Automated Anomaly Detection Using Qdrant Medoids in n8n
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This n8n workflow automates anomaly detection by identifying medoids within Qdrant collections. It leverages `httpRequest` and `code` nodes to process data and determine cluster centers, enhancing data analysis capabilities.
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About This Workflow
Overview
This n8n workflow is designed for anomaly detection within vector databases, specifically using Qdrant. It automates the process of identifying "medoids" – representative data points within clusters – which can then be used to flag anomalies or outliers. The workflow starts by calculating the total number of points in a collection, then proceeds to compute a distance matrix to identify potential cluster centers. A Python code node is used to efficiently process this matrix and determine the medoid ID. Subsequent httpRequest nodes are used to update the Qdrant database by marking the identified medoids and their corresponding cluster thresholds, and to retrieve the medoid's vector for further analysis. This approach is particularly useful for identifying unusual data points or trends in large datasets stored in Qdrant.
Key Features
- Automated medoid identification using Qdrant's search and cluster capabilities.
- Utilization of a Python
codenode for efficient sparse matrix processing. - Dynamic calculation of cluster thresholds based on medoid vectors.
- Updates Qdrant database with
is_medoidandis_medoid_cluster_thresholdflags. - Integrates with Qdrant API for data retrieval and manipulation.
How To Use
- Configure Qdrant Credentials: Ensure your Qdrant API credentials are set up correctly in n8n.
- Set Qdrant Variables: Provide your Qdrant cloud URL and collection name.
- Trigger Workflow: Initiate the workflow, typically by clicking 'Test workflow'.
- Monitor Execution: Observe the nodes executing and check the output for identified medoid IDs and threshold scores.
- Analyze Results: Examine the updated payloads in Qdrant to understand which data points have been flagged as medoids or associated with medoid cluster thresholds.
Apps Used
Workflow JSON
{
"id": "f93e36c1-d7bb-4301-bc29-f387da38dbcb",
"name": "Automated Anomaly Detection Using Qdrant Medoids in n8n",
"nodes": 0,
"category": "AI Research, RAG, and Data Analysis",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: f93e36c1-d7bb...
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Building complex chains with OpenAI, Claude, and LangChain.
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