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# Source code for langchain_core.embeddings.fake

    import hashlib
    from typing import List

    from langchain_core.embeddings import Embeddings
    from langchain_core.pydantic_v1 import BaseModel

    class FakeEmbeddings(Embeddings, BaseModel):
        """Fake embedding model."""

        size: int
        """The size of the embedding vector."""

        def _get_embedding(self) -> List[float]:
            import numpy as np  # type: ignore[import-not-found, import-untyped]

            return list(np.random.normal(size=self.size))

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            return [self._get_embedding() for _ in texts]

        def embed_query(self, text: str) -> List[float]:
            return self._get_embedding()

    class DeterministicFakeEmbedding(Embeddings, BaseModel):
        """
        Fake embedding model that always returns
        the same embedding vector for the same text.
        """

        size: int
        """The size of the embedding vector."""

        def _get_embedding(self, seed: int) -> List[float]:
            import numpy as np  # type: ignore[import-not-found, import-untyped]

            # set the seed for the random generator
            np.random.seed(seed)
            return list(np.random.normal(size=self.size))

        def _get_seed(self, text: str) -> int:
            """
            Get a seed for the random generator, using the hash of the text.
            """
            return int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 10**8

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]

        def embed_query(self, text: str) -> List[float]:
            return self._get_embedding(seed=self._get_seed(text))