode(GenflowNode):
    a: str = ""
    b: str = ""

    async def process(self, context: ProcessingContext) -> str:
      return self.a + self.b

Contribution

We welcome contributions from the community! To contribute to GenFlow, please adhere to our contribution guidelines. Your efforts help us improve and evolve this project.

License

GenFlow is made available under the terms of the GPL3 License, promoting open-source collaboration and sharing.

Contact

For inquiries, suggestions, or contributions, please reach out to the core team:

  • Matthias Georgi
  • David Buerer
  • Severin Schwanck

GitHub: https://github.com/Gen-Flow/genflow

Project details


Download files

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

Source Distribution

genflow_lib-0.1.6.tar.gz (10.8 MB view details)

Uploaded Source

Built Distribution

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

genflow_lib-0.1.6-py3-none-any.whl (11.7 MB view details)

Uploaded Python 3

File details

Details for the file genflow_lib-0.1.6.tar.gz.

File metadata

  • Download URL: genflow_lib-0.1.6.tar.gz
  • Upload date:
  • Size: 10.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for genflow_lib-0.1.6.tar.gz
Algorithm Hash digest
SHA256 f7cec15935ecfd1bc4fcc6714c4cca7b2123a69a49434f7553c67f041bd67002
MD5 629a30d1f6e818c2df5255291878cf2f
BLAKE2b-256 74b8ee7da0d54c2a19dee6d866ee7041c51ef197a721c40c36fd6ed103a55cb8

See more details on using hashes here.

File details

Details for the file genflow_lib-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: genflow_lib-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 11.7 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for genflow_lib-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 c8634c08ab93a39156407968c6cb667d2d9528f8bb128afbdc969fd2020ca688
MD5 d77e54aae50de38ee3fc9d03df402258
BLAKE2b-256 f01f3fe3b45a05e7d96176c3e10398454a6c70e8cd786602139de418e978a117

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