小红书AI文章生成技巧与WordPress集成开发高级实战指南
- Linkreate AI插件 文章
- 2025-08-29 20:42:19
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小红书AI文章生成基础架构
小红书平台的内容创作需要特定的风格和结构，通过AI技术可以大幅提升创作效率。基础架构主要包括内容分析、风格学习和文章生成三个核心模块。首先，我们需要建立一个能够分析小红书热门文章特征的系统，提取关键词、标题模式、段落结构和情感倾向。
import requests
from bs4 import BeautifulSoup
import jieba
import jieba.analyse
def analyze_xiaohongshu_post(url):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, '.parser')
提取文章内容
content = soup.find('div', class_='note-text').get_text()
提取关键词
keywords = jieba.analyse.extract_tags(content, topK=10, withWeight=True)
分析标题模式
title = soup.find('h1', class_='title').get_text()
return {
'content': content,
'keywords': keywords,
'title_pattern': analyze_title_pattern(title),
'structure': analyze_content_structure(content)
}
def analyze_title_pattern(title):
分析标题模式，如疑问句、数字列表、情感词等
patterns = {
'question': '?' in title,
'list': any(char.isdigit() for char in title),
'emotion': len(re.findall(r'[！!。.]', title)) > 0
}
return patterns
这段代码实现了小红书文章的基础分析功能，包括内容提取、关键词分析和标题模式识别。运行此代码前，请确保已安装requests、beautifulsoup4和jieba库。代码中的headers模拟了浏览器访问，避免被网站识别为爬虫。analyze_title_pattern函数特别关注小红书常见的标题模式，如疑问句、数字列表和情感表达，这些是平台热门文章的典型特征。
WordPress与AI工具集成方法
将AI能力集成到WordPress中，可以通过API调用和插件开发两种方式实现。以下是使用WordPress REST API与AI服务集成的核心代码：
'POST',
'callback' => 'ai_generate_xiaohongshu_content',
'permission_callback' => function () {
return current_user_can('edit_posts');
}
));
});
// AI内容生成函数
function ai_generate_xiaohongshu_content(WP_REST_Request $request) {
$parameters = $request->get_params();
$topic = sanitize_text_field($parameters['topic']);
$style = sanitize_text_field($parameters['style']);
$keywords = sanitize_text_field($parameters['keywords']);
// 调用AI服务API
$ai_response = call_ai_service($topic, $style, $keywords);
if (is_wp_error($ai_response)) {
return new WP_Error('ai_error', 'Failed to generate content', array('status' => 500));
}
// 处理AI返回的内容
$content = process_ai_content($ai_response);
// 创建WordPress文章
$post_id = wp_insert_post(array(
'post_title' => $content['title'],
'post_content' => $content['body'],
'post_status' => 'draft',
'post_author' => get_current_user_id(),
'post_category' => array(1) // 默认分类
));
if (is_wp_error($post_id)) {
return new WP_Error('post_error', 'Failed to create post', array('status' => 500));
}
return array(
'success' => true,
'post_id' => $post_id,
'title' => $content['title'],
'preview' => substr($content['body'], 0, 200) . '...'
