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bard-builder
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1.3.13 • Public • Published

Bard

About it

TL;DR: Frameworks are messy and I created one to try to solved it for myself.

I am working with chatbots for some time now, and I gone through many frameworks (good and not so good ones).

No matter which one I choose, there will always be something that, in time, will make things harder than it should. That weak point, most of the time, was some difficulty to generalize code and/or to create custom functionalities.

Some of them had graphical interfaces, others don't, just plain code. I had more difficult to make custom pieces of code/functionality on graphical frameworks, although it can be fast/useful for simple projects. I preferred mostly plain code frameworks, but then I realize that customizability comes with a salty price.

I came up with some concepts to try to solve that, polished the best I could. The result was a simple framework that allow you to create a conversation flow (chatbot) and link it to any message broker that you wish to.

Bard

It is a chatbot framework that allows you to create your own chatbot and link it to any message broker. It is completely written in NodeJS/Typescript, but you can import it to your NodeJS/Javascript code too.

You can easily install it on your NodeJS project using NPM:

npm i --save bard-builder

Basically it is composed of two sections, the conversation flow and the message gateway.

Using its components, you can easily create a conversation flow that are interlinked. After that, you can connect it to your preferred message broker using the message gateway.

Chatbot

It is simple to create, just import the bard module and create a Bot instance:

import {Bot} from "bard-builder";
const bot = new Bot({name: "simple-bot"});
 
/* to start both conversation flow and message gateway */
bot.start();

Or using require/module.exports:

const bard = require("bard-builder");
const bot = new bard.Bot({name: "simple-bot"});
 
bot.start();

==Remeber to use the bard. prefix when not using ES6 modules import==

After you instantiate your chatbot, you can start creating the conversation flow.

Conversation flow

Conversation flow consists of layers, the incoming layer, the trailing layer and the outgoing layer. All of them can have multiple dialogs (layer's items) and each one have its own behavior (dialogs are compound of steps, in other words, an array of functions).

  • Incoming layer: acts almost like a queue, new dialogs will be inserted at the end of it and. Every chatbot interaction will land here first. It will execute all dialogs in the inserted order (first-in first-executed), ==if you want to proceed to the next dialog you must to ensure that course.next() is declared on the last step of that dialog==. From that, you can redirect the interaction to any trailing point you want to. If you don't redirect the interaction manually, at the end of all existent dialogs, it goes to the first inserted trailing dialog (you must specify on the last existent dialog that you want to continue, using course.next());

  • Trailing layer: is dynamic, meaning that dialogs are loose and must be linked using course.replace("dialog-name"). It have an entry point, which is the first dialog inserted. It receive all interactions that pass through the incoming layer;

  • Outgoing layer: is the last layer, it acts like a queue, similar to the incoming layer, but it can't redirect the interaction and it will only be triggered if the interaction reached the trailing layer;

Each dialog step (function) receives two objects, session and course. Session can be used to get the interaction information, to send messages and to end the session itself, resetting the progress. Course can be used to manage the flow, replace dialogs, jump to steps, etc.

Trailing Layer

A good example of a trailing layer dialog:

/*
    This conversation flow will ask the user a question and based on his response
    it will send some message, then it will say bye and end the session.
*/
bot.trailing("greetings", [
    (session, course) => {
        session.send("Hello, how are you?");
        /*
            stops flow and wait for the next interation,
            that will be handled by the next step-function
        */
        session.wait();
    },
    (session, course) => {
        /* get user message from session */
        const message = session.getMessage().data;
        
        if (message == "good") session.send("Nice!");
        else session.send("Bad :/");
        
        /* redirect the current interaction to the "goodbye" dialog */
        session.replace("goodbye");
    }
]);
 
bot.trailing("goodbye", [
    (session, course) => {
        session.send("Goodbye!");
        /* session.end() reset all conversation progress (steps, storage, etc) and delete the current session */
        session.end();
    }
]);

Above we have a simple conversation flow that asks a question and waits for a response. We can use session.send(message) to send messages to user and, in this case, ask the question How are you?. After that ==we must stop the flow and wait for the user response, otherwise it will continue directly to the next step function== with the same user message, making it useless. So, to do that we need to use course.wait(), it will save the course progress and wait for the next user interaction, that will be handled by the next step function. After that we can get the new user response and send a answer based on it. Done that, we want to say bye to our user, so we are redirecting the course to a new dialog using course.replace("goodbye"). There ("goodbye dialog") we have a bye message and a session.end() call that reset the session and all its members, like storage and user info.

Incoming Layer

We can evolve that in something more complex. Using incoming layer dialogs, we can extract intents from user input and use it to manage the flow.

