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ae5b105d11
Co-authored-by: Mohammad Mohtashim <45242107+keenborder786@users.noreply.github.com> Co-authored-by: Caspar Broekhuizen <caspar@langchain.dev> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Christophe Bornet <cbornet@hotmail.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Sadra Barikbin <sadraqazvin1@yahoo.com> Co-authored-by: Vadym Barda <vadim.barda@gmail.com>
122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
"""Integration tests for embeddings."""
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from langchain_core.embeddings import Embeddings
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from langchain_tests.unit_tests.embeddings import EmbeddingsTests
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class EmbeddingsIntegrationTests(EmbeddingsTests):
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"""Base class for embeddings integration tests.
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Test subclasses must implement the ``embeddings_class`` property to specify the
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embeddings model to be tested. You can also override the
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``embedding_model_params`` property to specify initialization parameters.
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Example:
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.. code-block:: python
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from typing import Type
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from langchain_tests.integration_tests import EmbeddingsIntegrationTests
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from my_package.embeddings import MyEmbeddingsModel
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class TestMyEmbeddingsModelIntegration(EmbeddingsIntegrationTests):
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@property
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def embeddings_class(self) -> Type[MyEmbeddingsModel]:
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# Return the embeddings model class to test here
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return MyEmbeddingsModel
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@property
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def embedding_model_params(self) -> dict:
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# Return initialization parameters for the model.
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return {"model": "model-001"}
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!!! note
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API references for individual test methods include troubleshooting tips.
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"""
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def test_embed_query(self, model: Embeddings) -> None:
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"""Test embedding a string query.
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??? note "Troubleshooting"
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If this test fails, check that:
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1. The model will generate a list of floats when calling ``.embed_query``
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on a string.
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2. The length of the list is consistent across different inputs.
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"""
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embedding_1 = model.embed_query("foo")
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assert isinstance(embedding_1, list)
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assert isinstance(embedding_1[0], float)
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embedding_2 = model.embed_query("bar")
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assert len(embedding_1) > 0
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assert len(embedding_1) == len(embedding_2)
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def test_embed_documents(self, model: Embeddings) -> None:
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"""Test embedding a list of strings.
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??? note "Troubleshooting"
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If this test fails, check that:
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1. The model will generate a list of lists of floats when calling
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``.embed_documents`` on a list of strings.
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2. The length of each list is the same.
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"""
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documents = ["foo", "bar", "baz"]
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embeddings = model.embed_documents(documents)
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assert len(embeddings) == len(documents)
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assert all(isinstance(embedding, list) for embedding in embeddings)
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assert all(isinstance(embedding[0], float) for embedding in embeddings)
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assert len(embeddings[0]) > 0
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assert all(len(embedding) == len(embeddings[0]) for embedding in embeddings)
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async def test_aembed_query(self, model: Embeddings) -> None:
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"""Test embedding a string query async.
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??? note "Troubleshooting"
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If this test fails, check that:
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1. The model will generate a list of floats when calling ``.aembed_query``
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on a string.
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2. The length of the list is consistent across different inputs.
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"""
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embedding_1 = await model.aembed_query("foo")
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assert isinstance(embedding_1, list)
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assert isinstance(embedding_1[0], float)
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embedding_2 = await model.aembed_query("bar")
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assert len(embedding_1) > 0
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assert len(embedding_1) == len(embedding_2)
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async def test_aembed_documents(self, model: Embeddings) -> None:
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"""Test embedding a list of strings async.
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??? note "Troubleshooting"
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If this test fails, check that:
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1. The model will generate a list of lists of floats when calling
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``.aembed_documents`` on a list of strings.
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2. The length of each list is the same.
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"""
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documents = ["foo", "bar", "baz"]
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embeddings = await model.aembed_documents(documents)
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assert len(embeddings) == len(documents)
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assert all(isinstance(embedding, list) for embedding in embeddings)
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assert all(isinstance(embedding[0], float) for embedding in embeddings)
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assert len(embeddings[0]) > 0
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assert all(len(embedding) == len(embeddings[0]) for embedding in embeddings)
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