Content Component 25.3
Content Component Help

# Vector Search

A vector query returns documents that contain an indexed vector that is similar to a vector that you provide.

A vector is an array of numbers that represents an object, which might be text or an image. You can generate vectors by using many different models, and index them into the Content component in VectorType fields.

The Query action `VECTOR` operator allows you to search for vectors in your documents that are similar to the vector that you provide.

The syntax for the `VECTOR` operator is:

    text=VECTOR{v1,v2,...,vn:maxdist}[:maxneighbors][*multiplier]:fieldname

where,

`v1,v2,...,vn` | is the comma-separated list of values that makes up the vector for which you want to find similar vectors. The vector that you provide must be from the same model, and have the same number of dimensions, as the vectors that you use in your index.
`maxdist` | (optional) is the maximum distance from the search vector that you want to find. Content calculates this distance according to the value you set for DistanceMetric, which you set according to the requirements of your vector model. The default value is positive infinity (that is, there is no maximum distance). When DistanceMetric is set to L2 or Cosine, you can also use any negative number to mean there is no maximum distance.
`maxneighbors` | (optional) is the maximum number of similar vectors to return in the results.
`multiplier` | (optional) is the weight by which to multiply the relevance for vectors that return in the results. You can use this option to modify the weighting for parts of your vector queries.
`fieldname` | is the name of the field in your indexed documents that contains the vectors that you want to search.

For a vector search, the Content component returns the documents where the specified field contains the closest vectors to the vector that you provide, according to the DistanceMetric. If a document contains more than one instance of the vector field, Content uses the closest one to the search vector.

## Vector Search with Section-Breaking

When you use section breaking in your documents, the vector search can return the document ID of the matching section, as long as source and offset metadata is available in the vector field.

Content uses the source metadata to determine that the embedding was generated from text in the section-breaking field. It then uses the offset values in your vector field to calculate the section that the vector source text is from. Content calculates offsets relative to the start of the whole text of the field (before section-breaking), and uses the configured section size to calculate the correct section.

For example, if the `DRECONTENT` field is split into sections of 1000 characters each, `source=DRECONTENT[123:456]` represents an embedding generated from section 0, and `source=DRECONTENT[2234:2345]` represents an embedding generated from section 2.

NOTE: If the source offsets for a single embedding span two or more sections, only the first of these sections returns when this embedding matches a `VECTOR` query.

## Highlight Values in Vector Search

When you use the Highlight parameter with a `VECTOR` query, Content highlights the appropriate section of the source text for the vector, as long as you print the source field in the query results.

Vector highlighting works for all highlight options. For example, with `Sentences` highlighting, Content highlights all sentences that overlap with the vector source text.

For `Terms` highlighting, Content highlights only entire words, even if the vector offset is in the middle of a word.

NOTE: `Proximity` highlighting is the same as terms highlighting, as there is no defined proximity between vector and non-vector fields.

Copyright 2025 Open Text
Version 25.3 | Last updated July 2025

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Product: Knowledge Discovery Content Component 25.3

Topic Title: Vector Search