# Hugging Face Transformers示例
from transformers import AutoModelForCausalLM, AutoTokenizer

# 加载模型
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 模型配置
model.config.max_length = 4096
model.config.temperature = 0.7

class HybridGenerativeAI:
    def __init__(self):
        self.local_model = None  # 本地模型
        self.api_clients = {}    # API客户端
        self.caching_layer = None  # 缓存层
    
    def route_request(self, request_type, content):
        """智能路由请求到最适合的处理方式"""
        # 根据复杂度和成本选择处理方式
        if self._should_use_local(content):
            return self._process_local(content)
        else:
            return self._process_api(content)

from transformers import pipeline

# 文本生成管道
text_generator = pipeline(
    "text-generation",
    model="gpt2",
    device=0  # 使用GPU
)

# 图像生成管道
image_generator = pipeline(
    "text-to-image",
    model="stabilityai/stable-diffusion-2-1"
)

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

# 创建模板
prompt_template = PromptTemplate(
    input_variables=["topic", "length"],
    template="关于{topic}的{length}字介绍："
)

# 创建链
llm = OpenAI(temperature=0.7)
chain = LLMChain(llm=llm, prompt=prompt_template)

# 使用
result = chain.run(topic="人工智能", length="500")

class GenerativeAIFramework:
    def __init__(self, config):
        self.config = config
        self.models = {}
        self.prompts = {}
        self.memory = MemorySystem()
    
    def register_model(self, name, model_config):
        """注册模型"""
        self.models[name] = self._load_model(model_config)
    
    def generate_response(self, prompt, context=None):
        """生成响应"""
        # 上下文处理
        processed_prompt = self._process_prompt(prompt, context)
        
        # 模型选择
        model = self._select_model(processed_prompt)
        
        # 生成响应
        response = model.generate(processed_prompt)
        
        # 后处理
        return self._post_process(response)

import re
import pandas as pd
from text_preprocessing import TextCleaner

class DataPreprocessor:
    def __init__(self, config):
        self.config = config
        self.cleaner = TextCleaner()
    
    def clean_text(self, text):
        """文本清洗"""
        # 去除HTML标签
        text = re.sub(r'<[^>]+>', '', text)
        
        # 去除特殊字符
        text = re.sub(r'[^\w\s一-鿿]', '', text)
        
        # 标准化空格
        text = ' '.join(text.split())
        
        # 去除停用词
        text = self.cleaner.remove_stopwords(text)
        
        return text
    
    def filter_quality(self, text, min_length=10, max_length=512):
        """质量控制"""
        if len(text) < min_length or len(text) > max_length:
            return False
        
        # 检查语言质量
        if self._contains_inappropriate_content(text):
            return False
        
        return True
    
    def process_dataset(self, dataset_path):
        """处理数据集"""
        df = pd.read_csv(dataset_path)
        
        # 清洗文本
        df['cleaned_text'] = df['text'].apply(self.clean_text)
        
        # 质量过滤
        df = df[df['cleaned_text'].apply(
            lambda x: self.filter_quality(x)
        )]
        
        # 去重
        df = df.drop_duplicates()
        
        return df

import nlpaug.augmenter as naa
from sklearn.utils import shuffle

class DataAugmentor:
    def __init__(self):
        self.augmenters = {
            'synonym': naa.SynonymAug(),
            'insert': naa.ContextualWordEmbsAug(),
            'swap': naa.RandomWordAug(action="swap"),
            'delete': naa.RandomWordAug(action="delete")
        }
    
    def augment_text(self, text, augment_type='synonym', n_augment=3):
        """文本增强"""
        if augment_type not in self.augmenters:
            return [text]
        
        augmenter = self.augmenters[augment_type]
        augmented_texts = augmenter.augment(text, n=n_augment)
        
        return augmented_texts
    
    def create_balanced_dataset(self, df, target_ratio=0.8):
        """创建平衡数据集"""
        # 计算类别分布
        class_counts = df['label'].value_counts()
        max_count = class_counts.max()
        
        balanced_data = []
        
        for label in class_counts.index:
            # 获取当前类别的数据
            class_data = df[df['label'] == label]
            
