AISearch

These dataclasses are used in the SDK to represent API requests and responses for services in the databricks.sdk.service.aisearch module.

class databricks.sdk.service.aisearch.ColumnInfo(name: str | None = None, type_text: str | None = None)

Column information (name and data type) for an index column. Surfaced on Index.column_info.

name: str | None = None

Name of the column.

type_text: str | None = None

Data type of the column (e.g., “string”, “int”, “array<float>”).

as_dict() → dict

Serializes the ColumnInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ColumnInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ColumnInfo

Deserializes the ColumnInfo from a dictionary.

class databricks.sdk.service.aisearch.CustomTag(key: str, value: str | None = None)

User-defined key/value tag attached to an AI Search endpoint for cost attribution and access control.

key: str

Key field for an AI Search endpoint tag.

value: str | None = None

[Optional] Value field for an AI Search endpoint tag.

as_dict() → dict

Serializes the CustomTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CustomTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CustomTag

Deserializes the CustomTag from a dictionary.

class databricks.sdk.service.aisearch.DataModificationResult(failed_primary_keys: List[str] | None = None, success_row_count: int | None = None)

Per-row outcome of a data-plane upsert or delete operation.

failed_primary_keys: List[str] | None = None

Primary keys of rows that failed to process.

success_row_count: int | None = None

Count of rows processed successfully.

as_dict() → dict

Serializes the DataModificationResult into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DataModificationResult into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DataModificationResult

Deserializes the DataModificationResult from a dictionary.

class databricks.sdk.service.aisearch.DataModificationStatus

Overall outcome of a data-plane upsert or delete. Mirrors the legacy databricks.brickindexscheduler.UpsertDeleteDataStatus value-for-value.

FAILURE = "FAILURE"

PARTIAL_SUCCESS = "PARTIAL_SUCCESS"

SUCCESS = "SUCCESS"

class databricks.sdk.service.aisearch.DeltaSyncIndexSpec(pipeline_type: PipelineType, columns_to_sync: List[str] | None = None, embedding_source_columns: List[EmbeddingSourceColumn] | None = None, embedding_vector_columns: List[EmbeddingVectorColumn] | None = None, embedding_writeback_table: str | None = None, pipeline_id: str | None = None, source_table: str | None = None)

Specification for a Delta Sync index — the index is kept in sync with a source Delta table.

pipeline_type: PipelineType

Pipeline execution mode. Required on create — the backend rejects an unset value. Storage Optimized endpoints accept only TRIGGERED; Standard endpoints accept both. No explicit stage — a REQUIRED field staged below its service would be dropped from combined specs while remaining in required, tripping the OpenAPI required-vs-properties consistency check. The field inherits the service’s launch stage.

columns_to_sync: List[str] | None = None

[Optional] Select the columns to sync with the index. If left blank, all columns from the source table are synced. The primary key column and embedding source or vector column are always synced.

embedding_source_columns: List[EmbeddingSourceColumn] | None = None

The columns that contain the embedding source.

embedding_vector_columns: List[EmbeddingVectorColumn] | None = None

The columns that contain the embedding vectors.

embedding_writeback_table: str | None = None

[Optional] Name of the Delta table to sync the index contents and computed embeddings to.

pipeline_id: str | None = None

The ID of the pipeline that is used to sync the index.

source_table: str | None = None

The full name of the source Delta table.

as_dict() → dict

Serializes the DeltaSyncIndexSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeltaSyncIndexSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeltaSyncIndexSpec

Deserializes the DeltaSyncIndexSpec from a dictionary.

class databricks.sdk.service.aisearch.DirectAccessIndexSpec(embedding_source_columns: List[EmbeddingSourceColumn] | None = None, embedding_vector_columns: List[EmbeddingVectorColumn] | None = None, schema_json: str | None = None)

Specification for a Direct Access index — the customer manages vectors and metadata directly.

