Capstone: Your Private AI Platform

Congratulations! You have reached the end of the course. To earn your "Sovereign AI Engineer" status, you must now combine every module into a single, cohesive project.

The Goal: Build a system that can:

1. Ingest personal documents (RAG).
2. Toggle between specialized personas (Modelfiles).
3. Interact with your local computer (Tool Calling).
4. Display results in a beautiful interface (API + UI).

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1. Project Requirements

Part A: The Brain (Ollama)

• Define a base model (Llama 3 or Mistral).
• Create three custom Modelfiles: ExpertWriter, SecurityAuditor, and PersonalAssistant.
• Each must have a distinct SYSTEM prompt and PARAMETER settings.

Part B: The Memory (RAG)

• Use ChromaDB and mxbai-embed-large.
• Index a folder of your own lecture notes or personal projects.
• Implement a "Search" function that finds relevant context before prompting.

Part C: The Hands (Tool Calling)

• Create a Python function get_system_time() or check_disk_space().
• Register this tool so the AI can report on your hardware's health when asked.

Part D: The Face (Frontend)

• Use Open WebUI (via Docker Compose) OR a custom FastAPI dashboard.
• Ensure the connection is secure (localhost only).

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2. Implementation Guide

Step 1: The Stack Launch your Docker Compose file from Module 13 to get the infrastructure running.

Step 2: The Logic Write a Python "Coordinator" script that sits between your users and Ollama. This script will determine when to use RAG and when to call a Tool.

Step 3: The Hardening Add the guardrails from Module 9. Ensure the platform refuses to answer questions about topics you deem "Off-limits" for your private setup.

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3. Submission / Success Criteria

Your project is successful if you can sit at your computer and say: "Hey AI, read my project plan from the knowledge folder, tell me the deadline, and find any security flaws in the architecture."

• Ollama runs the model.
• RAG finds the project plan.
• The Modelfile (SecurityAuditor) provides the perspective.
• Tool Calling (optional) checks if you have enough disk space to save the report.

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Final Thoughts

The world of AI is moving at lightning speed. By choosing to run models locally, you have secured your privacy, your data sovereignty, and your freedom.

There is no "End" to this journey. New models are released every week. New quantization methods appear every month. But the foundations you learned here—how to think about Hardware, Context, Vectors, and Prompts—will stay relevant for years to daytime.

Go forth and build something incredible.

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Course Checklist (Complete!)

• Module 1: Foundations
• Module 2: Installation
• Module 3: Models
• Module 4: Internals
• Module 5: Modelfiles
• Module 6: Importing
• Module 7: Optimization
• Module 8: API
• Module 9: Prompting
• Module 10: RAG
• Module 11: Fine-tuning
• Module 12: Security
• Module 13: Scaling
• Module 14: Operations
• Capstone Project