Build AI Virtual Assistant

Automate experiences with kandi 1-Click Solution kit

The AI-powered virtual assistant has become a must for customer service. It can answer questions, resolve issues and even learn from past interactions.  

Virtual Assistant can do everything from booking flights and ordering groceries to control your home heating system. But how do you create a voice-activated assistant? The answer is the kandi 1-click solution kit for AI-Powered Virtual Assistant.

The AI-powered virtual assistant has become a must for customer service. It can answer questions, resolve issues and even learn from past interactions. You don't need to be an expert in programming or machine learning to create your own virtual assistant.

With this kit, you can build personal assistant that will help you with all sorts of tasks. This solution kit has various features such as Text to Speech, Speech recognition and image recognition. You can integrate all these features into your existing application and enhance the user experience of your application.

Build your AI-based Virtual Assistant in minutes with this fully editable source code. The entire solution is available as a package to download from the source code repository.

• Build an NLP based chatbot/ virtual agent
• Provide 24/7 support for an interactive experience
• Deploy in minutes and customize source code as per your requirements

Troubleshooting

1. Install the Microsoft Visual C++ Redistributable for Visual Studio 2022 in case the kit doesn't successfully run on your windows system.
2. In case, step 1 doesn't solve your issue go ahead and setup Microsoft build Tools.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

For building Virtual Agent, we use pandas to load, view and analyse data from csv file; and we use numpy to find out the indices of maximum values of an array.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

We use py-lingualytics to manage pre-processing of data like removing numerical values, stopwords, punctuations and so on.

Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics.

For generating sentence embedding, we use sentence-transformers with pretrained models. We reference bert-cosine-sim to build similarity engine for comparing two sets of input text.

Request servicing via REST API

Web frameworks help build serving solution as REST APIs. The resources involved for servicing request can be handled by containerising and hosting on hyperscalers.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.