mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
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import numpy as np
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import pytest
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from langchain_community.embeddings.infinity_local import InfinityEmbeddingsLocal
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try:
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import torch # noqa
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import infinity_emb # noqa
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IMPORTED_TORCH = True
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except ImportError:
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IMPORTED_TORCH = False
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@pytest.mark.skipif(not IMPORTED_TORCH, reason="torch not installed")
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@pytest.mark.asyncio
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async def test_local_infinity_embeddings() -> None:
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embedder = InfinityEmbeddingsLocal(
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model="TaylorAI/bge-micro-v2",
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device="cpu",
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backend="torch",
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revision=None,
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batch_size=2,
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model_warmup=False,
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)
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async with embedder:
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embeddings = await embedder.aembed_documents(["text1", "text2", "text1"])
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assert len(embeddings) == 3
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# model has 384 dim output
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assert len(embeddings[0]) == 384
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assert len(embeddings[1]) == 384
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assert len(embeddings[2]) == 384
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# assert all different embeddings
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assert (np.array(embeddings[0]) - np.array(embeddings[1]) != 0).all()
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# assert identical embeddings, up to floating point error
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np.testing.assert_array_equal(embeddings[0], embeddings[2])
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if __name__ == "__main__":
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import asyncio
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asyncio.run(test_local_infinity_embeddings())
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