How does llm.bind_tools work in langchain?

[anmol-aidora]

I need to understand how exactly does langchain convert information from code to LLM prompt, because end of the day, the LLM will need only text to be passed to it.

If I am incorrect somewhere in my understanding, kindly point it out as well when answering the question

This is the bind_tools function:

class BaseChatOpenAI(BaseChatModel):
...
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
...
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.

    Assumes model is compatible with OpenAI tool-calling API.
    """
    ...
    return super().bind(tools=formatted_tools, **kwargs)

On going to definition of super():

class Runnable(Generic[Input, Output], ABC):
...
def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
"""
Bind arguments to a Runnable, returning a new Runnable.

    Useful when a Runnable in a chain requires an argument that is not

    """
    return RunnableBinding(bound=self, kwargs=kwargs, config={})

On going to RunnableBinding:

class RunnableBinding(RunnableBindingBase[Input, Output]):
"""Wrap a Runnable with additional functionality.

A RunnableBinding can be thought of as a "runnable decorator" that
preserves the essential features of Runnable; i.e., batching, streaming,
and async support, while adding additional functionality.

Any class that inherits from Runnable can be bound to a `RunnableBinding`.
Runnables expose a standard set of methods for creating `RunnableBindings`
or sub-classes of `RunnableBindings` (e.g., `RunnableRetry`,
`RunnableWithFallbacks`) that add additional functionality.

These methods include:

- ``bind``: Bind kwargs to pass to the underlying Runnable when running it.
- ``with_config``: Bind config to pass to the underlying Runnable when running it.
- ``with_listeners``:  Bind lifecycle listeners to the underlying Runnable.
- ``with_types``: Override the input and output types of the underlying Runnable.
- ``with_retry``: Bind a retry policy to the underlying Runnable.
- ``with_fallbacks``: Bind a fallback policy to the underlying Runnable.

Example:

`bind`: Bind kwargs to pass to the underlying Runnable when running it.

    .. code-block:: python

        # Create a Runnable binding that invokes the ChatModel with the
        # additional kwarg `stop=['-']` when running it.
        from langchain_community.chat_models import ChatOpenAI
        model = ChatOpenAI()
        model.invoke('Say "Parrot-MAGIC"', stop=['-']) # Should return `Parrot`
        # Using it the easy way via `bind` method which returns a new
        # RunnableBinding
        runnable_binding = model.bind(stop=['-'])
        runnable_binding.invoke('Say "Parrot-MAGIC"') # Should return `Parrot`

    Can also be done by instantiating a RunnableBinding directly (not recommended):

    .. code-block:: python

        from langchain_core.runnables import RunnableBinding
        runnable_binding = RunnableBinding(
            bound=model,
            kwargs={'stop': ['-']} # <-- Note the additional kwargs
        )
        runnable_binding.invoke('Say "Parrot-MAGIC"') # Should return `Parrot`
"""
...
def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
    """Bind additional kwargs to a Runnable, returning a new Runnable.

    Args:
        **kwargs: The kwargs to bind to the Runnable.

    Returns:
        A new Runnable with the same type and config as the original,
        but with the additional kwargs bound.
    """
    return self.__class__(
        bound=self.bound,
        config=self.config,
        kwargs={**self.kwargs, **kwargs},
        custom_input_type=self.custom_input_type,
        custom_output_type=self.custom_output_type,
    )

After this, I am not able to understand how exactly the bind_tools function pass info to the LLM.

[dosubot[bot]]

Hey @anmol-aidora! I'm here to assist you with any bugs, questions, or contributions you have. Let me know how I can help!

The bind_tools method in LangChain is designed to bind tool-like objects to a chat model, specifically for models compatible with the OpenAI tool-calling API. Here's how it works:

1. Tool Conversion: The bind_tools method first converts each tool into a format compatible with OpenAI using the convert_to_openai_tool function. This function ensures that the tools are properly formatted for integration with OpenAI-compatible components.

2. Tool Formatting: After conversion, the tools are formatted into a list of functions that can be used by the model. This involves extracting the "function" component from each converted tool.

3. Binding Tools: The formatted tools are then passed to the super().bind method. This method is part of the Runnable class, which BaseChatOpenAI inherits from. The bind method creates a RunnableBinding, which acts as a decorator to add additional functionality to the Runnable.

4. RunnableBinding: The RunnableBinding class wraps the original Runnable (in this case, the chat model) and binds additional keyword arguments (kwargs) to it. This allows the model to be invoked with these additional arguments when executed.

5. Execution: When the RunnableBinding is executed, it passes the bound arguments to the underlying Runnable, ensuring that the tools and any other specified configurations are used during the model's operation.

In summary, bind_tools prepares and formats tools for use with a language model, and RunnableBinding ensures these tools are correctly passed and utilized during execution ^[1][2].

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[anmol-aidora]

You are explaining what I can already see by looking through the code. Need to ask a Human