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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;
  • 对第一个文档应用一个过程;
  • 根据下一个文档优化或更新结果;
  • 重复遍历文档序列直到完成。

在这种情况下应用的一个常见过程是摘要,即在处理长文本的各个部分时,逐步修改运行中的摘要。这对于那些相对于给定LLM的上下文窗口来说较大的文本特别有用。

一个LangGraph实现为这个问题带来了许多优势:

  • RefineDocumentsChain通过类内部的for循环优化摘要的地方,LangGraph实现允许您逐步执行以监控或在需要时进行引导。
  • LangGraph 实现支持执行步骤和单个令牌的流式传输。
  • 由于它是由模块化组件组装而成,因此扩展或修改也很简单(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例子来说明RefineDocumentsChain和相应的LangGraph实现,以便更好地理解。

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

让我们通过一个例子来总结一系列文档。我们首先生成一些简单的文档用于说明目的:

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 Reference:Document

遗留问题

Details

下面我们展示了一个使用RefineDocumentsChain的实现。我们为初始摘要和连续细化定义了提示模板,为这两个目的实例化了单独的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 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。当我们使用最终文档的内容更新摘要时,该过程完成。

LangGraph

Details

下面我们展示这个过程的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进行推理。
  • 我们生成第二个refine_summary_chain,它在每个后续文档上操作,细化初始摘要。

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())

我们可以按照以下步骤逐步执行,并在优化时打印出摘要:

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中流式传输令牌,包括来自中间步骤的令牌:

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

请参阅本教程以获取更多基于LLM的摘要策略。

查看LangGraph文档以获取使用LangGraph构建的详细信息。


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