hugegraph-llm/src/hugegraph_llm/models/embeddings/base.py

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from abc import ABC, abstractmethod
from enum import Enum
from typing import List, Union

import numpy as np
from typing_extensions import deprecated


class SimilarityMode(str, Enum):
    """Modes for similarity/distance."""

    DEFAULT = "cosine"
    DOT_PRODUCT = "dot_product"
    EUCLIDEAN = "euclidean"


def similarity(
    embedding1: Union[List[float], np.ndarray],
    embedding2: Union[List[float], np.ndarray],
    mode: SimilarityMode = SimilarityMode.DEFAULT,
) -> float:
    """Get embedding similarity."""
    if isinstance(embedding1, list):
        embedding1 = np.array(embedding1)
    if isinstance(embedding2, list):
        embedding2 = np.array(embedding2)
    if mode == SimilarityMode.EUCLIDEAN:
        # Using - Euclidean distance as similarity to achieve the same ranking order
        return -float(np.linalg.norm(embedding1 - embedding2))
    if mode == SimilarityMode.DOT_PRODUCT:
        return np.dot(embedding1, embedding2)
    product = np.dot(embedding1, embedding2)
    norm = np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
    return product / norm


class BaseEmbedding(ABC):
    """Embedding wrapper should take in a text and return a vector."""

    # TODO: replace all the usage by get_texts_embeddings() & remove it in the future
    @deprecated("Use get_texts_embeddings() instead in the future.")
    @abstractmethod
    def get_text_embedding(self, text: str) -> List[float]:
        """Comment"""

    @abstractmethod
    def get_embedding_dim(
        self,
    ) -> int:
        """Get the dimension of the embedding."""

    @abstractmethod
    def get_texts_embeddings(self, texts: List[str], batch_size: int = 32) -> List[List[float]]:
        """Get embeddings for multiple texts with automatic batch splitting.

        This method should efficiently process multiple texts at once by leveraging
        the embedding model's batching capabilities, which is typically more efficient
        than processing texts individually.

        Parameters
        ----------
        texts : List[str]
            A list of text strings to be embedded.

        Returns
        -------
        List[List[float]]
            A list of embedding vectors, where each vector is a list of floats.
            The order of embeddings should match the order of input texts.
        """

    @abstractmethod
    async def async_get_texts_embeddings(self, texts: List[str], batch_size: int = 32) -> List[List[float]]:
        """Get embeddings for multiple texts asynchronously with automatic batch splitting.

        This method should efficiently process multiple texts at once by leveraging
        the embedding model's batching capabilities, which is typically more efficient
        than processing texts individually.

        Parameters
        ----------
        texts : List[str]
            A list of text strings to be embedded.

        Returns
        -------
        List[List[float]]
            A list of embedding vectors, where each vector is a list of floats.
            The order of embeddings should match the order of input texts.
        """

    @staticmethod
    def similarity(
        embedding1: Union[List[float], np.ndarray],
        embedding2: Union[List[float], np.ndarray],
        mode: SimilarityMode = SimilarityMode.DEFAULT,
    ) -> float:
        """Get embedding similarity."""
        if isinstance(embedding1, list):
            embedding1 = np.array(embedding1)
        if isinstance(embedding2, list):
            embedding2 = np.array(embedding2)
        if mode == SimilarityMode.EUCLIDEAN:
            # Using - Euclidean distance as similarity to achieve the same ranking order
            return -float(np.linalg.norm(embedding1 - embedding2))
        if mode == SimilarityMode.DOT_PRODUCT:
            return np.dot(embedding1, embedding2)
        product = np.dot(embedding1, embedding2)
        norm = np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
        return product / norm