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HTTP Request node
Google Cloud Storage node
+3

Uploading image datasets to Qdrant [1/3 anomaly][1/2 KNN]

Vector database (Qdrant) as a data analysis tool Working with images, embedding model - Voyage AI. For anomaly detection 1. This is the first pipeline to upload (crops) dataset to Qdrant's collection. The second pipeline is to set up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant (crops) collection. For KNN (k nearest neighbours) classification 1. This is the first pipeline to upload (lands) dataset to Qdrant's collection. The second is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage In general, pipelines are adaptable to any dataset of images. [This workflow] Batch Uploading Dataset to Qdrant This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are). First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later. Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing). Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API. Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.
mrscoopers
Jenny

Supported Actions

Create
Create a new Bucket
Delete
Delete an empty Bucket
Get
Get metadata for a specific Bucket
Get Many
Get list of Buckets
Update
Update the metadata of a bucket
Create
Create an object
Delete
Delete an object
Get
Get object data or metadata
Get Many
Retrieve a list of objects
Update
Update an object's metadata
Google Cloud Storage node

About Google Cloud Storage

Data can be stored and accessed on the Google Cloud Platform infrastructure using Google Cloud Storage, a RESTful online file storage web service. The service combines cutting-edge security and sharing features with the performance and scalability of Google's cloud.

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FAQ about Google Cloud Storage integrations

  • How can I set up Google Cloud Storage integration in n8n?

      To use Google Cloud Storage integration in n8n, start by adding the Google Cloud Storage node to your workflow. You'll need to authenticate your Google Cloud Storage account using supported authentication methods. Once connected, you can choose from the list of supported actions or make custom API calls via the HTTP Request node, for example: you can configure triggers and actions related to file management, such as uploading, downloading, or deleting files. Make sure to set the appropriate credentials and permissions in your Google Cloud Console to ensure proper access. Finally, test your integration to verify that everything is functioning as expected.

  • Do I need any special permissions or API keys to integrate Google Cloud Storage with n8n?

  • Can I combine Google Cloud Storage with other apps in n8n workflows?

  • What are some common use cases for Google Cloud Storage integrations with n8n?

  • How does n8n’s pricing model benefit me when integrating Google Cloud Storage?

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