embedding_source_columns: List[EmbeddingSourceColumn] | None = None

The columns that contain the embedding source.

embedding_vector_columns: List[EmbeddingVectorColumn] | None = None

The columns that contain the embedding vectors.

schema_json: str | None = None

The schema of the index in JSON format. Supported types are integer, long, float, double, boolean, string, date, timestamp. Supported types for vector columns: array<float>, array<double>.

as_dict() → dict

Serializes the DirectAccessIndexSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DirectAccessIndexSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DirectAccessIndexSpec

Deserializes the DirectAccessIndexSpec from a dictionary.

class databricks.sdk.service.aisearch.EmbeddingSourceColumn(embedding_model_endpoint: str | None = None, model_endpoint_name_for_query: str | None = None, name: str | None = None)

Name of an embedding source column and its associated embedding model endpoint.

embedding_model_endpoint: str | None = None

Name of the embedding model endpoint, used by default for both ingestion and querying.

model_endpoint_name_for_query: str | None = None

Name of the embedding model endpoint which, if specified, is used for querying (not ingestion).

name: str | None = None

Name of the source column.

as_dict() → dict

Serializes the EmbeddingSourceColumn into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EmbeddingSourceColumn into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EmbeddingSourceColumn

Deserializes the EmbeddingSourceColumn from a dictionary.

class databricks.sdk.service.aisearch.EmbeddingVectorColumn(embedding_dimension: int | None = None, name: str | None = None)

Name and dimension of an embedding vector column.

embedding_dimension: int | None = None

Dimension of the embedding vector.

name: str | None = None

Name of the column.

as_dict() → dict

Serializes the EmbeddingVectorColumn into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EmbeddingVectorColumn into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EmbeddingVectorColumn

Deserializes the EmbeddingVectorColumn from a dictionary.

class databricks.sdk.service.aisearch.Endpoint(endpoint_type: EndpointType, budget_policy_id: str | None = None, create_time: Timestamp | None = None, creator: str | None = None, custom_tags: List[CustomTag] | None = None, effective_budget_policy_id: str | None = None, endpoint_status: EndpointStatus | None = None, id: str | None = None, index_count: int | None = None, last_updated_user: str | None = None, name: str | None = None, replica_count: int | None = None, scaling_info: EndpointScalingInfo | None = None, target_qps: int | None = None, throughput_info: EndpointThroughputInfo | None = None, update_time: Timestamp | None = None, usage_policy_id: str | None = None)

An AI Search endpoint — compute infrastructure that hosts AI Search indexes and serves queries against them. Customers create, query, and delete endpoints; the system manages provisioning, scaling, and health status.

endpoint_type: EndpointType

Type of endpoint. Required on create and immutable thereafter.

budget_policy_id: str | None = None

The user-selected budget policy id for the endpoint.

create_time: Timestamp | None = None

Time the endpoint was created.

creator: str | None = None

Creator of the endpoint

custom_tags: List[CustomTag] | None = None

The custom tags assigned to the endpoint

effective_budget_policy_id: str | None = None

The budget policy id applied to the endpoint

endpoint_status: EndpointStatus | None = None

Current status of the endpoint

id: str | None = None

Unique identifier of the endpoint

index_count: int | None = None

Number of indexes on the endpoint

last_updated_user: str | None = None

User who last updated the endpoint

name: str | None = None

Name of the AI Search endpoint. Server-assigned full resource path (workspaces/{workspace}/endpoints/{endpoint}) on output. On create, the user-supplied short name is conveyed via CreateEndpointRequest.endpoint_id; the server composes the full name and returns it on the response.

replica_count: int | None = None

The client-supplied desired number of replicas for the endpoint, applied at create/update time. Mutually exclusive with target_qps.

scaling_info: EndpointScalingInfo | None = None

Scaling information for the endpoint

target_qps: int | None = None

Target QPS for the endpoint. Mutually exclusive with replica_count. Best-effort; the system does not guarantee this QPS will be achieved.

