feat: add support for multimodal model and add embed_image

libs/vertexai/.gitignore

@@ -1 +1,2 @@
 __pycache__
+.mypy_cache_test

libs/vertexai/langchain_google_vertexai/embeddings.py

@@ -3,6 +3,7 @@
 import string
 import threading
 from concurrent.futures import ThreadPoolExecutor, wait
+from enum import Enum, auto
 from typing import Any, Dict, List, Literal, Optional, Tuple, Type
 
 from google.api_core.exceptions import (
@@ -19,6 +20,11 @@
     TextEmbeddingInput,
     TextEmbeddingModel,
 )
+from vertexai.vision_models import (  # type: ignore
+    Image,
+    MultiModalEmbeddingModel,
+    MultiModalEmbeddingResponse,
+)
 
 from langchain_google_vertexai._base import _VertexAICommon
 
@@ -29,6 +35,19 @@
 _MIN_BATCH_SIZE = 5
 
 
+class GoogleEmbeddingModelType(str, Enum):
+    TEXT = auto()
+    MULTIMODAL = auto()
+
+    @classmethod
+    def _missing_(cls, value: Any) -> Optional["GoogleEmbeddingModelType"]:
+        if "textembedding-gecko" in value.lower():
+            return GoogleEmbeddingModelType.TEXT
+        elif "multimodalembedding" in value.lower():
+            return GoogleEmbeddingModelType.MULTIMODAL
+        return None
+
+
 class VertexAIEmbeddings(_VertexAICommon, Embeddings):
     """Google Cloud VertexAI embedding models."""
 
@@ -46,7 +65,15 @@ def validate_environment(cls, values: Dict) -> Dict:
                 "textembedding-gecko@001"
             )
             values["model_name"] = "textembedding-gecko@001"
-        values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"])
+        if (
+            GoogleEmbeddingModelType(values["model_name"])
+            == GoogleEmbeddingModelType.MULTIMODAL
+        ):
+            values["client"] = MultiModalEmbeddingModel.from_pretrained(
+                values["model_name"]
+            )
+        else:
+            values["client"] = TextEmbeddingModel.from_pretrained(values["model_name"])
         return values
 
     def __init__(
@@ -83,6 +110,20 @@ def __init__(
             "embeddings_task_type_supported"
         ] = not self.client._endpoint_name.endswith("/textembedding-gecko@001")
 
+        retry_errors: List[Type[BaseException]] = [
+            ResourceExhausted,
+            ServiceUnavailable,
+            Aborted,
+            DeadlineExceeded,
+        ]
+        self.instance["retry_decorator"] = create_base_retry_decorator(
+            error_types=retry_errors, max_retries=self.max_retries
+        )
+
+    @property
+    def model_type(self) -> str:
+        return GoogleEmbeddingModelType(self.model_name)
+
     @staticmethod
     def _split_by_punctuation(text: str) -> List[str]:
         """Splits a string by punctuation and whitespace characters."""
@@ -321,6 +362,8 @@ def embed_documents(
         Returns:
             List of embeddings, one for each text.
         """
+        if self.model_type != GoogleEmbeddingModelType.TEXT:
+            raise NotImplementedError("Not supported for multimodal models")
         return self.embed(texts, batch_size, "RETRIEVAL_DOCUMENT")
 
     def embed_query(self, text: str) -> List[float]:
@@ -332,5 +375,25 @@ def embed_query(self, text: str) -> List[float]:
         Returns:
             Embedding for the text.
         """
+        if self.model_type != GoogleEmbeddingModelType.TEXT:
+            raise NotImplementedError("Not supported for multimodal models")
         embeddings = self.embed([text], 1, "RETRIEVAL_QUERY")
         return embeddings[0]
+
+    def embed_image(self, image_path: str) -> List[float]:
+        """Embed an image.
+
+        Args:
+            image_path: Path to image (local or Google Cloud Storage) to generate
+            embeddings for.
+
+        Returns:
+            Embedding for the image.
+        """
+        if self.model_type != GoogleEmbeddingModelType.MULTIMODAL:
+            raise NotImplementedError("Only supported for multimodal models")
+
+        embed_with_retry = self.instance["retry_decorator"](self.client.get_embeddings)
+        image = Image.load_from_file(image_path)
+        result: MultiModalEmbeddingResponse = embed_with_retry(image=image)
+        return result.image_embedding

