Answer electronics questions with Google Gemini, SerpAPI, and Supabase
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 16 nodes and runs when a chat message arrives (plus 1 other trigger). Along the way it connects Supabase Vector Store, Embeddings Google Gemini, Google Gemini Chat Model and HTTP Request. It also calls serpapi.com over HTTP.
How it runs
Trigger: When a chat message arrives (plus 1 other trigger).
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 3 tools (HTTP Request, Update Database, Supabase Vector Store); memory: Window Buffer Memory
- HTTP Request: calls an HTTP API
- Respond to Chat (Chat): uses Chat
- Extract from File: extracts data from a file (pdf)
- Supabase Vector Store: stores documents in the Supabase vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
Services it uses
HTTP calls to: serpapi.com
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 AI Agent1 (v1.6): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent1 (v1.6) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (14)
- Supabase Vector Store
@n8n/n8n-nodes-langchain.vectorStoreSupabase - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Token Splitter
@n8n/n8n-nodes-langchain.textSplitterTokenSplitter - Extract from File
n8n-nodes-base.extractFromFile - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - HTTP Request
n8n-nodes-base.httpRequest - Chat
@n8n/n8n-nodes-langchain.chat - Execute Workflow Trigger
n8n-nodes-base.executeWorkflowTrigger - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Http Request Tool
n8n-nodes-base.httpRequestTool - Call n8n Workflow Tool
@n8n/n8n-nodes-langchain.toolWorkflow
From the author
Quick Overview This chat-based workflow uses Google Gemini and a Supabase vector store to answer electronic component questions from indexed PDF datasheets, and when information is missing it searches Google via SerpAPI for a public datasheet PDF, downloads…