Generating Image Embeddings via Textual Summarisation
Explore this Workflow. Built for next generation AI app & agent building.
This rantir template showcases a method for creating image embeddings to enable fast, context-based image search. Potential applications include organizing a personal photo library, generating product recommendations, or searching video footage.
How It Works
- A photo is imported into the workflow from Google Drive.
- The photo is processed by an edit image node to extract color information, which becomes part of the semantic metadata used for identifying the image.
- A vision-capable model analyzes the image, generating a brief description with relevant keywords.
- This metadata and descriptive information are combined to create a document that describes the image.
- The document is then added to a vector store as a text embedding associated with the image.
- Users can query the vector store, which retrieves references or links to relevant images based on the search context.
Requirements
- Google account to access image files from Google Drive.
- OpenAI account for Vision-capable AI and embedding models.
Customizing the Workflow
Text summarization is one of many techniques for generating image embeddings. For more advanced results, consider using specialized image embedding models, such as Google's Vertex AI multimodal embeddings.
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