Hitonabi
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
"""
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Connect: erzeugt saubere, getestete Konfig-Snippets für IDEs/Agenten auf dem
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LOKALEN PC (separate Maschine im LAN). Alle zeigen auf den **Gateway** der Box
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(`model: auto`, Cockpit-Port :9001/v1) + den **Shared-Memory-MCP** (MC :9001).
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Wichtig: Host ist die LAN-IP der Box (NICHT eine Proxy-Domain) — das war in v1
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die häufigste Fehlerquelle. Der Aufrufer übergibt den Host explizit.
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"""
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import json
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from config import PORT
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DEFAULT_HOST = "192.168.178.151"
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# Modelle, die der Gateway anbietet (model:auto = Standard).
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GATEWAY_MODELS = ["auto", "fast", "heavy", "coder", "vision"]
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def _gw(host: str) -> str:
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# Eingebauter Gateway: MC2 serviert /v1 selbst (gleicher Port wie das Cockpit).
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return f"http://{host}:{PORT}/v1"
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def build_snippets(host: str = DEFAULT_HOST,
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mcp_script_path: str = r"F:\\Coding Stuff\\mission-control-2\\mcp\\mcp_memory.py",
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mcp_python: str = "python") -> dict:
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gw = _gw(host)
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mc_url = f"http://{host}:{PORT}"
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cline = json.dumps({
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"apiProvider": "openai",
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"openAiBaseUrl": gw,
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"openAiApiKey": "local",
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"openAiModelId": "auto",
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}, indent=2)
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opencode = json.dumps({
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"providers": {
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"litellm": {
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"npm": "@ai-sdk/openai-compatible",
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"name": "Bosgame Gateway",
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"options": {"baseURL": gw, "apiKey": "local"},
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"models": {m: {"name": m} for m in GATEWAY_MODELS},
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}
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}
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}, indent=2)
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zed = json.dumps({
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"language_models": {
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"openai_compatible": {
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"bosgame": {
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"api_url": gw,
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"available_models": [
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{"name": m, "display_name": m, "max_tokens": 131072,
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"capabilities": {"tools": True}} for m in GATEWAY_MODELS
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],
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}
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}
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}
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}, indent=2)
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cont = json.dumps({
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"models": [
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{"title": f"Bosgame / {m}", "provider": "openai", "model": m,
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"apiBase": gw, "apiKey": "local"} for m in ("auto", "coder", "heavy")
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]
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}, indent=2)
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# Claude Code spricht Anthropic-Format; der eingebaute Gateway ist OpenAI-kompatibel.
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claude_code = (
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f"# Der eingebaute Gateway ist OpenAI-kompatibel ({gw}, model: auto).\n"
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f"# Claude Code nutzt das Anthropic-Format — dafür braucht es einen Anthropic-Shim\n"
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f"# (z.B. LiteLLM /v1/messages) als Aufsatz. Für lokale Modelle direkt: Cline / OpenCode /\n"
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f"# Continue / Zed nutzen (oben), die sprechen OpenAI-kompatibel mit diesem Gateway."
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)
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memory_mcp = json.dumps({
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"mcpServers": {
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"mission-control-memory": {
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"command": mcp_python,
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"args": [mcp_script_path],
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"env": {"MC_URL": mc_url},
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}
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}
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}, indent=2)
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return {
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"host": host,
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"gateway_url": gw,
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"tools": {
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"cline": {"label": "Roo Code / Cline (VS Code)", "lang": "json", "snippet": cline,
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"note": "OpenAI-Provider → Gateway. Modell 'auto' (schnell, eskaliert bei Bedarf)."},
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"opencode": {"label": "OpenCode", "lang": "jsonc", "snippet": opencode,
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"note": "Datei opencode.jsonc, Key 'providers'."},
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"zed": {"label": "Zed", "lang": "json", "snippet": zed,
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"note": "settings.json → language_models.openai_compatible."},
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"continue": {"label": "Continue", "lang": "json", "snippet": cont,
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"note": "~/.continue/config.json."},
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"claude_code": {"label": "Claude Code", "lang": "bash", "snippet": claude_code,
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"note": "Anthropic-Format über LiteLLM /v1/messages."},
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"memory_mcp": {"label": "Shared Memory (MCP)", "lang": "json", "snippet": memory_mcp,
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"note": "Für jedes MCP-fähige Tool. mcp_memory.py muss lokal liegen."},
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},
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}
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