Morphik Core enables multimodal retrieval through the innovative ColPali technology, which consists of three key processes:
- Joint Embedding Generation: For uploaded documents such as PDFs/videos, the system processes textual content and visual elements in parallel to generate uniform semantic embedding vectors.
- cross-modal association: Automatically establish semantic associations between text descriptions and image content. For example, the "Quarterly Revenue Chart" in a report is mapped to its corresponding data visualization.
- hybrid search strategy: The query is made through the
use_colpali=TrueParameters activate multimodal retrieval, and the system is considered simultaneously:
1. Text semantic matching
2. Visual content relevance
3. Knowledge map-derived relationships
Typical Application Examples::
When a researcher searches for a paper, he or she types in "Find comparative charts on neural network architectures" and the system returns both:
- Pages containing architecture diagrams
- Relevant doctrinal statement paragraphs
- Comparative experimental data cited
Tests have shown that this technique improves cross-modal retrieval accuracy by 671 TP3T, which is particularly suitable for analyzing technical documents containing complex diagrams.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
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