RAGs Support Chatbot Demo

Experience our AI-powered customer support system that uses RAG technology and LLaMA 3.2 to provide intelligent responses based on your document knowledge base.

Key Features

RAG

RAG (Retrieval-Augmented Generation)

Combines intelligent document retrieval with AI generation to produce contextually accurate answers.

Model Context Protocol (MCP): Maintains persistent context, roles, and session state across interactions, ensuring coherent multi-turn dialogues and admin-level traceability.

LangChain Framework: Orchestrates prompt templates, vector stores, and external tool integrations for a modular and extensible RAG pipeline.

LLM

LLM Model (Current system uses LLaMA 3.2 Integration with Groq)

(can use any LLM model with versions)

Built on Meta's latest LLaMA 3.2 model, delivering high-performance, natural language interactions.

Context switching and memory handled via Model Context Protocol (MCP) for enhanced multi-turn conversations.

Document Knowledge Base

Supports uploading and processing of 500+ PDFs to build a robust, queryable knowledge base.

Integrated with LangChain document loaders and vector store orchestration.

Admin

Admin Panel & Controls

Includes real-time chat monitoring, human-in-the-loop chat takeover, and document management.

Supports model context tracing and role-based access to system memory via MCP.

RLHF

Adaptive Learning Engine

Uses Reinforcement Learning with Human Feedback (RLHF) to continually refine model responses.

Works in conjunction with MCP to track session feedback and improve long-term model behaviour.

Live Chat Demo

How It Works

Step 1
Uploading Information (Admin Side)

The system starts when an admin uploads important documents (like PDFs). These documents contain all the knowledge the AI will use to answer questions. Admins can update, delete, or add new documents anytime. They also have tools to monitor chats and step in if needed.

Step 2
Visitors Start Chatting

A visitor opens the website and sees a chat box. They can type their question just like they would in a conversation. The AI reads the question and looks into the documents to find the best answer. Then, it replies instantly in a natural, human-like way.

Step 3
Give Feedback

After receiving an answer, the visitor can rate the response (e.g., 1 to 5 stars). This feedback helps the AI learn and improve future responses automatically.

Step 4
Ask for a Human

If the visitor feels the AI isn't helpful, they can click a button like 'Talk to a Human.' This sends a request to an admin. The admin can then join the chat and continue the conversation personally.

Step 5
What Happens in the Background

The system remembers all conversations so admins can review them later. It also shows helpful details like where the answer came from (which document, which section). Over time, the system keeps learning and gives better answers.

Technical Specifications

AI & Processing

Language Model:LLaMA 3.2 (Meta)

Acceleration:Groq Hardware

Retrieval:MCP + Vector Search

Learning:Reinforcement Learning (PPO)

Infrastructure

Backend:FastAPI + WebSocket

Frontend:React + TypeScript

Database:PostgreSQL + Vector DB

Storage:AWS S3

Ready to Implement This Solution?

Transform your customer support with our AI-powered RAGs chatbot. Get intelligent, document-based responses that improve over time.