Building a Slack Bot with Go and Wit.ai

Table of Contents

• Introduction
• Building a Simple Slide Board with Natural Language Processing Functionality
• The Need for Natural Language Understanding (NLU)
• Creating a Bot in Slack
• Setting Up Real-Time Messaging
• Integrating with Wit.ai for Natural Language Understanding
• Training the Model with Wit.ai
• Using the Wit.ai API in Your Application
• Creating a Wolfram Alpha Application
• Creating a Wolfram Alpha Client in Go
• Replying to User Inputs Based on Entity Recognition
• Conclusion

Introduction

In this article, we will explore how to build a simple slide board with natural language processing functionality. The slide board will be able to extract information from user input and provide Relevant responses. We will use the Wit.ai platform for natural language understanding and the Wolfram Alpha API for retrieving useful information. The slide board will be implemented as a bot in Slack, utilizing real-time messaging for seamless interaction. By the end of this article, you will have a solid understanding of how to create a bot that can understand and respond to user queries using natural language processing.

Building a Simple Slide Board with Natural Language Processing Functionality

To build a simple slide board with natural language processing functionality, we will first create a bot in Slack. This bot will act as the interface for users to interact with the slide board. To add a new bot in Slack, go to Apps and search for the BOTS application. Add a new configuration and choose a username for your bot. Once the bot is added, you will receive an API token that we will use to authenticate our bot.

After adding the bot to Slack, we need to set up real-time messaging so that the bot can receive and respond to user messages in real-time. We will use the Slack API's real-time messaging capabilities to achieve this. By managing the connection with Slack's real-time messaging object, we can listen for incoming message events and handle them accordingly.

The Need for Natural Language Understanding (NLU)

The reason we need natural language understanding (NLU) in our slide board is that we want the bot to be able to understand and respond to user queries in a conversational manner. Users may ask questions in various ways or make typographical errors, and our bot should be able to interpret their queries correctly. By utilizing NLU, we can extract useful information from user messages and provide accurate responses.

There are various platforms available for NLU, such as Luis, AI, Rational, and Wit.ai. In this article, we will use Wit.ai, which is a natural language processing platform acquired by Facebook. Wit.ai provides the ability to extract entities from custom messages, allowing us to understand the intent and context of user queries.

Creating a Bot in Slack

To create a bot in Slack, follow these steps:

1. Go to the Apps page of your Slack team.
2. Search for the BOTS application and add a new configuration.
3. Choose a username for your bot, for example, "Package-main".
4. Copy the generated API token, as we will need it later.

Once the bot is added, you will see it listed on the main page of your Slack workspace. However, the bot will be offline initially. To make the bot functional, we need to create a program that receives messages from users and sends appropriate replies.

Setting Up Real-Time Messaging

To set up real-time messaging in our slide board, we will use the Slack API's real-time messaging capabilities. This will allow our bot to receive events from Slack's real-time messaging system and handle them accordingly. We will utilize the "github.com/nlopes/slack" package, which provides a Go client for the Slack API.

To get started, we first create a Slack client by importing the "github.com/nlopes/slack" package and calling the "slack.New" function. We need to provide our API token as the argument to the "slack.New" function. However, instead of hard-coding the token, we can store it in an environment variable for security purposes. We can retrieve the token using the "os.Getenv" function and pass it to the "slack.New" function.

Once the Slack client is created, we can set up the real-time messaging capabilities by calling the "RTM" method of the client. This will return a real-time messaging object. We should manage the connection to Slack's real-time messaging system in a separate goroutine to ensure uninterrupted message handling.

To handle incoming events, we can iterate over the "IncomingEvents" Channel of the real-time messaging object. We can use a switch statement to determine the type of each event and call the appropriate function to handle it. For this article, we will focus on the "MessageEvent" type of events. We can write a separate function to handle message events and call it using a goroutine.

Integrating with Wit.ai for Natural Language Understanding

To provide natural language understanding capabilities to our slide board, we will integrate with Wit.ai. Wit.ai is a natural language processing platform that allows us to extract entities from custom messages. We can use these entities to understand the intent and context of user queries.

To get started, we need to create an application in the Wit.ai platform. This can be done by going to the Wit.AI Website and signing in with your preferred account. After signing in, you can create a new application by providing a name and description for it. You can choose to make the application open or private, depending on your requirements.

Once the application is created, we can define entities within it. Entities are specific types of information that we want to extract from user messages. Wit.ai provides some predefined entities, such as greetings and thanks, but we can also create custom entities if needed.

For example, let's create a greetings entity. We can define different variations of greetings, such as "hi" or "hello," and mark them as valid greetings. We can also assign a confidence level to each variation. This allows Wit.ai to understand similar greetings even if there are typos or variations in the user's input.

