📖 Generate contextual recommendations from Slack using Pinecone
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Description
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 recommended_action, rationale, and optional risk_note.
📨 Sends Final Output Back to Slack Posts the recommendation directly to the channel as a reply.
Required Integrations
- Slack Bot with channels:history & app_mentions: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
- Combines live Slack interaction, file ingestion, and Pinecone retrieval into a fully RAG-powered response system.
- AI prompts are carefully scoped for actionable, context-aware, and time-bound responses.
- No-code setup with modular components for scaling or adapting to new use cases (e.g., different roles or goals).
🔗 Nodes Used
Slack, Google Drive, AI Agent, Auto-fixing Output Parser, Structured Output Parser, Pinecone Vector Store
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File