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从 RefineDocumentsChain 迁移

RefineDocumentsChain 实现了一种分析长文本的策略。该策略如下:

  • 将文本分割成较小的文档;
  • 将一个过程应用于第一个文档;
  • 根据下一个文档改进或更新结果;
  • 重复执行文档序列,直到完成。

在此上下文中应用的一个常见过程是摘要,其中在处理长文本块时修改运行摘要。这对于与给定 LLM 的上下文窗口相比很大的文本特别有用。

LangGraph 实现为这个问题带来了一些优势:

  • RefineDocumentsChain 通过类中的 for 循环改进摘要,而 LangGraph 实现允许您逐步执行,以便在需要时进行监控或控制。
  • LangGraph 实现支持执行步骤和单个令牌的流式传输。
  • 因为它是由模块化组件组装而成,因此也很容易扩展或修改(例如,合并工具调用或其他行为)。

下面我们将通过一个简单的示例来说明 RefineDocumentsChain 和相应的 LangGraph 实现。

让我们首先加载一个聊天模型

pip install -qU langchain-openai
import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import ChatOpenAI

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

示例

让我们来看一个总结一系列文档的示例。首先,为了说明目的,我们生成一些简单的文档。

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:文档 (Document)

旧版

详情

下面展示了使用 RefineDocumentsChain 的实现。我们为初始摘要和后续改进定义提示模板,为这两个目的实例化单独的 LLMChain 对象,并使用这些组件实例化 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了

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

LangSmith 跟踪由三个 LLM 调用组成:一个用于初始摘要,另外两个用于更新该摘要。当我们使用最后一个文档的内容更新摘要时,该过程完成。

LangGraph

详情

下面展示了此过程的 LangGraph 实现

  • 我们使用与之前相同的两个模板。
  • 我们为初始摘要生成一个简单的链,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,它在每个后续文档上运行,从而完善初始摘要。

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())

我们可以按如下步骤执行,并打印出摘要的改进过程

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 跟踪中,我们再次恢复了三个 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(包括中间步骤)流式传输令牌。

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 的摘要策略,请参阅本教程

请查看 LangGraph 文档,了解有关使用 LangGraph 构建的详细信息。


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