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https://github.com/hwchase17/langchain
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49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
"""Test openai embeddings."""
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import numpy as np
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import openai
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from langchain.embeddings.openai import OpenAIEmbeddings
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def test_openai_embedding_documents() -> None:
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"""Test openai embeddings."""
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documents = ["foo bar"]
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embedding = OpenAIEmbeddings()
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output = embedding.embed_documents(documents)
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assert len(output) == 1
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assert len(output[0]) == 1536
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def test_openai_embedding_documents_multiple() -> None:
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"""Test openai embeddings."""
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documents = ["foo bar", "bar foo", "foo"]
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embedding = OpenAIEmbeddings(chunk_size=2)
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embedding.embedding_ctx_length = 8191
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output = embedding.embed_documents(documents)
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assert len(output) == 3
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assert len(output[0]) == 1536
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assert len(output[1]) == 1536
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assert len(output[2]) == 1536
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def test_openai_embedding_query() -> None:
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"""Test openai embeddings."""
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document = "foo bar"
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embedding = OpenAIEmbeddings()
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output = embedding.embed_query(document)
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assert len(output) == 1536
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def test_openai_embedding_with_empty_string() -> None:
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"""Test openai embeddings with empty string."""
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document = ["", "abc"]
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embedding = OpenAIEmbeddings()
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output = embedding.embed_documents(document)
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assert len(output) == 2
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assert len(output[0]) == 1536
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expected_output = openai.Embedding.create(input="", model="text-embedding-ada-002")[
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"data"
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][0]["embedding"]
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assert np.allclose(output[0], expected_output)
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assert len(output[1]) == 1536
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