feat: bootstrap vela UI and gateway workspace

Establish the monorepo, tooling, and starter apps so UI and gateway development can begin from a documented, runnable baseline.
This commit is contained in:
2026-04-08 17:49:46 +02:00
commit bba0095bc0
23 changed files with 2023 additions and 0 deletions

129
docs/architecture.md Normal file
View File

@@ -0,0 +1,129 @@
# Vela Architecture
## High-Level Architecture
```text
[ Browser (PWA UI) ]
|
WebSocket
|
[ Vela Gateway (NanoPi R6S) ]
|
+--> STT (local or NAS)
+--> Ollama (NAS GPU)
+--> Kokoro TTS (NAS or NanoPi)
+--> Home Assistant
+--> SearXNG
```
## Core Components
## Repository Structure
```text
apps/
vela-ui/
vela-gateway/
```
The repository now includes separate runnable workspaces for the UI and gateway so implementation can proceed independently while staying aligned through shared documentation.
### Frontend — `vela-ui`
#### Tech
- SvelteKit
- PWA enabled
- WebSocket client
The current implementation is a minimal SvelteKit app with a single starter page. PWA behavior, microphone capture, and the WebSocket client are later increments.
#### Responsibilities
- audio capture from microphone
- audio playback for TTS
- UI state rendering
- session management
- interrupt handling
#### Main Screen
- large mic button
- live transcript
- streamed assistant response text
- state indicator:
- idle
- listening
- thinking
- speaking
- interrupt button during speaking
### Backend — `vela-gateway`
#### Tech
- Fastify (Node)
- WebSocket-based session layer
The current implementation is a minimal Fastify service with `/` and `/health` HTTP endpoints. The WebSocket session layer is a later increment.
#### Responsibilities
- session lifecycle
- audio ingestion
- STT orchestration
- LLM orchestration
- tool execution
- TTS orchestration
- event streaming
## Voice Pipeline
```text
Mic → Gateway → STT → Transcript
→ LLM → Tool Calls → Results
→ LLM → Final Response
→ TTS → Audio Stream → UI
```
## Gateway Internal Flow
```text
1. Receive audio
2. Run STT (streaming)
3. Emit partial transcripts
4. On final:
→ call LLM
5. LLM decides:
→ direct response OR tool call
6. Execute tool
7. Feed result back to LLM
8. Generate final response
9. Send text stream
10. Send TTS stream
```
## LLM Layer
### Location
- NAS with RTX 3050 8GB
### Role
- intent parsing
- tool selection
- response generation
### Constraints
- must use a tool-calling schema
- must not directly control systems
- target approximately 7B-class models because of hardware limits
## Naming
- system: **Vela**
- gateway: `vela-gateway`
- UI: `vela-ui`
- voice profile: `vela-neutral`