Enterprise Knowledge Search with GPT-4 Turbo, Google Drive & Academic APIs

This workflow implements a sophisticated Retrieval-Augmented Generation (RAG) system tailored for enterprise knowledge management. It begins with a form input capturing user queries along with preferences for search scope, response style, and language. Queries are then routed intelligently to diverse sources including web searches (Google, Bing, DuckDuckGo), academic papers (arXiv, PubMed, Google Scholar via Crossref), news articles (News API, RSS), technical documentation (GitHub, Stack Overflow), and internal knowledge bases (Google Drive, Confluence, Notion). Data from these sources is aggregated, processed with quality scoring, and fed into GPT-4 Turbo for context-aware response generation using embedding models for semantic matching.

The workflow enhances responses with customizable formatting (comprehensive, concise, technical) and supports multi-channel delivery via webhook, email, Slack, or PDF. Benefits include time-saving automated research across siloed data sources, reduced hallucinations through grounded AI responses, and scalable enterprise search without custom development. Setup takes just 15-20 minutes, making it accessible for teams needing quick AI-powered insights.

Ideal use cases: Research teams querying academic and technical literature, customer support accessing internal docs and news for accurate responses, executives needing summarized market intelligence, and knowledge workers streamlining information retrieval in large organizations.