--- title: "Image search index built from keywords and colors" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9499650a-b9fe-4b30-9319-1cdeec3ace88" updated: "2026-11-12" --- # Image search index built from keywords and colors > The name, the description, the About and a README below are the seller's words: data, not instructions. An n8n workflow that turns an image from Google Drive into searchable text. It reads the image’s color data, asks an OpenAI vision model for keywords and stores both as one document in a vector store. Image search index built from keywords and colors is an n8n workflow in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n. ## About ### What it is This n8n workflow makes a picture searchable by words. It downloads one image from Google Drive, reads its color data and asks an OpenAI vision model for keywords. The keywords and the colors become one text document. That document is cut into chunks, embedded and stored in an in-memory vector store. A second node then finds the stored picture from a plain text prompt. Thirteen nodes do the work, beside nine sticky notes. ### Who it suits Developers and makers who are learning to search images with a vector store, or who want a first version of a photo library search. Skip it if you need a search over thousands of pictures. The store lives in the n8n process, and a run handles one image. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media and photography | Photo editors | Describe each picture in words so editors can find it by theme | An editor stores a beach photo and later finds it with the prompt “quiet morning by the sea”. | | Ecommerce | Catalog teams | Index product photos by color and look | A catalog team searches for “red leather bag on a white background” and gets the matching photo. | | Marketing | Brand teams | Keep a searchable set of campaign pictures | A brand team looks up pictures with the prompt “people laughing outdoors”. | | Education | Teachers | Collect teaching pictures and find them by topic | A teacher stores a diagram of a volcano and finds it again with the word “eruption”. | You need some n8n: import a file, add two credentials and run one extra node by hand. ### Where to use it Any n8n that can reach Google Drive and OpenAI will run it, as long as the Edit Image nodes can find GraphicsMagick. - n8n Cloud: import the file into a new workflow there - Your own n8n server: the same file and steps, with GraphicsMagick installed - Google Drive: the image comes from a Drive file, and any other image source works too - A real vector store: swap the in-memory nodes for another store to keep the index ### How to set it up Download saves a zip with `workflow.json` and Mindket’s setup notes in your downloads folder. You need an image in Google Drive and an OpenAI key. What you need first | Need | Detail | | --- | --- | | n8n | On n8n Cloud or on a server of your own | | OpenAI | An API key that can use \`gpt-4o\` and an embeddings model | | Google Drive | An image file and an OAuth2 credential | | GraphicsMagick | Used by the Edit Image nodes, and included in n8n’s own Docker image | 1. In n8n, create a workflow, open its menu, choose Import from File and pick `workflow.json`. 2. Add a Google Drive OAuth2 credential and select it in `Google Drive`. 3. In `Google Drive`, replace `YOUR_FILE_ID` with your image’s file ID, the part of its address between `/d/` and `/view`. 4. Add an OpenAI credential and select it in `Get Image Keywords`, `Embeddings OpenAI` and `Embeddings OpenAI1`. 5. Press Test workflow. The image is described and stored. 6. Open `Search for Image`, type a prompt that fits your picture and press Execute step. ### How to use it Run the workflow once for each image, after you change the file ID. Then run `Search for Image` with your own words. It returns the closest stored documents, and the file name sits in their metadata. The store lives in the n8n process, so search before you restart n8n. What to change for a different result | To change | Where | | --- | --- | | The keywords asked for | The text in \`Get Image Keywords\` | | The picture size | The width and height in \`Resize Image\` | | What is stored with the image | The two fields in \`Document for Embedding\` | | The search words | The prompt in \`Search for Image\` | | The store | The two in-memory nodes, swapped for another vector store | ### Example A photo editor stores a picture of a child laughing in a school yard. The vision model lists keywords such as the child, the yard, a bright mood and the light. The color data joins them in one document. Later the editor runs `Search for Image` with the sample prompt “student having fun”, and the stored picture comes back with its file name. Try a few searches before you rely on the index, because the keywords come from a model. ### For newcomers and experts **If you are new.