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VectorisedGraph #

Bases: object

VectorisedGraph object that contains embedded documents that correspond to graph entities.

edges_by_similarity(query, limit, window=None) #

Perform a similarity search between each edge's associated document and a specified query. Returns a number of edges up to a specified limit ranked in ascending order of distance.

Parameters:

Name Type Description Default
query str | list

The text or the embedding to calculate the distance from.

required
limit int

The maximum number of new edges in the results.

required
window Tuple[int | str, int | str]

The window that documents need to belong to in order to be considered.

None

Returns:

Type Description
VectorSelection

The vector selection resulting from the search.

empty_selection() #

Return an empty selection of entities.

entities_by_similarity(query, limit, window=None) #

Perform a similarity search between each entity's associated document and a specified query. Returns a number of entities up to a specified limit ranked in ascending order of distance.

Parameters:

Name Type Description Default
query str | list

The text or the embedding to calculate the distance from.

required
limit int

The maximum number of new entities in the result.

required
window Tuple[int | str, int | str]

The window that documents need to belong to in order to be considered.

None

Returns:

Type Description
VectorSelection

The vector selection resulting from the search.

nodes_by_similarity(query, limit, window=None) #

Perform a similarity search between each node's associated document and a specified query. Returns a number of nodes up to a specified limit ranked in ascending order of distance.

Parameters:

Name Type Description Default
query str | list

The text or the embedding to calculate the distance from.

required
limit int

The maximum number of new nodes in the result.

required
window Tuple[int | str, int | str]

The window where documents need to belong to in order to be considered.

None

Returns:

Type Description
VectorSelection

The vector selection resulting from the search.

optimize_index() #

Optmize the vector index