Create a self-updating RAG chatbot with Google Drive, Gemini, and Supabase
Ready for 3.0What this workflow does
This AI workflow has 34 nodes and runs when a chat message arrives (plus 4 other triggers). Along the way it connects Reranker Cohere, Supabase Vector Store, Google Drive and Supabase and 4 more.
How it runs
Trigger: When a chat message arrives (plus 4 other triggers).
- RAG Agent (AI Agent): runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Supabase Vector Store); memory: Postgres Chat Memory
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- Search files and folders (Google Drive): work with file folder
- Get File IDs (Google Drive): work with file folder
- Supabase: get many
- Get File IDs (Google Drive): work with file folder
- Supabase: get many
- Set File ID (Edit Fields (Set)): sets 4 fields: file_id, file_type, file_title…
- Merge: merges the data from its input branches
- Merge: merges the data from its input branches
…plus 11 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
Nothing in this workflow is removed in n8n 3.0, so it should import and run after the upgrade.
Nodes used (21)
- Reranker Cohere
@n8n/n8n-nodes-langchain.rerankerCohere - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Drive Trigger
n8n-nodes-base.googleDriveTrigger - Extract from File
n8n-nodes-base.extractFromFile - Supabase
n8n-nodes-base.supabase - Edit Fields (Set)
n8n-nodes-base.set - Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - Switch
n8n-nodes-base.switch - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Postgres
n8n-nodes-base.postgres - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Google Drive
n8n-nodes-base.googleDrive - Merge
n8n-nodes-base.merge - Code
n8n-nodes-base.code - Schedule Trigger
n8n-nodes-base.scheduleTrigger - Manual Trigger
n8n-nodes-base.manualTrigger - Postgres Chat Memory
@n8n/n8n-nodes-langchain.memoryPostgresChat - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini
From the author
This template creates a comprehensive, production-ready Retrieval-Augmented Generation (RAG) system. It builds a sophisticated AI agent that can answer questions based on documents stored in a specific Google Drive folder, and it automatically keeps its…