docs: Update for decomposed repo structure

- AGENT-ONBOARDING: New repo map with daviestechlabs Gitea repos
- TECH-STACK: Reference handler-base instead of llm-workflows
- CODING-CONVENTIONS: Update project structure for new repos
- ADR 0006: Update GitRepository examples for Gitea repos

llm-workflows has been split into:
- handler-base, chat-handler, voice-assistant
- kuberay-images, argo, kubeflow, mlflow, gradio-ui

AGENT-ONBOARDING.md

@@ -15,9 +15,21 @@ You are working on a **homelab Kubernetes cluster** running:
 | Repo | What It Contains | When to Edit |
 |------|------------------|--------------|
 | `homelab-k8s2` | Kubernetes manifests, Talos config, Flux | Infrastructure changes |
-| `llm-workflows` | NATS handlers, Argo/KFP workflows | Workflow/handler changes |
-| `companions-frontend` | Go server, HTMX UI, VRM avatars | Frontend changes |
 | `homelab-design` (this) | Architecture docs, ADRs | Design decisions |
+| `companions-frontend` | Go server, HTMX UI, VRM avatars | Frontend changes |
+
+### AI/ML Repos (git.daviestechlabs.io/daviestechlabs)
+
+| Repo | Purpose |
+|------|---------|
+| `handler-base` | Shared Python library for NATS handlers |
+| `chat-handler` | Text chat with RAG pipeline |
+| `voice-assistant` | Voice pipeline (STT → RAG → LLM → TTS) |
+| `kuberay-images` | GPU-specific Ray worker Docker images |
+| `argo` | Argo Workflows (training, batch inference) |
+| `kubeflow` | Kubeflow Pipeline definitions |
+| `mlflow` | MLflow integration utilities |
+| `gradio-ui` | Gradio demo apps (embeddings, STT, TTS) |
 
 ## 🏗️ System Architecture (30-Second Version)
 
@@ -74,22 +86,39 @@ talos/
 │   └── nvidia/nvidia-runtime.yaml
 ```
 
-### Workflows (`llm-workflows`)
+### AI/ML Services (Gitea daviestechlabs org)
 
 ```
-workflows/                    # NATS handler deployments
-├── chat-handler.yaml
-├── voice-assistant.yaml
-└── pipeline-bridge.yaml
+handler-base/                 # Shared handler library
+├── handler_base/             #   Core classes
+│   ├── handler.py            #   Base Handler class
+│   ├── nats_client.py        #   NATS wrapper
+│   └── clients/              #   Service clients (STT, TTS, LLM, etc.)
+
+chat-handler/                 # RAG chat service
+├── chat_handler_v2.py        #   Handler-base version
+└── Dockerfile.v2
+
+voice-assistant/              # Voice pipeline service
+├── voice_assistant_v2.py     #   Handler-base version
+└── pipelines/voice_pipeline.py
 
 argo/                         # Argo WorkflowTemplates
-├── document-ingestion.yaml
 ├── batch-inference.yaml
-└── qlora-training.yaml
+├── qlora-training.yaml
+└── document-ingestion.yaml
 
-pipelines/                    # Kubeflow Pipeline Python
+kubeflow/                     # Kubeflow Pipeline definitions
 ├── voice_pipeline.py
-└── document_ingestion_pipeline.py
+├── document_ingestion_pipeline.py
+└── evaluation_pipeline.py
+
+kuberay-images/               # GPU worker images
+├── dockerfiles/
+│   ├── Dockerfile.ray-worker-nvidia
+│   ├── Dockerfile.ray-worker-strixhalo
+│   └── Dockerfile.ray-worker-rdna2
+└── ray-serve/                #   Serve modules
 ```
 
 ## 🔌 Service Endpoints (Internal)
@@ -138,14 +167,24 @@ f"ai.pipeline.status.{request_id}"     # Status updates
 ### Deploy a New AI Service
 
 1. Create InferenceService in `homelab-k8s2/kubernetes/apps/ai-ml/kserve/`
-2. Add endpoint to `llm-workflows/config/ai-services-config.yaml`
+2. Push to main → Flux deploys automatically
+
+### Add a New NATS Handler
+
+1. Create handler repo or add to existing (use `handler-base` library)
+2. Add K8s Deployment in `homelab-k8s2/kubernetes/apps/ai-ml/`
 3. Push to main → Flux deploys automatically
 
-### Add a New Workflow
+### Add a New Argo Workflow
 
-1. Create handler in `llm-workflows/chat-handler/` or `llm-workflows/voice-assistant/`
-2. Add Kubernetes Deployment in `llm-workflows/workflows/`
-3. Push to main → Flux deploys automatically
+1. Add WorkflowTemplate to `argo/` repo
+2. Push to main → Gitea syncs to cluster
+
+### Add a New Kubeflow Pipeline
+
+1. Add pipeline .py to `kubeflow/` repo
+2. Compile with `python pipeline.py`
+3. Upload YAML to Kubeflow UI
 
 ### Create Architecture Decision