从 RefineDocumentsChain 迁移

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

将文本拆分为更小的文档；
对第一个文档应用一个流程；
根据下一个文档优化或更新结果；
重复文档序列，直到完成。

在此上下文中常用的流程是摘要生成，即在处理长文本的片段时不断修改运行中的摘要。这对于与给定大型语言模型上下文窗口相比规模较大的文本尤其有用。

A LangGraph 实现为此问题带来了诸多优势：

其中RefineDocumentsChain通过类内部的for循环细化摘要，LangGraph 实现允许您逐步执行以监控或在需要时引导它。
LangGraph 实现支持执行步骤和单个 token 的流式传输。
由于它是从模块化组件组装而成的，因此也很容易扩展或修改（例如，以集成工具调用或其他行为）。

下面我们将通过一个简单示例，分别介绍 RefineDocumentsChain 和对应的 LangGraph 实现，以便说明。

让我们首先加载一个聊天模型：

选择聊天模型:

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.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

示例

让我们通过一个示例来了解如何对一系列文档进行总结。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 参考:文档

遗留

详细信息

下面展示了一个使用 RefineDocumentsChain 的实现。我们为初始摘要和后续优化定义提示模板，分别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,
)

API 参考:LLMChain |RefineDocumentsChain |聊天提示模板 |提示模板 |ChatOpenAI

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.'

The LangSmith 追踪由三个LLM调用组成：一个用于初始摘要，另外两个用于更新该摘要。当我们用最终文档的内容更新摘要时，该过程完成。

LangGraph

详细信息

下面我们将展示该过程的 LangGraph 实现：

我们使用与之前相同的两个模板。
我们生成一个简单的链，用于初始摘要，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，它对每个连续文档进行操作，优化初始摘要。

我们将需要安装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())

我们可以逐步执行，并在精炼过程中打印摘要：

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.

在 LangSmith 追踪中，我们再次恢复了三个 LLM 调用，执行了与之前相同的功能。

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

查看此教程了解更多基于大语言模型的摘要策略。

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

示例​

遗留​

LangGraph​

下一步​

示例

遗留

LangGraph

下一步