            # 计算需要增强的数量
            current_count = len(class_data)
            if current_count < max_count * target_ratio:
                # 需要增强
                n_augment = int(max_count * target_ratio - current_count)
                
                # 随机选择样本进行增强
                samples_to_augment = class_data.sample(n=min(n_augment, len(class_data)))
                
                for _, row in samples_to_augment.iterrows():
                    augmented_text = self.augment_text(row['text'])
                    for aug_text in augmented_text:
                        balanced_data.append({
                            'text': aug_text,
                            'label': label
                        })
        
        # 合并原始数据和增强数据
        balanced_df = pd.DataFrame(balanced_data)
        balanced_df = pd.concat([df, balanced_df], ignore_index=True)
        
        # 打乱数据
        balanced_df = shuffle(balanced_df)
        
        return balanced_df

class AutoLabeler:
    def __init__(self, labeling_model):
        self.model = labeling_model
        self.label_schema = {}
    
    def set_label_schema(self, schema):
        """设置标签架构"""
        self.label_schema = schema
    
    def predict_labels(self, texts, confidence_threshold=0.8):
        """预测标签"""
        results = []
        
        for text in texts:
            # 模型预测
            predictions = self.model.predict(text)
            
            # 过滤低置信度预测
            filtered_predictions = {
                label: score for label, score in predictions.items()
                if score >= confidence_threshold
            }
            
            results.append({
                'text': text,
                'predictions': filtered_predictions,
                'needs_review': len(filtered_predictions) == 0
            })
        
        return results
    
    def active_learning(self, unlabeled_data, n_samples=100):
        """主动学习选择样本"""
        # 选择不确定性的样本
 uncertainties = []
        
        for item in unlabeled_data:
            # 计算熵作为不确定性度量
            predictions = item['predictions']
            entropy = self._calculate_entropy(predictions)
            uncertainties.append({
                'item': item,
                'entropy': entropy
            })
        
        # 按熵排序
        uncertainties.sort(key=lambda x: x['entropy'], reverse=True)
        
        # 返回最不确定的样本
        return [u['item'] for u in uncertainties[:n_samples]]

import torch
from transformers import TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model

class LoRATrainer:
    def __init__(self, model_name, config):
        self.model_name = model_name
        self.config = config
        self.model = None
        self.tokenizer = None
    
    def setup_lora(self):
        """设置LoRA"""
        # 加载基础模型
        self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        
        # 设置LoRA配置
        lora_config = LoraConfig(
            r=self.config['r'],  # Rank
            lora_alpha=self.config['alpha'],
            target_modules=self.config['target_modules'],
            lora_dropout=self.config['dropout']
        )
        
        # 应用LoRA
        self.model = get_peft_model(self.model, lora_config)
        
        # 冻结基础参数
        for param in self.model.base_model.parameters():
            param.requires_grad = False
    
    def train(self, train_dataset, eval_dataset):
        """训练模型"""
        training_args = TrainingArguments(
            output_dir=self.config['output_dir'],
            num_train_epochs=self.config['epochs'],
            per_device_train_batch_size=self.config['batch_size'],
            per_device_eval_batch_size=self.config['eval_batch_size'],
            warmup_steps=self.config['warmup_steps'],
            weight_decay=self.config['weight_decay'],
            logging_dir=self.config['logging_dir'],
            logging_steps=self.config['logging_steps'],
            save_steps=self.config['save_steps'],
            eval_steps=self.config['eval_steps'],
            learning_rate=self.config['learning_rate']
        )
        
        trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset
        )
        
        trainer.train()
        return trainer
    
    def save_model(self, path):
        """保存模型"""
        self.model.save_pretrained(path)
        self.tokenizer.save_pretrained(path)

from torch.utils.data import Dataset, DataLoader

class GenerativeDataset(Dataset):
    def __init__(self, texts, tokenizer, max_length=512):
        self.texts = texts
        self.tokenizer = tokenizer
        self.max_length = max_length
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        text = self.texts[idx]
        
