锦中人工智能助手

// 数据加密示例：使用AES加密敏感信息

import javax.crypto.Cipher;

import javax.crypto.spec.SecretKeySpec;

public class AESUtil {

private static final String ALGORITHM = "AES";

private static final byte[] KEY = "1234567890abcdef".getBytes();

public static String encrypt(String data) throws Exception {

Cipher cipher = Cipher.getInstance(ALGORITHM);

SecretKeySpec keySpec = new SecretKeySpec(KEY, ALGORITHM);

cipher.init(Cipher.ENCRYPT_MODE, keySpec);

byte[] encrypted = cipher.doFinal(data.getBytes());

return Base64.getEncoder().encodeToString(encrypted);

}

public static String decrypt(String encryptedData) throws Exception {

Cipher cipher = Cipher.getInstance(ALGORITHM);

SecretKeySpec keySpec = new SecretKeySpec(KEY, ALGORITHM);

cipher.init(Cipher.DECRYPT_MODE, keySpec);

byte[] decrypted = cipher.doFinal(Base64.getDecoder().decode(encryptedData));

return new String(decrypted);

}

}

// 权限控制示例：使用Spring Security进行角色管理

@Configuration

@EnableWebSecurity

public class SecurityConfig extends WebSecurityConfigurerAdapter {

@Override

protected void configure(HttpSecurity http) throws Exception {

http

.authorizeRequests()

.antMatchers("/api/**").hasRole("USER")

.anyRequest().authenticated()

.and()

.formLogin();

}

@Autowired

public void configureGlobal(AuthenticationManagerBuilder auth) throws Exception {

auth.inMemoryAuthentication()

.withUser("admin").password("{noop}123456").roles("ADMIN")

.and()

.withUser("user").password("{noop}123456").roles("USER");

}

}

# 使用PyTorch训练一个简单的NLP分类器

import torch

from torch.utils.data import Dataset, DataLoader

class ChatDataset(Dataset):

def __init__(self, texts, labels):

self.texts = texts

self.labels = labels

def __len__(self):

return len(self.texts)

def __getitem__(self, idx):

return self.texts[idx], self.labels[idx]

model = torch.nn.Sequential(

torch.nn.Embedding(10000, 128),

torch.nn.LSTM(128, 64),

torch.nn.Linear(64, 2)

)

criterion = torch.nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 假设texts和labels已经准备好

dataset = ChatDataset(texts, labels)

dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

for epoch in range(10):

for inputs, labels in dataloader:

outputs = model(inputs)

loss = criterion(outputs, labels)

optimizer.zero_grad()

loss.backward()

optimizer.step()

print(f'Epoch {epoch+1}, Loss: {loss.item()}')