Enhancing the Vue JS bot by integrating open source services for improved functionality

I am currently in the process of developing a completely open-source bot using Vue JS. I aim to incorporate intelligence into the bot so that it can learn from user input over time. However, I prefer not to rely on services offered by major tech companies like Microsoft, Google, or IBM. Instead, I want to use an open-source solution that I can host and enhance on my own terms.

How can I achieve this?

Below is an example of how I display "Hello World" using the bot:

var botui = new BotUI('hello-world');
botui.message.add({
  content: 'Hello World from bot!'
}).then(function () { // wait till previous message has been shown.
  botui.message.add({
    delay: 1000,
    human: true,
    content: 'Hello World from human!'
  });
});

Currently, I am following this resource:

Answer №1

AIML: Artificial Intelligence Markup Language (AIML) is a powerful tool for creating conversational flows for your bot. It is user-friendly and built as an extension of XML.

Check out this simple example:

<category> marks the beginning of a category
<pattern>What the User Says</pattern>
<template>What the Bot Responds</template>
</category>

For more information, visit: Playground

Explore AIML on this Open Source Platform: Pandorabots

Answer №2

Open AI is exceptional because it operates as a non-profit entity and provides its services for free

Answer №3

If you have experience with Python, check out Spacy:

https://github.com/explosion/spaCy

Spacy provides pretrained word2vec models that can be utilized and hosted on your own server. The library also includes various high-level methods for calculating the similarity between sentences. You can see a demonstration of its functionality on their website:

Once you determine the similarity between sentences, all you need to do is maintain a list of questions and their corresponding answers in a database.

When a new question is asked, your program will calculate the similarity score with each question in the database. It will then select the question with the highest similarity score and provide the corresponding answer.

Keep in mind that this entire library is written in Python, so you will need to host it yourself and establish an API layer (potentially using Flask, etc). Your web application can then communicate with Spacy through API calls.

While Spacy already supports multiple languages, including English, you also have the option to train custom models.

Here is a code snippet example from their website:

import spacy
nlp = spacy.load('en_core_web_md')
doc1 = nlp(u"Hello how are you?")
doc2 = nlp(u"Hi! how are you doing?")
doc1.similarity(doc2)

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