QueryVectorCollectionRequest.md

QueryVectorCollectionRequest

Properties

Name	Type	Description	Notes
query	str	Natural language search query (mutually exclusive with vector)	[optional]
vector	List[float]	Pre-computed embedding vector (mutually exclusive with query). Array length must match collection dimension.	[optional]
limit	int	Maximum number of results to return	[optional] [default to 5]
threshold	float	Minimum similarity score (0-1, higher = more relevant)	[optional] [default to 0.7]
include_embeddings	bool	Include embedding vectors in response (for debugging)	[optional] [default to False]
filter	QueryVectorCollectionRequestFilter		[optional]
list_by_metadata	bool	If true, skip semantic search and return all documents matching the filter. Requires filter. Supports cursor pagination.	[optional] [default to False]
cursor	str	Pagination cursor for listByMetadata mode. Use nextCursor from previous response. Opaque format - do not construct manually.	[optional]
sort_by	str	Field to sort by in listByMetadata mode	[optional] [default to 'created_at']
sort_order	str	Sort direction in listByMetadata mode	[optional] [default to 'desc']

Example

from quantcdn.models.query_vector_collection_request import QueryVectorCollectionRequest

# TODO update the JSON string below
json = "{}"
# create an instance of QueryVectorCollectionRequest from a JSON string
query_vector_collection_request_instance = QueryVectorCollectionRequest.from_json(json)
# print the JSON string representation of the object
print(QueryVectorCollectionRequest.to_json())

# convert the object into a dict
query_vector_collection_request_dict = query_vector_collection_request_instance.to_dict()
# create an instance of QueryVectorCollectionRequest from a dict
query_vector_collection_request_from_dict = QueryVectorCollectionRequest.from_dict(query_vector_collection_request_dict)

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