From Pixels to Knowledge: How We Built Visual Search Using GraphRAG

Combining computer vision with knowledge graphs, the Memgraph Community Call showcased how images can be processed and queried semantically through a GraphRAG pipeline, transforming raw images into queryable knowledge. The session, led by Dino Duranovic and Ante Javor, illustrated the integration of image recognition with text-to-image embedding via CLIP, enabling natural language queries and zero-shot classification. Knowledge graphs were highlighted as a powerful tool for structuring visual information, allowing for accurate searches, intelligent navigation, and context-based query expansion. The live demo demonstrated the practical application of these technologies, where images were processed into a graph structure using the Gemini API and CLIP embeddings, enabling both image and text queries. The Q&A session underscored the advantages of using knowledge graphs over vector databases for contextual search and expandability, and addressed considerations for modeling and querying within this framework.

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