IPL cricket rules Q&A chat bot using RAG and Google Gemini API
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 12 nodes and runs manually, when you click “Execute workflow” (plus 1 other trigger). Along the way it connects Google Gemini Chat Model and Embeddings Google Gemini. It also calls documents.iplt20.com over HTTP.
How it runs
Trigger: Manually, when you click “Execute workflow” (plus 1 other trigger).
- HTTP Request: calls documents.iplt20.com
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Simple Vector Store); memory: Simple Memory
- Simple Vector Store: stores documents in the Simple vector store, with embeddings: Embeddings Google Gemini; document loader: Default Data Loader
Services it uses
HTTP calls to: documents.iplt20.com
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 (10)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Simple Vector Store
@n8n/n8n-nodes-langchain.vectorStoreInMemory - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - 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 - HTTP Request
n8n-nodes-base.httpRequest - Manual Trigger
n8n-nodes-base.manualTrigger
From the author
This workflow has 2 Broad Steps Step 1 - Vector store creation with set of ipl rules using Google Gemini Embedding. This will we used to drive RAG for model grouding Step 2 - Connecting the vector store with google gemini API model and enabling a chat…