curo1305
4c35d7a2a4
All JSON config files (AI settings, doc settings, appearance, themes) now live in the 'config' bucket of storage-service instead of a shared Docker volume. - backend/core/config_storage.py: new async HTTP helpers for config bucket r/w - backend/core/app_config.py: fully async rewrite; all load_*/save_*/seed_* functions use config_storage instead of filesystem - backend/routers/settings.py: all asyncio.to_thread() wrappers removed; direct await calls throughout; update_theme reads via load_theme_by_id() - backend/main.py: await seed_builtin_themes() directly (no to_thread) - ai-service: remove CONFIG_PATH, add STORAGE_SERVICE_URL; config_reader now fetches from storage-service via httpx - doc-service: config_reader rewritten to fetch/write via storage-service - docker-compose: remove app_config volume; add storage-service depends_on for ai-service; remove DATA_DIR and CONFIG_PATH from doc-service Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
"""
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Reads ai_service_config.json from the storage-service config bucket.
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30-second TTL cache + env var overrides (dev credentials stay out of git).
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Env var overrides (all optional):
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AI_PROVIDER — "lmstudio" | "ollama" | "anthropic"
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LMSTUDIO_BASE_URL, LMSTUDIO_API_KEY, LMSTUDIO_MODEL
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OLLAMA_BASE_URL, OLLAMA_MODEL, OLLAMA_API_KEY
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ANTHROPIC_API_KEY, ANTHROPIC_MODEL
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"""
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import json
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import os
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import time
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from copy import deepcopy
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import httpx
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from app.core.config import settings
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_CONFIG_KEY = "ai_service_config.json"
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_DEFAULT_CONFIG: dict = {
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"provider": "lmstudio",
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"timeout_seconds": 60,
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"max_retries": 2,
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"anthropic": {"api_key": "", "model": "claude-haiku-4-5-20251001"},
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"ollama": {"base_url": "http://host.docker.internal:11434/v1", "model": "llama3.2", "api_key": "ollama"},
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"lmstudio": {"base_url": "http://host.docker.internal:1234/v1", "model": "gemma-4-e4b-it", "api_key": "lm-studio"},
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}
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_cache: dict | None = None
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_cache_at: float = 0.0
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_CACHE_TTL = 30.0
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def _storage_url() -> str:
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return f"{settings.STORAGE_SERVICE_URL}/objects/config/{_CONFIG_KEY}"
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async def _fetch_config() -> dict:
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"""Fetch config from storage-service. Returns defaults if not found."""
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async with httpx.AsyncClient(timeout=10.0) as client:
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resp = await client.get(_storage_url())
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if resp.status_code == 404:
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return deepcopy(_DEFAULT_CONFIG)
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resp.raise_for_status()
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return resp.json()
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def _apply_env_overrides(config: dict) -> dict:
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cfg = deepcopy(config)
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if v := os.environ.get("AI_PROVIDER"):
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cfg["provider"] = v
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lms = cfg.setdefault("lmstudio", {})
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if v := os.environ.get("LMSTUDIO_BASE_URL"):
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lms["base_url"] = v
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if v := os.environ.get("LMSTUDIO_API_KEY"):
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lms["api_key"] = v
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if v := os.environ.get("LMSTUDIO_MODEL"):
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lms["model"] = v
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oll = cfg.setdefault("ollama", {})
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if v := os.environ.get("OLLAMA_BASE_URL"):
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oll["base_url"] = v
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if v := os.environ.get("OLLAMA_MODEL"):
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oll["model"] = v
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if v := os.environ.get("OLLAMA_API_KEY"):
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oll["api_key"] = v
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ant = cfg.setdefault("anthropic", {})
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if v := os.environ.get("ANTHROPIC_API_KEY"):
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ant["api_key"] = v
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if v := os.environ.get("ANTHROPIC_MODEL"):
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ant["model"] = v
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return cfg
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async def load_ai_config() -> dict:
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global _cache, _cache_at
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now = time.monotonic()
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if _cache is not None and (now - _cache_at) < _CACHE_TTL:
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return _cache
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raw = await _fetch_config()
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data = _apply_env_overrides(raw)
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_cache = data
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_cache_at = now
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return data
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