fix(chat_models): add bind_tools impl for MoonshotChat

libs/community/langchain_community/chat_models/moonshot.py

@@ -1,12 +1,18 @@
 """Wrapper around Moonshot chat models."""
 
-from typing import Dict
+from typing import Any, Callable, Dict, Sequence, Type, Union
 
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import AIMessage
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
 from langchain_core.utils import (
     convert_to_secret_str,
     get_from_dict_or_env,
     pre_init,
 )
+from langchain_core.utils.function_calling import convert_to_openai_tool
+from pydantic import BaseModel
 
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.llms.moonshot import MOONSHOT_SERVICE_URL_BASE, MoonshotCommon
@@ -172,9 +178,11 @@ def validate_environment(cls, values: Dict) -> Dict:
 
         client_params = {
             "api_key": values["moonshot_api_key"].get_secret_value(),
-            "base_url": values["base_url"]
-            if "base_url" in values
-            else MOONSHOT_SERVICE_URL_BASE,
+            "base_url": (
+                values["base_url"]
+                if "base_url" in values
+                else MOONSHOT_SERVICE_URL_BASE
+            ),
         }
 
         if not values.get("client"):
@@ -185,3 +193,22 @@ def validate_environment(cls, values: Dict) -> Dict:
             ).chat.completions
 
         return values
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, AIMessage]:
+        """Bind tool-like objects to this chat model.
+
+        Args:
+            tools: A list of tool definitions to bind to this chat model.
+                Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
+                models, callables, and BaseTools will be automatically converted to
+                their schema dictionary representation.
+            **kwargs: Any additional parameters to pass to the
+                :class:`~langchain.runnable.Runnable` constructor.
+        """
+
+        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        return super().bind(tools=formatted_tools, **kwargs)

libs/community/langchain_community/llms/moonshot.py

@@ -39,7 +39,7 @@ def completion(self, request: Any) -> Any:
 class MoonshotCommon(BaseModel):
     """Common parameters for Moonshot LLMs."""
 
-    client: Any
+    client: Any = Field(default=None)
     base_url: str = MOONSHOT_SERVICE_URL_BASE
     moonshot_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
     """Moonshot API key. Get it here: https://platform.moonshot.cn/console/api-keys"""

libs/community/tests/integration_tests/chat_models/test_moonshot.py

@@ -27,5 +27,5 @@ def test_usage_metadata(self, model: BaseChatModel) -> None:
 def test_chat_moonshot_instantiate_with_alias() -> None:
     """Test MoonshotChat instantiate when using alias."""
     api_key = "your-api-key"
-    chat = MoonshotChat(api_key=api_key)  # type: ignore[call-arg]
+    chat = MoonshotChat(api_key=api_key)
     assert cast(SecretStr, chat.moonshot_api_key).get_secret_value() == api_key

libs/community/tests/unit_tests/chat_models/test_moonshot.py

@@ -0,0 +1,14 @@
+from typing import Any
+
+import pytest
+
+from langchain_community.chat_models.moonshot import MoonshotChat
+
+mock_tool_list = [lambda: f"tool-id-{i}" for i in range(3)]
+
+
+@pytest.mark.requires("openai")
+def test_moonshot_bind_tools() -> None:
+    llm = MoonshotChat(name="moonshot")
+    ret: Any = llm.bind_tools(mock_tool_list)
+    assert len(ret.kwargs["tools"]) == 3

libs/community/tests/unit_tests/llms/test_moonshot.py

@@ -9,7 +9,7 @@
 
 @pytest.mark.requires("openai")
 def test_moonshot_model_param() -> None:
-    llm = Moonshot(model="foo")  # type: ignore[call-arg]
+    llm = Moonshot(model="foo")
     assert llm.model_name == "foo"
     llm = Moonshot(model_name="bar")  # type: ignore[call-arg]
     assert llm.model_name == "bar"