Chat with PDF, CSV, and JSON documents using Google Gemini RAG
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 11 nodes and runs when someone submits an n8n form (“Document Upload for Knowledge Base”) (plus 1 other trigger). Along the way it connects Embeddings Google Gemini and Google Gemini Chat Model.
How it runs
Trigger: When someone submits an n8n form (“Document Upload for Knowledge Base”) (plus 1 other trigger).
- Add Metadata (Edit Fields (Set)): sets 3 fields: filename, fileType, uploadDate
- Knowledge Base Agent (AI Agent): runs an AI agent, with model: Google Gemini Chat Model; 1 tool (Vector Store Retrieve); memory: Chat Memory
- Vector Store Insert (Simple Vector Store): stores documents in the Simple vector store, with embeddings: Embeddings Google Gemini; document loader: Document Loader
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 (10)
- n8n Form Trigger
n8n-nodes-base.formTrigger - Edit Fields (Set)
n8n-nodes-base.set - Simple Vector Store
@n8n/n8n-nodes-langchain.vectorStoreInMemory - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter
From the author
Overview Turn documents into an AI-powered knowledge base. Upload PDF, CSV, or JSON files and ask natural-language questions about their content using a Retrieval-Augmented Generation (RAG) workflow powered by Google Gemini. The workflow extracts, embeds, and…