);
}
// 调用AI服务
function call_ai_service($topic, $style, $keywords) {
$api_url = 'https://api.example.com/ai/generate';
$api_key = get_option('ai_service_api_key');
$response = wp_remote_post($api_url, array(
'headers' => array(
'Content-Type' => 'application/json',
'Authorization' => 'Bearer ' . $api_key
),
'body' => json_encode(array(
'topic' => $topic,
'style' => $style,
'keywords' => $keywords,
'platform' => 'xiaohongshu'
))
));
if (is_wp_error($response)) {
return $response;
}
$body = json_decode(wp_remote_retrieve_body($response), true);
if (isset($body['error'])) {
return new WP_Error('api_error', $body['error']);
}
return $body;
}
// 处理AI返回的内容
function process_ai_content($ai_response) {
// 提取标题和正文
$title = $ai_response['title'];
$body = $ai_response['content'];
// 添加小红书特有的标签和格式
$body = add_xiaohongshu_formatting($body);
return array(
'title' => $title,
'body' => $body
);
}
// 添加小红书特有的格式
function add_xiaohongshu_formatting($content) {
// 添加表情符号
$content = preg_replace('/(.|!|?)/', '$1✨', $content);
// 添加标签
$tags = array('小红书', '生活分享', '日常');
$content .= "nn" . implode(' ', $tags);
return $content;
}
这段代码实现了一个WordPress插件，通过REST API端点接收请求，调用AI服务生成小红书风格的内容，并自动创建WordPress文章。代码中包含了完整的错误处理机制，确保在API调用失败或文章创建失败时能够返回适当的错误信息。add_xiaohongshu_formatting函数特别添加了小红书平台特有的格式元素，如表情符号和标签，使生成的内容更符合平台风格。
自定义功能开发
为了进一步提升AI生成内容的质量和适用性，我们需要开发一些自定义功能。以下是一个基于用户反馈的内容优化系统：
// AI内容优化器
class AIContentOptimizer {
constructor(apiKey) {
this.apiKey = apiKey;
this.feedbackData = [];
this.optimizationRules = {
titleLength: { min: 10, max: 30 },
paragraphLength: { min: 50, max: 200 },
keywordDensity: { min: 0.01, max: 0.03 },
emotionScore: { min: 0.3, max: 0.8 }
};
}
// 分析内容质量
analyzeContent(content) {
const analysis = {
titleLength: content.title.length,
paragraphLength: this.calculateAverageParagraphLength(content.body),
keywordDensity: this.calculateKeywordDensity(content.body, content.keywords),
emotionScore: this.calculateEmotionScore(content.body),
readabilityScore: this.calculateReadabilityScore(content.body)
};
return analysis;
}
// 根据分析结果优化内容
optimizeContent(content, analysis) {
let optimizedContent = { ...content };
// 优化标题长度
if (analysis.titleLength this.optimizationRules.titleLength.max) {
optimizedContent.title = this.optimizeTitle(content.title);
}
// 优化段落长度
if (analysis.paragraphLength this.optimizationRules.paragraphLength.max) {
optimizedContent.body = this.optimizeParagraphLength(content.body);
}
// 优化关键词密度
if (analysis.keywordDensity this.optimizationRules.keywordDensity.max) {
optimizedContent.body = this.optimizeKeywordDensity(content.body, content.keywords);
}
// 优化情感表达
if (analysis.emotionScore this.optimizationRules.emotionScore.max) {
optimizedContent.body = this.optimizeEmotionExpression(content.body);
}
return optimizedContent;
}
// 优化标题
optimizeTitle(title) {
// 调用AI服务优化标题
return this.callAIService('optimize_title', { title });
}
// 优化段落长度
optimizeParagraphLength(body) {
const paragraphs = body.split('nn');
const optimizedParagraphs = paragraphs.map(paragraph => {
if (paragraph.length this.optimizationRules.paragraphLength.max) {
// 分割长段落
return this.splitParagraph(paragraph);
}
return paragraph;
});
return optimizedParagraphs.join('nn');
}
// 优化关键词密度
optimizeKeywordDensity(body, keywords) {
// 调用AI服务调整关键词密度
return this.callAIService('optimize_keyword_density', { body, keywords });
}
// 优化情感表达
optimizeEmotionExpression(body) {
// 调用AI服务增强情感表达
return this.callAIService('optimize_emotion', { body });
}
// 计算平均段落长度
calculateAverageParagraphLength(body) {
const paragraphs = body.split('nn').filter(p => p.trim().length > 0);
const totalLength = paragraphs.reduce((sum, p) => sum + p.length, 0);
return totalLength / paragraphs.length;
}
// 计算关键词密度
calculateKeywordDensity(body, keywords) {
const words = body.split(/s+/);
const keywordCount = keywords.reduce((count, keyword) => {
const regex = new RegExp(keyword, 'gi');
const matches = body.match(regex);
return count + (matches ? matches.length : 0);
}, 0);
