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Virtual Agent

Automate experiences with kandi 1-Click Solution kit

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
  • While you are downloading this kit, here are other 1-click ready to deploy projects to try: ✅ Build AI Powered Object Detector | ✅ Build Python Paraphrase Generator for NLP You can also find popular libraries for: 🔎 Virtual Assistant | 🔎 NLP | 🔎 ChatBot

    Training and Certification - How to build AI Powered Virtual Assistant

    Watch this self-guided tutorial on how you can use training data, Pre processing Library, Sentence Transformer, and Similarity Engine to build your own Virtual Assistant using Artificial Intelligence. You can also apply for a verified certificate. Completed the training? Apply for your Participation Certificate and Achievement Certificate now! Tag us on social media with a screenshot or video of your working application for a chance to be featured as an Open Source Champion and get a verified badge.

    Kit Deployment Instructions

    Download the 1-Click kit installer file to get started. After download, extract this zip, run the file and follow the next steps below. Note: Do ensure to extract the zip file before running it. Follow below instructions to run the solution. 1. After successful installation of the kit, press 'Y' to run the kit and execute cells in the notebook. 2. To run the kit manually, press 'N' and locate the zip file 'faq-virtual-agent.zip' 3. Extract the zip file and navigate to the directory 'faq-virtual-agent' 4. Open command prompt in the extracted directory 'faq-virtual-agent' and run the command 'jupyter notebook' 5. Locate and open the 'Virtual Agent for FAQ.ipynb' notebook from the Jupyter Notebook browser window. 6. Execute cells in the notebook 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

    Kit Solution Source

    FAQ Virtual Agents created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.

    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.


    If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.
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