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Module: Mongoid::SearchIndexable

Relationships & Source Files
Namespace Children
Modules:
ClassMethods
Classes:
Status
Extension / Inclusion / Inheritance Descendants
Included In:
Composable, Document, GlobalDiscriminatorKeyAssignment::InvalidFieldHost
Super Chains via Extension / Inclusion / Inheritance
Class Chain:
self, ActiveSupport::Concern
Defined in: lib/mongoid/search_indexable.rb

Overview

Encapsulates behavior around managing search indexes. This feature is only supported when connected to an Atlas cluster.

Instance Method Summary

DSL Calls

included

[ GitHub ] 


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# File 'lib/mongoid/search_indexable.rb', line 53

included do
  cattr_accessor :search_index_specs
  self.search_index_specs = []
end

Instance Method Details

#auto_embed_search(index: nil, path: nil, limit: 10, num_candidates: nil, filter: nil, exact: false, model: nil, pipeline: []) ⇒ Array<Mongoid::Document>

Performs an Atlas Vector Search query for documents with text similar to this document's stored text field, using auto-embedding. The current document is excluded from the results.

Examples:

Find articles with similar descriptions.

article.auto_embed_search(limit: 5, filter: { status: 'published' })

Parameters:

  • index (String | Symbol | nil) —

    The name of the auto-embed index to use (optional when only one is declared on the model).

  • path (String | Symbol | nil) —

    The indexed text field path (optional if unambiguous from the index definition).

  • limit (Integer) —

    Maximum number of results (default: 10).

  • num_candidates (Integer | nil) —

    Candidates for ANN search; defaults to limit * 10. Ignored when exact: true.

  • filter (Hash | nil) —

    Optional MongoDB filter for pre-filtering.

  • exact (true | false) —

    Use exact nearest-neighbor search (default: false).

  • model (String | nil) —

    Query-time embedding model override.

  • pipeline (Array) —

    Additional aggregation stages to append.

Returns:

[ GitHub ] 

  


# File 'lib/mongoid/search_indexable.rb', line 128



def auto_embed_search(index: nil, path: nil, limit: 10, num_candidates: nil, filter: nil, exact: false, model: nil, pipeline: []) # rubocop:disable Metrics/ParameterLists
  _index, resolved_path = self.class.send(:resolve_auto_embed_index, index, path)
  text = public_send(resolved_path)

  if text.nil?
    raise ArgumentError,
          "#{resolved_path} is nil on this document; cannot perform auto-embed search"
  end

  self_exclusion = { '$match' => { '_id' => { '$ne' => _id } } }
  post_pipeline = [ self_exclusion, { '$limit' => limit }, *Array(pipeline) ]
  effective_candidates = num_candidates || (limit * 10)

  self.class.auto_embed_search(
    text,
    index: index,
    path: path,
    limit: limit + 1,
    num_candidates: effective_candidates,
    filter: filter,
    exact: exact,
    model: model,
    pipeline: post_pipeline
  )
end


  








  

    #vector_search(index: nil, path: nil, limit: 10, num_candidates: nil, exact: false, filter: nil, pipeline: [])  ⇒ Array<Mongoid::Document> 
  



  

    
Performs a vector search for documents similar to this one, using
this document's stored embedding as the query vector. The document
itself is excluded from the results.

  





    

    
Examples:

        
Find articles similar to this one.

      
article.vector_search(limit: 5, filter: { status: 'published' })

  


Parameters:


    
  
  • 
    index
    (String | Symbol | nil)
—    
    The name of the vector search
index to use (optional if only one is declared on the model).
    
  
    • 
  
  • 
    path
    (String | Symbol | nil)
—    
    The field containing the stored
vector (optional if unambiguous from the index definition).
    
  
    • 
  
  • 
    limit
    (Integer)
—    
    The maximum number of results (default: 10).
    
  
    • 
  
  • 
    num_candidates
    (Integer | nil)
—    
    The number of candidates to
consider during the ANN search; defaults to limit * 10.
    
  
    • 
  
  • 
    exact
    (true | false)
—    
    Use exact nearest-neighbor (ENN) search
instead of ANN (default: false). When true, numCandidates is omitted.
Required when using a flat vector search index.
    
  
    • 
  
  • 
    filter
    (Hash | nil)
—    
    An optional MongoDB filter to pre-filter
candidates before scoring.
    
  
    • 
  
  • 
    pipeline
    (Array)
—    
    Additional aggregation stages to append after
the vector search and score projection.
    
  
    • 



Returns:





  [ GitHub ]


  

  


# File 'lib/mongoid/search_indexable.rb', line 82



def vector_search(index: nil, path: nil, limit: 10, num_candidates: nil, exact: false, filter: nil, pipeline: []) # rubocop:disable Metrics/ParameterLists
  _index, resolved_path = self.class.send(:resolve_vector_index, index, path)
  query_vector = public_send(resolved_path)

  if query_vector.nil?
    raise ArgumentError,
          "#{resolved_path} is nil on this document; cannot perform vector search"
  end

  self_exclusion = { '$match' => { '_id' => { '$ne' => _id } } }
  post_pipeline = [ self_exclusion, { '$limit' => limit }, *Array(pipeline) ]
  effective_candidates = num_candidates || (limit * 10)

  self.class.vector_search(
    query_vector,
    index: index,
    path: path,
    limit: limit + 1,
    num_candidates: effective_candidates,
    exact: exact,
    filter: filter,
    pipeline: post_pipeline
  )
end