Source code for langchain_core.embeddings.fake

"""Module contains a few fake embedding models for testing purposes."""

# Please do not add additional fake embedding model implementations here.
import hashlib
from typing import List

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


[docs]class FakeEmbeddings(Embeddings, BaseModel): """Fake embedding model for unit testing purposes. This embedding model creates embeddings by sampling from a normal distribution. Do not use this outside of testing, as it is not a real embedding model. Instantiate: .. code-block:: python from langchain_core.embeddings import FakeEmbeddings embed = FakeEmbeddings(size=100) Embed single text: .. code-block:: python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) .. code-block:: python [-0.700234640213188, -0.581266257710429, -1.1328482266445354] Embed multiple texts: .. code-block:: python input_texts = ["Document 1...", "Document 2..."] vectors = embed.embed_documents(input_texts) print(len(vectors)) # The first 3 coordinates for the first vector print(vectors[0][:3]) .. code-block:: python 2 [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257] """ 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))
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: return [self._get_embedding() for _ in texts]
[docs] def embed_query(self, text: str) -> List[float]: return self._get_embedding()
[docs]class DeterministicFakeEmbedding(Embeddings, BaseModel): """Deterministic fake embedding model for unit testing purposes. This embedding model creates embeddings by sampling from a normal distribution with a seed based on the hash of the text. Do not use this outside of testing, as it is not a real embedding model. Instantiate: .. code-block:: python from langchain_core.embeddings import DeterministicFakeEmbedding embed = DeterministicFakeEmbedding(size=100) Embed single text: .. code-block:: python input_text = "The meaning of life is 42" vector = embed.embed_query(input_text) print(vector[:3]) .. code-block:: python [-0.700234640213188, -0.581266257710429, -1.1328482266445354] Embed multiple texts: .. code-block:: python input_texts = ["Document 1...", "Document 2..."] vectors = embed.embed_documents(input_texts) print(len(vectors)) # The first 3 coordinates for the first vector print(vectors[0][:3]) .. code-block:: python 2 [-0.5670477847544458, -0.31403828652395727, -0.5840547508955257] """ 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
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: return [self._get_embedding(seed=self._get_seed(_)) for _ in texts]
[docs] def embed_query(self, text: str) -> List[float]: return self._get_embedding(seed=self._get_seed(text))