Custom LLM

This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.

There are only two required things that a custom LLM needs to implement:

• A _call method that takes in a string, some optional stop words, and returns a string.
• A _llm_type property that returns a string. Used for logging purposes only.

There is a second optional thing it can implement:

• An _identifying_params property that is used to help with printing of this class. Should return a dictionary.

Let’s implement a very simple custom LLM that just returns the first n characters of the input.

from typing import Any, List, Mapping, Optional

from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM

class CustomLLM(LLM):
    n: int

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

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        if stop is not None:
            raise ValueError("stop kwargs are not permitted.")
        return prompt[: self.n]

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {"n": self.n}

We can now use this as an any other LLM.

llm = CustomLLM(n=10)

llm.invoke("This is a foobar thing")

'This is a '

We can also print the LLM and see its custom print.

print(llm)

CustomLLM
Params: {'n': 10}