Virtual Assistant Kit (FAQ)

This is a Virtual Assistant Kit which predicts and answers FAQ. 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 10. Servers and webframeworks can be leveraged for servicing the request as REST API You can also find github reference to the Virtual Agent repo using this kit at the bottom for building your own Virtual Agents.

Development environment

Jupyter Notebook was used for development and debugging this kit.

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

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.

Vitual Assistant built

Please check out the link below to access my Github repository for the kit! https://github.com/RA1911003010940/Virtual-Assistant-Kit-FAQ-