langchain/docs/extras/modules/callbacks/custom_callbacks.ipynb

Custom callback handlers

You can create a custom handler to set on the object as well. In the example below, we'll implement streaming with a custom handler.

In [3]:

from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage


class MyCustomHandler(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs) -> None:
        print(f"My custom handler, token: {token}")


# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with our custom handler
chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[MyCustomHandler()])

chat([HumanMessage(content="Tell me a joke")])

Out [3]:

My custom handler, token: 
My custom handler, token: Why
My custom handler, token:  don
My custom handler, token: 't
My custom handler, token:  scientists
My custom handler, token:  trust
My custom handler, token:  atoms
My custom handler, token: ?
My custom handler, token:  


My custom handler, token: Because
My custom handler, token:  they
My custom handler, token:  make
My custom handler, token:  up
My custom handler, token:  everything
My custom handler, token: .
My custom handler, token: 

AIMessage(content="Why don't scientists trust atoms? \n\nBecause they make up everything.", additional_kwargs={}, example=False)

In [ ]: