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
+handler-base/                 # Shared handler library
-├── chat-handler.yaml
+├── handler_base/             #   Core classes
-├── voice-assistant.yaml
+│   ├── handler.py            #   Base Handler class
-└── pipeline-bridge.yaml
+│   ├── 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/`
+1. Add WorkflowTemplate to `argo/` repo
-2. Add Kubernetes Deployment in `llm-workflows/workflows/`
+2. Push to main → Gitea syncs to cluster
-3. Push to main → Flux deploys automatically
+
+### 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
 

CODING-CONVENTIONS.md

@@ -25,24 +25,34 @@ kubernetes/
 - Apps: lowercase with hyphens (`chat-handler`, `voice-assistant`)
 - Secrets: `{app}-{type}` (e.g., `milvus-credentials`)
 
-### llm-workflows (Orchestration)
+### AI/ML Repos (git.daviestechlabs.io/daviestechlabs)
 
 ```
-workflows/                   # Kubernetes Deployments for NATS handlers
+handler-base/                # Shared library for all handlers
-├── {handler}.yaml           # One file per handler
+├── handler_base/
+│   ├── handler.py           # Base Handler class
+│   ├── nats_client.py       # NATS wrapper
+│   ├── config.py            # Pydantic Settings
+│   ├── health.py            # K8s probes
+│   ├── telemetry.py         # OpenTelemetry
+│   └── clients/             # Service clients
+└── pyproject.toml
+
+chat-handler/                # Text chat service
+voice-assistant/             # Voice pipeline service
+├── {name}.py                # Standalone version
+├── {name}_v2.py             # Handler-base version (preferred)
+└── Dockerfile.v2
 
 argo/                        # Argo WorkflowTemplates
-├── {workflow-name}.yaml     # One file per workflow
+├── {workflow-name}.yaml
 
-pipelines/                   # Kubeflow Pipeline Python files
+kubeflow/                    # Kubeflow Pipelines
-├── {pipeline}_pipeline.py   # Pipeline definition
+├── {pipeline}_pipeline.py
-└── kfp-sync-job.yaml       # Upload job
 
-{handler}/                   # Python source code
+kuberay-images/              # GPU worker images
-├── __init__.py
+├── dockerfiles/
-├── {handler}.py            # Main entry point
+└── ray-serve/
-├── requirements.txt
-└── Dockerfile
 ```
 
 ---

TECH-STACK.md

@@ -237,20 +237,26 @@
 
 ---
 
-## Python Dependencies (llm-workflows)
+## Python Dependencies (handler-base)
+
+Core library for all NATS handlers: [handler-base](https://git.daviestechlabs.io/daviestechlabs/handler-base)
 
 ```toml
 # Core
 nats-py>=2.7.0          # NATS client
 msgpack>=1.0.0          # Binary serialization
-aiohttp>=3.9.0          # HTTP client
+httpx>=0.27.0           # HTTP client
 
 # ML/AI
 pymilvus>=2.4.0         # Milvus client
-sentence-transformers   # Embeddings
 openai>=1.0.0           # vLLM OpenAI API
 
-# Kubeflow
+# Observability
+opentelemetry-api>=1.20.0
+opentelemetry-sdk>=1.20.0
+mlflow>=2.10.0          # Experiment tracking
+
+# Kubeflow (kubeflow repo)
 kfp>=2.12.1             # Pipeline SDK
 ```
 

decisions/0006-gitops-with-flux.md

@@ -65,20 +65,23 @@ homelab-k8s2/
 ### Multi-Repository Sync
 
 ```yaml
-# GitRepository for llm-workflows
+# GitRepository for Gitea repos (daviestechlabs org)
+# Examples: argo, kubeflow, chat-handler, voice-assistant
 apiVersion: source.toolkit.fluxcd.io/v1
 kind: GitRepository
 metadata:
-  name: llm-workflows
+  name: argo-workflows
   namespace: flux-system
 spec:
-  url: ssh://git@github.com/Billy-Davies-2/llm-workflows
+  url: https://git.daviestechlabs.io/daviestechlabs/argo.git
   ref:
     branch: main
-  secretRef:
+  # Public repos don't need secretRef
-    name: github-deploy-key
 ```
 
+Note: The monolithic `llm-workflows` repo has been decomposed into separate repos
+in the daviestechlabs Gitea organization. See AGENT-ONBOARDING.md for the full list.
+
 ### SOPS Integration
 
 ```yaml