Automate GLPI Knowledge Base RAG Pipeline with Google Gemini and PostgreSQL

The workflow connects to the GLPI API to fetch FAQ articles, cleans and normalizes the content, and generates vector embeddings using Google Gemini. These embeddings are stored in a PostgreSQL database with the pgvector extension, making the data ready for integration with any RAG-ready LLM pipeline. This setup is ideal for creating intelligent support bots or documentation assistants that rely on up-to-date knowledge from your internal systems.