Module vexpresso.retrievers.base

View Source

import abc

from dataclasses import dataclass

from typing import Any, Iterable, List, Optional, Union

import numpy as np

@dataclass

class RetrievalOutput:

    embeddings: Any

    indices: Union[np.ndarray, Iterable[int]]

    scores: Union[np.ndarray, Iterable[float]]

    query_embeddings: Optional[Any] = None

class BaseRetriever(metaclass=abc.ABCMeta):

    SUPPORTED_TYPES = [np.dtype]

    @abc.abstractmethod

    def retrieve(

        self, query_embeddings: np.ndarray, embeddings: List[Any], *args, **kwargs

    ) -> Union[List[RetrievalOutput], RetrievalOutput]:

        """

        Queries embeddings with query embedding vector and returns nearest embeddings and their corresponding ids

        Args:

            query_embeddings (np.ndarray): query, used to find nearest embeddings in set.

            embeddings (List[Any]): embeddings set, query is compared to this.

        Returns:

            Union[List[QueryOutput], QueryOutput]: dataclasses containing returned embeddings and corresponding indices.

            When this has more than one entry, that means that the call was batched

        """

Classes

BaseRetriever

class BaseRetriever(
    /,
    *args,
    **kwargs
)

View Source

class BaseRetriever(metaclass=abc.ABCMeta):

    SUPPORTED_TYPES = [np.dtype]

    @abc.abstractmethod

    def retrieve(

        self, query_embeddings: np.ndarray, embeddings: List[Any], *args, **kwargs

    ) -> Union[List[RetrievalOutput], RetrievalOutput]:

        """

        Queries embeddings with query embedding vector and returns nearest embeddings and their corresponding ids

        Args:

            query_embeddings (np.ndarray): query, used to find nearest embeddings in set.

            embeddings (List[Any]): embeddings set, query is compared to this.

        Returns:

            Union[List[QueryOutput], QueryOutput]: dataclasses containing returned embeddings and corresponding indices.

            When this has more than one entry, that means that the call was batched

        """

---

Descendants

• vexpresso.retrievers.faiss.FaissRetriever
• vexpresso.retrievers.np.Retriever

Class variables

SUPPORTED_TYPES

Methods

retrieve

def retrieve(
    self,
    query_embeddings: numpy.ndarray,
    embeddings: List[Any],
    *args,
    **kwargs
) -> Union[List[vexpresso.retrievers.base.RetrievalOutput], vexpresso.retrievers.base.RetrievalOutput]

Queries embeddings with query embedding vector and returns nearest embeddings and their corresponding ids

Parameters:

Name	Type	Description	Default
query_embeddings	np.ndarray	query, used to find nearest embeddings in set.	None
embeddings	List[Any]	embeddings set, query is compared to this.	None

Returns:

Type	Description
Union[List[QueryOutput], QueryOutput]	dataclasses containing returned embeddings and corresponding indices. When this has more than one entry, that means that the call was batched

View Source

    @abc.abstractmethod

    def retrieve(

        self, query_embeddings: np.ndarray, embeddings: List[Any], *args, **kwargs

    ) -> Union[List[RetrievalOutput], RetrievalOutput]:

        """

        Queries embeddings with query embedding vector and returns nearest embeddings and their corresponding ids

        Args:

            query_embeddings (np.ndarray): query, used to find nearest embeddings in set.

            embeddings (List[Any]): embeddings set, query is compared to this.

        Returns:

            Union[List[QueryOutput], QueryOutput]: dataclasses containing returned embeddings and corresponding indices.

            When this has more than one entry, that means that the call was batched

        """

RetrievalOutput

class RetrievalOutput(
    embeddings: Any,
    indices: Union[numpy.ndarray, Iterable[int]],
    scores: Union[numpy.ndarray, Iterable[float]],
    query_embeddings: Optional[Any] = None
)

RetrievalOutput(embeddings: Any, indices: Union[numpy.ndarray, Iterable[int]], scores: Union[numpy.ndarray, Iterable[float]], query_embeddings: Optional[Any] = None)

View Source

@dataclass

class RetrievalOutput:

    embeddings: Any

    indices: Union[np.ndarray, Iterable[int]]

    scores: Union[np.ndarray, Iterable[float]]

    query_embeddings: Optional[Any] = None

---

Class variables

query_embeddings