MatrixBot is a smart AI chatbot solution built with LLM, RAG, and context-aware conversation features. It helps businesses automate customer support, provide human-like replies, track orders, create tickets, and deliver accurate answers from business knowledge base data.

AI-Powered Chatbot: Built with modern LLM technology for smart and natural responses.

RAG-Based Knowledge Base: Uses Retrieval-Augmented Generation to provide accurate answers from your business data.

Context-Wise Conversation: Understands previous messages and gives replies based on the conversation flow.

Human-Like Conversation: Provides natural, friendly, and customer-focused responses.

Order Tracking: Allows customers to check their order status directly through the chatbot.

Ticket Creation: Helps customers create support tickets for issues, questions, or service requests.

Conversation History: Stores previous chat records so users and support teams can review past conversations.

Customer Support Automation: Handles common customer questions and reduces manual support workload.

Webhook/API Integration: Easily connects with backend systems, CRM, order systems, or third-party services.

Rasa Middleware Support: Supports Rasa-based chatbot workflow and custom conversation logic.

Scalable Architecture: Frontend and backend are separated for better performance, flexibility, and future growth.

Customizable UI: Easy to customize based on business branding and product requirements. 

Python Version: Python 3.10 is required to run the backend project.   

Virtual Environment: Recommended to use a Python virtual environment for package management.

Backend Framework: Flask-based backend application.

Frontend Technology: HTML, CSS, JavaScript, and Tailwind CSS.

Rasa Requirement: Rasa should be installed and configured for chatbot conversation flow.

LLM/API Access: Requires an LLM API key or supported local LLM configuration.

RAG Support: Requires a knowledge base or document data source for RAG-based answers.

Database: A supported database is required to store users, conversations, orders, and tickets.

Package Installation: Install all required Python packages using the requirements.txt file.

Webhook/API URL: Backend webhook URL must be configured properly for chatbot communication.

Internet Connection: Required if you are using cloud-based LLM APIs or external integrations.

Server Requirement: Can be deployed on VPS, cloud server, or local server based on your needs.