92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
"""Application configuration via environment variables / .env file.
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All settings are read from the environment (or a .env file in the project
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root). Import :func:`get_settings` wherever you need configuration; the
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returned object is cached after the first call.
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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 pydantic import AliasChoices, Field
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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"""Centralised, env-driven configuration for the codex application."""
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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case_sensitive=False,
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extra="ignore",
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)
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# ------------------------------------------------------------------
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# Database
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# ------------------------------------------------------------------
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database_url: str = Field(
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default="postgresql://researcher:change_me@localhost:5432/papers",
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description=(
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"libpq-compatible connection string consumed by psycopg. "
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"Example: postgresql://user:pass@host:5432/dbname"
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),
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)
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# ------------------------------------------------------------------
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# External services
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# ------------------------------------------------------------------
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grobid_url: str = Field(
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default="http://localhost:8070",
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description="Base URL of the GROBID HTTP API (containerised).",
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)
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nougat_url: str = Field(
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default="http://localhost:8080",
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validation_alias=AliasChoices("NOUGAT_URL", "nougat_url"),
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description="Base URL of the Nougat OCR HTTP API (containerised).",
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)
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ollama_base_url: str = Field(
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default="http://localhost:11434",
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description="Base URL of the local Ollama endpoint (optional Q&A layer).",
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)
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# ------------------------------------------------------------------
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# Embeddings
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# ------------------------------------------------------------------
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embedding_model: str = Field(
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default="BAAI/bge-m3",
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description=(
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"sentence-transformers model identifier. "
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"Must match EMBEDDING_DIM. Default: BAAI/bge-m3 (1024 dims)."
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),
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)
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embedding_dim: int = Field(
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default=1024,
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gt=0,
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description=(
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"Dimension of the dense embedding vectors. "
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"Must match the output dimension of EMBEDDING_MODEL."
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),
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)
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# ------------------------------------------------------------------
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# API etiquette
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# ------------------------------------------------------------------
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openalex_mailto: str = Field(
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default="",
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description=(
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"E-mail address for the OpenAlex Polite Pool (faster rate limits). "
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"Required by OpenAlex ToS for automated access."
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),
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)
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@lru_cache(maxsize=1)
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def get_settings() -> Settings:
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"""Return the cached application settings singleton."""
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return Settings()
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