~1h 36minest. build time
6.5/10difficulty
+325 XPon completion
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Build a GLPI Knowledge Base RAG Pipeline with Google Gemini and PostgreSQL

📖 Overview

AI Builder · Operations Manager · Data Analyst
Build a GLPI Knowledge Base RAG Pipeline with Google Gemini and PostgreSQL
Build a GLPI Knowledge Base RAG Pipeline with Google Gemini and PostgreSQL automates a workflow using AI Agent, AI Memory, Google Gemini, n8n Chat. It connects 8 nodes to complete the full process end-to-end.
AI AgentAI MemoryGoogle Geminin8n Chat

🏗️ Architecture

8 nodes
🚀
Entry Point
Triggered by chat Trigger. The workflow activates when this condition is met.
🤖
AI Layer
7 AI node(s) handle reasoning, language generation, or memory.

▶️ Animated Playback

Watch it run step by step

Build Challenge

Drag nodes onto the canvas, connect them in order, then grade or reveal the solution.

n8n
Make
Zapier
Activepieces
Power Automate

🗺️ Real Workflow Canvas

Actual n8n node layout · click nodes to inspect

🏗️ Guided Build Along

Step-by-step with config snippets

🎯 Sandbox Missions

Extend · modify · prove you understand

📝 Practice Questions

Test your understanding

🧩 Mock Builder

Rebuild from scratch · graded

🔬 Deep Execution Playback

Agent thoughts · data flow · real output

🟠 Make Scenario

Mapped from n8n
🧠
OpenAI
Create a Completion
🔗
Webhooks
Custom webhook

🏗️ Guided Build — Make

Step-by-step
1
Create a new scenario
Click Create a new scenario in Make. Search for Webhooks and select Custom webhook as your trigger. Save immediately to generate the webhook URL.
💡 Give your scenario a descriptive name before saving — Make uses it in logs and error emails.
2
Send a test payload
Copy the webhook URL and POST a sample JSON payload to it. Make captures the structure and makes fields available for mapping in later modules.
💡 Click the data bubble on the webhook module after a test run to inspect the received data structure.
3
Add the OpenAI module
Click + after the webhook and search for OpenAI. Select Create a Completion. Map the input fields from your webhook payload into the prompt using the variable picker.
💡 Set Max Tokens and Temperature explicitly — Make does not set defaults, so leaving them blank can produce inconsistent results.
4
Chain output modules and activate
Add any output modules (HTTP, Google Sheets, NocoDB). Set the scenario schedule or leave it as instant. Toggle the scenario to Active.
💡 Run the scenario once manually before activating. Check the Operations counter to see how many Make operations each run consumes.

🎯 Sandbox Mission

Extend it on Make
Add an error handler route
Make has a built-in error handling system using the Router module. Add an error route from your most critical module and configure it to send a Slack or email alert when the scenario fails.
Right-click any module → Add error handler. Choose Rollback, Ignore, or Resume as the directive, then wire the error path to a notification module.

⚡ Zapier Scenario

Mapped from n8n
⚡
Trigger
Webhooks by Zapier
Catch Hook
TRIGGER
⚙
Action
Formatter by Zapier
Utilities

🏗️ Guided Build — Zapier

Step-by-step
1
Create a Zap with Webhooks trigger
Click Create Zap. In the trigger step choose Webhooks by Zapier and select Catch Hook. Click Continue and copy the webhook URL shown.
💡 Webhooks by Zapier requires a paid plan. Use a native app trigger like Google Sheets: New Row to stay on free.
2
Send a test webhook
POST a sample JSON payload to the webhook URL. Zapier captures the structure and makes all fields available as variables in later steps.
💡 Use Postman or reqbin.com to send a quick test — paste your webhook URL and send any JSON body.
3
Add an OpenAI action
Click the + button. Search for OpenAI (ChatGPT) and choose Send Prompt. Map the data from your trigger into the prompt using the variable picker.
💡 Zapier waits for the full OpenAI response with no streaming. For long prompts, increase Zap timeout in settings or split into sub-Zaps.
4
Add output steps and publish
Chain any output actions (Google Sheets: Update Row, Gmail: Send Email). Click Publish Zap and toggle it on.
💡 Test each action step individually using the Test step button before publishing — Zapier will not warn you about missing fields until runtime.

🎯 Sandbox Mission

Extend it on Zapier
Add a Filter step to control when the Zap runs
Add a Filter step between your trigger and the OpenAI action. Set conditions so the Zap only continues when specific fields are populated — this prevents unnecessary API calls.
Click the + icon and choose Filter by Zapier. Add a condition based on any field from your trigger step.

🧩 Activepieces Scenario

Mapped from n8n
🧠
Openai
Ask ChatGPT
🔗
Http
New Webhook

🏗️ Guided Build — Activepieces

Step-by-step
1
Add the Webhook trigger piece
Create a new flow. Click Add Trigger and search for HTTP. Select New Webhook Event. Copy the generated URL and save the flow.
💡 Activepieces keeps the same webhook URL until you delete the piece — safe to reuse across test runs.
2
Add the OpenAI piece
Click + after the trigger and search for OpenAI. Select Ask ChatGPT. In the prompt field, use the variable picker to reference fields from your webhook payload.
💡 The OpenAI piece version matters — newer versions support GPT-4o. Check the Connections panel to confirm which model your key has access to.
3
Map output fields
Add a Data Mapper piece after OpenAI to extract and reshape the AI response before passing it downstream.
💡 Use {{step.openai.choices[0].message.content}} in the expression editor to pull the text content from the ChatGPT response.
4
Add output pieces and activate
Add NocoDB, Google Sheets, or any output piece. Connect credentials in the Connections panel. Click Test flow then Enable.
💡 Activepieces is self-hostable — run your own instance for production so your webhook URLs never change.