throughput_info: EndpointThroughputInfo | None = None

Throughput information for the endpoint

update_time: Timestamp | None = None

Time the endpoint was last updated.

usage_policy_id: str | None = None

The usage policy id applied to the endpoint.

as_dict() → dict

Serializes the Endpoint into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Endpoint into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Endpoint

Deserializes the Endpoint from a dictionary.

class databricks.sdk.service.aisearch.EndpointScalingInfo(requested_target_qps: int | None = None, state: ScalingChangeState | None = None)

Scaling information for a Storage Optimized endpoint — current scaling state and the requested QPS target the system is scaling toward.

requested_target_qps: int | None = None

The requested QPS target for the endpoint. Best-effort; the system does not guarantee this QPS will be achieved.

state: ScalingChangeState | None = None

The current state of the scaling change request.

as_dict() → dict

Serializes the EndpointScalingInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EndpointScalingInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EndpointScalingInfo

Deserializes the EndpointScalingInfo from a dictionary.

class databricks.sdk.service.aisearch.EndpointStatus(message: str | None = None, state: EndpointStatusState | None = None)

Lifecycle and health state of an AI Search endpoint, along with any human-readable detail about that state.

message: str | None = None

Human-readable detail about the endpoint’s current state or the reason for a state transition.

state: EndpointStatusState | None = None

Current lifecycle state of the endpoint. See State for the meaning of each value.

as_dict() → dict

Serializes the EndpointStatus into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EndpointStatus into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EndpointStatus

Deserializes the EndpointStatus from a dictionary.

class databricks.sdk.service.aisearch.EndpointStatusState

Lifecycle state of an AI Search endpoint, used by both Standard and Storage Optimized SKUs.

DELETED = "DELETED"

OFFLINE = "OFFLINE"

ONLINE = "ONLINE"

PROVISIONING = "PROVISIONING"

RED_STATE = "RED_STATE"

YELLOW_STATE = "YELLOW_STATE"

class databricks.sdk.service.aisearch.EndpointThroughputInfo(change_request_message: str | None = None, change_request_state: ThroughputChangeRequestState | None = None, current_concurrency: float | None = None, current_concurrency_utilization_percentage: float | None = None, current_num_replicas: int | None = None, maximum_concurrency_allowed: float | None = None, minimal_concurrency_allowed: float | None = None, requested_concurrency: float | None = None, requested_num_replicas: int | None = None)

Throughput information for an AI Search endpoint, including requested and current concurrency settings.

change_request_message: str | None = None

Additional information about the throughput change request

change_request_state: ThroughputChangeRequestState | None = None

The state of the most recent throughput change request

current_concurrency: float | None = None

The current concurrency (total CPU) allocated to the endpoint

current_concurrency_utilization_percentage: float | None = None

The current utilization of concurrency as a percentage (0-100)

current_num_replicas: int | None = None

The current number of replicas allocated to the endpoint

maximum_concurrency_allowed: float | None = None

The maximum concurrency allowed for this endpoint

minimal_concurrency_allowed: float | None = None

The minimum concurrency allowed for this endpoint

requested_concurrency: float | None = None

The requested concurrency (total CPU) for the endpoint

requested_num_replicas: int | None = None

The requested number of replicas for the endpoint

as_dict() → dict

Serializes the EndpointThroughputInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EndpointThroughputInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EndpointThroughputInfo

Deserializes the EndpointThroughputInfo from a dictionary.

class databricks.sdk.service.aisearch.EndpointType

Type of endpoint.