return keywordCount / words.length;
}
// 计算情感得分
calculateEmotionScore(body) {
// 简单情感分析，实际应用中可使用更复杂的NLP模型
const emotionWords = ['喜欢', '爱', '开心', '快乐', '美好', '惊喜', '推荐', '棒'];
const regex = new RegExp(emotionWords.join('|'), 'gi');
const matches = body.match(regex);
const emotionCount = matches ? matches.length : 0;
const words = body.split(/s+/);
return Math.min(emotionCount / words.length 10, 1);
}
// 计算可读性得分
calculateReadabilityScore(body) {
// 简单可读性计算，实际应用中可使用更复杂的算法
const sentences = body.split(/[.!?]+/).filter(s => s.trim().length > 0);
const words = body.split(/s+/);
const avgWordsPerSentence = words.length / sentences.length;
// 简单评分：每句词数越少，可读性越高
return Math.max(0, 1 - (avgWordsPerSentence - 10) / 20);
}
// 调用AI服务
async callAIService(endpoint, params) {
try {
const response = await fetch(`https://api.example.com/ai/${endpoint}`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${this.apiKey}`
},
body: JSON.stringify(params)
});
const data = await response.json();
if (data.error) {
console.error('AI service error:', data.error);
return params.body || params.title; // 返回原始内容作为后备
}
return data.result;
} catch (error) {
console.error('Error calling AI service:', error);
return params.body || params.title; // 返回原始内容作为后备
}
}
// 记录用户反馈
recordFeedback(contentId, feedback) {
this.feedbackData.push({
contentId,
feedback,
timestamp: new Date().toISOString()
});
// 根据反馈调整优化规则
this.adjustOptimizationRules(feedback);
}
// 根据反馈调整优化规则
adjustOptimizationRules(feedback) {
// 简单示例：根据反馈调整情感得分范围
if (feedback.emotion === 'too_low') {
this.optimizationRules.emotionScore.min += 0.05;
} else if (feedback.emotion === 'too_high') {
this.optimizationRules.emotionScore.max -= 0.05;
}
// 确保规则在合理范围内
this.optimizationRules.emotionScore.min = Math.max(0.1, Math.min(0.5, this.optimizationRules.emotionScore.min));
this.optimizationRules.emotionScore.max = Math.max(0.4, Math.min(0.9, this.optimizationRules.emotionScore.max));
}
}
// 使用示例
const optimizer = new AIContentOptimizer('your-api-key');
// 分析内容
const content = {
title: '夏日清爽护肤小技巧',
body: '夏天到了，皮肤容易出油。今天分享几个清爽护肤的小技巧...',
keywords: ['护肤', '夏天', '清爽']
};
const analysis = optimizer.analyzeContent(content);
console.log('Content analysis:', analysis);
// 优化内容
const optimizedContent = optimizer.optimizeContent(content, analysis);
console.log('Optimized content:', optimizedContent);
// 记录用户反馈
optimizer.recordFeedback('post-123', {
emotion: 'too_low',
readability: 'good',
overall: 'satisfactory'
});
这段JavaScript代码实现了一个AI内容优化器类，可以分析生成内容的质量并根据预设规则进行优化。代码中包含了多种优化功能，如标题长度优化、段落长度调整、关键词密度优化和情感表达增强。特别值得注意的是，该系统还包含了用户反馈机制，可以根据用户反馈动态调整优化规则，使系统不断学习和改进。calculateEmotionScore函数使用简单的情感词匹配来评估内容的情感表达强度，实际应用中可以替换为更复杂的NLP模型以提高准确性。
高级模块实现
为了实现更高级的小红书AI文章生成功能，我们需要开发一些专门的模块。以下是一个基于深度学习的风格迁移模块，可以将普通内容转换为小红书风格：
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
import re
import jieba
from collections import Counter
class XiaohongshuStyleDataset(Dataset):
def __init__(self, content_files, style_files, max_length=512):
self.content_texts = self.load_texts(content_files)
self.style_texts = self.load_texts(style_files)
self.max_length = max_length
self.vocab = self.build_vocab(self.content_texts + self.style_texts)
self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)}
def load_texts(self, files):
texts = []
for file in files:
with open(file, 'r', encoding='utf-8') as f:
texts.extend([line.strip() for line in f if line.strip()])
return texts
def build_vocab(self, texts):
all_words = []
for text in texts:
words = jieba.lcut(text)
all_words.extend(words)
word_counts = Counter(all_words)
保留最常见的词汇
vocab = ['', ''] + [word for word, _ in word_counts.most_common(10000)]
return vocab
def text_to_sequence(self, text):
words = jieba.lcut(text)
sequence = [self.word_to_idx.get(word, self.word_to_idx['']) for word in words]
填充或截断到固定长度
if len(sequence) < self.max_length:
sequence += [self.word_to_idx['']] (self.max_length - len(sequence))
else:
sequence = sequence[:self.max_length]
