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