A [WIP] conversational search RAG (Retrieval-Augmented Generation) app based on TiDB Serverless Vector Storage, providing a out-of-the-box and embeddable QA robot experience based on your knowledge on official and documentation sites.
Live Demo: TiDB.AI
With this tool, you can achieve:
- Perplexity-style Conversational Search page: Our platform features an advanced built-in website crawler, designed to elevate your browsing experience. This crawler effortlessly navigates official and documentation sites, ensuring comprehensive coverage and streamlined search processes through sitemap URL scraping.
- Embeddable JavaScript Snippet: Integrate our conversational search window effortlessly into your website by copying and embedding a simple JavaScript code snippet. This widget, typically placed at the bottom right corner of your site, facilitates instant responses to product-related queries.
To deploy the application in a self-hosted environment, run the following command:
curl https://tidb.cloud/install.sh | sh
then:
ticloud create-app --template rag
For deploying the application to production, there are many options available:
- Next.js – Framework
- TypeScript – Language
- Tailwind – CSS
- shadcn/ui - Design
- TiDB – Database to store chat history, vector, json, and analytic
- Kysely - SQL query builder
- NextAuth.js – Auth
- Vercel – Deployments
- LlamaIndex - RAG framework
TiDB.AI is open-source under the Apache License, Version 2.0. You can find it here.