Background
Traditional RAG systems can only process plain text content, resulting in the loss of key information such as pictures and tables in the document, which affects the accuracy and completeness of the answer.
Core Solutions
RAG-Anything solves the problem in the following way:
- Built-in multimodal parser: Recognize images, tables and formulas with specialized analysis tools
- Knowledge graph construction: networking all elements and their relationships
- Visual language model: call GPT-4o and other models to analyze image content
- Hybrid search techniques: combining keyword matching and contextual understanding to locate information
procedure
- Select the 'all' option when installing:
pip install 'raganything[all]' - Enable image and table processing when configured:
enable_image_processing=True, enable_table_processing=True - Use hybrid mode when asking questions:
mode='hybrid'
caveat
LibreOffice needs to be installed to process Office documents and ensure image clarity for recognition.
This answer comes from the articleRAG-Anything: an all-in-one RAG system that can handle graphic formsThe
May not be reproduced without permission:AI productivity tools " How to solve the problem that traditional RAG systems cannot handle images and tables in documents?
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