n8n AI Data Management Engineering
Contributed by Sabrina Ramonov 🍄. From the community directory at agents.sabrina.dev, republished with permission and full credit.
This workflow is designed for one purpose only: to bulk-upload structured JSON articles from an FTP server into a Qdrant vector database for use in LLM-powered semantic search, RAG systems, or AI assistants.
The JSON files are pre-cleaned and contain metadata and rich text chunks, ready for vectorization. This workflow handles:
- Downloading from FTP
- Parsing & splitting
- Embedding with OpenAI-embedding
- Storing in Qdrant for future querying
JSON structure format for blog articles:json { "id": "article_001", "title": "reseguider", "language": "sv", "tags": ["london", "resa", "info"], "source": "alltomlondon.se", "url": "https://...", "embedded_at": "2025-04-08T15:27:00Z", "chunks": [ { "chunk_id": "article_001_01", "section_title": "Introduktion", "text": "Välkommen till London..." }, ... ] }
Benefits
Automated Vector Loading
Handles FTP JSON Qdrant in a hands-free pipeline.
Clean Embedding Input
Supports pre-validated chunks with metadata: titles, tags, language, and article ID.
AI-Ready Format
Perfect for Retrieval-Augmented Generation (RAG), semantic search, or assistant memory.
Flexible Architecture
Modular and swappable: FTP can be replaced with GDrive/Notion/S3, and embeddings can switch to local models like Ollama.
Community Friendly
This template helps others adopt best practices for vector DB feeding and LLM integration.
Tools used: FTP, Qdrant, OpenAI
Download the workflow template (JSON)
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