Release Date: July 2025

Feature Status: Major Enhancement

Breaking Changes: None - All existing Python code works unchanged

Feature Summary

HyperFlow's Python coding environment now supports sophisticated multi-modal content processing, rich media generation, and advanced RAG workflows. Process images, audio, and video inputs while generating charts, visualizations, and fully-formed knowledge structures - all with intelligent data-flow handling that automatically maps between compatible input and output types.

What's New

HyperFlow's Python coding framework has undergone a substantial upgrade in this new release, focusing on vastly improved data-flow handling through the Python-enabled nodes, with intelligent mapping of compatible input data to requested input type, for example mapping a content selection or import set to extracted & concatenated text if Text is the declated input type, or detailed content descriptors with download URLs if the declared input type is Content.

Similarly on the output side, the upgraded framework intelligently maps conformant python values to requested output data-flow types.

This enables capabilities that were simply not supported in earlier versions, such as the ability to process all content input types via their mimetype and URL descriptors, access all metadata and crucial structures in segments and knowledge input sets, process and generate fully multi-modally data, both on input and generating new media content assets on output, with internal support for PIL, matlplotlib, ffmpeg, and the ability to process and generate fully-formed segments or RAG knowledge sets for fully custom segmentation & knowledge ranking & reranking.

Rich Content Processing

Python scripts can now intelligently process complex content inputs:

• Content Collections: Access mimetype, URL descriptors, and metadata from import sets and content selections
• Segment Analysis: See all metadata, hierarchical structures, and embeddings in segment collections
• Multi-Modal Inputs: Process images, audio, video, and documents with their native properties preserved
• Knowledge Sets: Access and manipulate RAG knowledge structures with rankings, relevance scores, and source attribution

Multi-Modal Content Generation

Generate rich media outputs directly from Python:

• Image Creation: Charts, visualizations, processed images using PIL and matplotlib
• Data Visualizations: Scientific plots, statistical analysis, custom graphics
• Video Processing: Frame extraction, editing, and generation with ffmpeg support