STANDARD = "STANDARD"

STORAGE_OPTIMIZED = "STORAGE_OPTIMIZED"

class databricks.sdk.service.aisearch.FacetResultData(facet_array: List[List[any]] | None = None, facet_row_count: int | None = None)

Facet aggregation rows returned by a query.

facet_array: List[List[any]] | None = None

Facet rows; each row is [facet_column_name, value_or_range, count].

facet_row_count: int | None = None

Number of facet rows returned.

as_dict() → dict

Serializes the FacetResultData into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FacetResultData into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FacetResultData

Deserializes the FacetResultData from a dictionary.

class databricks.sdk.service.aisearch.Index(primary_key: str, index_type: IndexType, creator: str | None = None, delta_sync_index_spec: DeltaSyncIndexSpec | None = None, direct_access_index_spec: DirectAccessIndexSpec | None = None, endpoint: str | None = None, index_subtype: IndexSubtype | None = None, name: str | None = None, status: IndexStatus | None = None)

An AI Search index — a searchable collection of vectors and metadata hosted on an AI Search endpoint. Indexes are children of endpoints; customers create, get, list, and delete them. The {index} segment of the resource name is the index’s Unity Catalog table name.

primary_key: str

Primary key of the index. Set on create and immutable thereafter.

index_type: IndexType

Type of index. Required on create and immutable thereafter.

creator: str | None = None

Creator of the index.

delta_sync_index_spec: DeltaSyncIndexSpec | None = None

Specification for a Delta Sync index. Set when index_type is DELTA_SYNC.

direct_access_index_spec: DirectAccessIndexSpec | None = None

Specification for a Direct Access index. Set when index_type is DIRECT_ACCESS.

endpoint: str | None = None

Name of the endpoint associated with the index. Ignored on create — the endpoint is taken from CreateIndexRequest.parent; populated only on output.

index_subtype: IndexSubtype | None = None

The subtype of the index. Set on create and immutable thereafter.

name: str | None = None

Name of the AI Search index. Server-assigned full resource path (workspaces/{workspace}/endpoints/{endpoint}/indexes/{index}) on output, where {index} is the index’s Unity Catalog table name. On create, the user-supplied UC table name is conveyed via CreateIndexRequest.index_id; the server composes the full name and returns it on the response.

status: IndexStatus | None = None

Current status of the index.

as_dict() → dict

Serializes the Index into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Index into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Index

Deserializes the Index from a dictionary.

class databricks.sdk.service.aisearch.IndexStatus(index_url: str | None = None, indexed_row_count: int | None = None, message: str | None = None, ready: bool | None = None)

Lifecycle and health state of an AI Search index, along with human-readable detail about that state and basic indexing progress.

index_url: str | None = None

Index API URL used to perform operations on the index.

indexed_row_count: int | None = None

Number of rows indexed.

message: str | None = None

Human-readable detail about the index’s current state.

ready: bool | None = None

Whether the index is ready for search.

as_dict() → dict

Serializes the IndexStatus into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the IndexStatus into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → IndexStatus

Deserializes the IndexStatus from a dictionary.

class databricks.sdk.service.aisearch.IndexSubtype

The subtype of the AI Search index, determining the indexing and retrieval strategy. - VECTOR: Not a supported create value — do not select it. Use HYBRID (vector + hybrid search) or FULL_TEXT (full-text only). It is the proto2 default (= 0) solely to mirror the legacy index_v2.proto enum value-for-value; it is not an offered index subtype. - FULL_TEXT: An index that uses full-text search without vector embeddings. - HYBRID: An index that uses vector embeddings for similarity search and hybrid search.

FULL_TEXT = "FULL_TEXT"

HYBRID = "HYBRID"

VECTOR = "VECTOR"

class databricks.sdk.service.aisearch.IndexType

There are 2 types of AI Search indexes: - DELTA_SYNC: An index that automatically syncs with a source Delta Table, automatically and incrementally updating the index as the underlying data in the Delta Table changes. - DIRECT_ACCESS: An index that supports direct read and write of vectors and metadata through our REST and SDK APIs. With this model, the user manages index updates.

