如何加载网页
本指南介绍如何将网页加载到 LangChain Document 格式中，以便在后续流程中使用。网页包含文本、图像和其他多媒体元素，通常以 HTML 形式表示。它们可能包含指向其他页面或资源的链接。
LangChain 集成了多种适用于网页的解析器。合适的解析器取决于您的具体需求。下面我们将演示两种可能性：
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
如果您正在寻找嵌入在网页中的文本的简单字符串表示，以下方法适用。它将返回一个包含 Document 个对象的列表——每页一个对象——其中包含该页面文本的单个字符串。其底层使用的是 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 objects 的迭代器。我们将在下面使用这些内容。
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∏èüîó LangChain
Skip to main contentShare your thoughts on AI agents. Take the 3-min survey.IntegrationsAPI ReferenceMoreContributingPeopleLangSmithLangGraphLangChain HubLangChain JS/TSv0.3v0.3v0.2v0.1üí¨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
这本质上是从页面 HTML 中提取的文本内容。它可能包含多余的資訊，如标题和导航栏。如果您熟悉预期的 HTML 结构，可以通过 BeautifulSoup 指定所需的 <div> 类名及其他参数。下面我们仅解析文章的正文部分：
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，这需要您具备关于底层 HTML 中正文文本表示方式的先进技术知识。
我们可以使用多种设置来参数化 WebBaseLoader，从而指定请求头、速率限制器、解析器以及 BeautifulSoup 的其他关键字参数。详细信息请参阅其 API 参考。
高级解析
如果我们需要对页面内容进行更细粒度的控制或处理，这种方法较为合适。下面，我们不再为每页生成一个Document并通过BeautifulSoup控制其内容，而是生成多个Document对象，用以表示页面上不同的结构。这些结构可以包括章节标题及其对应的正文文本、列表或枚举、表格等更多内容。
底层它使用了 langchain-unstructured 库。有关使用 Unstructured 与 LangChain 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:非结构化加载器
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，在没有关于页面HTML结构的前置知识的情况下，我们恢复了正文的自然组织：
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
每个 Document 对象代表页面的一个元素。其元数据包含有用信息，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.
元素之间也可能存在父子关系——例如，一个段落可能属于带有标题的章节。如果某个章节特别重要（例如用于索引），我们可以隔离对应的 Document 对象。
作为示例，下面我们加载两个网页的“设置”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"}
页面内容的向量搜索
一旦我们将页面内容加载到 LangChain Document 对象中，我们就可以以通常的方式对它们进行索引（例如，用于 RAG 应用）。下面我们使用 OpenAI 嵌入模型，不过任何 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')
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.
其他网页加载器
对于可用的 LangChain 网页加载器列表，请参阅 此表格。