EmbeddingsIntegrationTests#

class langchain_tests.integration_tests.embeddings.EmbeddingsIntegrationTests[source]#

Base class for embeddings integration tests.

Test subclasses must implement the embeddings_class property to specify the embeddings model to be tested. You can also override the embedding_model_params property to specify initialization parameters.

Example:

from typing import Type

from langchain_tests.integration_tests import EmbeddingsIntegrationTests
from my_package.embeddings import MyEmbeddingsModel


class TestMyEmbeddingsModelIntegration(EmbeddingsIntegrationTests):
    @property
    def embeddings_class(self) -> Type[MyEmbeddingsModel]:
        # Return the embeddings model class to test here
        return MyEmbeddingsModel

    @property
    def embedding_model_params(self) -> dict:
        # Return initialization parameters for the model.
        return {"model": "model-001"}

Note

API references for individual test methods include troubleshooting tips.

Attributes

embedding_model_params

embeddings_class

Methods

model()

test_aembed_documents(model)

Test embedding a list of strings async.

test_aembed_query(model)

Test embedding a string query async.

test_embed_documents(model)

Test embedding a list of strings.

test_embed_query(model)

Test embedding a string query.
model() → Embeddings#
Return type:

Embeddings

async test_aembed_documents(model: Embeddings) → None[source]#

Test embedding a list of strings async.

Troubleshooting

If this test fails, check that:

  1. The model will generate a list of lists of floats when calling .aembed_documents on a list of strings.

  2. The length of each list is the same.

Parameters:

model (Embeddings)

Return type:

None

async test_aembed_query(model: Embeddings) → None[source]#

Test embedding a string query async.

Troubleshooting

If this test fails, check that:

  1. The model will generate a list of floats when calling .aembed_query on a string.

  2. The length of the list is consistent across different inputs.

Parameters:

model (Embeddings)

Return type:

None

test_embed_documents(model: Embeddings) → None[source]#

Test embedding a list of strings.

Troubleshooting

If this test fails, check that:

  1. The model will generate a list of lists of floats when calling .embed_documents on a list of strings.

  2. The length of each list is the same.

Parameters:

model (Embeddings)

Return type:

None

test_embed_query(model: Embeddings) → None[source]#

Test embedding a string query.

Troubleshooting

If this test fails, check that:

  1. The model will generate a list of floats when calling .embed_query on a string.

  2. The length of the list is consistent across different inputs.

Parameters:

model (Embeddings)

Return type:

None