JoyAgent-JDGenie's multimodal processing is characterized by three main technologies:
- Heterogeneous data fusion: Adoption of a unified intermediate representation layer to handle data in different formats, such as text, images, tables, etc.
- Intelligent Routing: Automatically selects the optimal processing pipeline based on the input type, e.g. image description calling CLIP+GPT combination
- context-sensitive: Support for maintaining semantic consistency across modalities in multi-round interactions
Specific types supported in the current version include:
- Input Type: JPEG/PNG images, PDF documents, CSV/Excel tables, Markdown text
- output capability: image description generation, document summarization, tables to visual charts, cross-format conversion
Typical usage scenarios are: uploading product images to automatically generate e-commerce descriptions, or parsing financial statements to generate PPT presentations. When dealing with multimodal tasks, it is recommended to prepare clear task descriptions, and if necessary, combine multiple intelligences to work together, for example, extracting image text through OCR intelligences first, and then handing it over to NLP intelligences for content processing.
This answer comes from the articleJoyAgent-JDGenie: an open source multi-intelligence framework to support automated processing of complex tasksThe
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