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Child Field Options

  • Capella Operational
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    You can configure a child field from the Couchbase Capella UI’s Advanced Mode to control how its data is added to your Search index.

    For more information about how to create a child field using Advanced Mode, see Create a Child Field in Advanced Mode.

    For more information about creating an index and child fields in Quick Mode, see Create a Search Index in Quick Mode.

    You can configure the following options for a child field in Advanced Mode, based on its type:

    Option Description

    Dimension (Vector Fields Only)

    For a vector child field, enter the total number of elements in the vector embedding array.

    From Couchbase Server version 7.6.2 and later, Vector Search indexes can support arrays with up to 4096 elements. Arrays can be an array of arrays.

    For more information about Vector Search indexes, see Use Vector Search for AI Applications or Create a Vector Search Index in Quick Mode.

    Similarity Metric (Vector Fields Only)

    For a vector child field, choose the method to calculate the similarity between the vector embedding in a Vector Search index and the vector embedding in a Vector Search query.

    It’s recommended to choose the same similarity metric for your Search index as the one used in your embedding model.

    • dot_product: Calculated by adding the result of multiplying a vector’s components, or the product of the magnitudes of the vectors and the cosine of the angle between them. The dot product of 2 vectors is affected by the length and direction of each of the vectors, rather than just taking a straight-line distance.

      Dot product similarity is commonly used by Large Language Models (LLMs). Use dot_product to get the best results with an embedding model that uses dot product similarity.

    • l2_norm: Also known as Euclidean distance. Uses the straight-line distance between 2 vectors to calculate similarity. Smaller euclidean distances mean that the values of each coordinate in the vectors are closer together.

      It’s best to use l2_norm similarity when your embeddings contain information about the count or measure of specific things, and your embedding model uses the same similarity metric.

    For more information about Vector Search indexes, see Use Vector Search for AI Applications or Create a Vector Search Index in Quick Mode.

    Optimized For (Vector Fields Only)

    For a vector child field, choose whether the Search Service should prioritize recall or latency when returning similar vectors in search results:

    • recall: The Search Service prioritizes returning the most accurate result. This may increase resource usage for Search queries.

      The Search Service uses an nprobe value to calculate the number of centroids to search when using recall priority. This value is calculated by taking the square root of the number of centroids in the index.

    • latency: The Search Service prioritizes returning results with lower latency. This may reduce the accuracy of results.

      The Search Service uses half the nprobe value calculated for recall priority.

    For more information about Vector Search indexes, see Use Vector Search for AI Applications or Create a Vector Search Index in Quick Mode.

    Searchable As

    Set a different name that you can use to search the field’s contents in a query.

    The default value is the value set in the Field field when you create a child field.

    Analyzer

    Choose an analyzer for the content in the child field.

    You can choose a default analyzer or a custom analyzer.

    For more information about the available default analyzers, see Default Analyzers.

    For more information about how to create a custom analyzer, see Create a Custom Analyzer.

    Index

    To include the child field in the index, select Index.

    To remove the child field from the index, clear Index.

    If you remove the child field from the index, it cannot appear in search results.

    Store

    To store the values from the field in the index and return them in search results, select Store. This increases your index’s size and indexing time.

    To remove the field’s values from the index, clear Store.

    Include in _all field

    The _all field is a composite field that has the content from multiple fields in an index. It allows searches to query the content of a field without specifying the field’s name.

    To include this field in the _all field, select Include in _all field.

    To exclude this field from the _all field, clear Include in _all field.

    To change the name of the _all field, see Default Field.

    Include Term Vectors

    Term vectors store the location of terms in a field for an index. You can use term vectors to highlight matching search terms in search results, and perform phrase searches. Term vectors increase your index’s size and indexing time.

    To enable term vectors for this field, select Include Term Vectors.

    To turn off term vectors, clear Include Term Vectors.

    To enable term vectors, you must also enable Store.

    Doc Values

    Use doc values for a field for Search Facets and sorting your search results.

    To store doc values and enable faceting and sorting for this field, select Doc Values.

    To exclude doc values from the index for this field, clear Doc Values.

    See Also