Generate contextual recommendations from Slack using Pinecone

This advanced Retrieval-Augmented Generation (RAG) automation template for n8n enables contextual, real-time recommendations using Slack messages as input. The workflow extracts referenced documents from Google Drive, performs semantic retrieval from Pinecone, and generates next-step advice using GPT-4o — tailored specifically for executives and knowledge workers. Perfect for AI copilots, Slack-based assistants, or CTO coaching tools, this no-code RAG implementation gives you the building blocks to combine unstructured inputs with memory-augmented intelligence. What This Template Does ✅ Triggers from a Slack Message or Mention Monitors a Slack channel using a bot, capturing user input in real-time. 🔍 Extracts Key Info from Message GPT-4o parses the message to identify the subject person and Google Drive link (if present). 📥 Downloads File from Google Drive Automatically fetches and extracts PDF content using the built-in extractor. 📇 Retrieves Metadata from Google Sheets & Pinecone Looks up user ID from Google Sheets and retrieves context from Pinecone based on embeddings and reranking. 🧠 Contextual Response via GPT-4o (RAG) Combines user data and document context to generate a single, actionable next step using a tightly scoped GPT-4o prompt. 🛠️ Auto-Fixes & Structures Output Ensures formatted response with recommendedaction, rationale, and optional risknote. 📨 Sends Final Output Back to Slack Posts the recommendation directly to the channel as a reply. Required Integrations - Slack Bot with channels:history & appmentions:read - Google Drive OAuth for file fetching - Google Sheets for ID mapping - Pinecone for vector document retrieval - Azure OpenAI or OpenAI GPT-4o for language processing - (Optional) Cohere for reranking results Ideal Use Cases 🧑‍💼 Executive coaching bots (e.g., for CTOs or founders) 🧠 Slack-based internal AI assistants 📄 AI-powered document summarization with memory 💬 Actionable recommendations based on real Slack conversations 📊 Enterprise knowledge augmentation from vector DBs Why This Template Stands Out 1. Combines live Slack interaction, file ingestion, and Pinecone retrieval into a fully RAG-powered response system. 2. AI prompts are carefully scoped for actionable, context-aware, and time-bound responses. 3. No-code setup with modular components for scaling or adapting to new use cases (e.g., different roles or goals).