Build a GLPI knowledge base RAG pipeline with Google Gemini and PostgreSQL
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 8 nodes and runs when a chat message arrives. Along the way it connects Google Gemini Chat Model, Embeddings Google Gemini and Postgres PGVector Store.
How it runs
Trigger: When a chat message arrives.
- AI Agent: runs an AI agent, with model: Google Gemini Chat Model; 1 tool (CONHECIMENTO_TI_GLPI); memory: Simple Memory
- CONFLUENCE_TI_CONFLUENCE_SGU_GPL (Postgres PGVector Store): uses the Postgres PGVector Store vector store, with embeddings: Embeddings Google Gemini
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in AI Agent (v1.8): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Agent (v1.8) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (6)
- AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - Embeddings Google Gemini
@n8n/n8n-nodes-langchain.embeddingsGoogleGemini - Postgres PGVector Store
@n8n/n8n-nodes-langchain.vectorStorePGVector - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
From the author
Description This workflow automates the creation of a Retrieval-Augmented Generation (RAG) pipeline using content from the GLPI Knowledge Base. It retrieves and processes FAQ articles directly via the GLPI API, cleans and vectorizes the content using pgvector…