> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vectoraidb.actian.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Configure cosine similarity

> Create a collection that measures angular similarity between vectors.

Cosine similarity measures the angular relationship between vectors, ignoring magnitude. It is well suited for text embeddings where semantic meaning matters more than vector length.

The following example creates a collection named `text_embeddings` configured to use cosine similarity as its distance metric.

<CodeGroup>
  ```python Python theme={null}
  from actian_vectorai import VectorAIClient, VectorParams, Distance

  # Connect to VectorAI DB server
  with VectorAIClient("localhost:6574") as client:
      # Create collection with cosine similarity
      client.collections.create(
          "text_embeddings",  # Collection name
          vectors_config=VectorParams(size=128, distance=Distance.Cosine)  # Cosine metric
      )
  ```

  ```javascript JavaScript theme={null}
  import { VectorAIClient } from '@actian/vectorai-client';

  async function main() {
      // Connect to VectorAI DB server
      const client = new VectorAIClient('localhost:6574');

      // Create collection with cosine similarity
      await client.collections.create('text_embeddings', {
          dimension: 128,
          distanceMetric: 'COSINE'
      });
  }

  main().catch(console.error);
  ```
</CodeGroup>

To confirm the collection was created successfully, call `client.collections.get_info("text_embeddings")` and check that the `status` field returns `Ready`.