How I Used GraphQL to Build a Smarter Chatbot for Customer Support

In today's fast-paced world, customers demand instant gratification when it comes to customer service. One of the best ways to address this is by leveraging chatbots to provide customers with quick and effective support. However, not all chatbots are created equal. In this blog post, I will share my experience of using GraphQL to build a smarter chatbot for customer support. I will explain the benefits of using GraphQL, the steps I took to build the chatbot, and how it improved the overall customer experience.

Introduction

The rise of chatbots in recent years has transformed the way businesses handle customer service. By leveraging artificial intelligence and machine learning, chatbots can provide quick and effective solutions to customer queries. However, to build a chatbot that truly meets the needs of customers, you need to use the right technology. GraphQL is one such technology that has proven to be effective in building smarter chatbots.

What is GraphQL?

GraphQL is a query language and runtime for APIs. It was developed by Facebook in 2012 and has since become an open-source technology. Unlike traditional RESTful APIs, GraphQL allows developers to request specific data and only receive the data they need. This makes it a more efficient and flexible way of accessing and manipulating data.

Why Use GraphQL for Chatbots?

When building a chatbot, you need to retrieve data from various sources and APIs. With traditional RESTful APIs, you would need to make multiple requests to retrieve all the data you need. This can lead to performance issues and slower response times. However, with GraphQL, you can retrieve all the data you need with a single request. This makes chatbots built with GraphQL faster, more efficient, and ultimately more effective.

Building a Chatbot with GraphQL

Now that we have established the benefits of using GraphQL, let's take a look at how to build a chatbot with GraphQL. In this example, we will be using the Apollo GraphQL platform.

Step 1: Define Your Schema

The first step is to define your schema. Your schema defines the types of data that your chatbot will use. For example, if your chatbot will be providing information about products, you will need to define a "Product" type in your schema. Here is an example of a simple schema:

type Query {
  hello: String
}

schema {
  query: Query
}

This schema defines a single query that returns a string. You can modify this schema to fit your chatbot's needs.

Step 2: Define Your Resolvers

Once you have defined your schema, you need to define your resolvers. Resolvers are functions that resolve the data for a specific field in your schema. For example, if you have a "Product" type in your schema, you will need to define a resolver function that retrieves the product data from your database. Here is an example of a resolver function:

const resolvers = {
  Query: {
    hello: () => 'Hello World!',
  },
};

This resolver function returns the string "Hello World!" when the "hello" query is executed.

Step 3: Connect to Your Data Sources

The next step is to connect to your data sources. This could be a database, an API, or any other data source that you need to retrieve data from. Once you have connected to your data sources, you can use your resolvers to retrieve the data.

Step 4: Build Your Chatbot

Now that you have defined your schema, defined your resolvers, and connected to your data sources, you can build your chatbot. There are many chatbot platforms available, but in this example, we will be using the Botpress platform. Botpress is an open-source chatbot platform that provides a visual interface for building chatbots.

To build a chatbot in Botpress, you need to create a flow. A flow is a sequence of steps that the chatbot will follow to provide a response to a user.

Here is an example of a Botpress flow with GraphQL Query node and Format Response node:

{
  "id": "graphql-example",
  "nodes": [
    {
      "id": "start",
      "name": "Start",
      "type": "entry",
      "next": "graphql-query"
    },
    {
      "id": "graphql-query",
      "name": "GraphQL Query",
      "type": "action",
      "action": {
        "name": "graphql.query",
        "args": {
          "query": "query { currentTime }",
          "variables": {}
        },
        "results": {
          "next": "format-response"
        }
      }
    },
    {
      "id": "format-response",
      "name": "Format Response",
      "type": "action",
      "action": {
        "name": "builtin.format",
        "args": {
          "text": "The current time is {{ results.graphql-query.currentTime }}"
        },
        "results": {
          "next": "end"
        }
      }
    },
    {
      "id": "end",
      "name": "End",
      "type": "exit"
    }
  ]
}

This flow responds to a user's message with the current time. The "GraphQL Query" node sends a query to the GraphQL API, and the "Format Response" node formats the response before sending it back to the user.

By combining the power of GraphQL and Botpress, you can build a chatbot that retrieves data from multiple sources and provides quick and effective support to customers.

Improving the Customer Experience with a Smarter Chatbot

By using GraphQL to build a smarter chatbot, you can improve the overall customer experience. Here are some ways that a smarter chatbot can improve customer service:

1. Faster response times: With GraphQL, you can retrieve all the data you need with a single request. This means that your chatbot can provide faster responses to customer queries.

2. More personalized responses: By using GraphQL to retrieve customer data, you can provide more personalized responses to customer queries.

3. Better customer engagement: A smarter chatbot can engage customers in a more meaningful way, providing a more positive customer experience.

Conclusion

In conclusion, building a chatbot with GraphQL can provide numerous benefits to businesses looking to improve their customer service. By leveraging the power of GraphQL, you can build a chatbot that retrieves data from multiple sources and provides quick and effective support to customers. With the right technology and platform, you can provide a smarter, more efficient, and ultimately more effective chatbot for better customer service.

Further reading:

• GraphQL documentation

• Botpress documentation

• "Why We Chose GraphQL Over REST for a New Mobile App API"

• "How to Build a Chatbot with GraphQL"