semantic_router.index.pinecone.PineconeIndex#

class semantic_router.index.pinecone.PineconeIndex(api_key: str | None = None, index_name: str = 'index', dimensions: int | None = None, metric: str = 'cosine', cloud: str = 'aws', region: str = 'us-west-2', host: str = '', namespace: str | None = '', base_url: str | None = 'https://api.pinecone.io', init_async_index: bool = False)#

Bases: BaseIndex

__init__(api_key: str | None = None, index_name: str = 'index', dimensions: int | None = None, metric: str = 'cosine', cloud: str = 'aws', region: str = 'us-west-2', host: str = '', namespace: str | None = '', base_url: str | None = 'https://api.pinecone.io', init_async_index: bool = False)#

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__`([api_key, index_name, dimensions, ...])	Create a new model by parsing and validating input data from keyword arguments.
`add`(embeddings, routes, utterances[, ...])	Add vectors to Pinecone in batches.
`aget_routes`()	Asynchronously get a list of route and utterance objects currently stored in the index.
`aquery`(vector[, top_k, route_filter])	Asynchronously search the index for the query vector and return the 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_all`()	Deletes all records from the index.
`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`()	Gets a list of route objects currently stored in the index.
`get_utterances`()	Gets a list of route and utterance objects currently stored in the index, including additional metadata.
`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 the 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_prefix`
`api_key`
`index_name`
`dimensions`
`metric`
`cloud`
`region`
`host`
`client`
`async_client`
`index`
`ServerlessSpec`
`namespace`
`base_url`
`routes`
`utterances`
`type`
`init_async_index`

add(embeddings: List[List[float]], routes: List[str], utterances: List[str], function_schemas: List[Dict[str, Any]] | None = None, metadata_list: List[Dict[str, Any]] = [], batch_size: int = 100)#: Add vectors to Pinecone in batches.

async aget_routes() → list[tuple]#

Asynchronously get a list of route and utterance objects currently stored in the index.

Returns:: A list of (route_name, utterance) objects.
Return type:: List[Tuple]

async aquery(vector: ndarray, top_k: int = 5, route_filter: List[str] | None = None, **kwargs: Any) → Tuple[ndarray, List[str]]#

Asynchronously search the index for the query vector and return the top_k results.

Parameters:

vector (np.ndarray) – The query vector to search for.
top_k (int, optional) – The number of top results to return, defaults to 5.
route_filter (Optional[List[str]], optional) – A list of route names to filter the search results, defaults to None.
kwargs (Any) – Additional keyword arguments for the query, including sparse_vector.
sparse_vector (Optional[dict]) – An optional sparse vector to include in the query.

Returns:

A tuple containing an array of scores and a list of route names.

Return type:

Tuple[np.ndarray, List[str]]

Raises:

ValueError – If the index is not populated.

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_all()#: Deletes all records from the index.

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() → List[Route]#

Gets a list of route objects currently stored in the index.

Returns:: A list of Route objects.
Return type:: List[Route]

get_utterances() → List[Utterance]#

Gets a list of route and utterance objects currently stored in the index, including additional metadata.

Returns:: A list of tuples, each containing route, utterance, function

schema and additional metadata. :rtype: List[Tuple]

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, **kwargs: Any) → Tuple[ndarray, List[str]]#

Search the index for the query vector and return the top_k results.

Parameters:

vector (np.ndarray) – The query vector to search for.
top_k (int, optional) – The number of top results to return, defaults to 5.
route_filter (Optional[List[str]], optional) – A list of route names to filter the search results, defaults to None.
kwargs (Any) – Additional keyword arguments for the query, including sparse_vector.
sparse_vector (Optional[dict]) – An optional sparse vector to include in the query.

Returns:

A tuple containing an array of scores and a list of route names.

Return type:

Tuple[np.ndarray, List[str]]

Raises:

ValueError – If the index is not populated.

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