        # 编码文本
        encoding = self.tokenizer(
            text,
            truncation=True,
            padding='max_length',
            max_length=self.max_length,
            return_tensors='pt'
        )
        
        return {
            'input_ids': encoding['input_ids'].squeeze(),
            'attention_mask': encoding['attention_mask'].squeeze(),
            'labels': encoding['input_ids'].squeeze()
        }

def create_data_loader(texts, tokenizer, batch_size=8, max_length=512):
    """创建数据加载器"""
    dataset = GenerativeDataset(texts, tokenizer, max_length)
    data_loader = DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True
    )
    return data_loader

import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

class ModelEvaluator:
    def __init__(self):
        self.metrics = {}
    
    def calculate_perplexity(self, model, test_data):
        """计算困惑度"""
        total_loss = 0
        total_tokens = 0
        
        for batch in test_data:
            outputs = model(
                input_ids=batch['input_ids'],
                labels=batch['labels']
            )
            
            loss = outputs.loss
            tokens = batch['input_ids'].numel()
            
            total_loss += loss.item() * tokens
            total_tokens += tokens
        
        avg_loss = total_loss / total_tokens
        perplexity = np.exp(avg_loss)
        
        return perplexity
    
    def evaluate_text_quality(self, generated_texts, reference_texts):
        """评估生成文本质量"""
        # BLEU分数
        bleu_scores = []
        
        # ROUGE分数
        rouge_scores = []
        
        # 相似度分数
        similarity_scores = []
        
        for gen_text, ref_text in zip(generated_texts, reference_texts):
            # 计算各种指标
            bleu = self._calculate_bleu(gen_text, ref_text)
            rouge = self._calculate_rouge(gen_text, ref_text)
            similarity = self._calculate_similarity(gen_text, ref_text)
            
            bleu_scores.append(bleu)
            rouge_scores.append(rouge)
            similarity_scores.append(similarity)
        
        return {
            'bleu': np.mean(bleu_scores),
            'rouge': np.mean(rouge_scores),
            'similarity': np.mean(similarity_scores)
        }
    
    def benchmark_models(self, models, test_data):
        """基准测试多个模型"""
        results = {}
        
        for model_name, model in models.items():
            # 性能测试
            time_scores = self._measure_inference_time(model, test_data)
            
            # 质量评估
            quality_scores = self.evaluate_text_quality(
                model.generate(test_data),
                test_data['references']
            )
            
            results[model_name] = {
                'performance': time_scores,
                'quality': quality_scores
            }
        
        return results

from transformers import BitsAndBytesConfig

class ModelOptimizer:
    def __init__(self):
        self.quantization_configs = {
            'int8': BitsAndBytesConfig(
                load_in_8bit=True,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type='nf4',
                bnb_4bit_compute_dtype=torch.bfloat16
            ),
            'int4': BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type='nf4',
                bnb_4bit_compute_dtype=torch.bfloat16
            )
        }
    
    def quantize_model(self, model, quant_type='int8'):
        """量化模型"""
        if quant_type in self.quantization_configs:
            config = self.quantization_configs[quant_type]
            model = model.to('cuda')
            model = quantize_model(model, config)
            return model
        else:
            raise ValueError(f"Unsupported quantization type: {quant_type}")
    
    def optimize_memory(self, model):
        """优化内存使用"""
        # 清理缓存
        torch.cuda.empty_cache()
        
        # 使用模型并行
        if torch.cuda.device_count() > 1:
            model = torch.nn.DataParallel(model)
        
        return model
    
    def prune_model(self, model, pruning_ratio=0.1):
        """剪枝模型"""
        # 获取参数
        parameters = list(model.named_parameters())
        
        # 计算重要性
        importances = {}
        for name, param in parameters:
            if 'weight' in name:
                importance = torch.abs(param).mean()
                importances[name] = importance
        
        # 排序
        sorted_importances = sorted(
            importances.items(),
            key=lambda x: x[1],
            reverse=True
        )
        
