similarity_search

similarity_search(
  self,
  query: str,
  k: int = 4,
  score_threshold: Optional[float] = 0.0,
  **kwargs: Any = {}
) -> List[Document]

Optional Args:

vector_field: Document field embeddings are stored in. Defaults to "vector_field".

text_field: Document field the text of the document is stored in. Defaults to "text".

metadata_field: Document field that metadata is stored in. Defaults to "metadata". Can be set to a special value "*" to include the entire document.

Optional Args for Approximate Search:

search_type: "approximate_search"; default: "approximate_search"

boolean_filter: A Boolean filter is a post filter consists of a Boolean query that contains a k-NN query and a filter.

subquery_clause: Query clause on the knn vector field; default: "must"

lucene_filter: the Lucene algorithm decides whether to perform an exact k-NN search with pre-filtering or an approximate search with modified post-filtering. (deprecated, use efficient_filter)

efficient_filter: the Lucene Engine or Faiss Engine decides whether to perform an exact k-NN search with pre-filtering or an approximate search with modified post-filtering.

Optional Args for Script Scoring Search:

search_type: "script_scoring"; default: "approximate_search"

space_type: "l2", "l1", "linf", "cosinesimil", "innerproduct", "hammingbit"; default: "l2"

pre_filter: script_score query to pre-filter documents before identifying nearest neighbors; default: {"match_all": {}}

Optional Args for Painless Scripting Search:

search_type: "painless_scripting"; default: "approximate_search"

space_type: "l2Squared", "l1Norm", "cosineSimilarity"; default: "l2Squared"

pre_filter: script_score query to pre-filter documents before identifying nearest neighbors; default: {"match_all": {}}