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从RefineDocumentsChain迁移

RefineDocumentsChain 实现了分析长文本的策略。该策略如下

  • 将文本拆分为更小的文档;
  • 对第一个文档应用一个处理过程;
  • 根据下一个文档精炼或更新结果;
  • 重复处理文档序列直到完成。

在此上下文中,常用的处理过程是总结,其中在处理长文本的各个块时,会修改运行中的摘要。这对于相对于给定 LLM 的上下文窗口来说较大的文本特别有用。

一个 LangGraph 实现为此问题带来了许多优势

  • RefineDocumentsChain 通过类内部的 `for` 循环精炼摘要,而 LangGraph 实现则允许您逐步执行以在需要时监控或引导它。
  • LangGraph 实现支持执行步骤和单个 token 的流式传输。
  • 由于它由模块化组件组装而成,因此也易于扩展或修改(例如,集成工具调用或其他行为)。

下面我们将通过一个简单的示例来演示 `RefineDocumentsChain` 及其对应的 LangGraph 实现,以作说明。

我们首先加载一个聊天模型

pip install -qU "langchain[google-genai]"
import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-2.0-flash", model_provider="google_genai")

示例

我们来看一个总结文档序列的例子。我们首先生成一些简单的文档以作说明

from langchain_core.documents import Document

documents = [
Document(page_content="Apples are red", metadata={"title": "apple_book"}),
Document(page_content="Blueberries are blue", metadata={"title": "blueberry_book"}),
Document(page_content="Bananas are yelow", metadata={"title": "banana_book"}),
]
API 参考:Document

旧版

详情

下面我们展示一个使用 `RefineDocumentsChain` 的实现。我们为初始总结和后续精炼定义了提示模板,为此两个目的实例化了独立的 LLMChain 对象,并使用这些组件实例化了 `RefineDocumentsChain`。

from langchain.chains import LLMChain, RefineDocumentsChain
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain_openai import ChatOpenAI

# This controls how each document will be formatted. Specifically,
# it will be passed to `format_document` - see that function for more
# details.
document_prompt = PromptTemplate(
input_variables=["page_content"], template="{page_content}"
)
document_variable_name = "context"
# The prompt here should take as an input variable the
# `document_variable_name`
summarize_prompt = ChatPromptTemplate(
[
("human", "Write a concise summary of the following: {context}"),
]
)
initial_llm_chain = LLMChain(llm=llm, prompt=summarize_prompt)
initial_response_name = "existing_answer"
# The prompt here should take as an input variable the
# `document_variable_name` as well as `initial_response_name`
refine_template = """
Produce a final summary.

Existing summary up to this point:
{existing_answer}

New context:
------------
{context}
------------

Given the new context, refine the original summary.
"""
refine_prompt = ChatPromptTemplate([("human", refine_template)])
refine_llm_chain = LLMChain(llm=llm, prompt=refine_prompt)
chain = RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
initial_response_name=initial_response_name,
)

我们现在可以调用我们的链

result = chain.invoke(documents)
result["output_text"]
'Apples are typically red in color, blueberries are blue, and bananas are yellow.'

LangSmith 追踪 由三次 LLM 调用组成:一次用于初始总结,另外两次用于更新该总结。当我们用最后一个文档的内容更新总结时,该过程完成。

LangGraph

详情

下面我们展示该过程的 LangGraph 实现

  • 我们使用与之前相同的两个模板。
  • 我们为初始总结生成一个简单的链,该链提取第一个文档,将其格式化为提示,并使用我们的 LLM 运行推理。
  • 我们生成第二个 `refine_summary_chain`,它对每个后续文档进行操作,精炼初始总结。

我们需要安装 langgraph

pip install -qU langgraph
import operator
from typing import List, Literal, TypedDict

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig
from langchain_openai import ChatOpenAI
from langgraph.constants import Send
from langgraph.graph import END, START, StateGraph

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

# Initial summary
summarize_prompt = ChatPromptTemplate(
[
("human", "Write a concise summary of the following: {context}"),
]
)
initial_summary_chain = summarize_prompt | llm | StrOutputParser()

# Refining the summary with new docs
refine_template = """
Produce a final summary.

Existing summary up to this point:
{existing_answer}

New context:
------------
{context}
------------

Given the new context, refine the original summary.
"""
refine_prompt = ChatPromptTemplate([("human", refine_template)])

refine_summary_chain = refine_prompt | llm | StrOutputParser()


# For LangGraph, we will define the state of the graph to hold the query,
# destination, and final answer.
class State(TypedDict):
contents: List[str]
index: int
summary: str


# We define functions for each node, including a node that generates
# the initial summary:
async def generate_initial_summary(state: State, config: RunnableConfig):
summary = await initial_summary_chain.ainvoke(
state["contents"][0],
config,
)
return {"summary": summary, "index": 1}


# And a node that refines the summary based on the next document
async def refine_summary(state: State, config: RunnableConfig):
content = state["contents"][state["index"]]
summary = await refine_summary_chain.ainvoke(
{"existing_answer": state["summary"], "context": content},
config,
)

return {"summary": summary, "index": state["index"] + 1}


# Here we implement logic to either exit the application or refine
# the summary.
def should_refine(state: State) -> Literal["refine_summary", END]:
if state["index"] >= len(state["contents"]):
return END
else:
return "refine_summary"


graph = StateGraph(State)
graph.add_node("generate_initial_summary", generate_initial_summary)
graph.add_node("refine_summary", refine_summary)

graph.add_edge(START, "generate_initial_summary")
graph.add_conditional_edges("generate_initial_summary", should_refine)
graph.add_conditional_edges("refine_summary", should_refine)
app = graph.compile()
from IPython.display import Image

Image(app.get_graph().draw_mermaid_png())

我们可以按如下方式逐步执行,并在精炼过程中打印出总结

async for step in app.astream(
{"contents": [doc.page_content for doc in documents]},
stream_mode="values",
):
if summary := step.get("summary"):
print(summary)
Apples are typically red in color.
Apples are typically red in color, while blueberries are blue.
Apples are typically red in color, blueberries are blue, and bananas are yellow.

LangSmith 追踪 中,我们再次获得了三次 LLM 调用,执行与之前相同的功能。

请注意,我们可以从应用程序流式传输 token,包括从中间步骤

async for event in app.astream_events(
{"contents": [doc.page_content for doc in documents]}, version="v2"
):
kind = event["event"]
if kind == "on_chat_model_stream":
content = event["data"]["chunk"].content
if content:
print(content, end="|")
elif kind == "on_chat_model_end":
print("\n\n")
Ap|ples| are| characterized| by| their| red| color|.|


Ap|ples| are| characterized| by| their| red| color|,| while| blueberries| are| known| for| their| blue| hue|.|


Ap|ples| are| characterized| by| their| red| color|,| blueberries| are| known| for| their| blue| hue|,| and| bananas| are| recognized| for| their| yellow| color|.|

下一步

有关更多基于 LLM 的总结策略,请参阅本教程

查看 LangGraph 文档了解使用 LangGraph 构建的详细信息。