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Generating image embeddings via textual summarisation

JimleukJimleuk
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2/3/2026
Lead GenerationWebhookHubSpotCRM IntegrationSalesforce
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This n8n template demonstrates an approach to image embeddings for purpose of building a quick image contextual search. Use-cases could for a personal photo library, product recommendations or searching through video footage.

How it works

  • A photo is imported into the workflow via Google Drive.
  • The photo is processed by the edit image node to extract colour information. This information forms part of our semantic metadata used to identify the image.
  • The photo is also processed by a vision-capable model which analyses the image and returns a short description with semantic keywords.
  • Both pieces of information about the image are combined with the metadata of the image to form a document describing the image.
  • This document is then inserted into our vector store as a text embedding which is associated with our image.
  • From here, the user can query the vector store as they would any document and the relevant image references and/or links should be returned.

Requirements

  • Google account to download image files from Google Drive.
  • OpenAI account for the Vision-capable AI and Embedding models.

Customise this workflow

Text summarisation is just one of many techniques to generate image embeddings. If the results are unsatisfactory, there are dedicated image embedding models such as Google's vertex AI multimodal embeddings.

Generating Image Embeddings via Textual Summarisation

This n8n workflow automates the process of generating image embeddings by first extracting textual content from images, then summarizing that content, and finally creating embeddings based on the summary. This can be useful for tasks like semantic search, content categorization, or data analysis where visual content needs to be understood in a textual context.

What it does

  1. Triggers Manually: The workflow is initiated manually by clicking 'Execute workflow'.
  2. Retrieves Image Data: It connects to Google Drive to retrieve image files.
  3. Loads Document Data: The Default Data Loader node processes the retrieved image files, likely converting them into a format suitable for text extraction.
  4. Edits Fields: The Edit Fields (Set) node is used to manipulate or set specific fields in the data, potentially preparing the image data for the next step.
  5. Extracts Text from Image: The Edit Image node is configured to perform an operation that extracts text from the image content.
  6. Splits Text: The Recursive Character Text Splitter node breaks down the extracted text into smaller, manageable chunks. This is crucial for processing large texts by language models.
  7. Summarizes Text with OpenAI: The OpenAI node (likely configured for a chat or completion task) takes the split text and generates a concise summary.
  8. Generates Embeddings with OpenAI: The Embeddings OpenAI node then takes the generated summary and converts it into numerical vector embeddings.
  9. Stores Embeddings: Finally, the Simple Vector Store node stores these embeddings, making them available for future use (e.g., similarity search).
  10. Merges Data: The Merge node combines data streams, likely merging the original image metadata with the generated embeddings or summaries.
  11. Sticky Note: A sticky note is included for documentation or internal notes within the workflow.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance.
  • Google Drive Account: Configured credentials for Google Drive to access image files.
  • OpenAI API Key: An API key for OpenAI, configured as a credential in n8n, to use their language models for summarization and embedding generation.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Drive credentials to allow n8n to access your image files.
    • Set up your OpenAI API key as a credential in n8n.
  3. Adjust Node Settings (Optional):
    • In the Google Drive node, specify the folder or files you want to process.
    • Review the Edit Image node settings to ensure it correctly extracts text from your specific image types.
    • Adjust the Recursive Character Text Splitter parameters (e.g., chunk size, overlap) if needed for optimal text splitting.
    • Configure the OpenAI node for summarization (e.g., model, prompt).
    • Configure the Embeddings OpenAI node (e.g., model for embeddings).
  4. Execute the Workflow: Click the 'Execute workflow' button in the Manual Trigger node to run the workflow.

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