150 lines
7.2 KiB
Python
150 lines
7.2 KiB
Python
"""
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Centralized configuration for the AI Product Catalog + RAG backend.
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SECURITY NOTE
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--------------
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The previous version of this project had a serious problem: `settings.py`
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hard-coded a *live* database host, port and password as Python literal
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fallbacks (`os.getenv("DB_HOST", "<real ip>")`, etc.). That means the
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real production credentials shipped inside the source code itself - in
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every copy, every zip export, and every git commit - regardless of
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whether a `.env` file was present.
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This rewrite removes every hard-coded secret. Every credential
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(DB_PASSWORD, S3 keys, Google API key, ...) is read ONLY from the
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environment (via a local `.env` file, loaded through python-dotenv, or
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real OS/container environment variables). Non-secret values (ports,
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feature flags, model names) keep sane, publicly-safe defaults so the
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project still boots out of the box for local development.
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If a secret-shaped variable is required for a feature that is enabled
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(e.g. `USE_PGVECTOR=true` but no `DB_PASSWORD` set), we raise a clear
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`RuntimeError` at settings-load time instead of silently connecting
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with an empty/placeholder password. Fail loudly, not insecurely.
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"""
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from __future__ import annotations
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import os
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from pathlib import Path
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try:
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from dotenv import load_dotenv
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# backend/.env (one level up from this file: app/infrastructure/settings.py)
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_env_path = Path(__file__).resolve().parents[2] / ".env"
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load_dotenv(_env_path)
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except ImportError:
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# python-dotenv not installed - fall back to whatever is already in the
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# process environment (e.g. set by the shell, Docker, systemd, CI, etc.)
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pass
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def _bool(name: str, default: str) -> bool:
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return os.getenv(name, default).strip().lower() in {"1", "true", "yes"}
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def _require(name: str, *, feature_flag: str) -> str:
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"""Read a required secret. Raises if missing and the owning feature is enabled."""
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value = os.getenv(name)
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if not value:
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raise RuntimeError(
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f"Missing required environment variable '{name}'. It is required because "
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f"'{feature_flag}' is enabled. Set it in backend/.env (copy from "
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f".env.example) or disable the feature by setting {feature_flag}=false."
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)
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return value
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# ---------------------------------------------------------------------------
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# Ollama (local LLM)
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# ---------------------------------------------------------------------------
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USE_OLLAMA = _bool("USE_OLLAMA", "true")
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
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OLLAMA_MODEL_NAME = os.getenv("OLLAMA_MODEL_NAME", "qwen2.5:1.5b")
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# Per-request generation timeout (seconds). Small CPU-only models on modest
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# hardware (e.g. 8GB RAM, no GPU) can take a while for longer RAG contexts.
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OLLAMA_TIMEOUT_SECONDS = int(os.getenv("OLLAMA_TIMEOUT_SECONDS", "120"))
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# ---------------------------------------------------------------------------
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# Embeddings (sentence-transformers, CPU-friendly)
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# ---------------------------------------------------------------------------
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USE_EMBEDDINGS = _bool("USE_EMBEDDINGS", "true")
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EMBEDDINGS_MODEL = os.getenv("EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
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EMBEDDINGS_DIM = int(os.getenv("EMBEDDINGS_DIM", "384"))
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# ---------------------------------------------------------------------------
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# Postgres / pgvector
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# ---------------------------------------------------------------------------
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USE_PGVECTOR = _bool("USE_PGVECTOR", "true")
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DB_HOST = os.getenv("DB_HOST", "localhost")
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DB_PORT = os.getenv("DB_PORT", "5432")
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DB_NAME = os.getenv("DB_NAME", "pgvector")
