AI Agents
Process and Store Documents in Vector Database
13 nodes
296
146
Automatic trigger
Workflow Description
An automated workflow that loads documents from FTP, splits them into text chunks, generates semantic embeddings via OpenAI, and stores them in Qdrant vector database for intelligent retrieval and search.
How it works
- 1.Trigger workflow manually or on file arrival event
- 2.Load documents from FTP server
- 3.Split text into sized chunks
- 4.Generate embeddings using OpenAI API
- 5.Store embeddings and metadata in Qdrant database
Use cases
- Build semantic search engine for document repositories
- Automate indexing of files from FTP sources
- Set up retrieval layer for RAG-based AI applications
Requirements
- Valid FTP credentials and read access permissions
- OpenAI API key with embedding model access
- Running Qdrant instance with network connectivity
- Support for multiple document formats
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Vector Store
OpenAI
Document Default Data Loader
Text Splitter
Ftp
Split In Batches
Details
Trigger
Automatic trigger
Nodes
13
Apps
6
Views
296
Downloads
146
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (13)
/
Qdrant Vector Store
Vector Store Qdrant
When clicking ‘Test workflow’
Manual Trigger
Embeddings OpenAI
OpenAI
Default Data Loader
Document Default Data Loader
Character Text Splitter
Text Splitter Character Text Splitter
Sticky Note
Sticky Note
Sticky Note1
Sticky Note
Sticky Note2
Sticky Note
Sticky Note3
Sticky Note
List all the files
Ftp
Loop over one item
Split In Batches
Downloading item
Ftp
Sticky Note4
Sticky Note