Add shared ai-service container as AI provider intermediary

All feature containers now POST messages to ai-service (port 8010) instead of calling AI providers directly. ai-service routes to LM Studio, Ollama, or Anthropic based on /config/ai_service_config.json. doc-service AI providers removed; replaced by httpx ai_client.py. Backend settings restructured to /api/settings/ai. Frontend gets dedicated AIAdminSettingsPage and AI Service card in AppsPage. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-14 12:30:45 +02:00
parent 52a2967f61
commit 88c1ea297e
47 changed files with 1354 additions and 497 deletions
@@ -0,0 +1,81 @@
+"""
+Reads ai_service_config.json from the shared config volume.
+30-second TTL cache + env var overrides (dev credentials stay out of git).
+
+Env var overrides (all optional):
+  AI_PROVIDER          — "lmstudio" | "ollama" | "anthropic"
+  LMSTUDIO_BASE_URL, LMSTUDIO_API_KEY, LMSTUDIO_MODEL
+  OLLAMA_BASE_URL, OLLAMA_MODEL, OLLAMA_API_KEY
+  ANTHROPIC_API_KEY, ANTHROPIC_MODEL
+"""
+import asyncio
+import json
+import os
+import time
+from copy import deepcopy
+from pathlib import Path
+
+from app.core.config import settings
+
+_DEFAULT_CONFIG: dict = {
+    "provider": "lmstudio",
+    "timeout_seconds": 60,
+    "max_retries": 2,
+    "anthropic": {"api_key": "", "model": "claude-haiku-4-5-20251001"},
+    "ollama": {"base_url": "http://host.docker.internal:11434/v1", "model": "llama3.2", "api_key": "ollama"},
+    "lmstudio": {"base_url": "http://host.docker.internal:1234/v1", "model": "local-model", "api_key": "lm-studio"},
+}
+
+_cache: dict | None = None
+_cache_at: float = 0.0
+_CACHE_TTL = 30.0
+
+
+def _read_config_sync() -> dict:
+    path = Path(settings.CONFIG_PATH)
+    if not path.exists():
+        return _apply_env_overrides(deepcopy(_DEFAULT_CONFIG))
+    with open(path) as f:
+        return _apply_env_overrides(json.load(f))
+
+
+def _apply_env_overrides(config: dict) -> dict:
+    cfg = deepcopy(config)
+
+    if v := os.environ.get("AI_PROVIDER"):
+        cfg["provider"] = v
+
+    lms = cfg.setdefault("lmstudio", {})
+    if v := os.environ.get("LMSTUDIO_BASE_URL"):
+        lms["base_url"] = v
+    if v := os.environ.get("LMSTUDIO_API_KEY"):
+        lms["api_key"] = v
+    if v := os.environ.get("LMSTUDIO_MODEL"):
+        lms["model"] = v
+
+    oll = cfg.setdefault("ollama", {})
+    if v := os.environ.get("OLLAMA_BASE_URL"):
+        oll["base_url"] = v
+    if v := os.environ.get("OLLAMA_MODEL"):
+        oll["model"] = v
+    if v := os.environ.get("OLLAMA_API_KEY"):
+        oll["api_key"] = v
+
+    ant = cfg.setdefault("anthropic", {})
+    if v := os.environ.get("ANTHROPIC_API_KEY"):
+        ant["api_key"] = v
+    if v := os.environ.get("ANTHROPIC_MODEL"):
+        ant["model"] = v
+
+    return cfg
+
+
+async def load_ai_config() -> dict:
+    global _cache, _cache_at
+    now = time.monotonic()
+    if _cache is not None and (now - _cache_at) < _CACHE_TTL:
+        return _cache
+    data = await asyncio.to_thread(_read_config_sync)
+    _cache = data
+    _cache_at = now
+    return data