curo1305
88c1ea297e
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>
137 lines
4.1 KiB
Python
137 lines
4.1 KiB
Python
"""
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Per-service runtime config helpers.
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Config files live on the shared `app_config` Docker volume at /config/.
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Each service has its own JSON file.
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Atomic write pattern: write to .tmp in same dir, then os.replace() so
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services never read a partial file.
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"""
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import copy
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import json
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import os
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from pathlib import Path
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from pydantic import BaseModel
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_CONFIG_DIR = Path(os.environ.get("APP_CONFIG_DIR", "/config"))
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# ── AI service config schemas ──────────────────────────────────────────────────
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class AnthropicConfig(BaseModel):
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api_key: str = ""
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model: str = "claude-haiku-4-5-20251001"
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class OllamaConfig(BaseModel):
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base_url: str = "http://host.docker.internal:11434/v1"
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model: str = "llama3.2"
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api_key: str = "ollama"
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class LMStudioConfig(BaseModel):
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base_url: str = "http://host.docker.internal:1234/v1"
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model: str = "local-model"
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api_key: str = "lm-studio"
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class AIServiceConfig(BaseModel):
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provider: str = "lmstudio"
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timeout_seconds: int = 60
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max_retries: int = 2
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anthropic: AnthropicConfig = AnthropicConfig()
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ollama: OllamaConfig = OllamaConfig()
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lmstudio: LMStudioConfig = LMStudioConfig()
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# ── Doc service config schemas ─────────────────────────────────────────────────
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class DocumentsConfig(BaseModel):
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max_pdf_bytes: int = 20 * 1024 * 1024
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class DocServiceConfig(BaseModel):
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documents: DocumentsConfig = DocumentsConfig()
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# ── Masking ────────────────────────────────────────────────────────────────────
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def _mask_key(key: str) -> str:
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if not key or len(key) <= 8:
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return "••••"
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return key[:7] + "••••"
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def _mask_ai_config(data: dict) -> dict:
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masked = copy.deepcopy(data)
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for provider in ("anthropic", "ollama", "lmstudio"):
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if provider in masked and "api_key" in masked[provider]:
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masked[provider]["api_key"] = _mask_key(masked[provider]["api_key"])
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return masked
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# ── Load / Save ────────────────────────────────────────────────────────────────
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def _config_path(service: str) -> Path:
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return _CONFIG_DIR / f"{service}_config.json"
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def load_service_config(service: str) -> dict:
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path = _config_path(service)
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if not path.exists():
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if service == "ai_service":
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return AIServiceConfig().model_dump()
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if service == "doc_service":
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return DocServiceConfig().model_dump()
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return {}
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with path.open() as f:
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return json.load(f)
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def save_service_config(service: str, data: dict) -> None:
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path = _config_path(service)
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path.parent.mkdir(parents=True, exist_ok=True)
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tmp = path.with_suffix(".tmp")
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tmp.write_text(json.dumps(data, indent=2))
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os.replace(tmp, path)
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# AI service helpers
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def load_ai_service_config() -> AIServiceConfig:
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raw = load_service_config("ai_service")
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return AIServiceConfig.model_validate(raw)
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def save_ai_service_config(config: AIServiceConfig) -> None:
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save_service_config("ai_service", config.model_dump())
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def load_ai_service_config_masked() -> dict:
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raw = load_service_config("ai_service")
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return _mask_ai_config(raw)
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# Doc service helpers
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def load_doc_service_config() -> DocServiceConfig:
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raw = load_service_config("doc_service")
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return DocServiceConfig.model_validate(raw)
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def save_doc_service_config(config: DocServiceConfig) -> None:
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save_service_config("doc_service", config.model_dump())
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def load_doc_service_config_masked() -> dict:
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return load_service_config("doc_service")
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def _merge_api_key(new_key: str, existing_key: str) -> str:
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"""If new_key is empty or a masked value, keep the existing key."""
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if not new_key or "••••" in new_key:
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return existing_key
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return new_key
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