curo1305
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>
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
"""
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OpenAI-compatible provider for Ollama and LM Studio.
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Both expose an OpenAI-compatible /v1/chat/completions endpoint.
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"""
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import json
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from openai import AsyncOpenAI
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from app.services.ai.base import AIProvider, SYSTEM_PROMPT, USER_PROMPT_TEMPLATE
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class OpenAICompatProvider(AIProvider):
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def __init__(self, config: dict) -> None:
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self._client = AsyncOpenAI(
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base_url=config["base_url"],
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api_key=config.get("api_key", "not-required"),
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)
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self._model = config["model"]
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async def classify_document(self, text: str) -> dict:
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response = await self._client.chat.completions.create(
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model=self._model,
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temperature=0,
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": USER_PROMPT_TEMPLATE.format(text=text[:100_000])},
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],
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)
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raw = response.choices[0].message.content.strip()
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return _parse_json(raw)
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def _parse_json(raw: str) -> dict:
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if raw.startswith("```"):
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raw = raw.split("\n", 1)[1].rsplit("```", 1)[0]
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return json.loads(raw)
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