We can create an incoming dialog that just do that:

/* create a incoming dialog to understand the user interaction intention */
bot.incoming("understand-intents", [
    (session, course) => {
        /* clear previous intent from storage (it is a storage bound to the session) */
        session.storage.set("intent", null);
        
        /* get message and its data from session */
        const message = session.getMessage().data;
 
        /* simulate a cognitive engine/API */
        let intent = null;
        if (data == "good") intent = "something-good";
        else if (data == "bad") intent = "something-bad";
        else if (data == "idiot") intent = "something-rude";
 
    /* set the intent into session storage, can be retrieve later on */
        if (intent) session.storage.set("intent", intent);
 
        /* ensure to keep the flow going */
        return course.next();
    }
]);
 
/* example of usage */
bot.trailing("begin-dialog", [
    (session, course) => {
        const intent = session.storage.get("intent");
        if (intent != null) session.send(`Your intention was: '${intent}'`);
        else session.send("Can't detected any intention");
        /* course.end() will reset progress, but it will keep the session and its values */
        course.end();
    }
]);

The main purpose of the example above is to show how easy is to connect/integrate APIs and cognitive engines into the flow. In the incoming dialog ("understand-intents") we used the user input to simulate an API call, then we set the result into the session.storage, that are a get/setter object that you can use to store/retrieve values wherever you want in the conversation flow. After that, we can retrieve that understanding value in the "begin-dialog" using session.storage.get("intent") and sends a message to the user with it. The course.end() call will reset the course progress ==but keep all session and its data, unlike session.end() that resets everything==. We could also put a filter that redirect the interaction to a dialog based on the user input or it meaning, making it easier to create a diverse flow. If you want to save some values at the end of the conversation, like analytics data, you can use the outgoing layer.

Outgoing Layer

Outgoing layer dialogs can be used to do some operations after the interaction completes (after trailing). An example of this:

/* create an outgoing dialog to save data after interaction */
bot.outgoing("save-data", [
    (session, course) => {
        /* set to known if already have one or more interations */
        session.storage.set("known", true);
    }
]);
 
/* example of usage */
bot.trailing("begin-dialog", [
    (session, course) => {
        /* get known data */
        const known = session.storage.get("known");
        
        /* if user already interacted, then send a different message to him */
        let greeting = "Hello, nice to meet you! How are you?";
        if (known) greeting = "Nice to see you again! How are you doing?";
        
        session.send(greeting);
        course.end();
    }
]);

The above example show us how we can use the outgoing layer to save and retrieve data to manipulate the conversation flow. Every time an user interacts with the chatbot a message will be choose, if the user already interacted, it will send a Nice to see you again! message, otherwise a Hello, nice to meet you! one. To know if the user already interacted we can set a "known" flag at the end of any trailing interaction using session.storage.set("known", true). When the user enters the trailing "begin-dialog", we can check if the flag "known" is set to true, than choose the respective message.

Course Functions

There are others course functions that may be helpful:

course.jump(2) /* jump to any step on the current dialog using its index */
course.mark("this-point") /* mark a step on flow */
course.hop("that-point") /* hop to a marked step on flow (above) */
course.back() /* back to the last marked mark */

We have some examples on the examples folder of this source code.

Events

Events are custom actions that can be triggered when something happen in the chatbot. Here is the list of events:

/* most used events */
ON_RECEIVE_MESSAGE = "Trigger when chatbot receives a message";
ON_SEND_MESSAGE = "Trigger when chatbot sends a message";
ON_CREATE_SESSION = "Trigger when a new session is created";
ON_DELETE_SESSION = "Trigger when a session is deleted";
ON_EXPIRE_SESSION = "Trigger when a session is expired (sessions have TTL)";

It will fire these actions every time an event occur. An example of it:

const {Events} = require("bard-builder");
 
bot.event(Events.ON_RECEIVE_MESSAGE, (params) => {
    console.log(`Incoming message ${params.message?.data}.`);
});
 
bot.event(Events.ON_SEND_MESSAGE, (params) => {
    console.log(`Outgoing message ${params.message?.data}.`);
});

Message Gateway

It is a mediator (not the pattern) between your message broker and the conversation flow. You can use bot.push(message) to insert a outgoing message or bot.pull() to retrieve a incoming message.

Receiving a Message

To insert a outgoing message you must to instantiate a message object first. You can use the Message class to do it:

const {Message, MessageTypes} = require("bard-builder");
 
const message = new Message(
    "user-contact", "user-session", "message-broker-origin",
    "message-data", MessageTypes.TEXT
);
 
...
 
/* and push it to the bot instance */
bot.push(message);

You probably are receiving from message broker by a webhook, so we will need to create one (you can use other frameworks, but to simplify we will just use "express", that is a excellent a reliable framework).

const {Bot, Message, MessageTypes} = require("bard-builder");
const express = require("express");
 
const bot = new Bot({name: "bot-name"});
... /* declare dialogs and start bot */
 
const server = express();
 
/* to parse JSON body */
server.use(express.json());
 
server.post("/receive/message", (request, response) => {
    const body = request.body;
    /* use bot.push(message_object) to send a message to the conversation flow */
    bot.push(new Message(body.contact, body.session, body.origin, body.data, MessageTypes.TEXT));
    return response.status(200).send("OK");
});
 
server.listen(8888);

You can create a switch to handle all incoming message types and set the respective one into the Message instance.

Above we are receiving a incoming message from a webhook and creating/inserting the Message instance into the conversation flow using bot.push(message).

Every time it happens a new interaction is executed in the conversation flow.

