langchain_experimental.sql.vector_sql.VectorSQLOutputParser¶
class langchain_experimental.sql.vector_sql.VectorSQLOutputParser[source]¶
Bases: BaseOutputParser[str]
Output Parser for Vector SQL
1. finds for NeuralArray() and replace it with the embedding
2. finds for DISTANCE() and replace it with the distance name in backend SQL
param distance_func_name: str = 'distance'¶
Distance name for Vector SQL
param model: langchain_core.embeddings.Embeddings [Required]¶
Embedding model to extract embedding for entity
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async ainvoke(input: str | langchain_core.messages.base.BaseMessage, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.