encoder
(DenseEncoder
): The dense encoder to use.sparse_encoder
(Optional[SparseEncoder]
): The sparse encoder to use.route
(Route
): The route to add.routes
(List[Route] | Route
): The route(s) to add.text
(Optional[str]
): The text to encode.vector
(Optional[List[float] | np.ndarray]
): The vector to encode.simulate_static
(bool
): Whether to simulate a static route.route_filter
(Optional[List[str]]
): The route filter to use.limit
(int | None
): The number of routes to return, defaults to 1. If set to None, no
limit is applied and all routes are returned.Optional[str]
0 (Optional[str]
1): The sparse vector to use.Optional[str]
2: A RouteChoice or a list of RouteChoices.
text
(Optional[str]
): The text to route.vector
(Optional[List[float] | np.ndarray]
): The vector to route.simulate_static
(bool
): Whether to simulate a static route (ie avoid dynamic route
LLM calls during fit or evaluate).route_filter
(Optional[List[str]]
): The route filter to use.sparse_vector
(dict[int, float] | SparseEmbedding | None
): The sparse vector to use.Optional[str]
0: The route choice.
X
(List[str]
): The input data.y
(List[str]
): The output data.batch_size
(int
): The batch size to use for fitting.max_iter
(int
): The maximum number of iterations to use for fitting.local_execution
(bool
): Whether to execute the fitting locally.X
(List[str]
): The input data.y
(List[str]
): The output data.batch_size
(int
): The batch size to use for evaluation.float
: The accuracy of the route selection.