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Vector Best Practices

When dealing with vector data in applications like semantic search, image recognition, or recommendation systems, selecting the right strategies can significantly enhance performance and accuracy. The following guidelines will help you work more effectively with vectors, covering aspects from choosing the appropriate distance metric to managing memory usage efficiently. Following these best practices ensures your vector operations are optimized for speed, accuracy, and efficiency, enabling you to get the most out of your data.

Choose the Right Distance Metric​

Different distance metrics, such as L2, IP, or COSINE, have different properties and might perform better for different types of data. For example, cosine similarity works best for text data, while L2 distance might be more suitable for numerical vectors.

For more information about distance metrics, refer to Distance Type.

Index Your Vectors Properly​

Proper indexing can speed up similarity searches considerably. In the context of Macrometa GDN, make sure you've chosen the right views for indexing. For example, consider opting for HNSW (Hierarchical Navigable Small World) indexing for faster similarity searches on large datasets.

For more information, refer to Index Type.

Optimize Vector Size and Dimensions for Efficient Searches​

Optimizing vector size, dimensions, and normalization is key to ensuring efficient and accurate vector comparisons. Normalizing vectors to a uniform length enables comparisons based on direction or content, crucial for methods like cosine similarity. It's important to avoid the curse of dimensionality by selecting vector dimensions that represent your data without unnecessary complexity, such as choosing between 128 to 256 dimensions for image searches. Large vectors can increase memory use and slow operations, so consider dimensionality reduction techniques like PCA to maintain efficiency without losing important information.

For more information, refer to Vector Size.

Prioritize Important Vector Dimensions​

Not all dimensions in a vector contribute equally to similarity. Identify and prioritize dimensions that play a significant role in your application. For example, consider prioritizing color and texture dimensions for an image similarity search in an e-commerce application.

Handle Outliers Effectively​

Outliers can skew similarity results. Implement techniques to detect and handle outliers in your vector dataset.

Memory Usage​

Memory consumed by the vectors and the underlying infrastructure. Regularly check for redundant vectors or unnecessary high dimensions. Consider using compression techniques. For example, consider using vector quantization to compress vectors and reduce memory usage.

Query Latency​

This measures the time it takes to complete a similarity search or any vector operation. Speed up searches by reducing vector dimensions.