POST body (kb_id rides the path, not the body).
| Name | Type | Description | Notes |
|---|---|---|---|
| query | str | [optional] | |
| method | str | Retrieval strategy: "keyword" (lexical), "vector" (embedding similarity) or "hybrid" (default, both). | [optional] [default to 'hybrid'] |
| page | int | Filter-only page number; query search supports page 1 only | [optional] [default to 1] |
| top_k | int | Maximum number of topics to return, 1 to 100 (default 10). On a query search the server also bounds the result by its rerank pool — 50 candidates by default — so asking for more than that returns what the pool held. On a filter-only request (tags without a query) it is the page size instead, and `page` walks the rest. | [optional] [default to 10] |
| score_threshold | float | [optional] | |
| include | List[str] | e.g. ['content'] | [optional] [default to []] |
| boost_tag_ids | List[str] | Reweight, do not filter: topics carrying these tags are pushed up, and topics without them still come back. Use `filters.tag_ids` when the intent is to exclude everything else. | [optional] [default to []] |
| filters | SearchFilters | Optional filters narrowing what is searched. | [optional] |
from everos_cloud.models.search_body import SearchBody
# TODO update the JSON string below
json = "{}"
# create an instance of SearchBody from a JSON string
search_body_instance = SearchBody.from_json(json)
# print the JSON string representation of the object
print(SearchBody.to_json())
# convert the object into a dict
search_body_dict = search_body_instance.to_dict()
# create an instance of SearchBody from a dict
search_body_from_dict = SearchBody.from_dict(search_body_dict)