AI全流程落地实战：从设计-开发-测试到运营一站式搞定

python# 使用Python进行简单的数据收集示例
import requests

def fetch_data(api_url):
    response = requests.get(api_url)
    if response.status_code == 200:
        return response.json()
    else:
        return None

data = fetch_data('https://api.example.com/data')

python# 使用MongoDB进行数据存储示例
from pymongo import MongoClient

client = MongoClient('mongodb://localhost:27017/')
db = client['knowledge_base']
collection = db['medical_data']

# 插入数据
collection.insert_many(data)

python# 使用TensorFlow进行简单的文本分类示例
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 假设已有训练数据
texts = ["Example text 1", "Example text 2"]
labels = [0, 1]

tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences, maxlen=100)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(10000, 128, input_length=100),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(padded_sequences, labels, epochs=10)

javascript// 使用Elasticsearch进行数据检索示例
const { Client } = require('@elastic/elasticsearch')
const client = new Client({ node: 'http://localhost:9200' })

async function search(query) {
  const result = await client.search({
    index: 'knowledge_base',
    body: {
      query: {
        match: { content: query }
      }
    }
  })
  return result.hits.hits
}

search('medical data').then(console.log)

python# 使用OpenAI Codex自动生成Python代码示例
import openai

openai.api_key = 'your-api-key'

prompt = "Write a Python function to sort a list of numbers."
response = openai.Completion.create(engine="davinci-codex", prompt=prompt, max_tokens=100)
print(response.choices[0].text.strip())

python# 使用SonarQube进行代码质量分析示例
# 假设已经安装并配置好SonarQube
import subprocess

subprocess.run(["sonar-scanner"])

python# 使用AI生成单元测试示例
import openai

prompt = "Generate a unit test for the following Python function:\n\n" \
         "def add(a, b):\n    return a + b\n"
response = openai.Completion.create(engine="davinci-codex", prompt=prompt, max_tokens=100)
print(response.choices[0].text.strip())

yaml# 使用GitHub Actions进行自动化部署示例
name: CI/CD Pipeline

on: [push]

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Run tests
      run: |
        pytest
    - name: Deploy to production
      run: |
        docker build -t myapp .
        docker run -d -p 80:80 myapp