DELTA_SYNC = "DELTA_SYNC"

DIRECT_ACCESS = "DIRECT_ACCESS"

class databricks.sdk.service.aisearch.ListEndpointsResponse(endpoints: List[Endpoint] | None = None, next_page_token: str | None = None)

Response for ListEndpoints carrying the page of endpoints and an optional continuation token.

endpoints: List[Endpoint] | None = None

The endpoints in the workspace.

next_page_token: str | None = None

A token that can be used to get the next page of results. Empty when there are no more results.

as_dict() → dict

Serializes the ListEndpointsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListEndpointsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListEndpointsResponse

Deserializes the ListEndpointsResponse from a dictionary.

class databricks.sdk.service.aisearch.ListIndexesResponse(indexes: List[Index] | None = None, next_page_token: str | None = None)

Response for ListIndexes carrying the page of indexes and an optional continuation token.

indexes: List[Index] | None = None

The indexes on the endpoint. The field is named indexes (not the irregular plural indices) to satisfy core::0132, which derives the response field name from the ListIndexes method. core::0158::response-plural-first-field independently computes the resource plural as indices and is satisfied via a scoped field exception below.

next_page_token: str | None = None

A token that can be used to get the next page of results. Empty when there are no more results.

as_dict() → dict

Serializes the ListIndexesResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListIndexesResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListIndexesResponse

Deserializes the ListIndexesResponse from a dictionary.

class databricks.sdk.service.aisearch.PipelineType

Pipeline execution mode for a Delta Sync index. Required on create for Delta Sync indexes; the legacy backend rejects an unset value with INVALID_PARAMETER_VALUE. - TRIGGERED: the pipeline stops after refreshing the source table once, using the data available when the update started. - CONTINUOUS: the pipeline processes new data as it arrives in the source table to keep the index fresh.

CONTINUOUS = "CONTINUOUS"

TRIGGERED = "TRIGGERED"

class databricks.sdk.service.aisearch.QueryIndexResponse(facet_result: FacetResultData | None = None, manifest: ResultManifest | None = None, result: ResultData | None = None)

Response for QueryIndex carrying the matched rows and their column metadata.

facet_result: FacetResultData | None = None

Facet aggregation rows, when facets were requested.

manifest: ResultManifest | None = None

Metadata describing the result columns.

result: ResultData | None = None

The matched result rows.

as_dict() → dict

Serializes the QueryIndexResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the QueryIndexResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → QueryIndexResponse

Deserializes the QueryIndexResponse from a dictionary.

class databricks.sdk.service.aisearch.RemoveDataResponse(result: DataModificationResult | None = None, status: DataModificationStatus | None = None)

Response for RemoveData.

result: DataModificationResult | None = None

Per-row outcome of the delete.

status: DataModificationStatus | None = None

Overall status of the delete.

as_dict() → dict

Serializes the RemoveDataResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RemoveDataResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RemoveDataResponse

Deserializes the RemoveDataResponse from a dictionary.

class databricks.sdk.service.aisearch.RerankerConfig(model: str | None = None, model_type: RerankerConfigModelType | None = None, parameters: RerankerConfigRerankerParameters | None = None)

Configuration for reranking query results with a reranker model.

model: str | None = None

Reranker identifier: “databricks_reranker” for the base model, or a Model Serving endpoint name when model_type is MODEL_TYPE_FINETUNED.

model_type: RerankerConfigModelType | None = None

Discriminator for how model is interpreted.

parameters: RerankerConfigRerankerParameters | None = None

Parameters controlling reranking.

as_dict() → dict

Serializes the RerankerConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RerankerConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RerankerConfig

Deserializes the RerankerConfig from a dictionary.

class databricks.sdk.service.aisearch.RerankerConfigModelType

How the model field is interpreted.

