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langchain-ai · Oct 31, 2024 · c2e9bd0 · c2e9bd0
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Showing 3 changed files with 2 additions and 22 deletions.
diff --git a/libs/vertexai/tests/integration_tests/conftest.py b/libs/vertexai/tests/integration_tests/conftest.py
@@ -30,12 +30,3 @@ def base64_image() -> str:
         "yuRj4Ik9is+hglfbkbfR3cnZm7chlUWLdwmprtCohX4HUtlOcQjLYCu+fzGJH2QRKvP3UN"
         "z8bWk1qMxjGTOMThZ3kvgLI5AzFfo379UAAAAASUVORK5CYII="
     )
-
-
-@pytest.fixture
-def tmp_image(tmp_path_factory: TempPathFactory, base64_image) -> str:
-    img_data = base64.b64decode(base64_image.split(",")[1])
-    image = Image(image_bytes=img_data)
-    fn = tmp_path_factory.mktemp("data") / "img.png"
-    image.save(str(fn))
-    return str(fn)
diff --git a/libs/vertexai/tests/integration_tests/test_chat_models.py b/libs/vertexai/tests/integration_tests/test_chat_models.py
@@ -1088,17 +1088,6 @@ def test_multimodal_pdf_input_url(multimodal_pdf_chain: RunnableSerializable) ->
     assert isinstance(response, AIMessage)
 
 
-@pytest.mark.release
-def test_multimodal_pdf_input_local(multimodal_pdf_chain: RunnableSerializable) -> None:
-    url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf"
-    request_response = requests.get(url, allow_redirects=True)
-
-    with tempfile.NamedTemporaryFile(suffix=".pdf") as localfile:
-        localfile.write(request_response.content)
-        response = multimodal_pdf_chain.invoke(dict(image=localfile.name))
-        assert isinstance(response, AIMessage)
-
-
 @pytest.mark.release
 def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) -> None:
     url = "https://abc.xyz/assets/95/eb/9cef90184e09bac553796896c633/2023q4-alphabet-earnings-release.pdf"

diff --git a/libs/vertexai/tests/integration_tests/test_embeddings.py b/libs/vertexai/tests/integration_tests/test_embeddings.py
@@ -71,13 +71,13 @@ def test_langchain_google_vertexai_embedding_query(model_name, embeddings_dim) -
     [(None, 1408), (512, 512)],
 )
 def test_langchain_google_vertexai_image_embeddings(
-    dim, expected_dim, tmp_image
+    dim, expected_dim, base64_image
 ) -> None:
     model = VertexAIEmbeddings(model_name="multimodalembedding")
     kwargs = {}
     if dim:
         kwargs["dimensions"] = dim
-    output = model.embed_images([tmp_image for i in range(3)], **kwargs)
+    output = model.embed_images([base64_image for i in range(3)], **kwargs)
     assert len(output) == 3
     assert len(output[0]) == expected_dim