Elasticsearch dense_vector Field Data Type

The dense_vector field type in Elasticsearch is designed to store dense vector data, which are fixed-length arrays of floating-point numbers. This data type is particularly useful for machine learning applications, similarity search, and recommendation systems. It allows for efficient storage and retrieval of high-dimensional vector data, enabling vector search capabilities within Elasticsearch.

Dense vectors are preferred when dealing with fixed-length, continuous numerical representations of data, such as word embeddings, image features, or user preferences. An alternative to dense_vector is the sparse_vector type, which is more suitable for high-dimensional data with many zero values.

Example

PUT my-index
{
  "mappings": {
    "properties": {
      "product_embedding": {
        "type": "dense_vector",
        "dims": 128
      }
    }
  }
}

PUT my-index/_doc/1
{
  "product_embedding": [0.5, 10.0, -0.3, ...]
}

Common issues or misuses

1. Incorrect dimensionality: Ensure that the number of dimensions specified in the mapping matches the actual vector size in your data.
2. Performance impact: Large vector dimensions can significantly impact indexing and search performance.
3. Scaling issues: As the number of vectors grows, consider using approximate nearest neighbor (ANN) algorithms for better search performance.
4. Limited query support: Not all query types are supported for dense_vector fields.

Frequently Asked Questions

Q: What is the maximum number of dimensions supported by the dense_vector field?
A: As of Elasticsearch 7.x, the maximum number of dimensions for a dense_vector field is 2048.

Q: Can I update a dense_vector field after indexing?
A: Yes, you can update a dense_vector field using the update API, but the entire vector must be provided in the update operation.

Q: How does Elasticsearch handle similarity search for dense vectors?
A: Elasticsearch uses cosine similarity or Euclidean distance to measure the similarity between dense vectors during search operations.

Q: Can I use dense_vector fields with aggregations?
A: Dense vector fields have limited support for aggregations. As of Elasticsearch 7.x, you can use them with the vector_tile aggregation for geospatial use cases.

Q: Is it possible to combine dense_vector search with text search?
A: Yes, you can combine dense_vector similarity search with traditional text search using a function score query or a custom script.