Request body for POST .../indexes
| Name | Type | Description | Notes |
|---|---|---|---|
| var_async | bool | When true, create the index as a background job and return a job ID for polling. | [optional] |
| columns | List[str] | Columns to index. Required for all index types. | |
| description | str | User-facing description of the embedding (e.g., "product descriptions"). | [optional] |
| dimensions | int | Output vector dimensions. Some models support multiple dimension sizes (e.g., OpenAI text-embedding-3-small supports 512 or 1536). If omitted, the model's default dimensions are used | [optional] |
| embedding_provider_id | str | Embedding provider ID. When set for a vector index, the source column is treated as text and embeddings are generated automatically. The vector index is then built on the generated embedding column (`{column}_embedding` by default). | [optional] |
| index_name | str | ||
| index_type | str | Index type: "sorted" (default), "bm25", or "vector" | [optional] |
| metric | str | Distance metric for vector indexes: "l2", "cosine", or "dot". When omitted, defaults to "l2" for float array columns or the provider's preferred metric for text columns with auto-embedding. | [optional] |
| output_column | str | Custom name for the generated embedding column. Defaults to `{column}_embedding`. | [optional] |
from hotdata.models.create_index_request import CreateIndexRequest
# TODO update the JSON string below
json = "{}"
# create an instance of CreateIndexRequest from a JSON string
create_index_request_instance = CreateIndexRequest.from_json(json)
# print the JSON string representation of the object
print(CreateIndexRequest.to_json())
# convert the object into a dict
create_index_request_dict = create_index_request_instance.to_dict()
# create an instance of CreateIndexRequest from a dict
create_index_request_from_dict = CreateIndexRequest.from_dict(create_index_request_dict)