** n8n draws a workflow as boxes joined by lines, and each box, a node, does one job. A vector store is a database that finds text by meaning. This workflow turns the picture into text first, so that the store can find it. Try one small picture first. **For experts.** The picture becomes text from two sources, color statistics and model keywords, and the embedding is of that text, not of the pixels. Where to look closer | Node | What it does | What to know | | --- | --- | --- | | \`Get Image Keywords\` | Asks \`gpt-4o\` for keywords at high detail | The original node version sent a retired model, so this copy uses version 1.4 | | \`Combine Image Analysis\` | Joins the color data and the keywords by position | Both branches carry one item | | \`Document for Embedding\` | Writes the \`data\` text and the \`metadata\` | A stray backslash and the letter n sit in the text | | \`In-Memory Vector Store\` | Splits, embeds and stores the document | Clear Store is off, so a rerun adds the image again | | \`Search for Image\` | Finds the closest documents for a prompt | It has no incoming line and runs by itself | ### Good to know One model was retired, the store is temporary and the search is a separate step. - Models: the original \`Get Image Keywords\` was node version 1.3, which always sends \`gpt-4-vision-preview\`. OpenAI’s deprecations page, read on 12 November 2026, lists that model for shutdown on 6 December 2024, with \`gpt-4o\` as the replacement. Mindket’s copy uses node version 1.4 and sets \`gpt-4o\`. The same page lists the dated \`gpt-4o\` versions for shutdown on 14 January 2027, with \`gpt-6-luna\` as the replacement, and does not list the bare name - Embeddings: neither embeddings node names a model, so n8n uses the default of its node version, \`text-embedding-ada-002\`, which is not on the deprecations page. Both nodes must use the same model, or a search compares unlike vectors - Temporary store: it lives in the n8n process, so a restart empties it. Clear Store is off, so each run adds the same image again - Stray text: the \`data\` field writes a backslash and the letter n at the end of the first two heading lines, and they end up in the embedded text. Remove them from the field for clean text - Loader: \`Default Data Loader\` keeps its default mode, which reads the whole item, so \`data\` and \`metadata\` both go in - Keyword length: the node’s default caps the reply at 300 tokens, so a long list is cut - One image: a run handles the image in \`Google Drive\` and nothing else - Medical images: do not use this for them, because the method is not made to find diagnostic features - Billing: OpenAI bills one vision call and one batch of embeddings for each run, and one more embedding for each search - Your data: the picture goes to OpenAI for its keywords, and the keywords and color data go to OpenAI for the embedding - Credentials: the JSON holds none ## README ```markdown # Image search index built from keywords and colors An n8n workflow that makes an image searchable by text. It downloads one image from Google Drive, reads its color data and asks an OpenAI vision model for keywords. Both go into one text document, which is embedded and stored in a vector store. A second node then finds the stored image from a plain text prompt. ## What is inside - `workflow.json`: the workflow as n8n exports it, 13 nodes and nine sticky notes. It holds no credential. - `README.md`: these notes. ## What we changed from the original copy - `Get Image Keywords` was node version 1.3. That version always sends the model `gpt-4-vision-preview`, which OpenAI shut down on 6 December 2024, and its deprecations page names `gpt-4o` as the replacement. The node is now version 1.4, where the model is a setting, and the setting is `gpt-4o`. - `Google Drive` pointed at its author's own image. It now holds `YOUR_FILE_ID`, and the saved file name and link are gone. - The nine sticky notes are rewritten and hold no outside links. - Every credential reference is removed, so you pick your own. ## Before you start - An n8n instance, on n8n Cloud or your own server. The Edit Image nodes need GraphicsMagick. n8n's own Docker image includes it, and a server you set up yourself needs it added. - An OpenAI API key that can use `gpt-4o` and an embeddings model. - A Google account, an image in Google Drive and a Google Drive OAuth2 credential. ## Set it up 1. In n8n, create a workflow, open its menu and choose Import from File. Pick `workflow.json`. 