2025年AI Agents崛起：开发者需要掌握哪些关键技能？

# 使用Hugging Face Transformers库加载预训练的BERT模型

from transformers import BertTokenizer, BertModel



tokenizer = BertTokenizer.from\_pretrained('bert-base-uncased')

model = BertModel.from\_pretrained('bert-base-uncased')



# 对输入文本进行编码

text = "Hello, world!"

inputs = tokenizer(text, return\_tensors="pt")



# 获取模型输出

outputs = model(\*\*inputs)



# 输出编码层的隐藏状态

print(outputs.last\_hidden\_state)

# 使用NLTK库进行文本预处理

import nltk

from nltk.corpus import stopwords

from nltk.stem import PorterStemmer



nltk.download('stopwords')

nltk.download('punkt')



# 分词

text = "This is an example of text preprocessing."

tokens = nltk.word\_tokenize(text)



# 去除停用词

stop\_words = set(stopwords.words('english'))

filtered\_tokens = [word for word in tokens if word.casefold() not in stop\_words]



# 词干提取

stemmer = PorterStemmer()

stemmed\_tokens = [stemmer.stem(word) for word in filtered\_tokens]



print(stemmed\_tokens)

# 使用scikit-learn库进行线性回归

from sklearn.linear\_model import LinearRegression

import numpy as np



# 准备训练数据

X = np.array([[1], [2], [3], [4], [5]])

y = np.array([2, 4, 6, 8, 10])



# 创建线性回归模型并训练

model = LinearRegression()

model.fit(X, y)



# 预测新数据

X\_test = np.array([[6]])

y\_pred = model.predict(X\_test)



print(y\_pred)  # 输出预测结果

# 使用TensorFlow和OpenAI Gym进行强化学习

import tensorflow as tf

import gym



# 创建环境

env = gym.make('CartPole-v1')



# 定义神经网络模型

model = tf.keras.models.Sequential([

    tf.keras.layers.Dense(32, activation='relu', input\_shape=(4,)),

    tf.keras.layers.Dense(32, activation='relu'),

    tf.keras.layers.Dense(2)

])



# 定义优化器和损失函数

optimizer = tf.keras.optimizers.Adam(learning\_rate=0.001)

loss\_fn = tf.keras.losses.SparseCategoricalCrossentropy(from\_logits=True)



# 训练循环

episodes = 1000

for episode in range(episodes):

    state = env.reset()

    total\_reward = 0

    with tf.GradientTape() as tape:

        while True:

            # 获取模型的预测动作

            logits = model(tf.expand\_dims(state, axis=0))

            action = tf.random.categorical(logits, 1)[0, 0].numpy()



            # 执行动作并获取下一个状态和奖励

            next\_state, reward, done, \_ = env.step(action)

            total\_reward += reward



            # 计算损失

            loss\_value = loss\_fn(tf.expand\_dims(action, axis=0), logits)



            # 如果完成任务，跳出循环

            if done:

                break

            state = next\_state



    # 反向传播更新模型

    grads = tape.gradient(loss\_value, model.trainable\_variables)

    optimizer.apply\_gradients(zip(grads, model.trainable\_variables))



    # 打印训练信息

    if episode % 100 == 0:

        print(f"Episode {episode}, Total Reward: {total\_reward}")



env.close()

# 使用FastAPI构建RESTful API

from fastapi import FastAPI

from pydantic import BaseModel



app = FastAPI()



class Query(BaseModel):

    text: str



@app.post("/analyze")

async def analyze(query: Query):

    # 在这里调用AI模型进行分析

    result = {"sentiment": "positive", "confidence": 0.95}

    return result

# 使用Celery进行异步任务处理

from celery import Celery



app = Celery('tasks', broker='pyamqp://guest@localhost//')



@app.task

def analyze\_text(text):

    # 在这里执行耗时的文本分析任务

    result = {"sentiment": "positive", "confidence": 0.95}

    return result

# 使用加密技术保护数据

from cryptography.fernet import Fernet



# 生成密钥

key = Fernet.generate\_key()

cipher\_suite = Fernet(key)



# 加密数据

data = b"Sensitive user data"

cipher\_text = cipher\_suite.encrypt(data)



# 解密数据

plain\_text = cipher\_suite.decrypt(cipher\_text)



print(plain\_text.decode())

# 使用SHAP库解释模型决策

import shap

import xgboost



# 训练XGBoost模型

X, y = shap.datasets.adult()

model = xgboost.train({"learning\_rate": 0.01}, xgboost.DMatrix(X, label=y), 100)



# 创建SHAP解释器

explainer = shap.TreeExplainer(model)

shap\_values = explainer.shap\_values(X)



# 可视化SHAP值

shap.summary\_plot(shap\_values, X)