Google Drive to Pinecone vector storage workflow
Needs changesWhat this workflow does
This AI workflow has 32 nodes and runs when a chat message arrives (plus 3 other triggers). Along the way it connects OpenAI Chat Model, Embeddings OpenAI, Google Drive and Supabase and 1 more.
How it runs
Trigger: When a chat message arrives (plus 3 other triggers).
- Edit Fields (Edit Fields (Set)): sets 2 fields: chatInput, sessionId
- Set File ID (Edit Fields (Set)): sets 3 fields: file_id, file_type, file_name
- RAG AI Agent (AI Agent): runs an AI agent, with model: OpenAI Chat Model; 2 tools (Calculator, Cars Data Vector Store); memory: Simple Memory
- Split Out: splits a list into separate items
- Respond to Webhook: sends the response back to the webhook caller
- Loop Over Items (Loop Over Items (Split in Batches)): loops over the items in batches
- FinishNode (Edit Fields (Set)): sets 1 field: response
- Delete Old Doc Rows (Supabase): delete
- Download File (Google Drive): download
- Switch: routes items into 3 branches
…plus 10 more steps. See the full node list below.
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in RAG AI Agent (v1.6): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) RAG AI Agent (v1.6) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (22)
- OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Google Drive
n8n-nodes-base.googleDrive - Google Drive Trigger
n8n-nodes-base.googleDriveTrigger - Extract from File
n8n-nodes-base.extractFromFile - Supabase
n8n-nodes-base.supabase - Respond to Webhook
n8n-nodes-base.respondToWebhook - Edit Fields (Set)
n8n-nodes-base.set - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Webhook
n8n-nodes-base.webhook - Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - Summarize
n8n-nodes-base.summarize - AI Agent
@n8n/n8n-nodes-langchain.agent - Switch
n8n-nodes-base.switch - Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Calculator
@n8n/n8n-nodes-langchain.toolCalculator - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Wait
n8n-nodes-base.wait - Split Out
n8n-nodes-base.splitOut - Code
n8n-nodes-base.code - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
From the author
Document Chat Bot with Automated RAG System This workflow creates a conversational assistant that can answer questions based on your Google Drive documents. It automatically processes various file types and uses Retrieval-Augmented Generation (RAG) to provide…