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
- class databricks_ai_bridge.genie.Genie(space_id, client: WorkspaceClient | None = None)
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.FilterItem
Bases:
BaseModel
- class databricks_ai_bridge.vector_search_retriever_tool.VectorSearchRetrieverToolInput
Bases:
BaseModel
- param filters: List[FilterItem] | None = None
Optional filters to refine vector search results as an array of key-value pairs. Supports the following operators:
Inclusion: [{“key”: “column”, “value”: value}] or [{“key”: “column”, “value”: [value1, value2]}] (matches if the column equals any of the provided values)
Exclusion: [{“key”: “column NOT”, “value”: value}]
Comparisons: [{“key”: “column <”, “value”: value}], [{“key”: “column >=”, “value”: value}], etc.
Pattern match: [{“key”: “column LIKE”, “value”: “word”}] (matches full tokens separated by whitespace)
OR logic: [{“key”: “column1 OR column2”, “value”: [value1, value2]}] (matches if column1 equals value1 or column2 equals value2; matches are position-specific)
- 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 include_score: bool | None = False
When true, will return the similarity score with the metadata.
- param primary_key: str | None = None
Identifies the chunk that the document is a part of. This is used by some evaluation metrics.
- param workspace_client: WorkspaceClient | None = None
When specified, will use workspace client credential strategy to instantiate VectorSearchClient
- classmethod validate_tool_name(tool_name)