RAG Starter Template using Simple Vector Stores, Form trigger and OpenAI
VerifiedEnable RAG-powered AI agents with PDF uploads and vector storage.
What this workflow does
This template loads custom PDF knowledge into a vector store and enables an AI agent to retrieve relevant information during chat interactions.
It is designed for users who want to quickly add RAG capabilities to AI agents using basic n8n nodes and OpenAI models.
Who is this for?
Developers, AI tinkerers, and small teams who want to prototype giving custom knowledge to AI agents via RAG inside n8n.
What problem it solves
Manually feeding documents into agents is repetitive and error-prone. This template provides a ready-made RAG flow so users can upload a PDF once and immediately chat with an agent that uses that knowledge.
What it automates
Internal policy Q&A
Upload company handbook PDF and let support staff query it through the form trigger instead of searching files.
Personal research assistant
Load research papers or notes into the vector store so the agent can answer questions grounded in those documents.
Client onboarding bot
Feed product specs or contracts into the workflow so new clients can ask questions and receive accurate answers.
How the workflow works
The 5 nodes in this automation, in order.
- 1AI Agent@n8n/n8n-nodes-langchain.agent
- 2Embeddings OpenAI@n8n/n8n-nodes-langchain.embeddingsOpenAi
- 3OpenAI Chat Model@n8n/n8n-nodes-langchain.lmChatOpenAi
- 4Simple Vector Store@n8n/n8n-nodes-langchain.vectorStoreInMemory
- 5Default Data Loader@n8n/n8n-nodes-langchain.documentDefaultDataLoader
Apps & integrations used
How to set up RAG Starter Template using Simple Vector Stores, Form trigger and OpenAI
- 1Import the workflow JSON into your n8n instance
- 2Add OpenAI API credentials to the Chat Model and Embeddings nodes
- 3Click Execute Workflow to activate the form trigger
- 4Upload your PDF through the form and wait for the vector store to index it
- 5Open the chat interface and start asking questions about the uploaded document
How to customize this workflow
- →Edit the AI Agent system prompt to add custom instructions or tone
- →Replace the Default Data Loader with Google Drive or Google Sheets nodes
- →Swap Simple Vector Store for a production-ready store such as Pinecone or Qdrant
- →Change the Form trigger to a webhook or chat trigger for other interfaces
RAG Starter Template using Simple Vector Stores, Form trigger and OpenAI: pros & cons
Pros
- +Zero-setup RAG demo that runs in minutes
- +Clear separation of embedding, storage, and agent nodes
- +Uses only native n8n AI nodes
- +Easy to inspect and understand the retrieval flow
Cons
- –Simple Vector Store is in-memory and resets on restart
- –Requires a PDF upload each time the workflow is re-executed
- –No built-in chunking or metadata options
Frequently asked questions
It loads a PDF into a vector store, then lets you chat with an OpenAI-powered agent that retrieves relevant chunks before answering.
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