Index local documents for RAG using Ollama embeddings and Qdrant
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 7 nodes and runs manually, when you click “Execute workflow”. Along the way it connects Qdrant Vector Store and Embeddings Ollama.
How it runs
Trigger: Manually, when you click “Execute workflow”.
- Read/Write Files from Disk: reads a file from disk
- Extract from File: extracts data from a file (text)
- Qdrant Vector Store: stores documents in the Qdrant vector store, with embeddings: Embeddings Ollama; document loader: Default Data 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 (7)
- Manual Trigger
n8n-nodes-base.manualTrigger - Read/Write Files from Disk
n8n-nodes-base.readWriteFile - Extract from File
n8n-nodes-base.extractFromFile - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Embeddings Ollama
@n8n/n8n-nodes-langchain.embeddingsOllama - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter
From the author
Quick overview This workflow ingests a local document file, splits it into chunks, generates embeddings with Ollama (nomic-embed-text), and stores the vectors in a Qdrant collection for on-prem RAG indexing. How it works 1. Runs manually when you click…