如何加载网页

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

LangChain 与许多适用于网页的解析器集成。正确的解析器将取决于您的需求。下面我们演示了两种可能性：

简单快速的解析，其中我们恢复了一个Document每个网页，其内容表示为“扁平化”字符串;
高级解析，其中我们恢复多个Documentobjects 的 per page 中，允许识别和遍历部分、链接、表格和其他结构。

设置

对于“简单快速”的解析，我们需要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

如果要查找嵌入在网页中的文本的简单字符串表示形式，则以下方法很合适。它将返回一个Document对象 -- 每页一个 -- 包含页面文本的单个字符串。在后台，它使用beautifulsoup4Python 库。

LangChain 文档加载器实现lazy_load及其异步变体alazy_load，它们返回Document objects.我们将在下面使用这些。

import bs4
from langchain_community.document_loaders import WebBaseLoader

page_url = "https://python.langchain.com/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.com/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

这实质上是页面 HTML 中的文本转储。它可能包含无关信息，如标题和导航栏。如果您熟悉预期的 HTML，则可以指定所需的<div>类和其他参数。下面我们只解析文章的正文：

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.com/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，这需要有关正文文本在底层 HTML 中的表示方式的高级技术知识。

我们可以参数化WebBaseLoader具有多种设置，允许为 BeautifulSoup 指定请求标头、速率限制、解析器和其他 kwargs。有关详细信息，请参阅其 API 参考。

高级解析

如果我们希望对页面内容进行更精细的控制或处理，则此方法是合适的。，而不是生成一个Document并通过 BeautifulSoup 控制其内容，我们生成多个Document表示页面上不同结构的对象。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。

在后台，它使用langchain-unstructured库。有关将 Unstructured 与 LangChain 结合使用的更多信息，请参阅集成文档。

from langchain_unstructured import UnstructuredLoader

page_url = "https://python.langchain.com/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，在没有事先了解页面 HTML 结构的情况下，我们恢复了正文文本的自然组织：

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

从特定部分提取内容

每Documentobject 表示页面的一个元素。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，段落可能属于具有标题的章节。如果某个部分特别感兴趣（例如，用于索引），我们可以分离出相应的Document对象。

例如，下面我们加载两个网页的 “Setup” 部分的内容：

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.com/docs/how_to/chatbots_memory/",
    "https://python.langchain.com/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：文档

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.com/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.com/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"}

对页面内容进行矢量搜索

一旦我们将页面内容加载到 LangChain 中Document对象，我们可以像通常的方式为它们建立索引（例如，对于 RAG 应用程序）。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 | OpenAI 嵌入

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.com/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.com/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.

其他网页加载程序

有关可用的 LangChain 网页加载器的列表，请参阅此表。