        # 剪枝
        pruned_model = copy.deepcopy(model)
        
        for name, importance in sorted_importances:
            if len(sorted_importances) * pruning_ratio < 0:
                break
            
            # 剪枝参数
            param = pruned_model.get_parameter(name)
            mask = torch.abs(param) > importance * 0.1
            param.data[~mask] = 0
        
        return pruned_model

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import uvicorn

class GenerationRequest(BaseModel):
    prompt: str
    max_length: int = 512
    temperature: float = 0.7
    top_p: float = 0.9
    model: str = "default"

class GenerationResponse(BaseModel):
    text: str
    tokens: int
    time_taken: float
    model_used: str

class GenerativeAIService:
    def __init__(self, model_registry):
        self.model_registry = model_registry
        self.cache = {}
    
    def generate_text(self, request: GenerationRequest):
        """生成文本"""
        # 检查缓存
        cache_key = self._generate_cache_key(request)
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        # 获取模型
        model = self.model_registry.get_model(request.model)
        if not model:
            raise HTTPException(status_code=404, detail="Model not found")
        
        # 生成文本
        start_time = time.time()
        result = model.generate(
            request.prompt,
            max_length=request.max_length,
            temperature=request.temperature,
            top_p=request.top_p
        )
        end_time = time.time()
        
        response = GenerationResponse(
            text=result,
            tokens=len(result.split()),
            time_taken=end_time - start_time,
            model_used=request.model
        )
        
        # 缓存结果
        self.cache[cache_key] = response
        
        return response
    
    def _generate_cache_key(self, request: GenerationRequest):
        """生成缓存键"""
        return hashlib.md5(
            f"{request.prompt}_{request.temperature}_{request.top_p}".encode()
        ).hexdigest()

# FastAPI应用
app = FastAPI(title="Generative AI API")

# 添加CORS中间件
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# 初始化服务
model_registry = ModelRegistry()
service = GenerativeAIService(model_registry)

@app.post("/generate", response_model=GenerationResponse)
async def generate_text(request: GenerationRequest):
    """文本生成接口"""
    return service.generate_text(request)

@app.post("/batch-generate")
async def batch_generate(requests: List[GenerationRequest]):
    """批量生成接口"""
    results = []
    for req in requests:
        result = await generate_text(req)
        results.append(result)
    return results

@app.get("/models")
async def list_models():
    """列出可用模型"""
    return {"models": model_registry.list_models()}

from load_balancer import LoadBalancer
from health_checker import HealthChecker

class AIApplicationService:
    def __init__(self, config):
        self.config = config
        self.load_balancer = LoadBalancer(config['servers'])
        self.health_checker = HealthChecker()
        self.circuit_breaker = CircuitBreaker(
            failure_threshold=5,
            recovery_timeout=30
        )
    
    def handle_request(self, request):
        """处理请求"""
        # 检查熔断器状态
        if self.circuit_breaker.is_open():
            raise HTTPException(status_code=503, detail="Service unavailable")
        
        # 获取健康的服务器
        healthy_servers = self.health_checker.get_healthy_servers()
        
        if not healthy_servers:
            raise HTTPException(status_code=503, detail="No healthy servers available")
        
        # 负载均衡
        server = self.load_balancer.select_server(healthy_servers)
        
        try:
            # 处理请求
            result = self._forward_request(server, request)
            self.circuit_breaker.record_success()
            return result
        except Exception as e:
            self.circuit_breaker.record_failure()
            raise HTTPException(status_code=500, detail="Internal server error")
    
    def _forward_request(self, server, request):
        """转发请求到指定服务器"""
        # 这里可以实现实际的请求转发逻辑
        # 包括连接管理、错误处理等
        pass

import redis
from cachetools import TTLCache

class MultiLevelCache:
    def __init__(self, config):
        self.config = config
        
        # 内存缓存
        self.memory_cache = TTLCache(
            maxsize=config['memory_size'],
            ttl=config['memory_ttl']
        )
        
        # Redis缓存
        self.redis_cache = redis.Redis(
            host=config['redis_host'],
            port=config['redis_port'],
            db=config['redis_db']
        )
        
