0d34867a69
- New `features/doc-service` FastAPI microservice: PDF upload, async text extraction (pdfplumber), AI classification via Anthropic/Ollama/ LM Studio, per-user categories, file download - Alembic migration isolated with `alembic_version_doc_service` table - Main backend: httpx proxy routers for /api/documents/* and /api/documents/categories/*, admin settings API at /api/settings/* - Runtime config in /config/doc_service_config.json (shared Docker volume); api_key masking on reads; atomic write with os.replace() - Frontend: DocumentsPage, DocumentAdminSettingsPage, updated AppsPage launcher hub, simplified Nav (removed Settings link), new routes - docker-compose: doc-service service, doc_data + app_config volumes, removed internal:true from backend-net for outbound AI API calls - Fix pre-commit hook: probe Docker socket path so git subprocess picks up Docker Desktop on macOS - Fix security_check.py: use sys.executable for bandit so venv python is used instead of system python Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
156 lines
5.1 KiB
Python
156 lines
5.1 KiB
Python
"""
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Admin-only settings API for per-service runtime configuration.
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All endpoints require the caller to be an admin (Depends(get_current_admin)).
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Config files live on the shared app_config volume (/config/).
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"""
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import asyncio
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel
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from app.core.app_config import (
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DocServiceConfig,
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_merge_api_key,
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load_doc_service_config,
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load_doc_service_config_masked,
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save_doc_service_config,
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)
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from app.deps import get_current_admin
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from app.models.user import User
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router = APIRouter()
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# ── Pydantic request bodies ────────────────────────────────────────────────────
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class AIProviderUpdate(BaseModel):
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provider: str
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anthropic_api_key: str = ""
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anthropic_model: str = ""
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ollama_base_url: str = ""
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ollama_model: str = ""
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ollama_api_key: str = ""
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lmstudio_base_url: str = ""
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lmstudio_model: str = ""
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lmstudio_api_key: str = ""
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class LimitsUpdate(BaseModel):
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max_pdf_mb: int
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# ── Documents settings ─────────────────────────────────────────────────────────
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@router.get("/documents")
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async def get_documents_settings(
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_: User = Depends(get_current_admin),
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) -> dict:
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return load_doc_service_config_masked()
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@router.patch("/documents/ai")
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async def update_documents_ai(
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body: AIProviderUpdate,
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_: User = Depends(get_current_admin),
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) -> dict:
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valid_providers = ("anthropic", "ollama", "lmstudio")
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if body.provider not in valid_providers:
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raise HTTPException(status_code=422, detail=f"provider must be one of {valid_providers}")
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config = load_doc_service_config()
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config.ai.provider = body.provider
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# Anthropic
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if body.anthropic_api_key:
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config.ai.anthropic.api_key = _merge_api_key(
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body.anthropic_api_key, config.ai.anthropic.api_key
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)
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if body.anthropic_model:
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config.ai.anthropic.model = body.anthropic_model
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# Ollama
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if body.ollama_base_url:
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config.ai.ollama.base_url = body.ollama_base_url
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if body.ollama_model:
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config.ai.ollama.model = body.ollama_model
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if body.ollama_api_key:
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config.ai.ollama.api_key = _merge_api_key(body.ollama_api_key, config.ai.ollama.api_key)
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# LM Studio
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if body.lmstudio_base_url:
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config.ai.lmstudio.base_url = body.lmstudio_base_url
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if body.lmstudio_model:
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config.ai.lmstudio.model = body.lmstudio_model
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if body.lmstudio_api_key:
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config.ai.lmstudio.api_key = _merge_api_key(
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body.lmstudio_api_key, config.ai.lmstudio.api_key
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)
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await asyncio.to_thread(save_doc_service_config, config)
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return load_doc_service_config_masked()
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@router.post("/documents/ai/test")
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async def test_documents_ai(
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_: User = Depends(get_current_admin),
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) -> dict:
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"""Test the configured AI connection with a minimal prompt."""
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from app.core.app_config import load_service_config
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raw = await asyncio.to_thread(load_service_config, "doc_service")
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ai_cfg = raw.get("ai", {})
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provider_name = ai_cfg.get("provider", "anthropic")
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try:
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if provider_name == "anthropic":
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import anthropic
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client = anthropic.AsyncAnthropic(api_key=ai_cfg["anthropic"]["api_key"])
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msg = await client.messages.create(
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model=ai_cfg["anthropic"].get("model", "claude-haiku-4-5-20251001"),
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max_tokens=16,
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messages=[{"role": "user", "content": "Reply with: ok"}],
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)
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return {"ok": True, "provider": provider_name, "response": msg.content[0].text}
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elif provider_name in ("ollama", "lmstudio"):
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import openai
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pcfg = ai_cfg[provider_name]
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client = openai.AsyncOpenAI(
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base_url=pcfg["base_url"],
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api_key=pcfg.get("api_key") or "none",
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)
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resp = await client.chat.completions.create(
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model=pcfg["model"],
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messages=[{"role": "user", "content": "Reply with: ok"}],
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max_tokens=16,
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temperature=0,
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)
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return {
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"ok": True,
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"provider": provider_name,
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"response": resp.choices[0].message.content,
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}
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else:
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raise HTTPException(status_code=422, detail=f"Unknown provider: {provider_name}")
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except Exception as exc:
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return {"ok": False, "provider": provider_name, "error": str(exc)}
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@router.patch("/documents/limits")
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async def update_documents_limits(
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body: LimitsUpdate,
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_: User = Depends(get_current_admin),
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) -> dict:
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if body.max_pdf_mb < 1 or body.max_pdf_mb > 200:
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raise HTTPException(status_code=422, detail="max_pdf_mb must be between 1 and 200")
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config = load_doc_service_config()
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config.documents.max_pdf_bytes = body.max_pdf_mb * 1024 * 1024
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await asyncio.to_thread(save_doc_service_config, config)
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return load_doc_service_config_masked()
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