> For the complete documentation index, see [llms.txt](https://developers.oxylabs.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developers.oxylabs.io/integrations/cn/wang-ye-pa-chong-api-ji-cheng/langchain.md).

# LangChain

该 **LangChain** 与 [**Oxylabs 网页爬虫API**](https://oxylabs.io/products/scraper-api/web) 的集成使您能够在同一工作流中通过 LLM（大型语言模型）收集和处理网页数据。

## 概述

**LangChain** 是一个用于构建将 LLM 与工具、API 和网页数据结合使用的应用框架。它同时支持 Python 和 JavaScript。将其与 [**Oxylabs 网页爬虫API** ](http://developers.oxylabs.io/scraper-apis/web-scraper-api?_gl=1*1ljhay3*_gcl_aw*R0NMLjE3NDYxODM0ODcuQ2owS0NRancydEhBQmhDaUFSSXNBTlp6RFdvSXlSNVg3blQtd0ZEakxHOUlvdUhyQmtoRTRCeUNwc054dFJVMmh0Z3dZTTR3Nm90SjVKOGFBbHhhRUFMd193Y0I.*_gcl_au*MjU4NDEzMTkwLjE3NDExNzU2MzI.)一起使用，以便：

* 抓取结构化数据，而无需处理 CAPTCHA、IP 封锁或 JS 渲染
* 在同一管道中使用 LLM 处理结果
* 构建从提取到 AI 驱动输出的端到端工作流

## 快速开始

**创建您的 API 用户凭据**：注册免费试用或在 [**Oxylabs 控制面板**](https://dashboard.oxylabs.io/en/registration) 中购买产品，以创建您的 API 用户凭据（`USERNAME` 和 `PASSWORD`).

{% hint style="warning" %}
如果您的账户需要多个 API 用户，请联系我们的 [**客户支持**](mailto:support@oxylabs.io) 或给我们的 24/7 在线聊天支持发送消息。
{% endhint %}

在本指南中，我们将使用 Python 编程语言。使用 pip 安装所需库：

```bash
pip install -qU langchain-oxylabs langchain-openai langgraph requests python-dotenv
```

## 环境设置

创建一个 `.env` 文件到您的项目目录中，并填入您的 Oxylabs API 用户和 OpenAI 凭据：

```
OXYLABS_USERNAME=your-username
OXYLABS_PASSWORD=your-password
OPENAI_API_KEY=your-openai-key
```

在您的 Python 脚本中加载这些环境变量：

```python
import os
from dotenv import load_dotenv

load_dotenv()
```

## 集成方式

将 Oxylabs 网页爬虫API 与 LangChain 集成主要有两种方式：

### 使用 langchain-oxylabs 包

对于 Google 搜索查询，请使用专用的 [`langchain-oxylabs`](https://python.langchain.com/docs/integrations/tools/oxylabs/) 包，它提供了开箱即用的集成：

```python
import os
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent
from langchain_oxylabs import OxylabsSearchAPIWrapper, OxylabsSearchRun

load_dotenv()

# 初始化您首选的 LLM 模型
llm = init_chat_model(
    "gpt-4o-mini",
    model_provider="openai",
    api_key=os.getenv("OPENAI_API_KEY")
)

# 初始化 Google 搜索工具
search = OxylabsSearchRun(
    wrapper=OxylabsSearchAPIWrapper(
        oxylabs_username=os.getenv("OXYLABS_USERNAME"),
        oxylabs_password=os.getenv("OXYLABS_PASSWORD")
    )
)

# 创建一个使用 Google 搜索工具的代理
agent = create_react_agent(llm, [search])

# 使用示例
user_input = "When and why did the Maya civilization collapse?"
response = agent.invoke({"messages": user_input})
print(response["messages"][-1].content)
```

