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import hashlib
from typing import List
import numpy as np
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]:
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))
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