I am very new to langchain. I am trying to build a agent which uses a custom LLM (eg llama-3) and have tool calling capability. I have build my own CustomChatModel class which inherits from BaseChatModel class. I have implemented my own _generate() function. For the bind_tools() function i have taken the implmentation from one of the predifined chatModel for eg. ChatOpenAI().
But the i think it is not working. When asked about the current information it is not calling the tool.

model = CustomChatModel()
model_with_tools = model.bind_tools(tools)

response = model_with_tools.invoke([
        SystemMessage(content=f'''You are a helpful assistant. 
                      You also have tools the following tools available to you if you need you can call those:
                      {tool_info}  
                      Don't mention about tools to user'''),
                      HumanMessage(content="What's the today weather in SF?")])

print(f"ContentString: {response.content}")
print(f"ToolCalls: {response.tool_calls}")

You can see the tools are not called ->

ContentString: According to my knowledge, the current weather in San Francisco is mostly cloudy with a high of 63°F (17°C) and a low of 55°F (13°C). There's a gentle breeze blowing at about 7 mph (11 km/h).
ToolCalls: []

Here is the custom class implementation

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def convert_messages(langchain_messages):
    converted_messages = []
    role_mapping = {
        'SystemMessage': 'system',
        'HumanMessage': 'user',
        'AIMessage': 'assistant'
    }
    
    for message in langchain_messages:
        message_dict = {
            "role": role_mapping[type(message).__name__],
            "content": message.content
        }
        converted_messages.append(message_dict)
    
    return converted_messages


def llama3_instruct(messages):

    msgs = convert_messages(messages)
    input_ids = tokenizer.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)


    terminators = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<|eot_id|>")
    ]

    outputs = model.generate(
        input_ids,
        max_new_tokens=256,
        eos_token_id=terminators,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
    response = outputs[0][input_ids.shape[-1]:]

    return tokenizer.decode(response, skip_special_tokens=True)
    

class CustomChatModel(BaseChatModel):
    
    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        """Override the _generate method to implement the chat model logic.

        This can be a call to an API, a call to a local model, or any other
        implementation that generates a response to the input prompt.

        Args:
            messages: the prompt composed of a list of messages.
            stop: a list of strings on which the model should stop generating.
                  If generation stops due to a stop token, the stop token itself
                  SHOULD BE INCLUDED as part of the output. This is not enforced
                  across models right now, but it's a good practice to follow since
                  it makes it much easier to parse the output of the model
                  downstream and understand why generation stopped.
            run_manager: A run manager with callbacks for the LLM.
        """
        
        res = llama3_instruct(messages)
            
        message = AIMessage(
            content=res,
            additional_kwargs={},  # Used to add additional payload (e.g., function calling request)
            response_metadata={  # Use for response metadata
                "time_in_seconds": 3,
            },
        )
        ##

        generation = ChatGeneration(message=message)
        return ChatResult(generations=[generation])

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        """Stream the output of the model.

        This method should be implemented if the model can generate output
        in a streaming fashion. If the model does not support streaming,
        do not implement it. In that case streaming requests will be automatically
        handled by the _generate method.

        Args:
            messages: the prompt composed of a list of messages.
            stop: a list of strings on which the model should stop generating.
                  If generation stops due to a stop token, the stop token itself
                  SHOULD BE INCLUDED as part of the output. This is not enforced
                  across models right now, but it's a good practice to follow since
                  it makes it much easier to parse the output of the model
                  downstream and understand why generation stopped.
            run_manager: A run manager with callbacks for the LLM.
        """

        res = self._generate(messages).generations[0].text
    
        for token in res:
            chunk = ChatGenerationChunk(message=AIMessageChunk(content=token))

            if run_manager:
                # This is optional in newer versions of LangChain
                # The on_llm_new_token will be called automatically
                run_manager.on_llm_new_token(token, chunk=chunk)

            yield chunk

        # Let's add some other information (e.g., response metadata)
        chunk = ChatGenerationChunk(
            message=AIMessageChunk(content="", response_metadata={"time_in_sec": 3})
        )
        if run_manager:
            # This is optional in newer versions of LangChain
            # The on_llm_new_token will be called automatically
            run_manager.on_llm_new_token(token, chunk=chunk)
        yield chunk

    @property
    def _llm_type(self) -> str:
        """Get the type of language model used by this chat model."""
        return "echoing-chat-model-advanced"

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Return a dictionary of identifying parameters.

        This information is used by the LangChain callback system, which
        is used for tracing purposes make it possible to monitor LLMs.
        """
        return {
            "model_name": "llama-3",
        }
    
    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        *,
        tool_choice: Optional[
            Union[dict, str, Literal["auto", "any", "none"], bool]
        ] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        """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.
            tool_choice: Which tool to require the model to call.
                Must be the name of the single provided function,
                "auto" to automatically determine which function to call
                with the option to not call any function, "any" to enforce that some
                function is called, 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]
        if tool_choice is not None and tool_choice:
            if isinstance(tool_choice, str) and (
                tool_choice not in ("auto", "any", "none")
            ):
                tool_choice = {"type": "function", "function": {"name": tool_choice}}
            if isinstance(tool_choice, dict) and (len(formatted_tools) != 1):
                raise ValueError(
                    "When specifying `tool_choice`, you must provide exactly one "
                    f"tool. Received {len(formatted_tools)} tools."
                )
            if isinstance(tool_choice, dict) and (
                formatted_tools[0]["function"]["name"]
                != tool_choice["function"]["name"]
            ):
                raise ValueError(
                    f"Tool choice {tool_choice} was specified, but the only "
                    f"provided tool was {formatted_tools[0]['function']['name']}."
                )
            if isinstance(tool_choice, bool):
                if len(tools) > 1:
                    raise ValueError(
                        "tool_choice can only be True when there is one tool. Received "
                        f"{len(tools)} tools."
                    )
                tool_name = formatted_tools[0]["function"]["name"]
                tool_choice = {
                    "type": "function",
                    "function": {"name": tool_name},
                }

            kwargs["tool_choice"] = tool_choice
        return super().bind(tools=formatted_tools, **kwargs)

Anyone knows how to implement the bind_tools() function in custom chat models