MODEL_TYPE_BASE = "MODEL_TYPE_BASE"

MODEL_TYPE_FINETUNED = "MODEL_TYPE_FINETUNED"

class databricks.sdk.service.aisearch.RerankerConfigRerankerParameters(columns_to_rerank: List[str] | None = None)

Parameters controlling how the reranker processes results.

columns_to_rerank: List[str] | None = None

Columns whose values are concatenated and sent to the reranker.

as_dict() → dict

Serializes the RerankerConfigRerankerParameters into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RerankerConfigRerankerParameters into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RerankerConfigRerankerParameters

Deserializes the RerankerConfigRerankerParameters from a dictionary.

class databricks.sdk.service.aisearch.ResultData(data_array: List[List[any]] | None = None, row_count: int | None = None)

The rows of a query result set.

data_array: List[List[any]] | None = None

Result rows; each row is a list of column values aligned with the manifest columns.

row_count: int | None = None

Number of rows in the result set.

as_dict() → dict

Serializes the ResultData into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ResultData into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ResultData

Deserializes the ResultData from a dictionary.

class databricks.sdk.service.aisearch.ResultManifest(column_count: int | None = None, columns: List[ColumnInfo] | None = None, facet_column_count: int | None = None, facet_columns: List[ColumnInfo] | None = None)

Metadata describing the columns of a query result set.

column_count: int | None = None

Number of columns in the result set.

columns: List[ColumnInfo] | None = None

Information about each column in the result set.

facet_column_count: int | None = None

Number of columns in the facet result.

facet_columns: List[ColumnInfo] | None = None

Information about each facet column.

as_dict() → dict

Serializes the ResultManifest into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ResultManifest into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ResultManifest

Deserializes the ResultManifest from a dictionary.

class databricks.sdk.service.aisearch.ScalingChangeState

State of the most recent scaling change request for a Storage Optimized endpoint.

SCALING_CHANGE_APPLIED = "SCALING_CHANGE_APPLIED"

SCALING_CHANGE_IN_PROGRESS = "SCALING_CHANGE_IN_PROGRESS"

SCALING_CHANGE_UNSPECIFIED = "SCALING_CHANGE_UNSPECIFIED"

class databricks.sdk.service.aisearch.ScanIndexResponse(data: List[Dict[str, any]] | None = None, next_page_token: str | None = None)

Response for ScanIndex carrying a page of rows and an optional continuation token.

data: List[Dict[str, any]] | None = None

The rows in this page, each a struct of column name to value.

next_page_token: str | None = None

Token for the next page; empty when the scan is exhausted.

as_dict() → dict

Serializes the ScanIndexResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ScanIndexResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ScanIndexResponse

Deserializes the ScanIndexResponse from a dictionary.

class databricks.sdk.service.aisearch.SyncIndexResponse

Response for SyncIndex. Empty today; reserved so future sync metadata (e.g. an operation handle) can be added without breaking the wire contract.

as_dict() → dict

Serializes the SyncIndexResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SyncIndexResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SyncIndexResponse

Deserializes the SyncIndexResponse from a dictionary.

class databricks.sdk.service.aisearch.ThroughputChangeRequestState

State of the most recent throughput change request issued against a Storage Optimized endpoint. Surfaced on EndpointThroughputInfo.change_request_state.

CHANGE_ADJUSTED = "CHANGE_ADJUSTED"

CHANGE_FAILED = "CHANGE_FAILED"

CHANGE_IN_PROGRESS = "CHANGE_IN_PROGRESS"

CHANGE_REACHED_MAXIMUM = "CHANGE_REACHED_MAXIMUM"

CHANGE_REACHED_MINIMUM = "CHANGE_REACHED_MINIMUM"

CHANGE_SUCCESS = "CHANGE_SUCCESS"

class databricks.sdk.service.aisearch.UpsertDataResponse(result: DataModificationResult | None = None, status: DataModificationStatus | None = None)

Response for UpsertData.

result: DataModificationResult | None = None

Per-row outcome of the upsert.

status: DataModificationStatus | None = None

Overall status of the upsert.

as_dict() → dict

Serializes the UpsertDataResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpsertDataResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpsertDataResponse

Deserializes the UpsertDataResponse from a dictionary.