return torch.tensor(sequence, dtype=torch.long)
def __len__(self):
return min(len(self.content_texts), len(self.style_texts))
def __getitem__(self, idx):
content_seq = self.text_to_sequence(self.content_texts[idx])
style_seq = self.text_to_sequence(self.style_texts[idx])
return content_seq, style_seq
class StyleTransferModel(nn.Module):
def __init__(self, vocab_size, embedding_dim=256, hidden_dim=512, num_layers=2):
super(StyleTransferModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
self.encoder = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True, dropout=0.2)
self.style_encoder = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True, dropout=0.2)
self.decoder = nn.LSTM(embedding_dim + hidden_dim, hidden_dim, num_layers, batch_first=True, dropout=0.2)
self.output_layer = nn.Linear(hidden_dim, vocab_size)
def forward(self, content_input, style_input, target_input=None):
编码内容
content_embedded = self.embedding(content_input)
content_encoded, (content_h, content_c) = self.encoder(content_embedded)
编码风格
style_embedded = self.embedding(style_input)
style_encoded, (style_h, style_c) = self.style_encoder(style_embedded)
获取风格特征
style_features = style_encoded[:, -1, :].unsqueeze(1).repeat(1, content_encoded.size(1), 1)
训练阶段
if target_input is not None:
target_embedded = self.embedding(target_input)
将内容编码和风格特征拼接
decoder_input = torch.cat([content_encoded, style_features], dim=2)
解码
decoder_output, _ = self.decoder(decoder_input)
output = self.output_layer(decoder_output)
return output
推理阶段
else:
batch_size = content_input.size(0)
seq_length = content_input.size(1)
初始化解码器输入
decoder_input = torch.cat([content_encoded, style_features], dim=2)
解码
decoder_output, _ = self.decoder(decoder_input)
output = self.output_layer(decoder_output)
return output
class XiaohongshuStyleTransfer:
def __init__(self, model_path=None):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = None
self.vocab = None
self.word_to_idx = None
self.idx_to_word = None
if model_path:
self.load_model(model_path)
def load_model(self, model_path):
checkpoint = torch.load(model_path, map_location=self.device)
重建词汇表
self.vocab = checkpoint['vocab']
self.word_to_idx = {word: idx for idx, word in enumerate(self.vocab)}
self.idx_to_word = {idx: word for idx, word in enumerate(self.vocab)}
重建模型
vocab_size = len(self.vocab)
self.model = StyleTransferModel(vocab_size).to(self.device)
self.model.load_state_dict(checkpoint['model_state_dict'])
self.model.eval()
def train(self, content_files, style_files, epochs=10, batch_size=32, learning_rate=0.001):
准备数据集
dataset = XiaohongshuStyleDataset(content_files, style_files)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
保存词汇表
self.vocab = dataset.vocab
self.word_to_idx = dataset.word_to_idx
self.idx_to_word = {idx: word for idx, word in enumerate(self.vocab)}
初始化模型
vocab_size = len(self.vocab)
self.model = StyleTransferModel(vocab_size).to(self.device)
定义损失函数和优化器
criterion = nn.CrossEntropyLoss(ignore_index=0) 忽略填充标记
optimizer = optim.Adam(self.model.parameters(), lr=learning_rate)
训练循环
for epoch in range(epochs):
total_loss = 0
for batch_idx, (content_seqs, style_seqs) in enumerate(dataloader):
content_seqs = content_seqs.to(self.device)
style_seqs = style_seqs.to(self.device)
前向传播
outputs = self.model(content_seqs, style_seqs, style_seqs)
计算损失
loss = criterion(outputs.view(-1, outputs.size(-1)), style_seqs.view(-1))
反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
if batch_idx % 100 == 0:
print(f'Epoch {epoch+1}/{epochs}, Batch {batch_idx}/{len(dataloader)}, Loss: {loss.item():.4f}')
avg_loss = total_loss / len(dataloader)
print(f'Epoch {epoch+1}/{epochs}, Average Loss: {avg_loss:.4f}')
保存模型
self.save_model('xiaohongshu_style_transfer_model.pth')
def save_model(self, path):
torch.save({
'model_state_dict': self.model.state_dict(),
'vocab': self.vocab
}, path)
def transfer_style(self, content_text):
if self.model is None:
raise ValueError("Model not loaded. Please train or load a model first.")
