Files
langchain/libs/community/langchain_community/embeddings/fake.py
Harrison Chase 8516a03a02 langchain-community[major]: Upgrade community to pydantic 2 (#26011)
This PR upgrades langchain-community to pydantic 2.


* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2

Breaking Changes:

- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492

Other changes:

- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2024-09-05 14:07:10 -04:00

50 lines
1.5 KiB
Python

import hashlib
from typing import List
import numpy as np
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel
class FakeEmbeddings(Embeddings, BaseModel):
"""Fake embedding model."""
size: int
"""The size of the embedding vector."""
def _get_embedding(self) -> List[float]:
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]:
# 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))