libs/vertexai/tests/integration_tests/conftest.py

@@ -0,0 +1,39 @@
+import base64
+
+import pytest
+from _pytest.tmpdir import TempPathFactory
+from vertexai.vision_models import Image  # type: ignore
+
+
+@pytest.fixture
+def base64_image() -> str:
+    return (
+        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABgAAAAYCAYAAADgdz34AAAA"
+        "BHNCSVQICAgIfAhkiAAAAAlwSFlzAAAApgAAAKYB3X3/OAAAABl0RVh0U29mdHdhcmUAd3"
+        "d3Lmlua3NjYXBlLm9yZ5vuPBoAAANCSURBVEiJtZZPbBtFFMZ/M7ubXdtdb1xSFyeilBap"
+        "ySVU8h8OoFaooFSqiihIVIpQBKci6KEg9Q6H9kovIHoCIVQJJCKE1ENFjnAgcaSGC6rEnx"
+        "BwA04Tx43t2FnvDAfjkNibxgHxnWb2e/u992bee7tCa00YFsffekFY+nUzFtjW0LrvjRXr"
+        "CDIAaPLlW0nHL0SsZtVoaF98mLrx3pdhOqLtYPHChahZcYYO7KvPFxvRl5XPp1sN3adWiD"
+        "1ZAqD6XYK1b/dvE5IWryTt2udLFedwc1+9kLp+vbbpoDh+6TklxBeAi9TL0taeWpdmZzQD"
+        "ry0AcO+jQ12RyohqqoYoo8RDwJrU+qXkjWtfi8Xxt58BdQuwQs9qC/afLwCw8tnQbqYAPs"
+        "gxE1S6F3EAIXux2oQFKm0ihMsOF71dHYx+f3NND68ghCu1YIoePPQN1pGRABkJ6Bus96Cu"
+        "tRZMydTl+TvuiRW1m3n0eDl0vRPcEysqdXn+jsQPsrHMquGeXEaY4Yk4wxWcY5V/9scqOM"
+        "OVUFthatyTy8QyqwZ+kDURKoMWxNKr2EeqVKcTNOajqKoBgOE28U4tdQl5p5bwCw7BWqua"
+        "ZSzAPlwjlithJtp3pTImSqQRrb2Z8PHGigD4RZuNX6JYj6wj7O4TFLbCO/Mn/m8R+h6rYS"
+        "Ub3ekokRY6f/YukArN979jcW+V/S8g0eT/N3VN3kTqWbQ428m9/8k0P/1aIhF36PccEl6E"
+        "hOcAUCrXKZXXWS3XKd2vc/TRBG9O5ELC17MmWubD2nKhUKZa26Ba2+D3P+4/MNCFwg59oW"
+        "VeYhkzgN/JDR8deKBoD7Y+ljEjGZ0sosXVTvbc6RHirr2reNy1OXd6pJsQ+gqjk8VWFYmH"
+        "rwBzW/n+uMPFiRwHB2I7ih8ciHFxIkd/3Omk5tCDV1t+2nNu5sxxpDFNx+huNhVT3/zMDz"
+        "8usXC3ddaHBj1GHj/As08fwTS7Kt1HBTmyN29vdwAw+/wbwLVOJ3uAD1wi/dUH7Qei66Pf"
+        "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)

libs/vertexai/tests/integration_tests/test_embeddings.py

@@ -4,8 +4,13 @@
 `gcloud auth login` first).
 """
 import pytest
+from vertexai.language_models import TextEmbeddingModel  # type: ignore
+from vertexai.vision_models import MultiModalEmbeddingModel  # type: ignore
 
-from langchain_google_vertexai.embeddings import VertexAIEmbeddings
+from langchain_google_vertexai.embeddings import (
+    GoogleEmbeddingModelType,
+    VertexAIEmbeddings,
+)
 
 
 @pytest.mark.release
@@ -74,3 +79,24 @@ def test_warning(caplog: pytest.LogCaptureFixture) -> None:
         "Feb-01-2024. Currently the default is set to textembedding-gecko@001"
     )
     assert record.message == expected_message
+
+
+@pytest.mark.release
+def test_langchain_google_vertexai_image_embeddings(tmp_image) -> None:
+    model = VertexAIEmbeddings(model_name="multimodalembedding")
+    output = model.embed_image(tmp_image)
+    assert len(output) == 1408
+
+
+@pytest.mark.release
+def test_langchain_google_vertexai_text_model() -> None:
+    embeddings_model = VertexAIEmbeddings(model_name="textembedding-gecko@001")
+    assert isinstance(embeddings_model.client, TextEmbeddingModel)
+    assert embeddings_model.model_type == GoogleEmbeddingModelType.TEXT
+
+
+@pytest.mark.release
+def test_langchain_google_vertexai_multimodal_model() -> None:
+    embeddings_model = VertexAIEmbeddings(model_name="multimodalembedding@001")
+    assert isinstance(embeddings_model.client, MultiModalEmbeddingModel)
+    assert embeddings_model.model_type == GoogleEmbeddingModelType.MULTIMODAL

libs/vertexai/tests/integration_tests/test_vision_models.py

@@ -131,28 +131,3 @@ def test_vertex_ai_image_generation_and_edition():
 
