semantic_router.index.base.BaseIndex#

class semantic_router.index.base.BaseIndex(*, index: Any | None = None, routes: ndarray | None = None, utterances: ndarray | None = None, dimensions: int | None = None, type: str = 'base', init_async_index: bool = False, sync: str | None = None)#

Bases: BaseModel

Base class for indices using Pydantic’s BaseModel. This class outlines the expected interface for index classes. Actual method implementations should be provided in subclasses.

__init__(**data: Any) → None#

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`add`(embeddings, routes, utterances)	Add embeddings to the index.
`aget_routes`()	Asynchronously get a list of route and utterance objects currently stored in the index.
`aquery`(vector[, top_k, route_filter])	Search the index for the query_vector and return top_k results.
`construct`([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
`copy`(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
`delete`(route_name)	Deletes route by route name.
`delete_index`()	Deletes or resets the index.
`describe`()	Returns a dictionary with index details such as type, dimensions, and total vector count.
`dict`(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
`from_orm`(obj)
`get_routes`()	Retrieves a list of routes and their associated utterances from the index.
`json`(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`query`(vector[, top_k, route_filter])	Search the index for the query_vector and return top_k results.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)

Attributes

`index`
`routes`
`utterances`
`dimensions`
`type`
`init_async_index`
`sync`

add(embeddings: List[List[float]], routes: List[str], utterances: List[Any])#: Add embeddings to the index. This method should be implemented by subclasses.

aget_routes()#

Asynchronously get a list of route and utterance objects currently stored in the index. This method should be implemented by subclasses.

Returns:: A list of tuples, each containing a route name and an associated utterance.
Return type:: list[tuple]
Raises:: NotImplementedError – If the method is not implemented by the subclass.

async aquery(vector: ndarray, top_k: int = 5, route_filter: List[str] | None = None) → Tuple[ndarray, List[str]]#: Search the index for the query_vector and return top_k results. This method should be implemented by subclasses.

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns:

new model instance

delete(route_name: str)#: Deletes route by route name. This method should be implemented by subclasses.

delete_index()#: Deletes or resets the index. This method should be implemented by subclasses.

describe() → Dict#: Returns a dictionary with index details such as type, dimensions, and total vector count. This method should be implemented by subclasses.

dict(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

get_routes()#

Retrieves a list of routes and their associated utterances from the index. This method should be implemented by subclasses.

Returns:: A list of tuples, each containing a route name and an associated utterance.
Return type:: list[tuple]
Raises:: NotImplementedError – If the method is not implemented by the subclass.

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → str#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

query(vector: ndarray, top_k: int = 5, route_filter: List[str] | None = None) → Tuple[ndarray, List[str]]#: Search the index for the query_vector and return top_k results. This method should be implemented by subclasses.

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.