v1.0.0 to v1.0.0

---

name: ai-portable-setup

version: 1.0.0

type: tool

author: Lukas Geiger

created: 2026-03-12

updated: 2026-03-12

description: Creates a portable AI workspace on a USB drive or any drive. RAG pipeline with local LLM models (Ollama), vector database (ChromaDB), and preconfigured prompts.

standalone: true

anthropic_compatible: true

bach_compatible: false

bach_origin: true

category: infrastructure

tags: [portable, rag, ollama, usb-drive, offline, local-llm]

language: de

status: active

dependencies: {'tools': [], 'services': ['ollama'], 'protocols': [], 'python': ['chromadb', 'ollama']}

provenance: {'origin': 'bach', 'origin_path': 'MODULAR_AGENTS/AI-Portable', 'origin_version': '0.1.0', 'origin_repo': 'github.com/ellmos-ai/bach', 'last_sync_from_origin': '2026-03-12', 'last_sync_to_origin': None, 'local_changes_since_sync': False}

---

> **Deutsch** — Offizielle Deutsch-Version / Documento Oficial en Deutsch.

# AI Portable Setup (Deutsch)

Creates a portable AI workspace with a local RAG pipeline.

Designed for USB drives or external drives — works offline

with Ollama and local embedding models.

## Quick Start

```bash

# Create structure on USB drive (Deutsch)

python setup_portable.py E:\AI-Portable

# Then on the target system: (Deutsch)

cd E:\AI-Portable

python -m venv venv

venv\Scripts\activate # Windows

pip install chromadb ollama

# Index documents (Deutsch)

python rag/ingest.py

# Query (Deutsch)

python rag/query.py "My question..."

python rag/query.py --mode icf "Observations about the client"

```

## Generated Directory Structure

```

AI-Portable/

models/

llm/ Local LLM models (Mistral, Llama, etc.)

embeddings/ Embedding models (nomic-embed-text, bge-small)

tts/ Optional: Text-to-Speech (Piper, Coqui)

db/

chroma/ ChromaDB vector database

sqlite/ Metadata DB

documents/

code/ Code snippets, projects

general/ General documents

rag/

ingest.py Ingest and index files

query.py RAG queries with mode selection

pipeline.py Main RAG pipeline (embed + query + LLM)

prompts/

coding.txt Coding assistant prompt

icf.txt ICF structuring prompt

rpg.txt Pen-and-paper game master prompt

templates/ Word/PDF templates

venv/ Portable Python environment

```

## RAG Pipeline

```

Documents -> Chunking -> Embedding (nomic-embed-text)

|

v

ChromaDB (local)

|

Query -> Embedding -> Similarity Search -> Top-K Chunks

|

v

Context + Prompt -> Ollama (Mistral)

|

v

Response

```

## Query Modes

| Mode | Prompt | Usage |

|------|--------|-------|

| `default` | General assistant | Standard queries |

| `icf` | ICF structuring | Classify observations according to ICF |

| `coding` | Coding assistant | Write and explain code |

| `rpg` | Game master | Pen-and-paper role-playing |

## Requirements on Target System

- Python 3.10+

- Ollama (with `mistral:instruct` and `nomic-embed-text`)

- ~8 GB RAM for Mistral

## Änderungsprotokoll

### 1.0.0 (2026-03-12)

- Consolidation from MODULAR_AGENTS/AI-Portable

- Setup script as single-file skill

- 4 preconfigured prompts (coding, icf, rpg, default)

- RAG pipeline (ingest, query, pipeline)