"""Test Vertex AI API wrapper. Your end-user credentials would be used to make the calls (make sure you've run `gcloud auth login` first). """ import pytest from langchain_google_vertexai.embeddings import VertexAIEmbeddings def test_initialization() -> None: """Test embedding model initialization.""" VertexAIEmbeddings() def test_langchain_google_vertexai_embedding_documents() -> None: documents = ["foo bar"] model = VertexAIEmbeddings() output = model.embed_documents(documents) assert len(output) == 1 assert len(output[0]) == 768 assert model.model_name == model.client._model_id assert model.model_name == "textembedding-gecko@001" def test_langchain_google_vertexai_embedding_query() -> None: document = "foo bar" model = VertexAIEmbeddings() output = model.embed_query(document) assert len(output) == 768 def test_langchain_google_vertexai_large_batches() -> None: documents = ["foo bar" for _ in range(0, 251)] model_uscentral1 = VertexAIEmbeddings(location="us-central1") model_asianortheast1 = VertexAIEmbeddings(location="asia-northeast1") model_uscentral1.embed_documents(documents) model_asianortheast1.embed_documents(documents) assert model_uscentral1.instance["batch_size"] >= 250 assert model_asianortheast1.instance["batch_size"] < 50 def test_langchain_google_vertexai_paginated_texts() -> None: documents = [ "foo bar", "foo baz", "bar foo", "baz foo", "bar bar", "foo foo", "baz baz", "baz bar", ] model = VertexAIEmbeddings() output = model.embed_documents(documents) assert len(output) == 8 assert len(output[0]) == 768 assert model.model_name == model.client._model_id def test_warning(caplog: pytest.LogCaptureFixture) -> None: _ = VertexAIEmbeddings() assert len(caplog.records) == 1 record = caplog.records[0] assert record.levelname == "WARNING" expected_message = ( "Model_name will become a required arg for VertexAIEmbeddings starting from " "Feb-01-2024. Currently the default is set to textembedding-gecko@001" ) assert record.message == expected_message