如何加载网页

本指南介绍了如何将加载网页到 LangChain Document 格式中，以便我们在下游使用。网页包含文本、图像和其他多媒体元素，通常以 HTML 表示。它们可能包含指向其他页面或资源的链接。

LangChain 集成了许多适用于网页的解析器。合适的解析器将取决于你的需求。下面我们演示两种可能性

简单快速的解析，其中我们为每个网页恢复一个 Document，其内容表示为“扁平化”的字符串；
高级解析，其中我们为每个页面恢复多个 Document 对象，从而可以识别和遍历 sections（章节）、链接、表格和其他结构。

设置

对于“简单快速”的解析，我们将需要 langchain-community 和 beautifulsoup4 库

%pip install -qU langchain-community beautifulsoup4

对于高级解析，我们将使用 langchain-unstructured

%pip install -qU langchain-unstructured

简单快速的文本提取

如果你正在寻找网页中嵌入文本的简单字符串表示形式，则以下方法是合适的。它将返回一个 Document 对象列表（每个页面一个），其中包含页面文本的单个字符串。在底层，它使用 beautifulsoup4 Python 库。

LangChain 文档加载器实现了 lazy_load 及其异步变体 alazy_load，它们返回 Document 对象的迭代器。我们将在下面使用这些。

import bs4
from langchain_community.document_loaders import WebBaseLoader

page_url = "https://python.langchain.ac.cn/docs/how_to/chatbots_memory/"

loader = WebBaseLoader(web_paths=[page_url])
docs = []
async for doc in loader.alazy_load():
    docs.append(doc)

assert len(docs) == 1
doc = docs[0]

API 参考:WebBaseLoader

USER_AGENT environment variable not set, consider setting it to identify your requests.

print(f"{doc.metadata}\n")
print(doc.page_content[:500].strip())

{'source': 'https://python.langchain.ac.cn/docs/how_to/chatbots_memory/', 'title': 'How to add memory to chatbots | \uf8ffü¶úÔ∏è\uf8ffüîó LangChain', 'description': 'A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:', 'language': 'en'}

How to add memory to chatbots | ü¶úÔ∏èüîó LangChain

Skip to main contentShare your thoughts on AI agents. Take the 3-min survey.IntegrationsAPI ReferenceMoreContributingPeopleLangSmithLangGraphLangChain HubLangChain JS/TSv0.3v0.3v0.2v0.1üí¨SearchIntroductionTutorialsBuild a Question Answering application over a Graph DatabaseTutorialsBuild a Simple LLM Application with LCELBuild a Query Analysis SystemBuild a ChatbotConversational RAGBuild an Extraction ChainBuild an AgentTaggingd

这本质上是页面 HTML 中的文本转储。它可能包含多余的信息，例如标题和导航栏。如果你熟悉预期的 HTML，你可以通过 BeautifulSoup 指定所需的 <div> 类和其他参数。下面我们仅解析文章的正文文本

loader = WebBaseLoader(
    web_paths=[page_url],
    bs_kwargs={
        "parse_only": bs4.SoupStrainer(class_="theme-doc-markdown markdown"),
    },
    bs_get_text_kwargs={"separator": " | ", "strip": True},
)

docs = []
async for doc in loader.alazy_load():
    docs.append(doc)

assert len(docs) == 1
doc = docs[0]

print(f"{doc.metadata}\n")
print(doc.page_content[:500])

{'source': 'https://python.langchain.ac.cn/docs/how_to/chatbots_memory/'}

How to add memory to chatbots | A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including: | Simply stuffing previous messages into a chat model prompt. | The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. | More complex modifications like synthesizing summaries for long running conversations. | We'll go into more detail on a few techniq

print(doc.page_content[-500:])

a greeting. Nemo then asks the AI how it is doing, and the AI responds that it is fine.'), | HumanMessage(content='What did I say my name was?'), | AIMessage(content='You introduced yourself as Nemo. How can I assist you today, Nemo?')] | Note that invoking the chain again will generate another summary generated from the initial summary plus new messages and so on. You could also design a hybrid approach where a certain number of messages are retained in chat history while others are summarized.

请注意，这需要预先了解正文文本如何在底层 HTML 中表示的技术知识。

我们可以使用各种设置参数化 WebBaseLoader，从而可以指定请求标头、速率限制以及 BeautifulSoup 的解析器和其他 kwargs。有关详细信息，请参阅其 API 参考。

高级解析

如果我们想要更精细地控制或处理页面内容，则此方法是合适的。下面，我们不是为每个页面生成一个 Document 并通过 BeautifulSoup 控制其内容，而是生成多个 Document 对象，这些对象表示页面上不同的结构。这些结构可以包括章节标题及其相应的正文文本、列表或枚举、表格等等。

在底层，它使用 langchain-unstructured 库。有关将 Unstructured 与 LangChain 一起使用的更多信息，请参阅集成文档。

from langchain_unstructured import UnstructuredLoader

page_url = "https://python.langchain.ac.cn/docs/how_to/chatbots_memory/"
loader = UnstructuredLoader(web_url=page_url)

docs = []
async for doc in loader.alazy_load():
    docs.append(doc)

API 参考:UnstructuredLoader

INFO: Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
INFO: NumExpr defaulting to 8 threads.

