从 MapRerankDocumentsChain 迁移

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

将文本分割成更小的文档；
对文档集应用一个处理过程，该过程包括生成一个分数；
按分数对结果进行排序并返回最大值。

在这种场景中，一个常见的处理过程是使用文档中的上下文片段进行问题回答。强制模型在生成答案的同时生成分数有助于仅选择由相关上下文生成的答案。

LangGraph 实现允许在此问题中结合工具调用及其他功能。下面，我们将通过一个简单的示例，分别介绍 MapRerankDocumentsChain 和相应的 LangGraph 实现，以作说明。

示例

让我们通过一个分析文档集的示例。我们将使用以下 3 个文档：

from langchain_core.documents import Document

documents = [
    Document(page_content="Alice has blue eyes", metadata={"title": "book_chapter_2"}),
    Document(page_content="Bob has brown eyes", metadata={"title": "book_chapter_1"}),
    Document(
        page_content="Charlie has green eyes", metadata={"title": "book_chapter_3"}
    ),
]

API Reference:Document

旧版

Details

下面我们展示了使用 MapRerankDocumentsChain 的实现。我们为问答任务定义了提示模板，并为此实例化一个 LLMChain 对象。我们定义了文档如何格式化到提示中，并确保各种提示中的键保持一致。

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()
# The prompt here should take as an input variable the
# `document_variable_name`
# The actual prompt will need to be a lot more complex, this is just
# an example.
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",
)

API Reference:LLMChain | MapRerankDocumentsChain | RegexParser | PromptTemplate | OpenAI

response = chain.invoke(documents)
response["output_text"]

/langchain/libs/langchain/langchain/chains/llm.py:369: UserWarning: The apply_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
  warnings.warn(

'Brown'

检查上面运行的 LangSmith trace，我们可以看到三个 LLM 调用——每个文档一个——并且评分机制可以防止幻觉。

LangGraph

Details

下面我们展示了此过程的 LangGraph 实现。请注意，我们的模板是简化的，因为我们通过 .with_structured_output 方法将格式化指令委托给聊天模型的工具调用功能。

这里我们遵循基本的 map-reduce 工作流来并行执行 LLM 调用。

我们将需要安装 langgraph：

pip install -qU langgraph

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)

# The below chain formats context from a document into a prompt, then
# generates a response structured according to the AnswerWithScore schema.
map_chain = prompt | llm.with_structured_output(AnswerWithScore)

# Below we define the components that will make up the graph


# This will be the overall state of the graph.
# It will contain the input document contents, corresponding
# answers with scores, and a final answer.
class State(TypedDict):
    contents: List[str]
    answers_with_scores: Annotated[list, operator.add]
    answer: str


# This will be the state of the node that we will "map" all
# documents to in order to generate answers with scores
class MapState(TypedDict):
    content: str


# Here we define the logic to map out over the documents
# We will use this an edge in the graph
def map_analyses(state: State):
    # We will return a list of `Send` objects
    # Each `Send` object consists of the name of a node in the graph
    # as well as the state to send to that node
    return [
        Send("generate_analysis", {"content": content}) for content in state["contents"]
    ]


# Here we generate an answer with score, given a document
async def generate_analysis(state: MapState):
    response = await map_chain.ainvoke(state["content"])
    return {"answers_with_scores": [response]}


# Here we will select the top answer
def pick_top_ranked(state: State):
    ranked_answers = sorted(
        state["answers_with_scores"], key=lambda x: -int(x["score"])
    )
    return {"answer": ranked_answers[0]}


# Construct the graph: here we put everything together to construct our graph
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()

API Reference:ChatPromptTemplate | ChatOpenAI | Send | StateGraph

from IPython.display import Image

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

result = await app.ainvoke({"contents": [doc.page_content for doc in documents]})
result["answer"]

{'answer': 'Bob has brown eyes.', 'score': 10}

检查上面运行的 LangSmith 跟踪，我们可以看到像之前一样的三个 LLM 调用。使用模型的工具调用功能也使我们能够移除解析步骤。

后续步骤

参阅这些分步指南以了解更多关于 RAG 的问答任务。

查阅 LangGraph 文档以详细了解如何使用 LangGraph 进行构建，包括本指南中关于 LangGraph 中 map-reduce 细节的内容。

示例​

旧版​

LangGraph​

后续步骤​

示例

旧版

LangGraph

后续步骤