VectorisedGraph
#
Bases: object
edges_by_similarity(query, limit, window=None)
#
Search the top scoring edges according to query with no more than limit edges
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query
|
str | list
|
the text or the embedding to score against |
required |
limit
|
int
|
the maximum number of new edges to search |
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 |
empty_selection()
#
Return an empty selection of documents
entities_by_similarity(query, limit, window=None)
#
Search the top scoring entities according to query with no more than limit entities
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query
|
str | list
|
the text or the embedding to score against |
required |
limit
|
int
|
the maximum number of new entities to search |
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 |
nodes_by_similarity(query, limit, window=None)
#
Search the top scoring nodes according to query with no more than limit nodes
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
query
|
str | list
|
the text or the embedding to score against |
required |
limit
|
int
|
the maximum number of new nodes to search |
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 |