vertexai: Add embeddings_task_type parameter to embed_query and embed_documents

libs/vertexai/langchain_google_vertexai/embeddings.py

@@ -45,6 +45,18 @@
 _MIN_BATCH_SIZE = 5
 
 
+EmbeddingTaskTypes = Literal[
+    "RETRIEVAL_QUERY",
+    "RETRIEVAL_DOCUMENT",
+    "SEMANTIC_SIMILARITY",
+    "CLASSIFICATION",
+    "CLUSTERING",
+    "QUESTION_ANSWERING",
+    "FACT_VERIFICATION",
+    "CODE_RETRIEVAL_QUERY",
+]
+
+
 class GoogleEmbeddingModelType(str, Enum):
     TEXT = auto()
     MULTIMODAL = auto()
@@ -63,6 +75,7 @@ class GoogleEmbeddingModelVersion(str, Enum):
     EMBEDDINGS_NOV_2023 = auto()
     EMBEDDINGS_DEC_2023 = auto()
     EMBEDDINGS_MAY_2024 = auto()
+    EMBEDDINGS_NOV_2024 = auto()
 
     @classmethod
     def _missing_(cls, value: Any) -> "GoogleEmbeddingModelVersion":
@@ -82,6 +95,8 @@ def _missing_(cls, value: Any) -> "GoogleEmbeddingModelVersion":
             or "text-multilingual-embedding-preview-0409" in value.lower()
         ):
             return GoogleEmbeddingModelVersion.EMBEDDINGS_MAY_2024
+        if "text-embedding-005" in value.lower():
+            return GoogleEmbeddingModelVersion.EMBEDDINGS_NOV_2024
 
         return GoogleEmbeddingModelVersion.EMBEDDINGS_JUNE_2023
 
@@ -376,17 +391,7 @@ def embed(
         self,
         texts: List[str],
         batch_size: int = 0,
-        embeddings_task_type: Optional[
-            Literal[
-                "RETRIEVAL_QUERY",
-                "RETRIEVAL_DOCUMENT",
-                "SEMANTIC_SIMILARITY",
-                "CLASSIFICATION",
-                "CLUSTERING",
-                "QUESTION_ANSWERING",
-                "FACT_VERIFICATION",
-            ]
-        ] = None,
+        embeddings_task_type: Optional[EmbeddingTaskTypes] = None,
         dimensions: Optional[int] = None,
     ) -> List[List[float]]:
         """Embed a list of strings.
@@ -406,6 +411,8 @@ def embed(
                                           for Semantic Textual Similarity (STS).
                     CLASSIFICATION - Embeddings will be used for classification.
                     CLUSTERING - Embeddings will be used for clustering.
+                    CODE_RETRIEVAL_QUERY - Embeddings will be used for
+                                           code retrieval for Java and Python.
                     The following are only supported on preview models:
                     QUESTION_ANSWERING
                     FACT_VERIFICATION
@@ -447,7 +454,11 @@ def embed(
         return embeddings
 
     def embed_documents(
-        self, texts: List[str], batch_size: int = 0
+        self,
+        texts: List[str],
+        batch_size: int = 0,
+        *,
+        embeddings_task_type: EmbeddingTaskTypes = "RETRIEVAL_DOCUMENT",
     ) -> List[List[float]]:
         """Embed a list of documents.
 
@@ -460,9 +471,14 @@ def embed_documents(
         Returns:
             List of embeddings, one for each text.
         """
-        return self.embed(texts, batch_size, "RETRIEVAL_DOCUMENT")
+        return self.embed(texts, batch_size, embeddings_task_type)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(
+        self,
+        text: str,
+        *,
+        embeddings_task_type: EmbeddingTaskTypes = "RETRIEVAL_QUERY",
+    ) -> List[float]:
         """Embed a text.
 
