Multi-source RAG system with GPT-4 Turbo, news & academic papers integration

Multi-Source RAG System with GPT-4 Turbo, News & Academic Papers Integration

This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo.

How it works

This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo.

Key Steps

1. Form Input - Collects user queries with customizable search scope, response style, and language preferences 2. Intelligent Search - Routes queries to appropriate sources (web, academic papers, news, internal documents) 3. Data Aggregation - Unifies and processes information from multiple sources with quality scoring 4. AI Processing - Uses GPT-4 Turbo to generate context-aware, source-grounded responses 5. Response Enhancement - Formats outputs in various styles (comprehensive, concise, technical, etc.) 6. Multi-Channel Delivery - Delivers results via webhook, email, Slack, and optional PDF generation

Data Sources & AI Models

Search Sources - Web Search: Google, Bing, DuckDuckGo integration - Academic Papers: arXiv, PubMed, Google Scholar - News Articles: News API, RSS feeds, real-time news - Technical Documentation: GitHub, Stack Overflow, documentation sites - Internal Knowledge: Google Drive, Confluence, Notion integration