Multi-Source RAG with GPT-4 Turbo for News & Academic Papers

This workflow implements an advanced Retrieval-Augmented Generation (RAG) system that pulls real-time data from diverse sources including web searches (Google, Bing, DuckDuckGo), academic papers (arXiv, PubMed, Google Scholar), news (News API, RSS), technical docs (GitHub, Stack Overflow), and internal knowledge bases (Google Drive, Confluence, Notion). It uses GPT-4 Turbo for generating accurate, context-aware responses grounded in retrieved data, with embedding models for semantic matching.

Key benefits include reduced hallucinations through source-grounded AI, time savings on research (15-20 min setup), and flexible outputs in styles like comprehensive, concise, or technical. It aggregates data with quality scoring, routes queries intelligently, and delivers via webhook, email, Slack, or PDF.

Ideal use cases: HR research on labor laws and trends, content generation for reports, academic analysis, news summarization, or enterprise knowledge queries. Enhances decision-making with multi-source intelligence and scales for team use.