Sending a Message

To send a reply for the messages sent by the conversation flow, in response to the ones received, we can use bot.pull() function. It will pull a outgoing message from the conversation flow. We can do it by creating a pulling system and sending all outgoing messages to our message broker:

const {Bot} = require("bard-builder");
const MyBroker = require("my-broker");
 
const bot = new Bot({name: "bot-name"});
... /* declare dialogs and start bot */
 
/* declare your message broker */
const message_broker = new MyBroker({token: "token"});
 
function pullProcess() {
    /* get message from chatbot */
    const message = bot.pull();
    /* if it is an Error instance, re-run this with delay (probably empty) */
    if (message instanceof Error) {
        return setTimeout(pullProcess, 500);
    }
 
    /* send message to message broker */
    message_broker.sendMessage(message);
 
    /* re-run this */
    return setImmediate(pullProcess);
}
 
/* start pulling messages and sending it to the message broker */
pullProcess();

We are declaring our message broker and creating a function that calls itself repeatedly to pull messages from the conversation flow. The pulling function try to get a message, and if fail will wait some time to run again (probably the queue is empty). If succeed, will send the message to our message broker and re-call the function immediately again. Using this mechanism we can ensure that we not lock the thread only by pulling messages. We are re-scheduling these calls to fit wherever it can (using setImmediate() and let the other parts of the code breath and run smoothly.

Putting it all together

Basically, to create our chatbot we will need to:

  • Create Bot instance;
  • Declare the conversation flow;
  • Set up the message gateway:
    • Set up the incoming message system (probably a webhook);
    • Set up the outgoing message system (pulling process);

So, lets put it all together:

const {Bot, Message, MessageTypes} = require("bard");
const express = require("express");
 
/* declare the chatbot with a simple conversation flow */
const bot = new Bot({name: "bot-name"});
setupConversationFlow(bot);
 
/* declare server and webhook that will receive the message from the message broker */
const server = express();
setupServer(server);
 
/* declare your message broker and start pulling messages */
const message_broker = {
    sendMessage: (message) => console.log(message)
};
 
pullProcess();
 
/* helper functions */
function setupConversationFlow(bot) {
    bot.outgoing("save-data", [
        (session, course) => {
            /* set to known if already have one or more interations */
            session.storage.set("known", true);
        }
    ]);
 
    /* example of usage */
    bot.trailing("begin-dialog", [
        (session, course) => {
            /* get known data */
            const known = session.storage.get("known");
        
            /* if user already interacted, then send a different message to him */
            let greeting = "Hello, nice to meet you! How are you?";
            if (known) greeting = "Nice to see you again! How are you doing?";
        
            session.send(greeting);
            course.end();
        }
    ]);
 
    /* start chatbot */
    bot.start();
}
 
function setupServer(server) {
    /* to parse JSON body */
    server.use(express.json());
 
    server.post("/receive/message", (request, response) => {
        const body = request.body;
        /* use bot.push(message_object) to send a message to the conversation flow */
        bot.push(new Message(body.contact, body.session, body.origin, body.data, MessageTypes.TEXT));
        return response.status(200).send("OK");
    });
    server.listen(8888);
}
 
function pullProcess() {
    /* get message from chatbot */
    const message = bot.pull();
    /* if it is an Error instance, re-run this with delay (probably empty) */
    if (message instanceof Error) {
        return setTimeout(pullProcess, 500);
    }
 
    /* send message to message broker */
    message_broker.sendMessage(message);
 
    /* re-run this */
    return setImmediate(pullProcess);
}

Above we have the whole chatbot put together. We are declaring a simple conversation flow, setting a webhook for incoming messages and a pulling system to the outgoing messages.

Organizing Dialogs

Obviously you can stripe all of these items into several folders/files. I suggest you to create a separated folder just to hold the dialog files and a flow file to manage them:

.
├── flow.js
└── dialogs
    ├── root_trailing.js
    ├── bye_trailing.js
    └── intent_incoming.js
/* flow.js */
 
...
 
const root_trailing = require("./dialogs/root_trailing");
const bye_trailing = require("./dialogs/bye_trailing");
const intent_incoming = require("./dialogs/intent_incoming");
 
/* declare dialog dependencies */
const mysql = new Mysql({settings: "..."});
 
/* bundle all dependencies */
const deps = {mysql};
 
/* declare dialogs */
bot.trailing("root", root_trailing(deps));
bot.trailing("bye", bye_trailing(deps));
bot.incoming("intent", intent_incoming(deps));
/* root_dialog.js */
 
/* you should use deps to pass instances through dialogs */
module.exports = function(deps) {
    return [
        async (session, course) => {
            const users = await deps.mysql.getUsers();
            /* use users */
            ...
        },
        ...
    ];
}

This is a good way to structure your dialogs, it can be a huge mess, believe me. Now you can pass dependencies through dialogs now, it will be very useful later on.

Tutorials

Examples

We have some examples in this repository too:

Considerations

I really worked on this project, tried to solve this problem for me. Found myself writing with more quality while using Bard. It really changed the way I developed/structured my chatbots. I hope that it will help you too.

Bard is under GNU GPLv3 license.

Author: Arnaldo Badin

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