Golang NLP工程师在班加罗尔

package main

import (
    "fmt"
    "strings"
    "sync"
    
    // 实际项目中可能会引入的NLP相关库
    // "github.com/jdkato/prose/v2"
    // "gonum.org/v1/gonum/mat"
)

// NLPPipeline 表示一个简化的NLP处理管道
type NLPPipeline struct {
    mu          sync.RWMutex
    models      map[string]Model
    preprocessor TextPreprocessor
}

// Model 接口定义NLP模型的基本行为
type Model interface {
    Predict(text string) ([]string, error)
    Train(data []TrainingData) error
    Evaluate() float64
}

// TextPreprocessor 文本预处理
type TextPreprocessor struct {
    stopWords map[string]bool
}

func NewTextPreprocessor() *TextPreprocessor {
    return &TextPreprocessor{
        stopWords: map[string]bool{
            "the": true, "a": true, "an": true,
            "in": true, "on": true, "at": true,
        },
    }
}

func (tp *TextPreprocessor) Clean(text string) string {
    words := strings.Fields(strings.ToLower(text))
    filtered := make([]string, 0, len(words))
    
    for _, word := range words {
        if !tp.stopWords[word] {
            filtered = append(filtered, word)
        }
    }
    return strings.Join(filtered, " ")
}

// IntentClassifier 意图分类器示例
type IntentClassifier struct {
    embeddings map[string][]float64
    intents    map[string][]string
}

func NewIntentClassifier() *IntentClassifier {
    return &IntentClassifier{
        embeddings: make(map[string][]float64),
        intents:    make(map[string][]string),
    }
}

func (ic *IntentClassifier) Predict(text string) ([]string, error) {
    // 简化的意图匹配逻辑
    text = strings.ToLower(text)
    for intent, patterns := range ic.intents {
        for _, pattern := range patterns {
            if strings.Contains(text, pattern) {
                return []string{intent}, nil
            }
        }
    }
    return []string{"unknown"}, nil
}

func (ic *IntentClassifier) Train(data []TrainingData) error {
    for _, d := range data {
        ic.intents[d.Intent] = append(ic.intents[d.Intent], d.Text)
    }
    return nil
}

func (ic *IntentClassifier) Evaluate() float64 {
    // 简化评估逻辑
    return 0.85
}

type TrainingData struct {
    Text   string
    Intent string
}

// NLPMonitor 监控NLP模型性能
type NLPMonitor struct {
    metrics chan Metric
}

type Metric struct {
    ModelName string
    Accuracy  float64
    Latency   int64 // 毫秒
    Timestamp int64
}

func (m *NLPMonitor) Start() {
    go func() {
        for metric := range m.metrics {
            fmt.Printf("监控数据 - 模型: %s, 准确率: %.2f, 延迟: %dms\n",
                metric.ModelName, metric.Accuracy, metric.Latency)
            // 实际项目中这里会有报警逻辑和指标存储
        }
    }()
}

// 示例：并发处理多个NLP请求
func ProcessBatch(pipeline *NLPPipeline, texts []string) map[string][]string {
    var wg sync.WaitGroup
    results := make(map[string][]string)
    var mu sync.Mutex
    
    for i, text := range texts {
        wg.Add(1)
        go func(idx int, t string) {
            defer wg.Done()
            
            pipeline.mu.RLock()
            model, exists := pipeline.models["intent_classifier"]
            pipeline.mu.RUnlock()
            
            if exists {
                cleaned := pipeline.preprocessor.Clean(t)
                prediction, err := model.Predict(cleaned)
                if err == nil {
                    mu.Lock()
                    results[fmt.Sprintf("text_%d", idx)] = prediction
                    mu.Unlock()
                }
            }
        }(i, text)
    }
    
    wg.Wait()
    return results
}

func main() {
    // 初始化NLP管道
    pipeline := &NLPPipeline{
        models:      make(map[string]Model),
        preprocessor: *NewTextPreprocessor(),
    }
    
    // 添加模型
    classifier := NewIntentClassifier()
    trainingData := []TrainingData{
        {"I want to book a flight", "book_flight"},
        {"What's the weather like", "check_weather"},
        {"Set an alarm for tomorrow", "set_alarm"},
    }
    
    classifier.Train(trainingData)
    pipeline.models["intent_classifier"] = classifier
    
    // 初始化监控
    monitor := &NLPMonitor{metrics: make(chan Metric, 100)}
    monitor.Start()
    
    // 示例处理
    texts := []string{
        "Book me a flight to Bangalore",
        "What's the weather in Bangalore?",
        "I need to set up a meeting",
    }
    
    results := ProcessBatch(pipeline, texts)
    
    // 输出结果
    for key, intents := range results {
        fmt.Printf("%s: %v\n", key, intents)
    }
    
    // 发送监控指标
    monitor.metrics <- Metric{
        ModelName: "intent_classifier",
        Accuracy:  classifier.Evaluate(),
        Latency:   45,
        Timestamp: 1672531200,
    }
    
    close(monitor.metrics)
}

这个示例展示了Go语言在NLP工程中的典型应用模式：

1. 并发处理：利用goroutine批量处理文本数据
2. 线程安全：使用sync包保护共享资源
3. 接口设计：通过接口定义模型标准行为
4. 监控集成：实时性能指标收集
5. 管道架构：模块化的文本处理流程

对于班加罗尔的NLP职位，Go的优势在于：

• 高性能的并发处理适合实时NLP服务
• 内存效率高，适合部署资源敏感的生产环境
• 强大的标准库支持网络服务和监控
• 与Python生态的良好互操作性（可通过cgo调用Python库）