预处理输入文本
content_seq = self.text_to_sequence(content_text).unsqueeze(0).to(self.device)
生成风格参考（使用随机选择的小红书风格文本）
style_texts = [
"今天分享一个超好用的小技巧！✨",
"姐妹们，这个真的太香了！强烈推荐！👍",
"挖到宝了！这个方法简直绝绝子！💯"
]
style_text = np.random.choice(style_texts)
style_seq = self.text_to_sequence(style_text).unsqueeze(0).to(self.device)
风格迁移
with torch.no_grad():
output = self.model(content_seq, style_seq)
获取预测的词索引
_, predicted = torch.max(output, dim=2)
predicted = predicted.squeeze(0).cpu().numpy()
将索引转换为文本
result_words = [self.idx_to_word[idx] for idx in predicted if idx != 0] 忽略填充标记
result_text = ''.join(result_words)
后处理：添加小红书特有的表情符号和标签
result_text = self.add_xiaohongshu_elements(result_text)
return result_text
def text_to_sequence(self, text):
words = jieba.lcut(text)
sequence = [self.word_to_idx.get(word, self.word_to_idx['']) for word in words]
填充或截断到固定长度
max_length = 512
if len(sequence) < max_length:
sequence += [self.word_to_idx['']] (max_length - len(sequence))
else:
sequence = sequence[:max_length]
return torch.tensor(sequence, dtype=torch.long)
def add_xiaohongshu_elements(self, text):
添加表情符号
text = re.sub(r'([。！？])', r'1✨', text)
添加标签
tags = ['生活分享', '日常', '推荐']
text += 'nn' + ' '.join(tags)
return text
使用示例
if __name__ == "__main__":
初始化风格迁移器
style_transfer = XiaohongshuStyleTransfer()
训练模型（如果有训练数据）
style_transfer.train(['content.txt'], ['xiaohongshu_style.txt'], epochs=5)
加载预训练模型
style_transfer.load_model('xiaohongshu_style_transfer_model.pth')
进行风格迁移
content = "今天我想分享一个护肤小技巧。每天早晚使用保湿霜可以让皮肤保持水润。"
styled_content = style_transfer.transfer_style(content)
print("原始内容:", content)
print("小红书风格内容:", styled_content)
这段Python代码实现了一个基于深度学习的小红书风格迁移系统。该系统使用LSTM神经网络模型，可以将普通内容转换为小红书特有的风格。代码中包含了完整的数据处理、模型训练和推理流程。XiaohongshuStyleDataset类负责加载和预处理文本数据，StyleTransferModel类定义了风格迁移的神经网络结构，XiaohongshuStyleTransfer类提供了训练和推理的高级接口。特别值得注意的是，add_xiaohongshu_elements方法会在生成的内容中添加小红书平台特有的表情符号和标签，使内容更符合平台风格。在实际应用中，你需要准备足够的小红书风格文本作为训练数据，以获得更好的风格迁移效果。
代码优化与性能提升
为了确保AI文章生成系统的高效运行，我们需要对代码进行优化和性能提升。以下是一个优化后的异步处理系统，可以大幅提升内容生成速度：
const { Worker, isMainThread, parentPort, workerData } = require('worker_threads');
const path = require('path');
const fs = require('fs').promises;
const { promisify } = require('util');
const redis = require('redis');
const { createClient } = redis;
// Redis客户端
const redisClient = createClient({
url: 'redis://localhost:6379'
});
redisClient.on('error', (err) => console.log('Redis Client Error', err));
const redisGet = promisify(redisClient.get).bind(redisClient);
const redisSet = promisify(redisClient.set).bind(redisClient);
// 异步任务队列
class TaskQueue {
constructor(concurrency = 4) {
this.concurrency = concurrency;
this.running = 0;
this.queue = [];
}
async run(task) {
return new Promise((resolve, reject) => {
this.queue.push({ task, resolve, reject });
this.next();
});
}
next() {
while (this.running {
this.running--;
this.next();
});
}
}
}
// AI内容生成器
class AIContentGenerator {
constructor(options = {}) {
this.apiKey = options.apiKey || '';
this.cacheEnabled = options.cacheEnabled !== false;
this.cacheTTL = options.cacheTTL || 3600; // 默认缓存1小时
this.workers = [];
this.workerCount = options.workerCount || 4;
