Generating image embeddings via textual summarisation
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 13 nodes and runs manually, when you click “Execute workflow”. Along the way it connects Google Drive, Embeddings OpenAI and OpenAI.
How it runs
Trigger: Manually, when you click “Execute workflow”.
- Google Drive: download
- Get Color Information (Edit Image): information
- Resize Image (Edit Image): resize
- Combine Image Analysis (Merge): merges the data from its input branches
- Get Image Keywords (OpenAI): analyze (image)
- Document for Embedding (Edit Fields (Set)): sets 2 fields: data, metadata
- In-Memory Vector Store (Simple Vector Store): stores documents in the Simple vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
- Search for Image (Simple Vector Store): searches the Simple vector store, with embeddings: Embeddings OpenAI
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (10)
- Manual Trigger
n8n-nodes-base.manualTrigger - Google Drive
n8n-nodes-base.googleDrive - Edit Image
n8n-nodes-base.editImage - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - Merge
n8n-nodes-base.merge - Edit Fields (Set)
n8n-nodes-base.set - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Simple Vector Store
@n8n/n8n-nodes-langchain.vectorStoreInMemory - OpenAI
@n8n/n8n-nodes-langchain.openAi
From the author
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…