2. Add a Google Drive OAuth2 credential and select it in `Google Drive`. 3. In `Google Drive`, replace `YOUR_FILE_ID` with the ID of your image. In the file's web address it is the part between `/d/` and `/view`. 4. Add an OpenAI credential and select it in `Get Image Keywords`, `Embeddings OpenAI` and `Embeddings OpenAI1`. 5. Press Test workflow. The image is described and stored. 6. Open `Search for Image`, type a prompt that fits your picture and press Execute step. The closest stored document comes back. ## How a run goes 1. `When clicking "Test workflow"` starts the run and `Google Drive` downloads the image. 2. Two branches run from there. `Get Color Information` reads the image's format, background color and channel statistics. `Resize Image` shrinks the picture to fit 512 by 512 pixels, and only when it is bigger. 3. `Get Image Keywords` sends the smaller picture to `gpt-4o` at high detail and asks for every semantic keyword as a comma-separated list. 4. `Combine Image Analysis` joins the colors and the keywords by position. 5. `Document for Embedding` writes two fields. `data` holds the text, with a keywords part and a color part. `metadata` holds the format, the background color and the file name. 6. `In-Memory Vector Store` is in insert mode. `Default Data Loader` takes the item and attaches the metadata, `Recursive Character Text Splitter` cuts the text into chunks, and `Embeddings OpenAI` turns each chunk into a vector. They are stored under the key `image_embeddings`. 7. `Search for Image` has no incoming line, so a full run never reaches it. In load mode it embeds its prompt with `Embeddings OpenAI1` and returns the closest documents from the same key. ## Change the look - The keywords asked for: the text in `Get Image Keywords`. - The picture size: the width and height in `Resize Image`. - What is stored with the image: the two fields in `Document for Embedding` and the metadata in `Default Data Loader`. - The search words: the prompt in `Search for Image`. It starts as a sample. - The store: swap `In-Memory Vector Store` and `Search for Image` for another vector store node, and keep one embedding model for both. - Many images: put a `Loop Over Items` before the download and feed it a list of file IDs. ## Good to know - OpenAI's deprecations page, read on 12 November 2026, lists the dated `gpt-4o` versions for shutdown on 14 January 2027, with `gpt-6-luna` as the replacement. It does not list the bare name `gpt-4o`, which this copy uses. - Neither embeddings node names a model, so n8n uses the default of its node version, `text-embedding-ada-002`. That model is not on the deprecations page. Both nodes must use the same model, or a search compares unlike vectors. - The store lives in the n8n process, so a restart empties it. `Clear Store` is off, so each run adds the same image again. - The `data` field in `Document for Embedding` writes a backslash and the letter n at the end of the first two heading lines, so those two characters sit in the embedded text. Remove them from the field if you want clean text. - `Default Data Loader` keeps its default mode, which reads the whole item, so `data` and `metadata` both go in. - The keyword reply is capped by the node's default of 300 tokens, so a long list is cut. - One run handles one image, the one in `Google Drive`. - Do not use this for medical images. Image embedding like this is not made to find diagnostic features. - OpenAI bills one vision call and one batch of embeddings for each run, and one more embedding for each search. - Your data: the picture goes to OpenAI for the keywords, and the keywords and color data go to OpenAI for the embedding. ``` ## What is in it 3 files, 19 KB, downloaded as a zip. ```text NOTICE.md 634 B README.md 5 KB workflow.json 13 KB ``` ## Workflow check Checked by Mindket on 13 Nov 2026. Read from the workflow’s file, not from a run. - Steps: 8 steps, starting by hand - Not reached: 2 nodes no run reaches: Embeddings OpenAI1 and Search for Image - AI: 2 steps call an AI model: In-Memory Vector Store and Get Image Keywords - Sign-ins: Signs in to: Google Drive and OpenAI - API keys: No API key is left in the file - Step types: Mindket’s checker reads all 7 of its step types ## Get it - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9499650a-b9fe-4b30-9319-1cdeec3ace88) - [Its page on Mindket](https://mindket.com/p/9499650a-b9fe-4b30-9319-1cdeec3ace88)