Fixed support for custom tool

libs/vertexai/langchain_google_vertexai/functions_utils.py

@@ -62,8 +62,14 @@ def _format_tools_to_vertex_tool(
     for tool in tools:
         if isinstance(tool, BaseTool):
             func = _format_tool_to_vertex_function(tool)
-        else:
+        elif isinstance(tool, type) and issubclass(tool, BaseModel):
             func = _format_pydantic_to_vertex_function(tool)
+        else:
+            func = {
+                "name": tool["name"],
+                "description": tool.get("description"),
+                "parameters": _get_parameters_from_schema(tool["parameters"]),
+            }
         function_declarations.append(FunctionDeclaration(**func))
 
     return [VertexTool(function_declarations=function_declarations)]

libs/vertexai/tests/integration_tests/test_tools.py

@@ -93,6 +93,38 @@ def test_tools() -> None:
     assert round(float(just_numbers), 2) == 2.16
 
 
+@pytest.mark.extended
+def test_custom_tool() -> None:
+    from langchain.agents import AgentExecutor, create_openai_functions_agent
+    from langchain.agents import tool
+
+    @tool
+    def search(query: str) -> str:
+        """Look up things online."""
+        return "LangChain"
+
+    tools = [search]
+
+    llm = ChatVertexAI(model_name="gemini-pro", temperature=0.0, convert_system_message_to_human=True)
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", "You are a helpful assistant"),
+            MessagesPlaceholder("chat_history", optional=True),
+            ("human", "{input}"),
+            MessagesPlaceholder("agent_scratchpad"),
+        ]
+    )
+
+    agent = create_openai_functions_agent(llm, tools, prompt)
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+    response = agent_executor.invoke({"input": "What is LangChain?"})
+    assert isinstance(response, dict)
+    assert response["input"] == "What is LangChain?"
+
+    assert "LangChain" in response["output"]
+
+
 @pytest.mark.extended
 def test_stream() -> None:
     from langchain.chains import LLMMathChain