> ## Documentation Index
> Fetch the complete documentation index at: https://docs.t3planet.de/en/latest/llms.txt
> Use this file to discover all available pages before exploring further.

# T3AC — Prerequisites Custom LLM

> T3AC — Prerequisites Custom LLM.

## Recommended AI Models Matrix

| TYPO3 Pages    | LLM (On-Prem)                                              | LLM (Cloud)                       | Embedding Model        | Vector DB          | Hardware Notes                     |
| -------------- | ---------------------------------------------------------- | --------------------------------- | ---------------------- | ------------------ | ---------------------------------- |
| 1K – 5K pages  | Mistral-7B, Llama 3 8B, Gemma 7B, Ollama’s GPT:OSS-120B    | GPT-3.5, Claude Haiku, Gemini Pro | all-MiniLM-L6-v2       | ChromaDB, Pinecone | 16 GB RAM, 4+ cores (GPU optional) |
| 5K – 20K pages | Llama 3 8B, Mistral-7B, Llama 3 70B, Ollama’s GPT:OSS-120B | GPT-4, Claude Sonnet              | text-embedding-3-small | ChromaDB, Pinecone | 32 GB RAM, GPU 8–24 GB VRAM        |
| 20K+ pages     | Llama 3 70B, Mistral 8x7B, Ollama’s GPT:OSS-120B           | GPT-4o, Claude Opus, Gemini Ultra | text-embedding-3-large | Pinecone, ChromaDB | 64 GB+ RAM, GPU 24+ GB VRAM        |

## Supported AI Providers and Models

| Provider           | Category  | Models                                                                                        |
| ------------------ | --------- | --------------------------------------------------------------------------------------------- |
| OpenAI             | openai    | gpt-5, nova-2-lite, gpt-4.1, gpt-4o, gpt-4, gpt-3.5-turbo                                     |
| Anthropic (Claude) | claude    | claude-3.5-sonnet-latest, claude-3.5-haiku-latest, claude-3-opus-latest                       |
| Google (Gemini)    | gemini    | gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, gemini-2.0-flash-lite, gemini-2.0-pro-exp |
| Mistral            | mistral   | mistral-large-latest                                                                          |
| Custom LLM         | customllm | User-defined (when enable\_custom\_llm\_model is enabled)                                     |

## Prerequisites of Hardware & Software

### Server Requirements

* **OS:** Ubuntu 20.04+ (Linux)
* **CPU:** 4+ cores
* **RAM:** 16 GB minimum (32+ GB recommended)
* **Disk:** 100 GB free + 10–100 GB storage for embeddings
* **GPU:** Based on the model matrix above

### System Access

* SSH access (terminal)
* Admin/root rights to install software and Docker

### TYPO3 Access

* TYPO3 Backend System Administrator access

### Network & Security

* Open ports (for example: 8000, 443)
* SSL/TLS for public endpoints
* Firewall configuration as required

### Required Software

* Python 3.8+ with pip
* Docker
* NVIDIA GPU drivers and CUDA (if GPU is used)
* Python packages: - `torch` - `transformers` - `sentence-transformers` - Additional packages as required

### Vector Database (Embedding/Search)

* **Local:** ChromaDB (recommended)
* **Cloud-managed:** Pinecone (recommended for large-scale setups)

# T3AC – Scope of Work (SOW) Custom AI LLM Integration

## Scope Overview

* Installation of LLM models
* Installation of required software and libraries
* Pre-processing of content (chunking and embedding)
* Secure embedding storage:
  * On-premise using ChromaDB
  * Cloud-based using Pinecone
* Deployment of on-prem open-source LLMs for RAG
* Secure and documented FastAPI delivery
* Daily or weekly incremental updates
* Administrative documentation
* Onboarding and training

## Workflow & Implementation Steps

1. **Data Processing & Embedding**

* Pre-process and chunk data
* Generate semantic embeddings (Sitemap, Website, PDF, Text, Q\&A).

2. **Vector Database Integration**

* Store embeddings in ChromaDB (on-prem)
* Store embeddings in Pinecone (cloud, if needed)

3. **LLM Deployment & API Layer**
4. **Training & Handover**

* Admin and technical documentation
* Live training sessions
* Go-live support