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DB_USER = os.getenv("DB_USER", "postgres")
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DB_PASSWORD = _require("DB_PASSWORD", feature_flag="USE_PGVECTOR") if USE_PGVECTOR else os.getenv("DB_PASSWORD", "")
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DATABASE_URL = os.getenv(
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"DATABASE_URL",
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f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}",
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)
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# ---------------------------------------------------------------------------
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# S3 / DigitalOcean Spaces (product image storage) - optional
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# ---------------------------------------------------------------------------
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USE_S3 = _bool("USE_S3", "false")
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S3_ACCESS_KEY = _require("S3_ACCESS_KEY", feature_flag="USE_S3") if USE_S3 else os.getenv("S3_ACCESS_KEY")
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S3_SECRET_KEY = _require("S3_SECRET_KEY", feature_flag="USE_S3") if USE_S3 else os.getenv("S3_SECRET_KEY")
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S3_ENDPOINT = _require("S3_ENDPOINT", feature_flag="USE_S3") if USE_S3 else os.getenv("S3_ENDPOINT")
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S3_BUCKET = _require("S3_BUCKET", feature_flag="USE_S3") if USE_S3 else os.getenv("S3_BUCKET")
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S3_REGION = os.getenv("S3_REGION", "sgp1")
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# ---------------------------------------------------------------------------
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# Google Custom Search (OPTIONAL image source - leave disabled if unset)
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# ---------------------------------------------------------------------------
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USE_GOOGLE_CSE = _bool("USE_GOOGLE_CSE", "false")
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GOOGLE_API_KEY = _require("GOOGLE_API_KEY", feature_flag="USE_GOOGLE_CSE") if USE_GOOGLE_CSE else os.getenv("GOOGLE_API_KEY")
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GOOGLE_CSE_ID = _require("GOOGLE_CSE_ID", feature_flag="USE_GOOGLE_CSE") if USE_GOOGLE_CSE else os.getenv("GOOGLE_CSE_ID")
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# ---------------------------------------------------------------------------
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# Open-source image sources (no API key needed for any of these three)
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# ---------------------------------------------------------------------------
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USE_DDG_IMAGES = _bool("USE_DDG_IMAGES", "true")
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USE_OPEN_FACTS = _bool("USE_OPEN_FACTS", "true")
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USE_WIKIMEDIA = _bool("USE_WIKIMEDIA", "true")
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# Last-resort headless-browser (Python Playwright) image fallback. Requires
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# `pip install playwright && playwright install chromium`; automatically
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# skipped (logged once) if that hasn't been done.
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USE_PLAYWRIGHT_FALLBACK = _bool("USE_PLAYWRIGHT_FALLBACK", "true")
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# Minimum byte size for a downloaded image to be accepted as "real" (filters
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# out 1x1 tracking pixels / broken placeholder images)
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MIN_IMAGE_BYTES = int(os.getenv("MIN_IMAGE_BYTES", "3000"))
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# ---------------------------------------------------------------------------
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# HTTP client defaults
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# ---------------------------------------------------------------------------
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USER_AGENT = os.getenv("USER_AGENT", "CatalogBot/1.0 (+https://example.com)")
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REQUEST_TIMEOUT_SECONDS = int(os.getenv("REQUEST_TIMEOUT_SECONDS", "20"))
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# ---------------------------------------------------------------------------
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# FastAPI / web server
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# ---------------------------------------------------------------------------
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API_CORS_ORIGINS = [
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origin.strip()
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for origin in os.getenv("API_CORS_ORIGINS", "http://localhost:5173,http://127.0.0.1:5173").split(",")
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if origin.strip()
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]
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# Default RAG behaviour
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RAG_DEFAULT_TOP_K = int(os.getenv("RAG_DEFAULT_TOP_K", "5"))
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RAG_MAX_TOP_K = int(os.getenv("RAG_MAX_TOP_K", "15"))
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RAG_MAX_CONTEXT_CHARS = int(os.getenv("RAG_MAX_CONTEXT_CHARS", "4000"))
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# Optional pgvector cosine-distance ceiling (0 = identical, 2 = opposite)
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# used to drop weak semantic matches before they reach the LLM. Left
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# unset (None) by default since the primary relevance guardrail is the
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# category-aware retrieval in rag_service.py; set RAG_MAX_DISTANCE (e.g.
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# "0.9") once you've inspected real distance scores for your embeddings
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# if you want an extra cutoff on top of that.
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_raw_max_distance = os.getenv("RAG_MAX_DISTANCE", "").strip()
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RAG_MAX_DISTANCE = float(_raw_max_distance) if _raw_max_distance else None
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