Databricks AI Bridge Python API

class databricks_ai_bridge.genie.GenieResponse(result: str | pandas.core.frame.DataFrame, query: str | None = '', description: str | None = '')

Bases: object

result: str | DataFrame

query: str | None = ''

description: str | None = ''

class databricks_ai_bridge.genie.Genie(space_id)

Bases: object

start_conversation(content)

create_message(conversation_id, content)

poll_for_result(conversation_id, message_id)

ask_question(question)

databricks_ai_bridge.vector_search_retriever_tool.vector_search_retriever_tool_trace(func)

Decorator factory to trace VectorSearchRetrieverTool with the tool name

class databricks_ai_bridge.vector_search_retriever_tool.VectorSearchRetrieverToolInput

Bases: BaseModel

param query: str [Required]

The string used to query the index with and identify the most similar vectors and return the associated documents.

class databricks_ai_bridge.vector_search_retriever_tool.VectorSearchRetrieverToolMixin

Bases: BaseModel

Mixin class for Databricks Vector Search retrieval tools. This class provides the common structure and interface that framework-specific implementations should follow.

param columns: List[str] | None = None

Columns to return when doing the search.

param filters: Dict[str, Any] | None = None

Filters to apply to the search.

param index_name: str [Required]

The name of the index to use, format: ‘catalog.schema.index’.

param num_results: int = 5

The number of results to return.

param query_type: str = 'ANN'

The type of this query. Supported values are ‘ANN’ and ‘HYBRID’.

param resources: List[dict] | None = None

Resources required to log a model that uses this tool.

param tool_description: str | None = None

A description of the tool.

param tool_name: str | None = None

The name of the retrieval tool.