Similarly, we can create a Wolfram search query entity. This entity can help us identify if the user wants to perform a search using the Wolfram Alpha API. We can define a variety of search queries, such as "What is the formula of ethanol?" and mark them as valid search queries. This entity can be particularly useful when the user asks specific questions that require a search operation.

After defining the entities, we need to train the model. This involves providing example queries to Wit.ai and validating the extracted entities. We can also sync the model to apply any changes made during training.

To use the Wit.ai API in our application, we need to obtain the API key. This can be done by going to the Settings page of your Wit.ai application and copying the Server Access Token. We should store this token in our keys file, as we will need it to authenticate with the Wit.ai API.

Training the Model with Wit.ai

Training the model in Wit.ai involves providing example queries and validating the extracted entities. By training the model, we help it understand different variations of user queries and improve its accuracy in recognizing entities.

To train the model, we can use the Wit.ai web platform. We can go to the Wit.ai website and select our application. From there, we can create new examples by providing sample queries and validating the extracted entities. This process helps the model learn and improve its understanding of user queries.

For example, let's say we have defined a greetings entity. We can create example queries like "hi," "hello," or "hola" and mark them as valid greetings. By providing several variations and validating them, we improve the model's ability to identify greetings in user messages.

Similarly, for the Wolfram search query entity, we can create example queries like "What is the meaning of life?" or "Who is the president of the United States?" and mark them as valid search queries. This helps the model understand when it should perform a search operation using the Wolfram Alpha API.

It's important to note that training the model is an iterative process. We can keep adding new examples and validating them to improve the model's accuracy over time.

Using the Wit.ai API in Your Application

To use the Wit.ai API in our slide board application, we need to make API calls to Wit.ai and parse the response to extract useful information. We can use the Wit.ai client provided by the "github.com/wit-ai/wit-go" package, which is designed specifically for the Wit.ai API.

To get started, we need to create a Wit.ai client by importing the "github.com/wit-ai/wit-go" package and calling the "wit.NewClient" function. We should provide the Wit.ai access token as an argument to the "wit.NewClient" function. This token can be retrieved from our keys file, where we stored the Server Access Token obtained from the Wit.ai web platform.

Once the Wit.ai client is created, we can use the "Client.Message" function to send a message to Wit.ai and retrieve the response. We need to pass the user's input text as the argument to the "Client.Message" function. The response will contain a list of entities extracted from the user's message, along with their confidence levels.

To extract the most relevant entity from the response, we can iterate over the map of entities and compare their confidence levels. We can store the entity with the highest confidence and use it to determine the appropriate response. It's important to note that there may be multiple entities with different confidence levels, so we should choose the one with the highest confidence.

We can also filter out entities with low confidence levels by setting a confidence threshold. This ensures that we only consider entities that are highly likely to be accurate.

Once we have identified the entity with the highest confidence, we can implement a switch statement to handle different types of entities. For example, if the entity is a greeting, we can reply with a suitable greeting message. If the entity is a Wolfram search query, we can use the Wolfram Alpha API to perform the search and provide the relevant answer.

We can send the response back to the user using the Slack client's "PostMessage" function. We need to provide the user's ID, the response text, and any necessary parameters. It's important to set the "user" parameter to true so that the message is sent as a user message, rather than a message from the bot.

If no relevant entities are found in the user's message, we can send a default "not found" message to the user. This ensures that the user receives a response even if their query does not match any defined entities.

By integrating Wit.ai into our slide board application, we can greatly enhance its ability to understand and respond to user queries using natural language processing.

Creating a Wolfram Alpha Application

To integrate the Wolfram Alpha API into our slide board, we need to create a Wolfram Alpha application. This can be done by going to the Wolfram Alpha developer portal and signing in with your preferred account. Once signed in, you can create a new application by providing a name and description for it. Note that there is a free tier available, but it has a limit of 2,000 API calls per month.

After creating the application, you will receive an App ID. This App ID is required to make API calls to the Wolfram Alpha API and retrieve useful information.

The Wolfram Alpha API is a powerful tool for getting answers to factual questions. It can provide information on a wide range of topics, including mathematics, science, history, geography, and much more. By integrating Wolfram Alpha into our slide board, we can ensure that our bot can retrieve accurate and relevant information for user queries.

Creating a Wolfram Alpha Client in Go

To interact with the Wolfram Alpha API in our slide board, we need to create a Wolfram Alpha client. We can do this by importing the appropriate Go package and using the provided functions.

There are several Go packages available for interacting with the Wolfram Alpha API, but it's important to choose a reliable one with good support and documentation. One such package is the "go-wolfram" package, which provides a client for the Wolfram Alpha API.

