{"product_id":"n8n-rag-chatbot-workflow-groq-pinecone-drive-ingest","title":"n8n RAG Chatbot Workflow: Groq + Pinecone + Drive Ingest","description":"\u003ch3\u003eTurn your Google Drive documents into a grounded RAG chatbot—automatically\u003c\/h3\u003e\n\u003cp\u003eThis n8n workflow delivers an AI RAG chatbot that answers incoming chat messages using a Groq Llama model grounded in a Pinecone knowledge base—and keeps that knowledge base fresh by ingesting new files from a specific Google Drive folder every minute.\u003c\/p\u003e\n\n\u003ch3\u003eWhat this workflow does\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003e\n\u003cstrong\u003eChat via public webhook trigger:\u003c\/strong\u003e Receives an incoming chat message using an n8n chat trigger exposed as a public webhook URL.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eGrounded, context-based responses:\u003c\/strong\u003e Runs a LangChain agent with a Groq Chat Model to answer \u003cem\u003eusing retrieved context\u003c\/em\u003e and maintains short-term conversation memory.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eRetrieval from Pinecone:\u003c\/strong\u003e Fetches relevant knowledge base chunks from a Pinecone vector index using Google Gemini embeddings, with Cohere reranking to improve relevance.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eReturns the grounded answer:\u003c\/strong\u003e Sends the grounded response back to the chat client.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eContinuous knowledge updates:\u003c\/strong\u003e Every minute, detects new files created in a watched Google Drive folder, downloads them, splits them into chunks, generates Gemini embeddings, and inserts the chunks into the same Pinecone index for future retrieval.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eUse cases\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003e\n\u003cstrong\u003eSaaS support assistant:\u003c\/strong\u003e Answer product questions using your documentation stored in Google Drive.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eInternal knowledge Q\u0026amp;A:\u003c\/strong\u003e Empower teams to ask policy or procedure questions with Pinecone-backed retrieval.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eOps-ready document ingestion:\u003c\/strong\u003e Add new PDFs or docs to the monitored folder and have them automatically available to the chatbot within minutes.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch3\u003eTechnical details\u003c\/h3\u003e\n\u003cul\u003e\n  \u003cli\u003e\n\u003cstrong\u003eIntegrations\/credentials:\u003c\/strong\u003e Groq, Pinecone, Cohere, Google Drive (OAuth2), and Google Gemini (PaLM) API.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003eKey workflow behavior:\u003c\/strong\u003e Public webhook-based \u003cem\u003echat trigger\u003c\/em\u003e; Google Drive folder \u003cem\u003etrigger\u003c\/em\u003e running every minute; LangChain \u003cem\u003eagent\u003c\/em\u003e with Groq; Pinecone retrieval with Gemini embeddings + Cohere reranking.\u003c\/li\u003e\n  \u003cli\u003e\n\u003cstrong\u003ePinecone setup:\u003c\/strong\u003e Create\/select the target index (set to \u003ccode\u003eerhan8n\u003c\/code\u003e in the workflow) and ensure vector dimensions match your chosen Gemini embedding model.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"N8N Commerce","offers":[{"title":"Default Title","offer_id":45756675424435,"sku":"N8N-17748","price":11.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0749\/6279\/6723\/files\/fZ5bP4_YuVbFRf3ZOao_J_fTv1f49O.png?v=1785747909","url":"https:\/\/buyflowscripts.com\/products\/n8n-rag-chatbot-workflow-groq-pinecone-drive-ingest","provider":"N8N Commerce","version":"1.0","type":"link"}