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"""Fake Embedding class for testing purposes."""
from typing import List
from langchain_core.embeddings import Embeddings
fake_texts = ["foo", "bar", "baz"]
class FakeEmbeddings(Embeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return simple embeddings.
Embeddings encode each text as its index."""
return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
return self.embed_documents(texts)
def embed_query(self, text: str) -> List[float]:
"""Return constant query embeddings.
Embeddings are identical to embed_documents(texts)[0].
Distance to each text will be that text's index,
as it was passed to embed_documents."""
return [float(1.0)] * 9 + [float(0.0)]
async def aembed_query(self, text: str) -> List[float]:
return self.embed_query(text)
class ConsistentFakeEmbeddings(FakeEmbeddings):
"""Fake embeddings which remember all the texts seen so far to return consistent
vectors for the same texts."""
def __init__(self, dimensionality: int = 10) -> None:
self.known_texts: List[str] = []
self.dimensionality = dimensionality
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return consistent embeddings for each text seen so far."""
out_vectors = []
for text in texts:
if text not in self.known_texts:
self.known_texts.append(text)
vector = [float(1.0)] * (self.dimensionality - 1) + [
float(self.known_texts.index(text))
]
out_vectors.append(vector)
return out_vectors
def embed_query(self, text: str) -> List[float]:
"""Return consistent embeddings for the text, if seen before, or a constant
one if the text is unknown."""
return self.embed_documents([text])[0]
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