elasticsearchHow can I use vector search in Elasticsearch?

Vector search in Elasticsearch is a powerful tool for finding documents that are similar to a given vector of values. It can be used to find similar items in a dataset, such as similar products in an e-commerce store. To use vector search in Elasticsearch, you need to create a vector field in your document mapping and store the vector values in that field.

Here is an example of how to create a vector field and store a vector of values in it:

PUT my_index
{
  "mappings": {
    "properties": {
      "vector_field": {
        "type": "dense_vector",
        "dims": 3
      }
    }
  }
}

PUT my_index/_doc/1
{
  "vector_field": [1.0, 2.0, 3.0]
}

Once the vector field has been created and the vector values stored in it, you can use the query_string query to search for documents that are similar to a given vector. The following example searches the my_index index for documents that are similar to the vector [1.0, 2.0, 3.0]:

GET my_index/_search
{
  "query": {
    "query_string": {
      "query": "vector_field: [1.0, 2.0, 3.0]"
    }
  }
}

If the query is successful, the output will be a list of documents that are similar to the given vector.

Parts of the example code:

• PUT my_index: Creates an index called my_index
• type": "dense_vector": Sets the type of the vector field to dense_vector
• dims": 3: Sets the number of dimensions in the vector field to 3
• PUT my_index/_doc/1: Creates a document in the my_index index
• vector_field": [1.0, 2.0, 3.0]: Stores a vector of values in the vector_field field
• GET my_index/_search: Searches the my_index index for documents
• query_string: Uses the query_string query to search for documents that are similar to a given vector

Helpful links

• Vector fields in Elasticsearch
• Query string query in Elasticsearch

Edit this code on GitHub