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