Parser of open free n8n templates with vector embeddings generation for semantic search. The system extracts titles, descriptions, tags, workflow authors, and generates 384-dimensional vectors using sentence-transformers. The search works semantically rather than by keyword matching — it finds relevant results even when the query is phrased differently.
Desktop GUI on PyQt: parser settings (pages count, rate limiting, user-agent), embedding generation, results table with relevance score sorting. Error handling for network timeouts and CloudFlare challenges. Export to JSON/CSV for database import.
Raw data in downloads/, processed results in results/, embeddings in binary format for fast download. Custom similarity search on cosine distance with a threshold of 0.7.
The result is semantic search in the n8n workflow database instead of manual browsing + the ability to use, copy, and work with these n8n templates.
In the future, we plan to teach AI to read and learn the necessary templates for automation automation.
Desktop GUI on PyQt: parser settings (pages count, rate limiting, user-agent), embedding generation, results table with relevance score sorting. Error handling for network timeouts and CloudFlare challenges. Export to JSON/CSV for database import.
Raw data in downloads/, processed results in results/, embeddings in binary format for fast download. Custom similarity search on cosine distance with a threshold of 0.7.
The result is semantic search in the n8n workflow database instead of manual browsing + the ability to use, copy, and work with these n8n templates.
In the future, we plan to teach AI to read and learn the necessary templates for automation automation.