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自定义回调处理程序

您还可以创建一个自定义处理程序并将其设置在对象上。在下面的示例中，我们将使用自定义处理程序实现流式处理。

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}")  

# 要启用流式处理，我们在ChatModel构造函数中传入`streaming=True`  
# 此外，我们还传入一个包含自定义处理程序的列表  
chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[MyCustomHandler()])  

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

API 参考:​

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