semantic_router.index.base.BaseIndex#

class semantic_router.index.base.BaseIndex(**data)#

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)#

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.
`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)	rtype: Model
`json`(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
`parse_file`(path, *[, content_type, ...])	rtype: Model
`parse_obj`(obj)	rtype: Model
`parse_raw`(b, *[, content_type, encoding, ...])	rtype: Model
`query`(vector[, top_k, route_filter])	Search the index for the query_vector and return top_k results.
`schema`([by_alias, ref_template])	rtype: DictStrAny
`schema_json`(*[, by_alias, ref_template])	rtype: `str`
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)	rtype: Model

Attributes

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

add(embeddings, routes, utterances)#: Add embeddings to the index. This method should be implemented by subclasses.

async aquery(vector, top_k=5, route_filter=None)#

Search the index for the query_vector and return top_k results. This method should be implemented by subclasses.

Return type:: Tuple[ndarray, List[str]]

classmethod construct(_fields_set=None, **values)#

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

Return type:: Model

copy(*, include=None, exclude=None, update=None, deep=False)#

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

Parameters:

include (Union[AbstractSetIntStr, MappingIntStrAny, None]) – fields to include in new model
exclude (Union[AbstractSetIntStr, MappingIntStrAny, None]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

Return type:

Model

Returns:

new model instance

delete(route_name)#: 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()#

Returns a dictionary with index details such as type, dimensions, and total vector count. This method should be implemented by subclasses.

Return type:: Dict

dict(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False)#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Return type:: DictStrAny

json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)#

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().

Return type:: str

query(vector, top_k=5, route_filter=None)#

Search the index for the query_vector and return top_k results. This method should be implemented by subclasses.

Return type:: Tuple[ndarray, List[str]]

classmethod update_forward_refs(**localns)#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Return type:: None