HTTP Request node
+4

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

Published 8 hours ago

Created by

mrscoopers
Jenny

Categories

Template description

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.
2. The second pipeline is to set up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores.
3. 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.
2. 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.

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