feat(community): add bind_tools function for ChatLiteLLM (#23823)

It's a follow-up to https://github.com/langchain-ai/langchain/pull/23765

Now the tools can be bound by calling `bind_tools`

```python
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_community.chat_models import ChatLiteLLM

class GetWeather(BaseModel):
    '''Get the current weather in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

class GetPopulation(BaseModel):
    '''Get the current population in a given location'''

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")

prompt = "Which city is hotter today and which is bigger: LA or NY?"
# tools = [convert_to_openai_tool(GetWeather), convert_to_openai_tool(GetPopulation)]
tools = [GetWeather, GetPopulation]

llm = ChatLiteLLM(model="claude-3-sonnet-20240229").bind_tools(tools)
ai_msg = llm.invoke(prompt)
print(ai_msg.tool_calls)
```

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

Co-authored-by: Igor Drozdov <idrozdov@gitlab.com>

libs/community/langchain_community/chat_models/litellm.py

@@ -12,6 +12,7 @@ from typing import (
     List,
     Mapping,
     Optional,
+    Sequence,
     Tuple,
     Type,
     Union,
@@ -21,6 +22,7 @@ from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
+from langchain_core.language_models import LanguageModelInput
 from langchain_core.language_models.chat_models import (
     BaseChatModel,
     agenerate_from_stream,
@@ -46,8 +48,11 @@ from langchain_core.outputs import (
     ChatGenerationChunk,
     ChatResult,
 )
-from langchain_core.pydantic_v1 import Field, root_validator
+from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool
 from langchain_core.utils import get_from_dict_or_env
+from langchain_core.utils.function_calling import convert_to_openai_tool
 
 logger = logging.getLogger(__name__)
 
@@ -411,6 +416,32 @@ class ChatLiteLLM(BaseChatModel):
         )
         return self._create_chat_result(response)
 
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        """Bind tool-like objects to this chat model.
+
+        LiteLLM expects tools argument in OpenAI format.
+
+        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.
+            tool_choice: Which tool to require the model to call.
+                Must be the name of the single provided function or
+                "auto" to automatically determine which function to call
+                (if any), or a dict of the form:
+                {"type": "function", "function": {"name": <<tool_name>>}}.
+            **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)
+
     @property
     def _identifying_params(self) -> Dict[str, Any]:
         """Get the identifying parameters."""