📚Widget Tech Stack

This system powers AI-driven document Q&A and contextual conversation experiences across decentralized applications (dApps), using a modular backend and a React-based frontend.

Backend Overview

Purpose: Provide an intelligent, multi-tenant backend system for Elsa-enabled dApps.

Highlights:

• RAG (Retrieval-Augmented Generation) for document Q&A

• Contextual chat with session memory

• Role-based access control

• Vector-based document search

• Isolated environments per DApp

Backend Technologies

Runtime & Language

Node.js, TypeScript (ES2020)

API Framework

Express.js, CORS, Multer, REST

Data & Storage

MySQL (via mysql2), Custom ORM

ChromaDB (vector database for embeddings)

Security & Auth

JWT, bcryptjs, UUID

Role-based access ( admin , dapp_admin )

AI & Embeddings

OpenAI GPT-4.1 + text-embedding-3-small

LangChain framework for RAG

ChromaDB client for semantic search

External Services (Backend)

Service	Purpose
OpenAI API	GPT-4.1 completions & embeddings
ChromaDB	Vector similarity search
MySQL	Relational data storage

Frontend Overview

Built as a modular React widget to integrate seamlessly into any dApp UI.

Frontend Technologies

Framework

React

= 16.8.0

– Core UI framework

TypeScript

5.3.3

– Type-safe development

React Hooks – State and lifecycle handling

Styling & UI

Inline Styles – React

CSSProperties

CSS-in-JS – Programmatic styling

PostCSS – CSS optimization pipeline

Responsive Design – Mobile-first layout

Web3 & Blockchain

Web3 Provider Detection (e.g., MetaMask)

EIP-1193 compliance

Multi-chain Support: Ethereum, Polygon, Base

Transaction signing, broadcasting, tracking

API & Communication

REST API integration

Fetch API for HTTP requests

WebSocket (planned) for real-time

CORS handling