Skip to content

Latest commit

 

History

History
 
 

RASA_Customer-support-bot

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
README.md
README.md
 
 
main.ipynb
main.ipynb
 
 

README.md

Open In Colab

Advanced Customer Support Chatbot

Welcome to the Advanced Customer Support Chatbot project! This chatbot leverages the powerful combination of Rasa, LanceDB, and OpenAI’s LLMs to deliver intelligent, context-aware, and efficient customer support solutions.

Overview

image

Rasa is an open-source framework for building sophisticated conversational agents and chatbots. It provides essential tools for natural language understanding (NLU) and dialogue management, enabling developers to create intelligent and contextually aware chatbots tailored to specific business needs. Rasa’s flexibility allows seamless integration with various APIs, databases, and machine learning models, making it an ideal choice for developing robust customer support systems.

Installation

For detailed installation instructions, please refer to our comprehensive guide available in the Google Colaboratory notebook:

Open In Colab

This guide will walk you through setting up each component of the RASA Customer Support bot, ensuring a seamless integration process.

Building the Customer Support Bot

Initialize the Rasa Project

rasa init --no-prompt

This command sets up the basic directory structure with sample data.

Utilizing LanceDB for Storage of customer support data

  • LanceDB serves as the knowledge base for the chatbot, storing FAQs and support information. This setup ensures that the knowledge base is populated with relevant support information, allowing the chatbot to retrieve accurate responses based on user queries.

Integrating RASA, LanceDB and Openai api in actions.py

  • Implement Custom Actions: Custom actions enable the chatbot to perform specific tasks, such as querying LanceDB and generating responses using OpenAI’s LLM. The actions.py file is the central location for defining these actions.

Implementation Steps

The RASA customer support bot involves several key steps:

  1. Define knowledge_data and store it in LanceDB
  2. Configure Rasa Files
  3. Implement Custom Actions (actions.py)
  4. Train the Model (rasa train)
  5. Run Servers
  6. Deploy and Test

Resources

  • Google Colab: For hands-on examples and detailed code snippets for implementing the RASA Chatbot with LanceDB and OpenAI api, visit our Google Colab Notebook.

  • Blog Post: For a more in-depth exploration of the concepts and methodologies discussed in this project, please visit our Blog Post.