How can I create a custom LLM chat node using LangChain?

Below is a general guideline for how you can create a custom LLM chat node within LangChain. The idea is to wrap your own LLM using the standard interfaces so that it integrates seamlessly with the rest of LangChain's ecosystem. Here’s how to get started:

1. Choose the Right Interface

For a chat model, you'll typically wrap your LLM with the standard BaseChatModel interface. This ensures that your chat model can be plugged into existing LangChain programs with little extra effort.

2. Implement the Required Methods

When creating your custom LLM (or chat node), you need to implement the two required items:

• _call: A method that takes a string (often a prompt or conversation history) along with any optional parameters (like stop tokens) and returns a string as the output. This method is used by the framework’s invoke functionality.
• _llm_type: A property that returns a string for logging or identifying the type of LLM.

3. Consider Optional Enhancements

To take full advantage of LangChain’s features, you can also implement:

• _identifying_params: A property that returns a dictionary with model-specific parameters helpful for debugging and logging.
• Async Implementations: Such as _acall (async counterpart for _call) and _astream (for streaming responses asynchronously).
• Streaming Support: Implement a _stream method that can return output token-by-token, making sure to invoke any callbacks (like on_llm_new_token) appropriately.

4. Example Skeleton

Below is an example skeleton of a custom chat node class:

PYTHONCopied
from langchain.chat_models.base import BaseChatModel

class MyCustomChatModel(BaseChatModel):
    def _call(self, prompt: str, stop: list[str] | None = None) -> str:
        # Your logic to process the input prompt and generate a response
        response = "Your custom logic response here."
        # Make sure any stop tokens are respected and included as needed
        return response

    @property
    def _llm_type(self) -> str:
        return "my_custom_chat_model"

    # Optionally, implement identifying parameters
    @property
    def _identifying_params(self) -> dict:
        return {"model_name": "MyCustomChatModel", "version": "1.0"}

    # Optionally, provide async methods if needed.
    async def _acall(self, prompt: str, stop: list[str] | None = None) -> str:
        # Your async logic here
        return self._call(prompt, stop)

    # If you want to support streaming responses, implement _stream and/or _astream
    def _stream(self, prompt: str, stop: list[str] | None = None):
        # Example: yield tokens one by one, ensuring "on_llm_new_token" callback is invoked before each token.
        for token in self._call(prompt, stop).split():
            yield token

5. Testing and Documentation

• Documentation: Add meaningful docstrings for initial arguments and for the class itself. Include references to any external APIs if your model connects with them.
• Tests: Write unit and integration tests, ensuring that methods like invoke, ainvoke, and any streaming functionality work correctly.
• Stop Token Behavior and Secret Management: Ensure you handle stop tokens as documented and, if using API keys, work with secure types like Pydantic’s SecretStr.

Following these steps and implementing the required (and optionally additional) methods according to the LangChain guidelines will set you on the right path for creating a custom chat node. This standardized approach not only simplifies integration but also leverages LangChain’s optimizations and asynchronous support out of the box.

Below is a concise guideline to help you get started on creating a custom chat node by extending LangChain’s functionality:

1. Wrap Your Chat Model

LangChain expects chat models to implement the standard interface (i.e. by subclassing from the provided chat model base class). Specifically, you can wrap your chat model with the standard BaseChatModel interface. This approach makes your model plug-and-play within the LangChain ecosystem with very few code changes.

2. Implement the Required Methods

For a custom LLM (or chat node) to work smoothly within LangChain, you need to implement two required elements:

• _call:
  This method receives a string (your prompt) along with optional stop tokens and returns the string output from your model (i.e., the chat response).

• _llm_type:
  This property returns a string (typically used for logging) that denotes the type of LLM you're implementing.

A simple example might look like this:

PYTHONCopied
from langchain.chat_models.base import BaseChatModel

class CustomChatModel(BaseChatModel):
    @property
    def _llm_type(self) -> str:
        return "custom_chat_model"

    def _call(self, prompt: str, stop: list = None) -> str:
        # Your custom logic to generate a response from the prompt
        response = "Your custom model's response"
        return response

3. Optional Implementations to Enhance Functionality

To take advantage of additional LangChain features (such as asynchronous operations, token streaming, and better identification/logging), consider implementing these:

• _acall:
  An async version of _call for asynchronous invocations.

• _stream / _astream:
  These methods allow streaming of output token by token.

• _identifying_params:
  A property that returns a dictionary with parameters that uniquely identify your model, which can be useful for logging and debugging.

4. Leverage Automatic Integration and Optimizations

By adhering to the standard interfaces, your chat node will automatically:

• Become a LangChain Runnable, meaning it can easily be connected into chains and workflows.
• Benefit from built-in optimizations and async support without additional modifications.

5. Additional Resources

For more details on chat model concepts and integrations, you may also refer to:

• The chat model conceptual guide
• Various how-to guides for chat models

Following these steps should help you start building a custom chat node that integrates seamlessly with LangChain while also addressing many of the common pitfalls others have encountered. If you run into specific errors, double-check that you are implementing all required methods and consider the optional ones to leverage additional features.