Virtual Agents have gained popularity due to the advancement of technologies in the area of Artificial Intelligence and Natural Language Understanding. They've become inevitable these days, particularly in support department of businesses as it can serve customers with quick turnaround.This kit aids rapid development of Virtual Agents by following below steps. 1. Select a development environment of your choice 2. Explore and analyse the dataset 3. Cleanse and get the noise-free data 4. Compute embeddings for the dataset - sentence or word embeddings 5. Preprocess the user query 6. Compute embeddings for user query 7. Compare and compute similarity score to find a best match 8. Look up the dataset for displaying answer of a best matched query 9. Precomputed embeddings can be persisted for later use
Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.
Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.
Environment Used
Jupyter Notebook is used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments.
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
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
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.
Virtual Agents built
FAQ Virtual Agents created using this kit are added in this section.
