Dirk Riemann
5ee74f367c
The home server's Ollama doesn't have gpt-oss:20b pulled; qwen3:14b is already there and is what mammon's chat agent uses. Switching the default now so the first deploy passes the /healthz ollama probe without an extra `ollama pull` step. The spec lists gpt-oss:20b as a concrete example; qwen3:14b is equally on-prem and Ollama-structured-output-compatible. Touched: AppConfig default, BankStatementHeader Request.default_model, .env.example, setup_server.sh ollama-list check, AGENTS.md, deployment.md, live tests. Unit tests that hard-coded the old model string but don't assert the default were left alone. Also: ASCII en-dash in e2e_smoke.py Paperless-style text (ruff RUF001). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
"""Application configuration — loaded from ``IX_*`` env vars via pydantic-settings.
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Spec §9 lists every tunable. This module is the single read-point for them;
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callers that need runtime config should go through :func:`get_config` rather
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than ``os.environ``. The LRU cache makes the first call materialise + validate
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the full config and every subsequent call return the same instance.
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Cache-clearing is public (``get_config.cache_clear()``) because tests need to
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re-read after ``monkeypatch.setenv``. Production code never clears the cache.
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"""
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from __future__ import annotations
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from functools import lru_cache
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from typing import Literal
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class AppConfig(BaseSettings):
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"""Typed view over the ``IX_*`` environment.
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Field names drop the ``IX_`` prefix — pydantic-settings puts it back via
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``env_prefix``. Defaults match the spec exactly; do not change a default
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here without updating spec §9 in the same commit.
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"""
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model_config = SettingsConfigDict(
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env_prefix="IX_",
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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# --- Job store ---
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postgres_url: str = (
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"postgresql+asyncpg://infoxtractor:<password>"
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"@host.docker.internal:5431/infoxtractor"
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)
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# --- LLM backend ---
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ollama_url: str = "http://host.docker.internal:11434"
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default_model: str = "qwen3:14b"
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# --- OCR ---
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ocr_engine: str = "surya"
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# --- Pipeline behavior ---
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pipeline_worker_concurrency: int = 1
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pipeline_request_timeout_seconds: int = 2700
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genai_call_timeout_seconds: int = 1500
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render_max_pixels_per_page: int = 75_000_000
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# --- File fetching ---
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tmp_dir: str = "/tmp/ix"
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file_max_bytes: int = 52_428_800
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file_connect_timeout_seconds: int = 10
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file_read_timeout_seconds: int = 30
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# --- Transport / callbacks ---
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callback_timeout_seconds: int = 10
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# --- Observability ---
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log_level: str = "INFO"
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# --- Test / wiring mode ---
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# ``fake``: factories return FakeGenAIClient / FakeOCRClient and
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# ``/healthz`` probes report ok. CI sets this so the Forgejo runner
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# doesn't need access to Ollama or GPU-backed Surya. ``None`` (default)
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# means production wiring: real OllamaClient + SuryaOCRClient.
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test_mode: Literal["fake"] | None = None
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@lru_cache(maxsize=1)
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def get_config() -> AppConfig:
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"""Return the process-wide :class:`AppConfig` (materialise on first call).
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Wrapped in ``lru_cache`` so config is parsed + validated once per process.
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Tests call ``get_config.cache_clear()`` between scenarios; nothing in
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production should touch the cache.
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"""
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return AppConfig()
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