FAQ#
About the project#
What is fluxus for?#
fluxus is a Python framework designed by BCG X to streamline the development of complex data processing pipelines (called flows), enabling users to quickly and efficiently build, test, and deploy highly concurrent workflows, making complex operations more manageable.
FLUXUS is inspired by the data stream paradigm and is designed to be simple, expressive, and composable.
Who developed fluxus, and why?#
fluxus was developed by the Responsible AI team at BCG X, primarily to provide a scalable and efficient way to rapidly stand up highly concurrent red teaming workloads for GenAI testing and evaluation.
Given that other use cases for fluxus are likely to emerge, we decided to publish the flow management portion of the codebase as a a separate open-source project.
What is the origin of the name fluxus?#
The name fluxus is derived from the Latin word for “flow” or “stream.” The name was chosen to reflect the project’s focus on data streams and the flow of data through a pipeline.
How does fluxus differ from other pipelining/workflow libraries?#
fluxus is designed to be simple, expressive, and composable. It is built on top of the asyncio library, which provides a powerful and flexible way to write concurrent code in Python.
fluxus is also designed to be highly extensible, allowing users to easily add new components and customize existing ones. It provides both a functional API for quickly and intuitively building flows using dictionaries as their primary data structures, as well as a class-based API for more complex flows that require custom data types.
Finally, fluxus is designed to be lightweight and efficient, managing the complexities of concurrency and parallelism behind the scenes so that users can focus on building their pipelines without worrying about the underlying implementation details.
What are examples of use cases for fluxus?#
fluxus is designed to be a general-purpose framework for building data processing pipelines, so it can be used in a wide variety of applications. It is particularly powerful for building highly concurrent workflows that make heavy use of I/O-bound operations, such as network requests, file I/O, and database queries.
Some examples of use cases for fluxus include:
Real-time data processing
ETL (Extract, Transform, Load) pipelines
Machine learning workflows
Data extraction
Data aggregation and analysis
Red teaming and security testing