Harnessing Vector Search: Step-by-Step Configuration with Elasticsearch ECK

Vector search is an advanced technique that can be used to identify similar items within a dataset based on their vector representations. This technique, when combined with Elasticsearch running on Kubernetes through Elasticsearch Operator (ECK), becomes a powerful tool for efficient and scalable similarity searches. We have created a comprehensive guide that walks you through a step-by-step configuration process to set up vector search on Elasticsearch with ECK. Once configured, you can easily leverage this advanced feature within your Kubernetes environment.

1. Set Up Elasticsearch Operator (ECK)

Before diving into vector search configuration, ensure you have Elasticsearch Operator (ECK) installed on your Kubernetes cluster. Follow these steps to deploy ECK:

– Download the ECK operator manifests from the Elastic website or GitHub repository.

– Apply the manifests to your Kubernetes cluster using kubectl apply.

– Verify that the ECK operator is running and ready to manage Elasticsearch clusters on your Kubernetes environment.

2. Deploy Elasticsearch Cluster

With ECK in place, you can now deploy an Elasticsearch cluster on Kubernetes. Define the Elasticsearch resource manifest with specifications such as cluster size, storage settings, and Elasticsearch version. Apply the manifest to create the Elasticsearch cluster:

apiVersion: elasticsearch.k8s.elastic.co/v1
kind: Elasticsearch
metadata:
  name: my-cluster
spec:
  version: 8.12.2
  nodeSets:
  - name: default
    count: 3
    config:
      node.master: true
      node.data: true
      node.ingest: true

Adjust the configuration according to your requirements, including the Elasticsearch version and node settings.

3. Install Vector Search Plugin

To enable vector search capabilities in Elasticsearch, you need to install the required plugin. Typically, this involves using Elasticsearch’s plugin manager to install plugins like the Open Distro for Elasticsearch K-NN plugin or the Elasticsearch Learning to Rank plugin. For example:

kubectl exec -it <elasticsearch-pod> -- bin/elasticsearch-plugin install https://d3g5vo6xdbdb9a.cloudfront.net/downloads/elasticsearch-plugins/opendistro-knn/opendistro-knn-1.13.0.0.zip

Replace `<elasticsearch-pod>` with the name of the Elasticsearch pod in your cluster.

4. Configure Elasticsearch

Configure Elasticsearch to enable the vector search plugin and define any additional settings required for vector search functionality. This typically involves modifying the Elasticsearch configuration file (`elasticsearch.yml`) within the Elasticsearch cluster. For instance:

opendistro.knn:
enabled: true
memory_index: true
autotune:
model_size_limit: 1000000

Ensure that you apply the configuration changes to all nodes within the Elasticsearch cluster.

5. Index Your Data

Once Elasticsearch is configured with vector search capabilities, index your dataset into Elasticsearch. Define an index and specify the mapping for your vector fields. Here’s an example:

PUT /my_index
{
  "mappings": {
    "properties": {
      "embedding_vector": {
        "type": "knn_vector",
        "dimension": 100
      }
    }
  }
}

Replace `my_index` with your desired index name and adjust the field specifications according to your data schema.

6. Perform Vector Searches

With your data indexed and Elasticsearch configured, you can execute vector searches using Elasticsearch’s query APIs. Craft a query that targets the vector field and specifies the desired search parameters. For example:

POST /my_index/_search
{
  "query": {
    "knn": {
      "embedding_vector": {
        "vector": [0.5, 0.5],
        "k": 5
      }
    }
  }
}

This query retrieves the 5 nearest neighbors to the provided vector `[0.5, 0.5]` in the `embedding_vector` field.

Conclusion

Vector search, when implemented in conjunction with Elasticsearch running on Kubernetes via Elasticsearch Operator (ECK), offers an advanced similarity search feature within your Kubernetes environment. By following a step-by-step configuration guide, you can seamlessly integrate vector search features into your Elasticsearch cluster on Kubernetes. This allows you to efficiently identify similar items within your dataset, regardless of whether you’re working with text, images, or other types of data. Mastering vector search with ECK enhances the search experience and facilitates insightful data exploration in Kubernetes-based environments.