added google_search_tool for gemini-2 (#677)

libs/vertexai/langchain_google_vertexai/chat_models.py

@@ -652,12 +652,7 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel):
     """Google Cloud Vertex AI chat model integration.
 
     Setup:
-        You must have the langchain-google-vertexai Python package installed
-        .. code-block:: bash
-
-            pip install -U langchain-google-vertexai
-
-        And either:
+        You must either:
             - Have credentials configured for your environment (gcloud, workload identity, etc...)
             - Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable
 
@@ -803,6 +798,16 @@ class GetPopulation(BaseModel):
 
         See ``ChatVertexAI.bind_tools()`` method for more.
 
+    Use Search with Gemini 2:
+        .. code-block:: python
+
+            import google.cloud.aiplatform_v1beta1.types import Tool as VertexTool
+            llm = ChatVertexAI(model="gemini-2.0-flash-exp")
+            resp = llm.invoke(
+                "When is the next total solar eclipse in US?",
+                tools=[VertexTool(google_search={})],
+            )
+
     Structured output:
         .. code-block:: python
 

libs/vertexai/langchain_google_vertexai/functions_utils.py

@@ -283,6 +283,8 @@ def _format_to_gapic_tool(tools: _ToolsType) -> gapic.Tool:
                 gapic_tool.google_search_retrieval = rt.google_search_retrieval
             if "function_declarations" in rt:
                 gapic_tool.function_declarations.extend(rt.function_declarations)
+            if "google_search" in rt:
+                gapic_tool.google_search = rt.google_search
         elif isinstance(tool, dict):
             # not _ToolDictLike
             if not any(

libs/vertexai/pyproject.toml

@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.9,<4.0"
 langchain-core = ">=0.3.27,<0.4"
-google-cloud-aiplatform = "^1.73.0"
+google-cloud-aiplatform = "^1.75.0"
 google-cloud-storage = "^2.18.0"
 # optional dependencies
 anthropic = { extras = ["vertexai"], version = ">=0.35.0,<1", optional = true }

libs/vertexai/tests/integration_tests/test_anthropic_cache.py

@@ -10,6 +10,7 @@
 
 
 @pytest.mark.extended
+@pytest.mark.skip(reason="claude-3-5-v2 not enabled")
 def test_anthropic_system_cache() -> None:
     """Test chat with system message having cache control."""
     project = os.environ["PROJECT_ID"]
@@ -35,6 +36,7 @@ def test_anthropic_system_cache() -> None:
 
 
 @pytest.mark.extended
+@pytest.mark.skip(reason="claude-3-5-v2 not enabled")
 def test_anthropic_mixed_cache() -> None:
     """Test chat with different cache control types."""
     project = os.environ["PROJECT_ID"]
@@ -72,6 +74,7 @@ def test_anthropic_mixed_cache() -> None:
 
 
 @pytest.mark.extended
+@pytest.mark.skip(reason="claude-3-5-v2 not enabled")
 def test_anthropic_conversation_cache() -> None:
     """Test chat conversation with cache control."""
     project = os.environ["PROJECT_ID"]
@@ -115,6 +118,7 @@ def test_anthropic_conversation_cache() -> None:
 
 
 @pytest.mark.extended
+@pytest.mark.skip(reason="claude-3-5-v2 not enabled")
 def test_anthropic_chat_template_cache() -> None:
     """Test chat template with structured content and cache control."""
     project = os.environ["PROJECT_ID"]

libs/vertexai/tests/integration_tests/test_chat_models.py

@@ -1175,6 +1175,7 @@ def test_multimodal_pdf_input_b64(multimodal_pdf_chain: RunnableSerializable) ->
         assert isinstance(response, AIMessage)
 
 
+@pytest.mark.xfail(reason="logprobs are subject to daily quotas")
 @pytest.mark.release
 def test_logprobs() -> None:
     llm = ChatVertexAI(model="gemini-1.5-flash", logprobs=2)