AI大模型应用开发入门-LangChain实现文档总结

一、整体思路

长网页文本往往超过 LLM 单次处理的 token 限制，我们需要设计一个 map-reduce 流水线来拆分、局部总结、归并：

1. 加载网页内容

2. 拆分成可控大小的 chunk

3. 对每个 chunk 做初步总结 (map)

4. 汇总所有初步总结 (reduce)

5. 如有需要递归 reduce 直到满足 token 限制

6. 输出最终总结

接下来我们用代码实现！

二、准备工作

1. 初始化 LLM

首先我们通过 init_chat_model 加载 LLM：

# llm_env.py
from langchain.chat_models import init_chat_model

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

三、主程序 main.py

1. 导入依赖 & 初始化

import os
import sys

sys.path.append(os.getcwd())

from langchain_community.document_loaders import WebBaseLoader
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains.llm import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_text_splitters import CharacterTextSplitter
import operator
from typing import Annotated, List, Literal, TypedDict
from langchain.chains.combine_documents.reduce import collapse_docs, split_list_of_docs
from langchain_core.documents import Document
from langgraph.constants import Send
from langgraph.graph import END, START, StateGraph

from llm_set import llm_env

llm = llm_env.llm

2. 加载网页

loader = WebBaseLoader("https://en.wikipedia.org/wiki/Artificial_intelligence")
docs = loader.load()

通过 WebBaseLoader 可以轻松加载网页文本到 docs 列表中。

3. 定义 Prompt 模板

- Map 阶段 Prompt

map_prompt = ChatPromptTemplate.from_messages(
    [("system", "Write a concise summary of the following: \\n\\n{context}")]
)

- Reduce 阶段 Prompt

reduce_template = """
The following is a set of summaries:
{docs}
Take these and distill it into a final, consolidated summary
of the main themes.
"""

reduce_prompt = ChatPromptTemplate([("human", reduce_template)])

4. 拆分文档 chunk

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
split_docs = text_splitter.split_documents(docs)
print(f"Split into {len(split_docs)} chunks")

将网页内容拆分成多个 chunk，chunk 大小设置 1000 tokens，便于单次处理。

5. 定义 Token 长度计算

token_max = 1000

def length_function(documents: List[Document]) -> int:
    return sum(llm.get_num_tokens(d.page_content) for d in documents)

计算输入文档 token 总量，用于判断是否需要继续 collapse。

6. 定义状态

主状态：

class OverallState(TypedDict):
    contents: List[str]
    summaries: Annotated[list, operator.add]
    collapsed_summaries: List[Document]
    final_summary: str

Map 阶段状态：

class SummaryState(TypedDict):
    content: str

7. 生成初步 summary (Map 阶段)

def generate_summary(state: SummaryState):
    prompt = map_prompt.invoke(state["content"])
    response = llm.invoke(prompt)
    return {"summaries": [response.content]}

8. Map 调度逻辑

def map_summaries(state: OverallState):
    return [
        Send("generate_summary", {"content": content}) for content in state["contents"]
    ]

9. 收集 summary

def collect_summaries(state: OverallState):
    return {
        "collapsed_summaries": [Document(summary) for summary in state["summaries"]]
    }

10. Reduce 逻辑

- 内部 reduce 函数

def _reduce(input: dict) -> str:
    prompt = reduce_prompt.invoke(input)
    response = llm.invoke(prompt)
    return response.content

- Collapse summaries

def collapse_summaries(state: OverallState):
    docs_lists = split_list_of_docs(
        state["collapsed_summaries"],
        length_function,
        token_max,
    )

    results = []
    for doc_list in docs_lists:
        combined = collapse_docs(doc_list, _reduce)
        results.append(combined)

    return {"collapsed_summaries": results}

11. 是否继续 collapse

def should_collapse(state: OverallState):
    num_tokens = length_function(state["collapsed_summaries"])
    if num_tokens > token_max:
        return "collapse_summaries"
    else:
        return "generate_final_summary"

12. 生成最终 summary

def generate_final_summary(state: OverallState):
    response = _reduce(state["collapsed_summaries"])
    return {"final_summary": response}

四、构建流程图 (StateGraph)

graph = StateGraph(OverallState)

graph.add_node("generate_summary", generate_summary)
graph.add_node("collect_summaries", collect_summaries)
graph.add_node("collapse_summaries", collapse_summaries)
graph.add_node("generate_final_summary", generate_final_summary)

graph.add_conditional_edges(START, map_summaries, ["generate_summary"])
graph.add_edge("generate_summary", "collect_summaries")
graph.add_conditional_edges("collect_summaries", should_collapse)
graph.add_conditional_edges("collapse_summaries", should_collapse)
graph.add_edge("generate_final_summary", END)

app = graph.compile()

五、执行总结流程

for step in app.stream(
    {"contents": [doc.page_content for doc in split_docs]},
    {"recursion_limit": 10},
):
    print(list(step.keys()))

通过 .stream() 启动整个流水线，传入切片后的 contents，流式输出每步结果，直到最终汇总完成。

六、总结

通过这个示例，你可以看到：

使用 LangChain + LLM 轻松实现 网页总结

设计了 自动 map-reduce 流程，支持长文本拆分和递归 reduce

通过 StateGraph 灵活编排流程、