        # 本地文件缓存
        self.disk_cache = DiskCache(config['disk_cache_path'])
    
    def get(self, key):
        """获取缓存数据"""
        # 先查内存
        if key in self.memory_cache:
            return self.memory_cache[key]
        
        # 再查Redis
        redis_value = self.redis_cache.get(key)
        if redis_value:
            value = json.loads(redis_value)
            # 回填到内存
            self.memory_cache[key] = value
            return value
        
        # 最后查磁盘
        disk_value = self.disk_cache.get(key)
        if disk_value:
            value = json.loads(disk_value)
            # 回填到上层缓存
            self.memory_cache[key] = value
            self.redis_cache.set(key, json.dumps(value))
            return value
        
        return None
    
    def set(self, key, value, ttl=None):
        """设置缓存数据"""
        # 设置到内存
        self.memory_cache[key] = value
        
        # 设置到Redis
        if ttl:
            self.redis_cache.setex(key, ttl, json.dumps(value))
        else:
            self.redis_cache.set(key, json.dumps(value))
        
        # 设置到磁盘
        self.disk_cache.set(key, json.dumps(value))
    
    def invalidate(self, key):
        """使缓存失效"""
        self.memory_cache.pop(key, None)
        self.redis_cache.delete(key)
        self.disk_cache.delete(key)
    
    def clear(self):
        """清空所有缓存"""
        self.memory_cache.clear()
        self.redis_cache.flushdb()
        self.disk_cache.clear()

import prometheus_client
from prometheus_client import Counter, Histogram, Gauge

class AIMonitor:
    def __init__(self):
        # 创建指标
        self.request_counter = Counter(
            'ai_requests_total',
            'Total number of AI requests',
            ['model', 'endpoint']
        )
        
        self.response_time = Histogram(
            'ai_response_time_seconds',
            'AI response time in seconds',
            ['model', 'endpoint']
        )
        
        self.error_counter = Counter(
            'ai_errors_total',
            'Total number of AI errors',
            ['error_type', 'model']
        )
        
        self.active_requests = Gauge(
            'ai_active_requests',
            'Number of active AI requests'
        )
    
    def record_request(self, model, endpoint, duration, success=True):
        """记录请求"""
        self.request_counter.labels(model=model, endpoint=endpoint).inc()
        
        if success:
            self.response_time.labels(model=model, endpoint=endpoint).observe(duration)
        else:
            self.error_counter.labels(
                error_type='generation_failure',
                model=model
            ).inc()
    
    def track_active_request(self, delta):
        """跟踪活跃请求数"""
        self.active_requests.inc(delta)

# 监控中间件
class MonitoringMiddleware:
    def __init__(self, monitor):
        self.monitor = monitor
    
    async def __call__(self, request, call_next):
        start_time = time.time()
        
        try:
            # 增加活跃请求数
            self.monitor.track_active_request(1)
            
            # 处理请求
            response = await call_next(request)
            
            # 记录成功请求
            duration = time.time() - start_time
            self.monitor.record_request(
                model=request.get('model', 'default'),
                endpoint=request.get('endpoint', 'unknown'),
                duration=duration,
                success=True
            )
            
            return response
        
        except Exception as e:
            # 记录失败请求
            duration = time.time() - start_time
            self.monitor.record_request(
                model=request.get('model', 'default'),
                endpoint=request.get('endpoint', 'unknown'),
                duration=duration,
                success=False
            )
            raise
        
        finally:
            # 减少活跃请求数
            self.monitor.track_active_request(-1)

from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import os

class DataEncryptor:
    def __init__(self, password, salt=None):
        self.password = password.encode()
        self.salt = salt or os.urandom(16)
        self.fernet = self._generate_key()
    
    def _generate_key(self):
        """生成加密密钥"""
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=self.salt,
            iterations=100000,
        )
        key = kdf.derive(self.password)
        return Fernet(key)
    
    def encrypt(self, data):
        """加密数据"""
        if isinstance(data, str):
            data = data.encode()
        return self.fernet.encrypt(data)
    
