Chunk response with search ranking score.
| Name | Type | Description | Notes |
|---|---|---|---|
| id | UUID | Chunk ID | |
| path_part_id | UUID | PathPart ID | |
| content_id | UUID | ChunkContent ID | |
| content | str | Chunk text content | |
| chunk_type | ChunkType | ||
| chunk_metadata | ChunkMetadata | ||
| num_tokens | int | Number of tokens in chunk content | [optional] |
| parent_path_id | UUID | Parent PathPart ID | |
| prev_sibling_path_id | UUID | Previous sibling PathPart ID | [optional] |
| next_sibling_path_id | UUID | Next sibling PathPart ID | [optional] |
| materialized_path | str | Full materialized path from root | |
| system_managed | bool | Whether this chunk is system-managed | |
| tenant_id | UUID | Tenant ID | |
| created_at | datetime | Creation timestamp | |
| updated_at | datetime | Last update timestamp | |
| asset_s3_urls | List[str] | Presigned URLs to download the chunk's visual assets (6-hour validity) | [optional] |
| document | ChunkDocumentResponse | Ancestor document info (populated when with_document=true) | [optional] |
| document_version | ChunkDocumentVersionResponse | Ancestor document version info (populated when with_document=true) | [optional] |
| score | float | Search ranking score returned by Qdrant |
from ksapi.models.scored_chunk_response import ScoredChunkResponse
# TODO update the JSON string below
json = "{}"
# create an instance of ScoredChunkResponse from a JSON string
scored_chunk_response_instance = ScoredChunkResponse.from_json(json)
# print the JSON string representation of the object
print(ScoredChunkResponse.to_json())
# convert the object into a dict
scored_chunk_response_dict = scored_chunk_response_instance.to_dict()
# create an instance of ScoredChunkResponse from a dict
scored_chunk_response_from_dict = ScoredChunkResponse.from_dict(scored_chunk_response_dict)