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Vector database as a big data analysis tool for AI agents [2/2 KNN]

Jenny Jenny
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2/3/2026
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Vector Database as a Big Data Analysis Tool for AI Agents

Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n".

This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases.

  1. Uploading (image) datasets to Qdrant
  2. Set up meta-variables for anomaly detection in Qdrant
  3. Anomaly detection tool
  4. KNN classifier tool

For anomaly detection

  1. The first pipeline to upload an image dataset to Qdrant.
  2. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection.
  3. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset.

For KNN (k nearest neighbours) classification

  1. The first pipeline to upload an image dataset to Qdrant.
  2. This pipeline is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset.

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.

[This workflow] KNN classification tool

This tool takes any image URL, and as output, it returns a class of the object on the image based on the image uploaded to the Qdrant dataset (lands).

  • An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage AI Multimodal Embeddings API to fetch its embedding.
  • The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes.
  • Majority voting is done for classes of neighbouring images.
  • A loop is used to resolve scenarios where there is a tie in Majority Voting, and we increase the number of neighbours to retrieve.
  • When the loop finally resolves, the identified class is returned to the calling workflow.

n8n Vector Database as a Big Data Analysis Tool for AI Agents (22 KNN)

This n8n workflow demonstrates a foundational pattern for interacting with external APIs and processing data conditionally. While the workflow name suggests a complex use case involving vector databases and AI agents, the current JSON definition provides a basic structure for making an HTTP request, evaluating its response, and performing subsequent actions.

What it does

This workflow outlines a process that:

  1. Triggers Execution: Starts when explicitly called by another n8n workflow (e.g., as a sub-workflow).
  2. Makes an HTTP Request: Initiates an API call to an external service. This is where you would configure your interaction with a vector database or any other data source.
  3. Processes API Response: Evaluates the data received from the HTTP request.
  4. Conditional Logic: Checks a condition based on the API response.
    • If the condition is true, it proceeds to edit the fields of the data.
    • If the condition is false, it executes custom JavaScript code.
  5. Transforms Data (Conditional):
    • If the condition was true, it uses a "Set" node to modify or add fields to the incoming data.
    • If the condition was false, it uses a "Code" node to perform custom data manipulation using JavaScript.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n to import and execute the workflow.
  • External API Endpoint: The URL and any necessary authentication (e.g., API keys, tokens) for the API you intend to call in the "HTTP Request" node. This would typically be your vector database or other data analysis service.

Setup/Usage

  1. Import the Workflow:
    • Copy the provided JSON code.
    • In your n8n instance, click "Workflows" in the left sidebar.
    • Click "New" -> "Import from JSON".
    • Paste the JSON code and click "Import".
  2. Configure the "HTTP Request" Node (Node 19):
    • Double-click the "HTTP Request" node.
    • Set the URL, Method (e.g., GET, POST), Headers, and Body according to the requirements of your external API.
    • If your API requires authentication (e.g., API Key, OAuth2), configure the appropriate credentials.
  3. Configure the "If" Node (Node 20):
    • Double-click the "If" node.
    • Define the condition(s) that will determine the flow of your data. For example, you might check if a specific field from the HTTP Request response exists or meets a certain value.
  4. Configure the "Edit Fields (Set)" Node (Node 38):
    • If the "If" node's condition evaluates to true, this node will be executed.
    • Double-click the "Edit Fields (Set)" node.
    • Add or remove fields, or set specific values based on your needs when the condition is met.
  5. Configure the "Code" Node (Node 834):
    • If the "If" node's condition evaluates to false, this node will be executed.
    • Double-click the "Code" node.
    • Write your custom JavaScript logic to process the data when the condition is not met.
  6. Activate the Workflow:
    • Ensure all necessary configurations are complete.
    • Click the "Activate" toggle in the top right corner of the workflow editor to enable it.

This workflow is designed to be triggered by another workflow, making it suitable for modularizing complex processes or creating reusable API interaction patterns.

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