this.taskQueue = new TaskQueue(this.workerCount);
// 初始化工作线程
this.initWorkers();
}
// 初始化工作线程
initWorkers() {
for (let i = 0; i {
console.error(`Worker ${i} error:`, err);
});
worker.on('exit', (code) => {
if (code !== 0) {
console.error(`Worker ${i} stopped with exit code ${code}`);
}
});
this.workers.push(worker);
}
}
// 生成内容
async generateContent(params) {
const { topic, style, keywords, length = 'medium' } = params;
// 生成缓存键
const cacheKey = `ai_content:${topic}:${style}:${keywords}:${length}`;
// 尝试从缓存获取
if (this.cacheEnabled) {
try {
const cachedContent = await redisGet(cacheKey);
if (cachedContent) {
return JSON.parse(cachedContent);
}
} catch (err) {
console.error('Cache retrieval error:', err);
}
}
// 使用任务队列处理请求
const content = await this.taskQueue.run(async () => {
// 选择工作线程（简单的轮询策略）
const workerIndex = Math.floor(Math.random() this.workerCount);
const worker = this.workers[workerIndex];
return new Promise((resolve, reject) => {
const requestId = Date.now().toString() + Math.random().toString().substr(2, 5);
// 设置超时
const timeout = setTimeout(() => {
reject(new Error('Content generation timeout'));
}, 30000); // 30秒超时
// 监听工作线程响应
const messageHandler = (msg) => {
if (msg.requestId === requestId) {
clearTimeout(timeout);
worker.removeListener('message', messageHandler);
if (msg.error) {
reject(new Error(msg.error));
} else {
resolve(msg.content);
}
}
};
worker.on('message', messageHandler);
// 发送任务到工作线程
worker.postMessage({
requestId,
task: 'generateContent',
params: { topic, style, keywords, length }
});
});
});
// 缓存结果
if (this.cacheEnabled && content) {
try {
await redisSet(cacheKey, JSON.stringify(content), 'EX', this.cacheTTL);
} catch (err) {
console.error('Cache storage error:', err);
}
}
return content;
}
// 批量生成内容
async generateBatchContent(tasks) {
const promises = tasks.map(task => this.generateContent(task));
return Promise.all(promises);
}
// 优化内容
async optimizeContent(content, options = {}) {
const { optimizeFor = 'engagement', platform = 'xiaohongshu' } = options;
// 使用任务队列处理优化请求
return this.taskQueue.run(async () => {
const workerIndex = Math.floor(Math.random() this.workerCount);
const worker = this.workers[workerIndex];
return new Promise((resolve, reject) => {
const requestId = Date.now().toString() + Math.random().toString().substr(2, 5);
// 设置超时
const timeout = setTimeout(() => {
reject(new Error('Content optimization timeout'));
}, 20000); // 20秒超时
// 监听工作线程响应
const messageHandler = (msg) => {
if (msg.requestId === requestId) {
clearTimeout(timeout);
worker.removeListener('message', messageHandler);
if (msg.error) {
reject(new Error(msg.error));
} else {
resolve(msg.optimizedContent);
}
}
};
worker.on('message', messageHandler);
// 发送任务到工作线程
worker.postMessage({
requestId,
task: 'optimizeContent',
params: { content, optimizeFor, platform }
});
});
});
}
// 关闭生成器
async close() {
// 终止所有工作线程
for (const worker of this.workers) {
await worker.terminate();
}
// 关闭Redis连接
await redisClient.quit();
}
}
// 工作线程代码 (ai-worker.js)
if (!isMainThread) {
const { workerData } = require('worker_threads');