    def decrypt(self, encrypted_data):
        """解密数据"""
        return self.fernet.decrypt(encrypted_data).decode()
    
    def encrypt_file(self, input_path, output_path):
        """加密文件"""
        with open(input_path, 'rb') as f:
            data = f.read()
        
        encrypted_data = self.encrypt(data)
        
        with open(output_path, 'wb') as f:
            f.write(encrypted_data)
    
    def decrypt_file(self, input_path, output_path):
        """解密文件"""
        with open(input_path, 'rb') as f:
            encrypted_data = f.read()
        
        decrypted_data = self.decrypt(encrypted_data)
        
        with open(output_path, 'wb') as f:
            f.write(decrypted_data.encode())

from fastapi import HTTPException, Depends
from fastapi.security import OAuth2PasswordBearer
from jose import JWTError, jwt
from passlib.context import CryptContext

class SecurityManager:
    def __init__(self, secret_key, algorithm="HS256"):
        self.secret_key = secret_key
        self.algorithm = algorithm
        self.password_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
        self.oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
    
    def verify_password(self, plain_password, hashed_password):
        """验证密码"""
        return self.password_context.verify(plain_password, hashed_password)
    
    def get_password_hash(self, password):
        """获取密码哈希"""
        return self.password_context.hash(password)
    
    def create_access_token(self, data, expires_delta=None):
        """创建访问令牌"""
        to_encode = data.copy()
        if expires_delta:
            expire = datetime.utcnow() + expires_delta
        else:
            expire = datetime.utcnow() + timedelta(minutes=15)
        
        to_encode.update({"exp": expire})
        encoded_jwt = jwt.encode(
            to_encode,
            self.secret_key,
            algorithm=self.algorithm
        )
        return encoded_jwt
    
    def verify_token(self, token: str):
        """验证令牌"""
        try:
            payload = jwt.decode(
                token,
                self.secret_key,
                algorithms=[self.algorithm]
            )
            username: str = payload.get("sub")
            if username is None:
                raise HTTPException(
                    status_code=401,
                    detail="Could not validate credentials"
                )
            return username
        except JWTError:
            raise HTTPException(
                status_code=401,
                detail="Could not validate credentials"
            )
    
    def require_role(self, required_role: str):
        """要求特定角色"""
        def role_checker(current_user: dict = Depends(self.get_current_user)):
            if current_user.get("role") != required_role:
                raise HTTPException(
                    status_code=403,
                    detail="Not enough permissions"
                )
            return current_user
        return role_checker

class ContentFilter:
    def __init__(self, config):
        self.config = config
        self.filters = {
            'profanity': self._check_profanity,
            'hate_speech': self._check_hate_speech,
            'sensitive_info': self._check_sensitive_info,
            'copyright': self._check_copyright
        }
    
    def filter_content(self, text):
        """过滤内容"""
        results = {}
        
        for filter_name, filter_func in self.filters.items():
            try:
                result = filter_func(text)
                results[filter_name] = result
            except Exception as e:
                results[filter_name] = {'error': str(e)}
        
        return results
    
    def _check_profanity(self, text):
        """检查亵渎内容"""
        # 这里可以使用预定义的亵渎词列表或API
        profanity_words = ['bad_word1', 'bad_word2']  # 示例
        found_words = [word for word in profanity_words if word in text.lower()]
        
        return {
            'is_profanity': len(found_words) > 0,
            'found_words': found_words,
            'severity': 'high' if len(found_words) > 5 else 'medium'
        }
    
    def _check_hate_speech(self, text):
        """检查仇恨言论"""
        # 这里可以使用机器学习模型来检测仇恨言论
        # 示例实现
        hate_patterns = [
            r'hate_pattern1',
            r'hate_pattern2'
        ]
        
        found_patterns = []
        for pattern in hate_patterns:
            if re.search(pattern, text, re.IGNORECASE):
                found_patterns.append(pattern)
        