To get started, we should import the "github.com/Krognol/go-wolfram" package and create a client using the provided functions. We can store the App ID obtained from the Wolfram Alpha developer portal in our keys file and retrieve it when creating the client.

The Wolfram Alpha client allows us to make various types of API calls, such as getting a short answer, full query results, or a specific subpod from a query result. In our slide board, we will primarily use the "ShortAnswerQuery" function, which returns a STRING with a concise answer to a given query.

To make a query to the Wolfram Alpha API, we need to provide the query text, units (e.g., metric or Imperial), and a timeout value. The query text can be obtained from the entity extracted by Wit.ai. We can cast the entity value to a string and use it as the query text. The units and timeout values can be set according to our requirements.

The Wolfram Alpha client returns a result and an error. If the error is not nil, it means that an error occurred while making the API call. In such cases, we can print an error message and return from the function. If the result is not empty, it means that we have received a valid response from the Wolfram Alpha API. We can send the result as the response to the user.

By creating a Wolfram Alpha client, we can leverage the powerful capabilities of the Wolfram Alpha API to provide accurate answers to user queries.

Replying to User Inputs Based on Entity Recognition

To reply to user inputs based on entity recognition, we will implement a function that handles different types of entities and sends appropriate replies. This function will take the entity key as an input and the entity itself as an argument.

In our switch statement, we can provide cases for different types of entities. For example, if the entity is a greeting, we can send a suitable greeting back to the user. We can use Slack's "PostMessage" function to send the message to the user. We need to provide the user ID, the response text, and any necessary parameters.

In the case of a Wolfram search query, we can use the Wolfram Alpha client to make the API call and retrieve the relevant answer. We can extract the query text from the entity value and pass it to the Wolfram Alpha client's "ShortAnswerQuery" function. The result will be a string with the answer, which we can send back to the user using Slack's "PostMessage" function.

If no relevant entities are found in the user's message, we can send a default "not found" message to the user. This ensures that the user receives a response even if their query does not match any defined entities.

By implementing this reply function, we can handle different types of user queries and send appropriate responses based on entity recognition. This allows our slide board to provide accurate and relevant information to users.

Conclusion

In this article, we have explored how to build a simple slide board with natural language processing functionality. We have seen the importance of natural language understanding (NLU) in understanding and responding to user queries in a conversational manner.

We started by creating a bot in Slack, which serves as the interface for users to interact with the slide board. We set up real-time messaging in Slack using the Slack API's capabilities, enabling the bot to receive and respond to user messages in real-time.

We integrated with the Wit.ai platform for natural language understanding, allowing us to extract entities from user messages and understand their intent and context. We trained the model by providing example queries and validating the extracted entities.

To enhance the slide board's ability to provide factual information, we integrated with the Wolfram Alpha API. We created a Wolfram Alpha client in Go, which allows us to make API calls and retrieve answers to user queries.

By leveraging the power of NLU and the Wolfram Alpha API, we can create a slide board that can understand and respond to user queries accurately and efficiently.

Now it's your turn to try building your own slide board and explore the possibilities of natural language processing. Don't forget to check out the links in the description for additional resources and code samples.

Highlights

• Building a simple slide board with natural language processing functionality
• Integrating with Wit.ai for natural language understanding
• Creating a bot in Slack for seamless interaction
• Training the model and improving accuracy over time
• Using the Wolfram Alpha API for retrieving factual information

FAQ

Q: How accurate is the natural language understanding (NLU) of the slide board?

A: The accuracy of NLU depends on the training of the model and the quality of the example queries provided. By providing diverse and validated examples, the slide board can achieve a high level of accuracy in understanding user queries.

Q: Can I customize the entities used by the slide board?

A: Yes, you can create custom entities in the Wit.ai platform to suit your specific needs. By defining custom entities, you can train the model to recognize and extract information that is relevant to your application.

Q: Is the Wolfram Alpha API limited to specific topics?

A: No, the Wolfram Alpha API can provide information on a wide range of topics, including mathematics, science, history, geography, and much more. It leverages the vast Knowledge Base of Wolfram Alpha to provide accurate and detailed answers to user queries.

Q: Can I use a different natural language processing platform instead of Wit.ai?

A: Yes, there are several natural language processing platforms available, such as Luis, AI, and Rational. You can choose the platform that best suits your requirements and integrate it into your slide board application.

Q: Are there any limitations on the usage of the Wolfram Alpha API?

A: Yes, the free tier of the Wolfram Alpha API has a limit of 2,000 API calls per month. If you anticipate higher usage or require additional features, you may need to upgrade to a paid plan.