Medoid and threshold for each crop group saved in Qdrant

Medoid and threshold for each crop group saved in Qdrant

An n8n workflow you start by hand. For every crop in a Qdrant collection it picks a medoid, the photo most similar to the others in its group, in two ways, and saves a similarity threshold on it for later anomaly checks.

What is inside

• workflow.json: the workflow as n8n exports it, forty-eight nodes. Twenty-six do the work and twenty-two are sticky notes that explain the steps. It holds no credential.
• README.md: these notes.

What we changed from the original copy

• The step that finds each group's medoid was a Python Code node that used scipy. In n8n 2, native Python offers only _items and _item, has no _input, and cannot import libraries such as scipy (n8n's docs, read on 12 November 2026). Mindket's copy, renamed Find Medoid, adds up each point's similarity scores in JavaScript and picks the point with the highest total, which is what the original did. One sticky note now says so.
• The Qdrant cluster address belonged to the original author. It is now the placeholder <YOUR_QDRANT_CLUSTER_URL>.
• Several nodes were linked to credentials in the author's n8n. The links are gone, so select your own.
• Nothing else. The thresholds, the crop descriptions and the other sticky notes are the file's own.

Before you start

• An n8n instance, on n8n Cloud or your own server.
• A Qdrant collection of labelled photos, made the way a batch upload does it: a named vector voyage from the Voyage model voyage-multimodal-3, a crop_name payload field on every point and a keyword payload index on crop_name. The file's collection is called agricultural-crops.
• A Qdrant cluster address and an API key.
• A Voyage AI API key, for the text branch.

Set it up

1. In n8n, create a workflow, open its menu and choose Import from File. Pick workflow.json.
2. Add a Qdrant API credential with your cluster address and key. Select it in every HTTP Request node that calls Qdrant. There are eleven, from Total Points in Collection to Set text medoid threshold score.
3. Add a Header Auth credential for Voyage. Name it Authorization and give it the value Bearer <YOUR_VOYAGE_KEY> with your own key. Select it in Embed text.
4. Open Qdrant cluster variables. Replace <YOUR_QDRANT_CLUSTER_URL> with your cluster address, with https:// in front and no slash at the end. Set collectionName to your collection.
5. Check Medoids Variables and Text Medoids Variables. Their furthestFromCenter decides which point sets the threshold. The file uses 5 and 1.
6. Open Textual (visual) crop descriptions and replace the 29 crops with your own classes, one sentence of how a typical specimen looks for each.
7. Press Execute workflow.

How a run goes

1. The manual trigger starts the run, and Qdrant cluster variables leads into two branches.
2. In the upper branch, Total Points in Collection counts the points exactly. Next, Crop Counts asks Qdrant for the values of crop_name and how many points each has. After that, Info About Crop Clusters keeps the crop names and the size of the biggest group, and Split Out makes one item for each crop.
3. For each crop, Cluster Distance Matrix asks Qdrant for the similarity between all the points of that crop. Find Medoid adds up the scores of each point and picks the highest. Set medoid id saves is_medoid on that point.
4. Get Medoid Vector reads the medoid's vector, and Prepare for Searching Threshold flips its sign. Searching Score then finds the points closest to the flipped vector, which are the points furthest from the medoid. Threshold Score takes the last score and Set medoid threshold score saves it as is_medoid_cluster_threshold.
5. In the lower branch, Textual (visual) crop descriptions holds one description for each crop. Merge pairs it with its crop, and Embed text turns it into a vector with Voyage, with input_type set to query.
6. Get Medoid by Text finds the photo of that crop nearest to the description. Set text medoid id saves is_text_anchor_medoid on it, and the same threshold steps save is_text_anchor_medoid_cluster_threshold.

Change the result

• Which point sets the threshold: furthestFromCenter in the two variable nodes. 1 is the furthest photo, 5 is the fifth furthest.
• The crops and their descriptions: Textual (visual) crop descriptions.
• The payload names: the bodies of the four nodes that start with Set.
• The collection: collectionName in Qdrant cluster variables.

Good to know

• The anomaly check that reads these thresholds is not in this file. The file only writes is_medoid, is_medoid_cluster_threshold, is_text_anchor_medoid and is_text_anchor_medoid_cluster_threshold.
• Qdrant saves a payload only on the points named in the call. If your data changed between runs, the old medoid keeps its is_medoid flag, so clear the old payloads first.
• A sticky note warns against the distance matrix call for groups of more than a couple of hundred photos.
• A crop with no description is left out of the text branch, because Merge pairs by crop name.
• Voyage bills the embeddings to your key. The descriptions go to Voyage, and the payloads are written to your Qdrant collection.
• The file holds no credential and no key.