### 使用网页爬虫API 

对于访问 Google 搜索之外的其他网站，您可以直接向网页爬虫API 发送请求：

```python
import os
import requests
from dotenv import load_dotenv
from langchain_openai import OpenAI
from langchain_core.prompts import PromptTemplate

load_dotenv()

def scrape_website(url):
    """使用 Oxylabs 网页爬虫API 抓取网站"""
    payload = {
        "source": "universal",
        "url": url,
        "parse": True
    }
    response = requests.post(
        "https://realtime.oxylabs.io/v1/queries",
        auth=(os.getenv("OXYLABS_USERNAME"), os.getenv("OXYLABS_PASSWORD")),
        json=payload
    )
    
    if response.status_code == 200:
        data = response.json()
        content = data["results"][0]["content"]
        return str(content)
    else:
        print(f"Failed to scrape website: {response.text}")
        return None

def process_content(content):
    """使用 LangChain 处理抓取的内容"""
    if not content:
        print("No content to process.")
        return None
        
    prompt = PromptTemplate.from_template(
        "Analyze the following website content and summarize key points: {content}"
    )
    chain = prompt | OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
    result = chain.invoke({"content": content})
    return result

def main(url):
    print("Scraping website...")
    scraped_content = scrape_website(url)
    if scraped_content:
        print("Processing scraped content with LangChain...")
        analysis = process_content(scraped_content)
        print("\nProcessed Analysis:\n", analysis)
    else:
        print("No content scraped.")

if __name__ == "__main__":
    url = "https://sandbox.oxylabs.io/products/1"
    main(url)
```

## 特定目标抓取器

Oxylabs 提供 [**专用抓取器**](/api-targets/cn/ren-yi-yu-ming.md) 用于各种热门网站。以下是一些可用源的示例：

| 网站      | source 参数        | 必需参数                   |
| ------- | ---------------- | ---------------------- |
| Google  | `google_search`  | `query`                |
| Amazon  | `amazon_search`  | `query`, `domain` （可选） |
| Walmart | `walmart_search` | `query`                |
| Target  | `target_search`  | `query`                |
| Kroger  | `kroger_search`  | `query`, `store_id`    |
| Staples | `staples_search` | `query`                |

要使用特定抓取器，请修改 `scrape_website` 函数中的 payload：

```python
# Amazon 搜索示例
payload = {
    "source": "amazon_search",
    "query": "smartphone",
    "domain": "com",
    "parse": True
}
```

## 高级配置

### 处理动态内容

网页爬虫API 可以通过添加 [**JavaScript 渲染**](/products/cn/web-scraper-api/features/js-rendering-and-browser-control.md) 来处理 `render` 参数：

```python
payload = {
    "source": "universal",
    "url": url,
    "parse": True,
    "render": "html"
}
```

### 设置用户代理类型

您可以指定不同的 [**用户代理**](/products/cn/web-scraper-api/features/http-context-and-job-management/user-agent-type.md) 以模拟不同设备：

```python
payload = {
    "source": "universal",
    "url": url,
    "parse": True,
    "render": "html",
    "user_agent_type": "mobile"
}
```

### 使用特定目标参数

许多 [**特定目标抓取器**](/api-targets/cn/ren-yi-yu-ming.md) 支持其他参数：

```python
# 带位置参数的 Kroger 示例
payload = {
    "source": "kroger_search",
    "query": "organic milk",
    "store_id": "01100002",
    "fulfillment_type": "pickup"
}
```

## 错误处理

为生产应用实现适当的错误处理：

```python
try:
    response = requests.post(
        "https://realtime.oxylabs.io/v1/queries",
        auth=(os.getenv("OXYLABS_USERNAME"), os.getenv("OXYLABS_PASSWORD")),
        json=payload,
        timeout=60
    )
    response.raise_for_status()
    # 处理响应
except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")
except requests.exceptions.ConnectionError as conn_err:
    print(f"Connection error occurred: {conn_err}")
except requests.exceptions.Timeout as timeout_err:
    print(f"Timeout error occurred: {timeout_err}")
except requests.exceptions.RequestException as req_err:
    print(f"An error occurred: {req_err}")
```


---

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```
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