Multi-source RAG system with GPT-4 Turbo, news & academic papers integration
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: OpenAI (legacy). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| OpenAI (legacy) 🤖 Advanced LLM Processor | OpenAI node (AI section) |
Rarely used nodes in this template: n8n-nodes-base.googleDriveSearch, n8n-nodes-base.webSearch
Nodes used (8)
- n8n Form Trigger
n8n-nodes-base.formTrigger - Code
n8n-nodes-base.code - If
n8n-nodes-base.if - Google Drive Search
n8n-nodes-base.googleDriveSearch - Web Search
n8n-nodes-base.webSearch - HTTP Request
n8n-nodes-base.httpRequest - OpenAI
n8n-nodes-base.openAi - Respond to Webhook
n8n-nodes-base.respondToWebhook
What it does
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 AI Models - GPT-4 Turbo: Primary language model for response generation - Embedding Models: For semantic search and similarity matching - Custom Prompts: Specialized prompts for different response styles Set up steps Setup time: 15-20 minutes 1. Configure API credentials - Set up OpenAI API, ScrapeGraphAI, Google Drive, and other service credentials 2. Set up search sources - Configure academic databases, news APIs, and internal knowledge sources 3. Connect analytics - Link Google Sheets for usage tracking and performance monitoring 4. Configure notifications - Set up Slack channels and email templates for automated alerts 5. Test the workflow - Run sample queries to verify all components are working correctly Keep detailed configuration notes in sticky notes inside your workflow