Automated workflow
Workflow Description
An intelligent automated workflow combining Qdrant vector storage with OpenAI embeddings to process documents, split them into batches, and store them efficiently. Enables semantic search and intelligent document retrieval through a unified pipeline with FTP output integration.
How it works
- 1.Trigger workflow manually to initiate the processing pipeline
- 2.Load documents from multiple data sources using the default data loader
- 3.Split text content into manageable chunks using character-level text splitter
- 4.Generate semantic embeddings for text chunks using OpenAI models
- 5.Organize embedded data into logical batches for efficient processing
- 6.Store vectors in Qdrant database and export results via FTP
Use cases
- Build intelligent search engines that understand query semantics and retrieve relevant documents
- Automate large-scale document processing and convert unstructured content into searchable vectors
- Create distributed knowledge management systems that retrieve contextually relevant information
Requirements
- Valid OpenAI API key with access to embedding models
- Configured Qdrant database instance with appropriate settings for data volume
- Valid FTP credentials for secure file transfer and backup operations
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
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
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List all the files
Ftp
Loop over one item
Split In Batches
Downloading item
Ftp
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