        return {
            'is_hate_speech': len(found_patterns) > 0,
            'found_patterns': found_patterns,
            'severity': 'high' if len(found_patterns) > 3 else 'medium'
        }
    
    def _check_sensitive_info(self, text):
        """检查敏感信息"""
        # 检查个人信息、银行卡号等
        sensitive_patterns = {
            'phone': r'\b\d{3}[-.\s]?\d{3}[-.\s]?\d{4}\b',
            'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            'id_card': r'\b\d{17}[\dXx]\b'
        }
        
        found_info = {}
        for info_type, pattern in sensitive_patterns.items():
            matches = re.findall(pattern, text)
            if matches:
                found_info[info_type] = matches
        
        return {
            'contains_sensitive_info': len(found_info) > 0,
            'found_info': found_info
        }
    
    def _check_copyright(self, text):
        """检查版权内容"""
        # 检查是否包含受版权保护的内容
        # 这里可以使用版权数据库或相似度检测
        return {
            'is_copyright_violation': False,  # 需要实际的检测逻辑
            'similarity_score': 0.0
        }

class MultimodalAI:
    def __init__(self, text_model, image_model, audio_model):
        self.text_model = text_model
        self.image_model = image_model
        self.audio_model = audio_model
        self.fusion_model = self._create_fusion_model()
    
    def _create_fusion_model(self):
        """创建融合模型"""
        # 使用Transformer架构进行多模态融合
        class FusionModel(nn.Module):
            def __init__(self, text_dim, image_dim, audio_dim):
                super().__init__()
                self.text_encoder = nn.Linear(text_dim, 256)
                self.image_encoder = nn.Linear(image_dim, 256)
                self.audio_encoder = nn.Linear(audio_dim, 256)
                
                self.fusion_layer = nn.MultiheadAttention(
                    embed_dim=256,
                    num_heads=8
                )
                
                self.output_layer = nn.Linear(256, 512)
            
            def forward(self, text, image, audio):
                # 编码不同模态
                text_emb = self.text_encoder(text)
                image_emb = self.image_encoder(image)
                audio_emb = self.audio_encoder(audio)
                
                # 拼接特征
                combined = torch.stack([text_emb, image_emb, audio_emb], dim=1)
                
                # 融合
                fused, _ = self.fusion_layer(combined, combined, combined)
                
                # 输出
                output = self.output_layer(fused[:, 0, :])
                return output
        
        return FusionModel(
            text_dim=768,
            image_dim=1024,
            audio_dim=512
        )
    
    def process_multimodal_input(self, text_input, image_input, audio_input):
        """处理多模态输入"""
        # 分别处理不同模态
        text_features = self.text_model.process(text_input)
        image_features = self.image_model.process(image_input)
        audio_features = self.audio_model.process(audio_input)
        
        # 融合特征
        fused_features = self.fusion_model(
            text_features,
            image_features,
            audio_features
        )
        
        return fused_features

class SelfSupervisedLearning:
    def __init__(self, model_config):
        self.model_config = model_config
        self.model = self._create_model()
    
    def _create_model(self):
        """创建自监督学习模型"""
        # 实现掩码语言模型、对比学习等
        class SSLModel(nn.Module):
            def __init__(self, vocab_size, hidden_size):
                super().__init__()
                self.encoder = nn.Embedding(vocab_size, hidden_size)
                self.mask_lm = nn.Linear(hidden_size, vocab_size)
                self.contrastive_head = nn.Linear(hidden_size, hidden_size)
            
            def forward(self, input_ids, masked_positions):
                # 编码
                hidden = self.encoder(input_ids)
                
                # 掩码语言模型
                lm_logits = self.mask_lm(hidden[masked_positions])
                
                # 对比学习特征
                contrastive_features = self.contrastive_head(hidden)
                
                return {
                    'lm_logits': lm_logits,
                    'contrastive_features': contrastive_features
                }
        
        return SSLModel(
            vocab_size=self.model_config['vocab_size'],
            hidden_size=self.model_config['hidden_size']
        )
    
    def train_ssl(self, unlabeled_data):
        """自监督训练"""
        for batch in unlabeled_data:
            # 掩码随机位置
            masked_positions = self._mask_random_positions(batch['input_ids'])
            