请注意，在不预先了解页面 HTML 结构的情况下，我们恢复了正文文本的自然组织结构

for doc in docs[:5]:
    print(doc.page_content)

How to add memory to chatbots
A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:
Simply stuffing previous messages into a chat model prompt.
The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.
More complex modifications like synthesizing summaries for long running conversations.
ERROR! Session/line number was not unique in database. History logging moved to new session 2747

从特定章节提取内容

每个 Document 对象代表页面的一个元素。其元数据包含有用的信息，例如其类别

for doc in docs[:5]:
    print(f'{doc.metadata["category"]}: {doc.page_content}')

Title: How to add memory to chatbots
NarrativeText: A key feature of chatbots is their ability to use content of previous conversation turns as context. This state management can take several forms, including:
ListItem: Simply stuffing previous messages into a chat model prompt.
ListItem: The above, but trimming old messages to reduce the amount of distracting information the model has to deal with.
ListItem: More complex modifications like synthesizing summaries for long running conversations.

元素也可能具有父子关系——例如，一个段落可能属于一个带有标题的章节。如果某个章节特别受关注（例如，用于索引），我们可以隔离相应的 Document 对象。

例如，下面我们加载两个网页“Setup”章节的内容

from typing import List

from langchain_core.documents import Document


async def _get_setup_docs_from_url(url: str) -> List[Document]:
    loader = UnstructuredLoader(web_url=url)

    setup_docs = []
    parent_id = -1
    async for doc in loader.alazy_load():
        if doc.metadata["category"] == "Title" and doc.page_content.startswith("Setup"):
            parent_id = doc.metadata["element_id"]
        if doc.metadata.get("parent_id") == parent_id:
            setup_docs.append(doc)

    return setup_docs


page_urls = [
    "https://python.langchain.ac.cn/docs/how_to/chatbots_memory/",
    "https://python.langchain.ac.cn/docs/how_to/chatbots_tools/",
]
setup_docs = []
for url in page_urls:
    page_setup_docs = await _get_setup_docs_from_url(url)
    setup_docs.extend(page_setup_docs)

API 参考:Document

from collections import defaultdict

setup_text = defaultdict(str)

for doc in setup_docs:
    url = doc.metadata["url"]
    setup_text[url] += f"{doc.page_content}\n"

dict(setup_text)

{'https://python.langchain.ac.cn/docs/how_to/chatbots_memory/': "You'll need to install a few packages, and have your OpenAI API key set as an environment variable named OPENAI_API_KEY:\n%pip install --upgrade --quiet langchain langchain-openai\n\n# Set env var OPENAI_API_KEY or load from a .env file:\nimport dotenv\n\ndotenv.load_dotenv()\n[33mWARNING: You are using pip version 22.0.4; however, version 23.3.2 is available.\nYou should consider upgrading via the '/Users/jacoblee/.pyenv/versions/3.10.5/bin/python -m pip install --upgrade pip' command.[0m[33m\n[0mNote: you may need to restart the kernel to use updated packages.\n",
 'https://python.langchain.ac.cn/docs/how_to/chatbots_tools/': "For this guide, we'll be using a tool calling agent with a single tool for searching the web. The default will be powered by Tavily, but you can switch it out for any similar tool. The rest of this section will assume you're using Tavily.\nYou'll need to sign up for an account on the Tavily website, and install the following packages:\n%pip install --upgrade --quiet langchain-community langchain-openai tavily-python\n\n# Set env var OPENAI_API_KEY or load from a .env file:\nimport dotenv\n\ndotenv.load_dotenv()\nYou will also need your OpenAI key set as OPENAI_API_KEY and your Tavily API key set as TAVILY_API_KEY.\n"}

网页内容向量搜索

一旦我们将页面内容加载到 LangChain Document 对象中，我们就可以像往常一样索引它们（例如，对于 RAG 应用）。下面我们使用 OpenAI 嵌入，尽管任何 LangChain 嵌入模型都足够了。

%pip install -qU langchain-openai

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

vector_store = InMemoryVectorStore.from_documents(setup_docs, OpenAIEmbeddings())
retrieved_docs = vector_store.similarity_search("Install Tavily", k=2)
for doc in retrieved_docs:
    print(f'Page {doc.metadata["url"]}: {doc.page_content[:300]}\n')

API 参考:InMemoryVectorStore | OpenAIEmbeddings

INFO: HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
INFO: HTTP Request: POST https://api.openai.com/v1/embeddings "HTTP/1.1 200 OK"
``````output
Page https://python.langchain.ac.cn/docs/how_to/chatbots_tools/: You'll need to sign up for an account on the Tavily website, and install the following packages:

Page https://python.langchain.ac.cn/docs/how_to/chatbots_tools/: For this guide, we'll be using a tool calling agent with a single tool for searching the web. The default will be powered by Tavily, but you can switch it out for any similar tool. The rest of this section will assume you're using Tavily.

其他网页加载器

有关可用 LangChain 网页加载器的列表，请参阅此表。

设置​

简单快速的文本提取​

高级解析​

从特定章节提取内容​

网页内容向量搜索​

其他网页加载器​

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设置

简单快速的文本提取

高级解析

从特定章节提取内容

网页内容向量搜索

其他网页加载器