Build a company website RAG chatbot using Apify, Pinecone and Gemini
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 14 nodes and runs on a schedule (every week) (plus 1 other trigger). Along the way it connects Pinecone Vector Store, Embeddings Google Gemini, Vector Store Question Answer and Google Gemini Chat Model and 1 more.
How it runs
Trigger: On a schedule (every week) (plus 1 other trigger).
- Scrape website data (Apify): run actor and get dataset
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Vector Store Tool); memory: Window Buffer Memory
- Pinecone Vector Store: stores documents in the Pinecone vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
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 AI Agent (v1.7): 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 Agent (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (11)
- Pinecone Vector Store
@n8n/n8n-nodes-langchain.vectorStorePinecone - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - AI Agent
@n8n/n8n-nodes-langchain.agent - Vector Store Question Answer Tool
@n8n/n8n-nodes-langchain.toolVectorStore - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Schedule Trigger
n8n-nodes-base.scheduleTrigger - Apify
@apify/n8n-nodes-apify.apify
From the author
AI chatbots are only as good as the data they learn from. Most large language models (LLM) rely only on their training datasets. If you want the chatbots to know more about your business, the best is to implement a retrieval-augmented generation (RAG)…