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Refine：类似于Map-Reduce，速度慢于MR。文档链通过循环遍历输入文档并逐步更新其答案来构建响应。对于每个文档，它将当前文档和最新的中间答案传递给LLM链，以获得新的答案。

初始化

import osfrom langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.summarize import load_summarize_chain
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain_text_splitters import CharacterTextSplitteros.environ['http_proxy'] = '127.0.0.1:7890'
os.environ['https_proxy'] = '127.0.0.1:7890'os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "LangchainDemo"
os.environ["LANGCHAIN_API_KEY"] = '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'
# os.environ["TAVILY_API_KEY"] = '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'# 创建模型
model = ChatOpenAI(model='gpt-3.5-turbo', temperature=0)# 加载我们的文档。我们将使用 WebBaseLoader 来加载博客文章：
loader = WebBaseLoader('https://lilianweng.github.io/posts/2023-06-23-agent/')
docs = loader.load()  # 得到整篇文章# 第三种： Refine
'''
Refine: RefineDocumentsChain 类似于map-reduce：
文档链通过循环遍历输入文档并逐步更新其答案来构建响应。对于每个文档，它将当前文档和最新的中间答案传递给LLM链，以获得新的答案。
'''
# 第一步： 切割阶段
# 每一个小docs为1000个token
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=1000, chunk_overlap=0)
split_docs = text_splitter.split_documents(docs)

chain refine

# 指定chain_type为： refine
chain = load_summarize_chain(model, chain_type='refine')result = chain.invoke(split_docs)
print(result['output_text'])

结合提示词

# 定义提示
prompt_template = """针对下面的内容，写一个简洁的总结摘要:
"{text}"
简洁的总结摘要:"""
prompt = PromptTemplate.from_template(prompt_template)refine_template = ("Your job is to produce a final summary\n""We have provided an existing summary up to a certain point: {existing_answer}\n""We have the opportunity to refine the existing summary""(only if needed) with some more context below.\n""------------\n""{text}\n""------------\n""\n""Given the new context, refine the original summary in Chinese""If the context isn't useful, return the original summary."
)# refine_template = (
#     "你的工作是做出一个最终的总结摘要。\n"
#     "我们提供了一个到某个点的现有摘要:{existing_answer}\n"
#     "我们有机会完善现有的摘要，基于下面更多的文本内容\n"
#     "------------\n"
#     "{text}\n"
#     "------------\n"
# )
refine_prompt = PromptTemplate.from_template(refine_template)chain = load_summarize_chain(llm=model,chain_type="refine",question_prompt=prompt,refine_prompt=refine_prompt,return_intermediate_steps=False,input_key="input_documents",output_key="output_text",
)text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=1000, chunk_overlap=0
)
split_docs = text_splitter.split_documents(docs)
result = chain.invoke({"input_documents": split_docs}, return_only_outputs=True)print(result["output_text"])