            # 前向传播
            outputs = self.model(
                batch['input_ids'],
                masked_positions
            )
            
            # 计算损失
            mlm_loss = self._compute_mlm_loss(
                outputs['lm_logits'],
                batch['input_ids'][masked_positions]
            )
            
            contrastive_loss = self._compute_contrastive_loss(
                outputs['contrastive_features']
            )
            
            # 总损失
            total_loss = mlm_loss + 0.5 * contrastive_loss
            
            # 反向传播
            total_loss.backward()
            
            # 更新参数
            self.optimizer.step()
            self.optimizer.zero_grad()

class IntelligentCustomerService:
    def __init__(self, config):
        self.config = config
        self.nlu_engine = NLUEngine(config['nlu'])
        self.dialog_manager = DialogManager(config['dialog'])
        self.knowledge_base = KnowledgeBase(config['kb'])
        self.llm = LanguageModel(config['llm'])
    
    def process_user_input(self, user_input, user_context):
        """处理用户输入"""
        # 意图识别
        intent = self.nlu_engine.detect_intent(user_input)
        
        # 对话状态更新
        dialog_state = self.dialog_manager.update_state(
            intent,
            user_context
        )
        
        # 知识检索
        knowledge = self.knowledge_base.search(
            user_input,
            intent,
            dialog_state
        )
        
        # 响应生成
        response = self.llm.generate_response(
            user_input,
            knowledge,
            dialog_state
        )
        
        # 后处理
        processed_response = self._post_process_response(response)
        
        return {
            'response': processed_response,
            'intent': intent,
            'dialog_state': dialog_state,
            'confidence': self._calculate_confidence(intent, response)
        }
    
    def handle_multiturn_conversation(self, conversation_history):
        """处理多轮对话"""
        # 维护对话状态
        dialog_state = self._initialize_dialog_state()
        
        for turn in conversation_history:
            # 处理每一轮对话
            result = self.process_user_input(
                turn['user_input'],
                dialog_state
            )
            
            # 更新对话状态
            dialog_state.update(result['dialog_state'])
            
            # 记录回复
            turn['system_response'] = result['response']
        
        return conversation_history

class CreativeContentGenerator:
    def __init__(self, config):
        self.config = config
        self.models = {
            'text': TextGenerationModel(config['text']),
            'image': ImageGenerationModel(config['image']),
            'music': MusicGenerationModel(config['music'])
        }
        self.creative_prompt_engine = CreativePromptEngine()
    
    def generate_creative_content(self, idea, style, constraints=None):
        """生成创意内容"""
        # 创意提示工程
        prompt = self.creative_prompt_engine.create_prompt(
            idea,
            style,
            constraints
        )
        
        # 多模态生成
        text_content = self.models['text'].generate(prompt['text'])
        image_content = self.models['image'].generate(prompt['image'])
        music_content = self.models['music'].generate(prompt['music'])
        
        # 内容融合与优化
        optimized_content = self._optimize_content(
            text_content,
            image_content,
            music_content
        )
        
        return {
            'text': optimized_content['text'],
            'image': optimized_content['image'],
            'music': optimized_content['music'],
            'metadata': {
                'idea': idea,
                'style': style,
                'generation_time': time.time()
            }
        }
    
    def generate_story(self, genre, characters, plot_elements):
        """生成故事"""
        # 构建故事大纲
        outline = self._create_story_outline(genre, characters, plot_elements)
        
        # 生成章节
        chapters = []
        for chapter_outline in outline['chapters']:
            chapter_content = self.models['text'].generate(
                chapter_outline,
                style='narrative'
            )
            chapters.append(chapter_content)
        
        # 生成配图
        chapter_images = []
        for chapter in chapters:
            image_prompt = self._extract_image_prompt(chapter)
            image_content = self.models['image'].generate(image_prompt)
            chapter_images.append(image_content)
        
        return {
            'chapters': chapters,
            'images': chapter_images,
            'outline': outline
        }