const { apiKey, workerId } = workerData;
// 模拟AI服务调用
async function callAIService(params) {
// 这里应该是实际的AI服务调用
// 为了示例，我们模拟一个延迟的响应
await new Promise(resolve => setTimeout(resolve, 1000 + Math.random() 2000));
// 模拟生成的内容
const content = {
title: `${params.topic}的小红书分享✨`,
body: `今天想和大家分享关于${params.topic}的一些心得！${params.keywords.map(k => `${k}`).join(' ')}nn` +
`这个${params.style}风格的内容真的太棒了！强烈推荐大家尝试一下！👍nn` +
`如果你有更好的建议，欢迎在评论区留言哦！💕`,
tags: ['小红书', '生活分享', '推荐']
};
return content;
}
// 模拟内容优化
async function optimizeContent(content, options) {
// 这里应该是实际的内容优化逻辑
// 为了示例，我们模拟一个延迟的响应
await new Promise(resolve => setTimeout(resolve, 500 + Math.random() 1000));
// 模拟优化后的内容
const optimizedContent = {
...content,
title: content.title + '（优化版）✨',
body: content.body.replace(/太棒了/g, '超级棒！').replace(/强烈推荐/g, '墙裂推荐！'),
optimized: true
};
return optimizedContent;
}
// 监听主线程消息
parentPort.on('message', async (msg) => {
const { requestId, task, params } = msg;
try {
let result;
if (task === 'generateContent') {
result = await callAIService(params);
parentPort.postMessage({ requestId, content: result });
} else if (task === 'optimizeContent') {
result = await optimizeContent(params.content, params);
parentPort.postMessage({ requestId, optimizedContent: result });
} else {
throw new Error(`Unknown task: ${task}`);
}
} catch (error) {
parentPort.postMessage({ requestId, error: error.message });
}
});
// 通知主线程工作线程已就绪
parentPort.postMessage({ workerId, status: 'ready' });
}
// 使用示例
async function main() {
try {
// 连接Redis
await redisClient.connect();
// 创建内容生成器
const generator = new AIContentGenerator({
apiKey: 'your-api-key',
cacheEnabled: true,
cacheTTL: 3600,
workerCount: 4
});
// 生成单篇内容
const content = await generator.generateContent({
topic: '夏日护肤',
style: '轻松活泼',
keywords: ['护肤', '夏天', '清爽'],
length: 'medium'
});
console.log('Generated content:', content);
// 优化内容
const optimizedContent = await generator.optimizeContent(content, {
optimizeFor: 'engagement',
platform: 'xiaohongshu'
});
console.log('Optimized content:', optimizedContent);
// 批量生成内容
const batchTasks = [
{ topic: '健身饮食', style: '专业严谨', keywords: ['健身', '饮食', '健康'] },
{ topic: '旅行攻略', style: '轻松活泼', keywords: ['旅行', '攻略', '假期'] },
{ topic: '读书笔记', style: '文艺清新', keywords: ['读书', '笔记', '思考'] }
];
const batchResults = await generator.generateBatchContent(batchTasks);
console.log('Batch results:', batchResults);
// 关闭生成器
await generator.close();
} catch (error) {
console.error('Error:', error);
} finally {
// 确保Redis连接关闭
await redisClient.quit();
}
}
// 运行示例
if (require.main === module) {
main();
}
module.exports = { AIContentGenerator, TaskQueue };
这段JavaScript代码实现了一个高性能的AI内容生成系统，使用了多种优化技术来提升性能。主要特点包括：
1. 多线程处理：使用Node.js的Worker Threads模块创建多个工作线程，并行处理内容生成任务，大幅提升处理速度。
2. 任务队列：实现了TaskQueue类，控制并发任务数量，避免系统过载。
3. 缓存机制：使用Redis缓存生成的内容，减少重复计算，提高响应速度。
4. 异步处理：全面采用异步编程模式，避免阻塞，提高系统吞吐量。
5. 批量处理：支持批量生成内容，进一步提高效率。
6. 超时控制：为每个任务设置超时时间，防止系统因长时间等待而卡死。
代码中的AIContentGenerator类是核心组件，提供了内容生成、内容优化和批量处理等功能。工作线程(ai-worker.js)负责实际的内容生成和优化工作，主线程负责任务调度和结果收集。这种架构可以充分利用多核CPU的性能，特别适合处理大量并发的AI内容生成请求。
在实际应用中，你需要将callAIService和optimizeContent函数替换为实际的AI服务调用，以实现真正的AI内容生成和优化功能。