Template description
This n8n workflow demonstrates how to automate indexing of images to build a object-based image search.
By utilising a Detr-Resnet-50 Object Classification model, we can identify objects within an image and store these associations in Elasticsearch along with a reference to the image.
How it works
- An image is imported into the workflow via HTTP request node.
- The image is then sent to Cloudflare's Worker AI API where the service runs the image through the Detr-Resnet-50 object classification model.
- The API returns the object associations with their positions in the image, labels and confidence score of the classification.
- Confidence scores of less the 0.9 are discarded for brevity.
- The image's URL and its associations are then index in an ElasticSearch server ready for searching.
Requirements
- A Cloudflare account with Workers AI enabled to access the object classification model.
- An ElasticSearch instance to store the image url and related associations.
Extending this workflow
Further enrich your indexed data with additional attributes or metrics relevant to your users.
Use a vectorstore to provide similarity search over the images.
Share Template
More AI workflow templates
AI agent chat
This workflow employs OpenAI's language models and SerpAPI to create a responsive, intelligent conversational agent. It comes equipped with manual chat triggers and memory buffer capabilities to ensure seamless interactions.
To use this template, you need to be on n8n version 1.50.0 or later.
n8n Team
+7
Scrape and summarize webpages with AI
This workflow integrates both web scraping and NLP functionalities. It uses HTML parsing to extract links, HTTP requests to fetch essay content, and AI-based summarization using GPT-4o. It's an excellent example of an end-to-end automated task that is not only efficient but also provides real value by summarizing valuable content.
Note that to use this template, you need to be on n8n version 1.50.0 or later.
n8n Team
+5
AI agent that can scrape webpages
⚙️🛠️🚀🤖🦾
This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results).
On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content.
The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters:
url** – an address of the page to fetch
method** = full / simplified
maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent
Page content fetching is a multistep process:
An HTTP Request mode tries to get the page content.
If the page content was successfuly retrieved, a series of post-processing begin:
Extract HTML BODY; content
Remove all unnecessary tags to recude the page size
Further eliminate external URLs and IMG scr values (based on the method query parameter)
Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure
The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node).
NB:
You can isolate the HTTP Request part into a separate workflow.
Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object.
Please reach out to Eduard is you need further assistance with you n8n workflows and automations!
Note that to use this template, you need to be on n8n version 1.19.4 or later.
Eduard
