Automated Image Embedding and Search Workflow

This n8n workflow automates the process of downloading an image from Google Drive, extracting its color and semantic features, creating an embedding document, and storing it in a vector database for efficient image retrieval. It combines image analysis techniques with OpenAI’s vision and language models to generate meaningful keywords and feature vectors, facilitating advanced image search capabilities.

The workflow starts with a manual trigger for testing purposes, then downloads the specified image file from Google Drive. It analyzes the image to extract color channel information and resizes the image to 512×512 pixels to optimize data for embedding. The process generates semantic keywords describing the image’s subjects, mood, and technical details using OpenAI’s image analysis models. These features, along with metadata about the image, are combined into a document suitable for embedding.

Next, the workflow inserts the document into an in-memory vector store, enabling fast similarity searches. Additional nodes demonstrate how to perform a basic search for images similar to a prompt, illustrating the workflow’s application in image retrieval and search systems. Sticky notes throughout the workflow provide helpful context and references for further learning.

This automation is useful in digital asset management, media libraries, or any scenario requiring organized, searchable image repositories with semantic and visual metadata for enhanced retrieval and classification.