Dense vector search is a method used primarily for finding items that are similar to a given item by comparing their numerical representations called dense vectors.
Understanding Dense Vectors
At its core, dense vector search relies on dense vectors of numeric values. These vectors are essentially arrays of numbers, often representing complex data like text, images, or audio after being processed by machine learning models (like embedding models). Unlike sparse vectors, which have many zero values and primarily represent the presence of features, dense vectors typically contain non-zero values throughout, capturing richer, more nuanced relationships between data points.
The dense_vector field type mentioned in the reference is designed specifically to store these dense vectors of numeric values. This allows databases or search engines to index and query this type of data effectively.
How Dense Vector Search Works: kNN
The primary application for storing and querying dense vectors is k-nearest neighbor (kNN) search. This is explicitly stated in the reference: "Dense vector fields are primarily used for k-nearest neighbor (kNN) search."
kNN search works by calculating the distance (or similarity) between the vector of a query item and the vectors of all other items in the dataset. The 'k' nearest neighbors are the 'k' items with the smallest distance (or highest similarity) to the query vector.
Why use dense vectors for kNN?
- Similarity: Dense vectors embedded from machine learning models often capture semantic or perceptual similarity. Vectors that are numerically "close" in the vector space represent items that are conceptually "similar".
- Efficiency: While calculating distance for all vectors can be computationally intensive, specialized indexing techniques (like Approximate Nearest Neighbor - ANN methods) are used to perform kNN search efficiently on large datasets of dense vectors.
Key Characteristics and Limitations
Based on the reference, the dense_vector field type, which underpins this search method, has specific characteristics:
- Data Type: Stores arrays of numeric values.
- Primary Use: k-nearest neighbor (kNN) search.
- Unsupported Operations: The dense_vector type does not support aggregations or sorting. This means you cannot directly calculate sums, averages, or sort results based on the vector field itself (though you can often sort by distance in a kNN query or sort by other fields).
Summary Table
| Feature | Description |
|---|---|
| Data Stored | Dense vectors of numeric values |
| Primary Use Case | k-nearest neighbor (kNN) search |
| Supported Ops | kNN search |
| Unsupported Ops | Aggregations, Sorting |
In essence, dense vector search is a powerful technique leveraging numerical vector representations for finding similar data points, making tasks like semantic search, image similarity search, and recommendation systems possible.
