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))