     response = editor.invoke(messages)
     assert isinstance(response, AIMessage)
-
-
-@pytest.fixture
-def base64_image() -> str:
-    return (
-        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABgAAAAYCAYAAADgdz34AAAA"
-        "BHNCSVQICAgIfAhkiAAAAAlwSFlzAAAApgAAAKYB3X3/OAAAABl0RVh0U29mdHdhcmUAd3"
-        "d3Lmlua3NjYXBlLm9yZ5vuPBoAAANCSURBVEiJtZZPbBtFFMZ/M7ubXdtdb1xSFyeilBap"
-        "ySVU8h8OoFaooFSqiihIVIpQBKci6KEg9Q6H9kovIHoCIVQJJCKE1ENFjnAgcaSGC6rEnx"
-        "BwA04Tx43t2FnvDAfjkNibxgHxnWb2e/u992bee7tCa00YFsffekFY+nUzFtjW0LrvjRXr"
-        "CDIAaPLlW0nHL0SsZtVoaF98mLrx3pdhOqLtYPHChahZcYYO7KvPFxvRl5XPp1sN3adWiD"
-        "1ZAqD6XYK1b/dvE5IWryTt2udLFedwc1+9kLp+vbbpoDh+6TklxBeAi9TL0taeWpdmZzQD"
-        "ry0AcO+jQ12RyohqqoYoo8RDwJrU+qXkjWtfi8Xxt58BdQuwQs9qC/afLwCw8tnQbqYAPs"
-        "gxE1S6F3EAIXux2oQFKm0ihMsOF71dHYx+f3NND68ghCu1YIoePPQN1pGRABkJ6Bus96Cu"
-        "tRZMydTl+TvuiRW1m3n0eDl0vRPcEysqdXn+jsQPsrHMquGeXEaY4Yk4wxWcY5V/9scqOM"
-        "OVUFthatyTy8QyqwZ+kDURKoMWxNKr2EeqVKcTNOajqKoBgOE28U4tdQl5p5bwCw7BWqua"
-        "ZSzAPlwjlithJtp3pTImSqQRrb2Z8PHGigD4RZuNX6JYj6wj7O4TFLbCO/Mn/m8R+h6rYS"
-        "Ub3ekokRY6f/YukArN979jcW+V/S8g0eT/N3VN3kTqWbQ428m9/8k0P/1aIhF36PccEl6E"
-        "hOcAUCrXKZXXWS3XKd2vc/TRBG9O5ELC17MmWubD2nKhUKZa26Ba2+D3P+4/MNCFwg59oW"
-        "VeYhkzgN/JDR8deKBoD7Y+ljEjGZ0sosXVTvbc6RHirr2reNy1OXd6pJsQ+gqjk8VWFYmH"
-        "rwBzW/n+uMPFiRwHB2I7ih8ciHFxIkd/3Omk5tCDV1t+2nNu5sxxpDFNx+huNhVT3/zMDz"
-        "8usXC3ddaHBj1GHj/As08fwTS7Kt1HBTmyN29vdwAw+/wbwLVOJ3uAD1wi/dUH7Qei66Pf"
-        "yuRj4Ik9is+hglfbkbfR3cnZm7chlUWLdwmprtCohX4HUtlOcQjLYCu+fzGJH2QRKvP3UN"
-        "z8bWk1qMxjGTOMThZ3kvgLI5AzFfo379UAAAAASUVORK5CYII="
-    )

libs/vertexai/tests/unit_tests/test_embeddings.py

@@ -0,0 +1,51 @@
+from typing import Any, Dict
+from unittest.mock import MagicMock
+
+import pytest
+from pydantic.v1 import root_validator
+
+from langchain_google_vertexai import VertexAIEmbeddings
+from langchain_google_vertexai.embeddings import GoogleEmbeddingModelType
+
+
+def test_langchain_google_vertexai_embed_image_multimodal_only() -> None:
+    mock_embeddings = MockVertexAIEmbeddings("textembedding-gecko@001")
+    assert mock_embeddings.model_type == GoogleEmbeddingModelType.TEXT
+    with pytest.raises(NotImplementedError) as e:
+        mock_embeddings.embed_image("test")
+        assert e.value == "Only supported for multimodal models"
+
+
+def test_langchain_google_vertexai_embed_documents_text_only() -> None:
+    mock_embeddings = MockVertexAIEmbeddings("multimodalembedding@001")
+    assert mock_embeddings.model_type == GoogleEmbeddingModelType.MULTIMODAL
+    with pytest.raises(NotImplementedError) as e:
+        mock_embeddings.embed_documents(["test"])
+        assert e.value == "Not supported for multimodal models"
+
+
+def test_langchain_google_vertexai_embed_query_text_only() -> None:
+    mock_embeddings = MockVertexAIEmbeddings("multimodalembedding@001")
+    assert mock_embeddings.model_type == GoogleEmbeddingModelType.MULTIMODAL
+    with pytest.raises(NotImplementedError) as e:
+        mock_embeddings.embed_query("test")
+        assert e.value == "Not supported for multimodal models"
+
+
+class MockVertexAIEmbeddings(VertexAIEmbeddings):
+    """
+    A mock class for avoiding instantiating VertexAI and the EmbeddingModel client
+    instance during init
+    """
+
+    def __init__(self, model_name, **kwargs: Any) -> None:
+        super().__init__(model_name, **kwargs)
+
+    @classmethod
+    def _init_vertexai(cls, values: Dict) -> None:
+        pass
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        values["client"] = MagicMock()
+        return values