How to use Go language for intelligent customer service development?

In modern society, people have extremely high demand for efficient and immediate customer service, and the emergence of intelligent customer service is precisely to meet this demand. The Go language is gradually favored by developers because of its excellent concurrency performance and ease of learning. This article will introduce how to use Go language for intelligent customer service development.

1. Understanding Intelligent Customer Service

Before starting to use Go language to develop intelligent customer service, we need to understand what intelligent customer service is. Simply put, intelligent customer service is a customer service method that uses artificial intelligence technology to realize automatic question and answer, automatic speech recognition, automatic semantic understanding, automatic recommendation and other functions. It can provide customers with quick and accurate answers and has 24-hour uninterrupted service capabilities. We need to apply these technologies to the Go language to achieve the goal of intelligent customer service.

2. Preparation

Before starting to use Go language for intelligent customer service development, we need to make the following preparations:

1. Install Go language

The installation of Go language is very simple. We can download the installation package of the corresponding operating system from the official website and install it (https://golang.org).

2. Installing dependent libraries

The Go language has many powerful open source libraries. We need to install some commonly used dependent libraries to assist us in developing intelligent customer service.

Commonly used dependent libraries include:

a. gin: A web framework similar to Flask in Python, which can help us quickly build web applications.

b. gRPC: an efficient, cross-language RPC framework that supports multiple serialization protocols.

c. TensorFlow: A powerful machine learning framework that can help us build the core model of intelligent customer service.

We can use the go get command to install the above dependent libraries.

3. Building Intelligent Customer Service

Here, we will take building a Web-based intelligent customer service system as an example to introduce the development process of intelligent customer service in Go language.

1. Build a Web server

We use the gin framework to build a Web service and use the following code to build an HTTP server.

package main

import "github.com/gin-gonic/gin"

func main() {
    r := gin.Default()
    r.GET("/", func(c *gin.Context) {
        c.JSON(200, gin.H{
            "message": "Welcome to the Smart Customer Service System!",
        })
    })
    r.Run() // listen and serve on 0.0.0.0:8080
}

After running the above code, open http://localhost:8080 in the browser and you can see the welcome message output.

2. Implementing Q&A robot

Using TensorFlow to implement intelligent Q&A, we can use the open source ChatBot sample code to implement our own answering system. The open source model provided by TensorFlow can help us complete natural language processing and intent recognition operations.

Before this, you need to add the trained model and text data to the project. You can check the official documentation of TensorFlow to learn how to do this step.

After we get the model and text data, we can build a processor to complete the question and answer function:

package main

import (
    "github.com/gin-gonic/gin"
    tf "github.com/galeone/tfgo"
    pb "github.com/galeone/tfgo/image.Tensorflow_inception_v3"
    "github.com/galeone/tfgo/tensorflow/core/framework/tensor_go"
    "github.com/gorilla/websocket"
    "log"
    "net/http"
)

var upgrader = websocket.Upgrader{
    ReadBufferSize:  1024,
    WriteBufferSize: 1024,
    CheckOrigin: func(r *http.Request) bool {
        return true
    },
}

type Message struct {
    Name string      `json:"name"`
    Body interface{} `json:"body"`
}

func main() {
    router := gin.Default()

    ai := NewAI()
    defer ai.Close()

    router.GET("/ws", func(c *gin.Context) {
        conn, err := upgrader.Upgrade(c.Writer, c.Request, nil)
        if err != nil {
            log.Println(err)
            return
        }
        defer conn.Close()

        for {
            var msg Message
            err := conn.ReadJSON(&msg)
            if err != nil {
                log.Println(err)
                break
            }

            response := ai.Query(msg.Body.(string))

            err = conn.WriteJSON(Message{
                Name: "response",
                Body: response,
            })
            if err != nil {
                log.Println(err)
                break
            }
        }
    })

    router.Run(":8080")
}

type AI struct {
    sess   *tf.Session
    graph  *tf.Graph
    labels []string
}

func NewAI() *AI {
    graph := tf.NewGraph()
    model, err := ioutil.ReadFile("model.pb")
    if err != nil {
        log.Fatalf("Unable to read %q: %v", "model.pb", err)
    }
    if err := graph.Import(model, ""); err != nil {
        log.Fatalf("Unable to read model %q: %v", "model.pb", err)
    }

    labels := make([]string, 0)
    file, err := os.Open("labels.txt")
    if err != nil {
        log.Fatalf("Unable to open labels file: %v", err)
    }
    defer file.Close()

    scanner := bufio.NewScanner(file)
    for scanner.Scan() {
        labels = append(labels, scanner.Text())
    }

    sess, err := tf.NewSession(graph, nil)
    if err != nil {
        log.Fatal(err)
    }

    return &AI{
        graph:  graph,
        sess:   sess,
        labels: labels,
    }
}

func (ai *AI) Query(input string) string {
    pb := tf.Output{
        Op:    ai.graph.Operation("input"),
        Index: 0,
    }

    prob := tf.Output{
        Op:    ai.graph.Operation("output"),
        Index: 0,
    }

    tensor, err := tensorflow.NewTensor(input)
    if err != nil {
        log.Fatalln("Cannot construct tensor: ", err)
    }

    result, err := ai.sess.Run(map[tf.Output]*tensorflow.Tensor{
        pb: tensor,
    }, []tf.Output{
        prob,
    }, nil)

    if err != nil {
        log.Fatal(err)
    }

    prob_result := result[0].Value().([][]float32)[0]
    max_prob_index := 0
    max_prob := prob_result[0]

    for i, prob := range prob_result {
        if prob > max_prob {
            max_prob = prob
            max_prob_index = i
        }
    }
    return ai.labels[max_prob_index]
}

func (ai *AI) Close() {
    ai.sess.Close()
}

In the above code, we implemented a basic chat robot and passed The WebSocket server provides a means of interacting with front-end pages.

4. Summary

This article introduces how to use Go language for intelligent customer service development. We first understand the concept of smart customer service, and then prepare for the work, including installing the Go language and other dependent libraries. Next, we built a Web-based intelligent customer service system, and took a simple question and answer robot as an example to introduce how to use Tensorflow to implement the question and answer system.

With the continuous development of artificial intelligence technology, the application of intelligent customer service has also received more and more attention. For developers, using Go language to develop intelligent customer service has good rapid iteration capabilities and excellent performance. I believe that in the near future, we will see the emergence of more interesting Go language intelligent customer service applications.

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