indexers.VectorStore.search

indexers.VectorStore.search(query, n_results=10, batch_size=None)

Queries the vectors attribute for the most similar documents.

Queries are processed in batches of batch_size, with each batch embedded using vectoriser.transform() and scored against all stored document embeddings via dot-product similarity (equivalent to cosine similarity when embeddings are L2-normalised). The top n_results documents are returned for each query, ordered by descending score.

Any preprocessing hooks set on the instance are applied to the input before searching, and any postprocessing hooks are applied to the output before it is returned.

Parameters

Name	Type	Description	Default
query	VectorStoreSearchInput	The input object containing the text query or list of queries to search for, with ids.	required
n_results	int	Number of top results to return for each query. Defaults to 10.	10
batch_size	int	The batch size for processing queries. Defaults to the batch_size set during initialisation.	None

Returns

Name	Type	Description
VectorStoreSearchOutput	VectorStoreSearchOutput	The output object containing search results with columns for query_id, query_text, doc_label, doc_text, rank, score, and any associated metadata columns.

Raises

Name	Type	Description
	DataValidationError	Raised if invalid arguments are passed.
	ConfigurationError	Raised if the VectorStore is not initialised.
	HookError	Raised if user-defined hooks fail.
	ClassifaiError	Raised if there is a package-specific error during the search operation.
	VectorisationError	Raised if query embedding fails.