Skip to content

Latest commit

 

History

History
 
 

week4

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
README.md
README.md
 
 
chatbot.ipynb
chatbot.ipynb
 
 
intents.json
intents.json
 
 

README.md

Chatbot for Queries

Formal Statement

This assignment deals with creating a chatbot capable of answering the queries and complaints raised by buyers on the marketplace website. The chatbot should be tailored to the needs of the potential buyers. The detailed process of building a chatbot might look as follows:-

  • Basic Requirement: Have a firm grip of the target audience and their needs in order to ensure that the chatbot works best. Try to gauge the length and depth of the conversations the chatbot might expect. Further, decide on when the chatbot may have to escalate the conversation to a human specialist. An idea of the website database will be beneficial.

  • Collecting a Dataset: A dataset is essential for building the backend of the chatbot. A good quality dataset is key when building a chatbot as it makes all the difference. The dataset typically contains past conversations, FAQs, etc. Transcripts of customer calls might also be helpful if available. The chatbot can simply respond to complaints or also have access to the store inventory. This is a design choice. In the latter case, the chatbot must have access to the database of the website. This will mean that some dummy store data is also required to train the model. The dataset can be organized into a CSV format for easy processing.

  • Types of Responses: Map out the possible variety of questions that might be asked into a logical flowchart which may allow the chatbot to determine which path to take. A basic decision tree after an analysis of the dataset will help build a prototype that can be fine-tuned.

  • Framework: The backend of the chatbot may be built using the same programming language as the website backend or can be integrated separately. Try out a few deep learning frameworks in Python or NodeJS once you have a decent dataset to get an idea of the working and optimize accordingly. The backend of a chatbot and the associated training is based on language models explained in detail in section 1.3.

  • Deployment: Once the chatbot is running locally, you can integrate it with the website either directly or by integrating it with an API. Note that providing the chatbot access to the current marketplace inventory will allow for more dynamic responses.

  • Improving the Bot: The chatbot can be improved greatly by utilizing live data over a certain period of time. Creating a re-training and re-deployment cycle will help automate this to some extent.

Key Requirements

  • Textual Data Collection, Processing and Cleaning

  • Natural Language Processing (NLP) Frameworks

  • Live Deployment

  • Integration to create a unified platform