LangChain

转换链#

本笔记展示了使用通用转换链。

以一个示例为例，我们将创建一个虚拟的转换，将一个超长文本过滤为仅前3段，并将其传递到LLMChain以对其进行摘要。

from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()

def transform_func(inputs: dict) -> dict:
    text = inputs["text"]
    shortened_text = "  ".join(text.split("  ")[:3])
    return {"output_text": shortened_text}
 
transform_chain = TransformChain(input_variables=["text"], output_variables=["output_text"], transform=transform_func)

template = """Summarize this text:
 
{output_text}
 
Summary:"""
prompt = PromptTemplate(input_variables=["output_text"], template=template)
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)

sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])

sequential_chain.run(state_of_the_union)

' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'

序列化（Serialization）Custom Chain