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NexusOS/CLAUDE.md
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jon b0aa0438af Initial commit: NexusOS project + desktop theme baseline
Captures the known-good state after theme consolidation and
consistency reconciliation:
- NexusOS GTK/xfwm4/icon theme assets (assets/themes, management/Mint-Y-Nexus)
- Tightened right-click menus, visible separators, no menu icons
- assets/themes/install-theme.sh: idempotent restore of all wiring
  (symlinks, xfconf xsettings+xfwm4, GTK 3/4 settings.ini)
- .gitignore excludes venv/ollama/models/runtime/db

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-05-18 13:39:48 -05:00

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4.8 KiB
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# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## What is NexusOS
NexusOS is a local AI assistant platform. It runs a Python/FastAPI backend (Synapse) that interfaces with a locally bundled Ollama instance, a dedicated memory microservice, and a React/Vite frontend. Everything runs on `localhost` with no external AI API calls — the LLM is served via Ollama.
## Running the Project
**Full stack (recommended):**
```bash
./launch_nexus.sh
```
This activates the `Promethean` venv, starts the memory service on port 8001, the Synapse backend on port 8000, and the Vite frontend (default port 5173).
**Individual services via CLI:**
```bash
# From nexus-core/ with Promethean venv active:
source Promethean/bin/activate
# Backend
uvicorn synapse.main:sio_app --host 0.0.0.0 --port 8000 --reload
# Memory service
uvicorn synapse.memory.service:app --host 0.0.0.0 --port 8001 --reload
# Frontend
cd interface/web && npm run dev
```
**Management CLI** (start/stop individual services with PID tracking):
```bash
./management/nexus-cli.sh start # starts backend + frontend
./management/nexus-cli.sh stop
./management/nexus-cli.sh start_backend / start_frontend
```
**Frontend lint:**
```bash
cd interface/web && npm run lint
```
**Frontend build:**
```bash
cd interface/web && npm run build
```
## Architecture
### Python venv
All Python code runs inside `Promethean/` (a local venv). Always activate it before running backend commands: `source Promethean/bin/activate`. The AMD ROCm PyTorch stack is installed here (`requirements.txt`).
### Synapse Backend (`synapse/`)
FastAPI app at `synapse/main.py`. Key responsibilities:
- `/chat` and `/chat/stream` — chat with Ollama; streaming uses SSE. After each exchange the stream endpoint calls the Memory Service to auto-extract persistent facts.
- `/playbooks` — CRUD for playbooks stored in SQLite via `synapse/playbooks/store.py`.
- `/memory` — CRUD for persistent facts (proxies the same SQLite store as the memory service).
- `/models` — lists, pulls, and deletes Ollama models by proxying `ollama/bin/ollama`'s HTTP API.
- `/conversations` — persists and retrieves full chat history from SQLite.
**System prompt assembly** (in `main.py` `chat_stream_endpoint`): the final system prompt is built by layering the active playbook instructions → reference playbook context → persistent memory facts → relevant past conversation snippets retrieved by `store.search_conversations`.
### Memory Service (`synapse/memory/`)
A separate FastAPI app on port 8001. `service.py` exposes `/memories/extract` which calls `extractor.py` — an Ollama prompt that decides whether to persist a new fact from a conversation exchange. The main Synapse backend calls this asynchronously after each streaming response. Both services share the same SQLite database (`synapse/memory/memory.db`).
### Playbook System (`synapse/playbooks/` + `synapse/playbook_manager.py`)
Playbooks are ordered records (title, goal, instructions, tags) stored in SQLite. The **first** playbook by order is the active system prompt; all subsequent playbooks are injected as reference context. `PlaybookManager` is the thin class the backend uses to retrieve and render them.
### Ollama (`ollama/bin/ollama`)
A bundled Ollama binary lives at `ollama/bin/ollama`. `OllamaManager` in `synapse/ollama_manager.py` manages its lifecycle (start/stop/health-check) and selects the best available model. GPU detection uses Vulkan (`vulkaninfo`) to prefer discrete AMD/NVIDIA GPUs. The Ollama HTTP API is at `http://127.0.0.1:11434` (overridable via `OLLAMA_HOST` env var).
### Frontend (`interface/web/`)
React 19 + Vite. No routing library — `App.jsx` manages page state in a single `currentPage` useState. All API calls hit `http://localhost:8000` (configured in `src/config.js`). Pages: Chatbot, Playbook editor, Conversation History, Models, Memory, Settings.
### Persistent Storage
All data lands in `synapse/memory/memory.db` (SQLite, WAL mode). Tables: memory facts, conversations, messages, playbooks, app settings. `synapse/memory/store.py` (`PersistentMemoryStore`) owns the schema and all queries. `nexus_config.py` defines all paths; it also ensures all required directories exist on import.
### Logs & Runtime State
- `runtime/backend.log`, `runtime/frontend.log` — service stdout
- `runtime/logs/ollama.log`, `runtime/logs/chat.log`
- `runtime/pids/backend.pid`, `runtime/pids/frontend.pid` — used by the management CLI
## Key Config
| Concern | Location |
|---|---|
| Ollama host | `OLLAMA_HOST` env var (default `http://127.0.0.1:11434`) |
| All filesystem paths | `synapse/nexus_config.py` `Settings` class |
| Frontend API base URL | `interface/web/src/config.js` |
| Python dependencies (AMD) | `requirements.txt` / `amd_requirements.txt` |
| Python dependencies (base) | `requirements-base.txt` |