RAG Chatbots (Embed Anywhere) — Train, Share, and Deploy AI-powered Q&A Bots
RAG Chatbots is a platform that lets you easily train and share smart chatbots powered by retrieval-augmented generation. You can train using diverse data sources (PDFs, URLs, HTML, Q&A formats), configure the AI model, and deploy via an embeddable iFrame on any website or platform. Ideal for adding on-site customer support, help desks, or knowledge-based assistants without heavy development effort.
How it works
- Train your data — Start by feeding your chatbot diverse data sources such as PDFs, HTML pages, and Q&A content to improve understanding and response accuracy.
- RAG Chatbots settings — Configure system prompts and choose the appropriate AI model (GPT, Google Gemini, or Llama3) based on your needs and performance considerations.
- Share via iFrame — Deploy your chatbot on any website by embedding it with an iFrame, making it accessible to users without complex integration.
How to build your unique RAG Chatbots
- Gather data from PDFs, URLs, HTML, and Q&A formats.
- Train the bot to build a robust knowledge base and retrieval capabilities.
- Configure prompts and select the AI model that best fits your use case.
- Generate an embeddable iFrame to deploy on your site or platform.
Use cases
- Add a responsive on-site help bot to Shopify stores or e-commerce platforms.
- Create knowledge assistants for internal teams (HR, IT, operations).
- Provide real-time Q&A for customer support portals.
Safety and privacy considerations
- Ensure data sources are compliant and appropriate for public or internal use.
- Review model configurations to avoid exposing sensitive prompts or data.
Core Features
- Easy three-step workflow: collect data, configure model, deploy via iFrame
- Support for PDFs, HTML, and Q&A data sources
- Choice of AI models: GPT, Google Gemini, Llama3
- Quick embeddable iframe deployment for any site
- Centralized management for multiple chatbots
- No complex backend integration required
- Infrastructure designed for fast, scalable RAG-based responses