         Args:
@@ -471,7 +487,7 @@ def embed_query(self, text: str) -> List[float]:
         Returns:
             Embedding for the text.
         """
-        return self.embed([text], 1, "RETRIEVAL_QUERY")[0]
+        return self.embed([text], 1, embeddings_task_type)[0]
 
     @deprecated(
         since="2.0.1", removal="3.0.0", alternative="VertexAIEmbeddings.embed_images()"

libs/vertexai/tests/integration_tests/test_embeddings.py

@@ -53,6 +53,25 @@ def test_langchain_google_vertexai_embedding_documents(
     assert model.model_name == model_name
 
 
+@pytest.mark.release
+@pytest.mark.parametrize(
+    "model_name, embeddings_dim",
+    _EMBEDDING_MODELS,
+)
+def test_langchain_google_vertexai_embedding_documents_with_task_type(
+    model_name: str,
+    embeddings_dim: int,
+) -> None:
+    documents = ["foo bar"] * 8
+    model = VertexAIEmbeddings(model_name)
+    output = model.embed_documents(documents)
+    assert len(output) == 8
+    for embedding in output:
+        assert len(embedding) == embeddings_dim
+    assert model.model_name == model.client._model_id
+    assert model.model_name == model_name
+
+
 @pytest.mark.release
 @pytest.mark.parametrize(
     "model_name, embeddings_dim",
@@ -65,6 +84,21 @@ def test_langchain_google_vertexai_embedding_query(model_name, embeddings_dim) -
     assert len(output) == embeddings_dim
 
 
+@pytest.mark.release
+@pytest.mark.parametrize(
+    "model_name, embeddings_dim",
+    _EMBEDDING_MODELS,
+)
+def test_langchain_google_vertexai_embedding_query_with_task_type(
+    model_name: str,
+    embeddings_dim: int,
+) -> None:
+    document = "foo bar"
+    model = VertexAIEmbeddings(model_name)
+    output = model.embed_query(document)
+    assert len(output) == embeddings_dim
+
+
 @pytest.mark.release
 @pytest.mark.parametrize(
     "dim, expected_dim",

libs/vertexai/tests/unit_tests/test_embeddings.py

@@ -1,12 +1,15 @@
 from typing import Any, Dict
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 from pydantic import model_validator
 from typing_extensions import Self
 
 from langchain_google_vertexai import VertexAIEmbeddings
-from langchain_google_vertexai.embeddings import GoogleEmbeddingModelType
+from langchain_google_vertexai.embeddings import (
+    EmbeddingTaskTypes,
+    GoogleEmbeddingModelType,
+)
 
 
 def test_langchain_google_vertexai_embed_image_multimodal_only() -> None:
@@ -29,6 +32,49 @@ def test_langchain_google_vertexai_no_dups_dynamic_batch_size() -> None:
     assert len(batches) == 2
 
 
+@patch.object(VertexAIEmbeddings, "embed")
+def test_embed_documents_with_question_answering_task(mock_embed) -> None:
+    mock_embeddings = MockVertexAIEmbeddings("text-embedding-005")
+    texts = [f"text {i}" for i in range(5)]
+
+    embedding_dimension = 768
+    embeddings_task_type: EmbeddingTaskTypes = "QUESTION_ANSWERING"
+
+    mock_embed.return_value = [[0.001] * embedding_dimension for _ in texts]
+
+    embeddings = mock_embeddings.embed_documents(
+        texts=texts, embeddings_task_type=embeddings_task_type
+    )
+
+    assert isinstance(embeddings, list)
+    assert len(embeddings) == len(texts)
+    assert len(embeddings[0]) == embedding_dimension
+
+    # Verify embed() was called correctly
+    mock_embed.assert_called_once_with(texts, 0, embeddings_task_type)
+
+
+@patch.object(VertexAIEmbeddings, "embed")
+def test_embed_query_with_question_answering_task(mock_embed) -> None:
+    mock_embeddings = MockVertexAIEmbeddings("text-embedding-005")
+    text = "text 0"
+
+    embedding_dimension = 768
+    embeddings_task_type: EmbeddingTaskTypes = "QUESTION_ANSWERING"
+
+    mock_embed.return_value = [[0.001] * embedding_dimension]
+
+    embedding = mock_embeddings.embed_query(
+        text=text, embeddings_task_type=embeddings_task_type
+    )
+
+    assert isinstance(embedding, list)
+    assert len(embedding) == embedding_dimension
+
+    # Verify embed() was called correctly
+    mock_embed.assert_called_once_with([text], 1, embeddings_task_type)
+
+
 class MockVertexAIEmbeddings(VertexAIEmbeddings):
     """
     A mock class for avoiding instantiating VertexAI and the EmbeddingModel client