Request body for chunk search (dense vector or full-text BM25).
| Name | Type | Description | Notes |
|---|---|---|---|
| query | str | Search query text | |
| search_type | SearchType | [optional] | |
| parent_path_ids | List[UUID] | Path part IDs to search within (non-CHUNK types). Defaults to tenant's /shared. | [optional] |
| tag_ids | List[UUID] | Filter by tag IDs (AND logic — chunks must have ALL specified tags) | [optional] |
| chunk_types | List[ChunkType] | Filter by chunk types (TEXT, TABLE, IMAGE, HTML, UNKNOWN). Only chunks matching one of the listed types are returned. | [optional] |
| ingestion_time_after | datetime | Only chunks ingested after this timestamp | [optional] |
| active_version_only | bool | Only return chunks from the active document version | [optional] [default to True] |
| top_k | int | Number of results (1-50) | [optional] [default to 5] |
| score_threshold | float | Minimum similarity score | [optional] [default to 0.3] |
| with_document | bool | Include ancestor document_id and document_version_id in each result | [optional] [default to False] |
from ksapi.models.chunk_search_request import ChunkSearchRequest
# TODO update the JSON string below
json = "{}"
# create an instance of ChunkSearchRequest from a JSON string
chunk_search_request_instance = ChunkSearchRequest.from_json(json)
# print the JSON string representation of the object
print(ChunkSearchRequest.to_json())
# convert the object into a dict
chunk_search_request_dict = chunk_search_request_instance.to_dict()
# create an instance of ChunkSearchRequest from a dict
chunk_search_request_from_dict = ChunkSearchRequest.from_dict(chunk_search_request_dict)