🎯 Sandbox Mission

Extend it on Activepieces
Swap OpenAI for Anthropic Claude
Activepieces has pieces for both OpenAI and Anthropic. Swap the OpenAI piece for @activepieces/piece-anthropic and compare output quality. Adjust the system message to suit Claude's style.
Claude 3 Haiku is significantly faster and cheaper than GPT-4o for simple extraction tasks. Benchmark both on 10 real inputs before committing.

🔵 Power Automate Scenario

Mapped from n8n
🔗
HTTP
When a HTTP request is received
TRIGGER
⚙
Data Operation
Compose

🏗️ Guided Build — Power Automate

Step-by-step
1
Create an Instant cloud flow
In Power Automate click Create and choose Instant cloud flow. Select When a HTTP request is received as the trigger. Click Save to generate the POST URL.
💡 The HTTP trigger URL only appears after the first save — the endpoint is provisioned server-side on save.
2
Parse the incoming JSON
Add Data Operation and select Parse JSON. Paste a sample payload into the Schema box and click Generate from sample. This maps fields for all downstream steps.
💡 If your payload shape changes later, regenerate the schema or Power Automate silently drops new fields with no error.
3
Call AI Builder
Add AI Builder and select Create text with GPT. In the Prompt field, combine your parsed JSON fields with a natural language instruction using the Dynamic content picker.
💡 AI Builder credits are separate from Power Automate credits. Check your Microsoft 365 license for AI Builder allocation before testing.
4
Add output connectors and save
Add connector actions for any outputs (Excel Online, SharePoint, Outlook). Click Save and use the Test button to send a live test run.
💡 Run the Flow Checker before saving — it catches missing required inputs and misconfigured connections before runtime.

🎯 Sandbox Mission

Extend it on Power Automate
Add a Teams notification for every AI output
Add Microsoft Teams → Post a message after your AI step. Format the AI response as an adaptive card and post it to a Teams channel for team visibility.
Power Automate has native Adaptive Card support for Teams. Use adaptivecards.io/designer to build the card JSON, then paste it into the flow.
🧠 LLM Compatibility
Recommended
GPT-4o / GPT-4o-mini
OpenAI
FastLow costJSON mode
Best balance of speed/quality for email drafting and summarisation.
Claude 3.5 Sonnet
Anthropic
Long contextSafe
Excellent for long documents, nuanced tone, and multi-turn support.
Gemini 1.5 Pro
Google
1M contextMultimodal
Use when processing large documents or needing image + text in one step.
Mistral 7B / Mixtral
Mistral AI
Open weightSelf-host
Run locally via Ollama for zero API cost. Best for simple classification and drafts.
Llama 3.1 70B
Meta via Ollama
FreeLocal
Strong open model for production quality replies. Requires GPU server.

All models work with the n8n OpenAI, Anthropic, Google Gemini, and Ollama nodes. Swap models without changing workflow logic — only credentials and model name change.

🏆 Challenge: Build a GLPI Knowledge Base RAG Pipeline with Google Gemini and PostgreSQL — Prysio AI Academy
Recreate the core node chain for this workflow
Study the animated playback above, then build the key node sequence in the canvas below. Match the trigger and processing steps this workflow uses.
⚡ When chat message received 🤖 AI Agent ⚙️ Google Gemini Chat Model Connect them left → right
Triggers
🔗
Webhook
Trigger
📧
Read Email
Trigger
AI
✍️
Generate Text
OpenAI / Claude
🏷️
Classify
AI Label
Output
📬
Send Email
Gmail / SMTP
📨
Send Message
Slack / Telegram
📤
Return Output
Response
Data
🔍
Extract Data
Parse fields
⚙️
Transform
Set / Code
↙️
Drag nodes from the left panel
Click another node to connect →
0%
Grading...

📊 Live Stats

~1h 36min
Est. Build Time
6.5/10
Difficulty
+325 XP
On Completion
8
Nodes
--
Builds Completed
--
Rebuilt from Scratch
--
Practice Passes
--
Capstones Shipped
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⚠️ Common Mistakes

From workflow analysis
❶
System prompt specificity: Vague system prompts produce inconsistent results. Be explicit about output format, tone, and what the AI should NOT do.
❷
Memory node port: Connect memory via the ai_memory port on the Agent node — NOT the main execution port. Wrong port = no memory.
❸
Token overflow: Large inputs can exceed context limits. Add a Code node to trim or summarise data before passing to the AI model.
❹
Temperature for structured output: Use temperature 0 when the AI needs to produce structured or deterministic output. Higher values introduce unpredictable formatting.

🗺️ Active Quests

Complete objectives · Earn XP · Track mastery

🏆 Achievements

🏗️
Build Complete
Finish the full guided build to earn this.
🧩
Mock Builder
Rebuild from scratch and submit to earn this.
🎯
Practice Pro
Score 70%+ on practice questions.
👑
Capstone Master
Ship the workflow in production.
Achievement Unlocked