| Name | Type | Description | Notes |
|---|---|---|---|
| results | List[QueryVectorCollection200ResponseResultsInner] | [optional] | |
| query | str | Original query text (null if vector or metadata search was used) | [optional] |
| search_mode | str | Search mode used: text (query provided), vector (pre-computed), metadata (listByMetadata) | [optional] |
| filter | object | Filter that was applied (if any) | [optional] |
| count | int | Number of results returned | [optional] |
| execution_time_ms | int | Query execution time in milliseconds | [optional] |
| collection_id | str | [optional] | |
| has_more | bool | True if more results available (listByMetadata mode only) | [optional] |
| next_cursor | str | Cursor for next page. Pass as cursor param to continue. Null when no more results. Only in listByMetadata mode. | [optional] |
| pagination | QueryVectorCollection200ResponsePagination | [optional] |
from quantcdn.models.query_vector_collection200_response import QueryVectorCollection200Response
# TODO update the JSON string below
json = "{}"
# create an instance of QueryVectorCollection200Response from a JSON string
query_vector_collection200_response_instance = QueryVectorCollection200Response.from_json(json)
# print the JSON string representation of the object
print(QueryVectorCollection200Response.to_json())
# convert the object into a dict
query_vector_collection200_response_dict = query_vector_collection200_response_instance.to_dict()
# create an instance of QueryVectorCollection200Response from a dict
query_vector_collection200_response_from_dict = QueryVectorCollection200Response.from_dict(query_vector_collection200_response_dict)