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e33977d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | """Central configuration loaded from environment variables.
All other modules import `settings` from here. Never call os.environ directly
in agent code — keeps the surface small and testable.
"""
from __future__ import annotations
import os
from dataclasses import dataclass
from pathlib import Path
from dotenv import load_dotenv
load_dotenv()
@dataclass(frozen=True)
class Settings:
# LLM provider switch
llm_provider: str # 'openai' or 'gemini'
# OpenAI
openai_api_key: str
openai_reasoning_model: str
openai_bulk_model: str
# Gemini
gemini_api_key: str
gemini_reasoning_model: str
gemini_bulk_model: str
# Convenience aliases (backward compat with older code that uses these)
reasoning_model: str
bulk_model: str
# Embeddings
sbert_model: str
# Paths
data_dir: Path
processed_dir: Path
chroma_persist_dir: Path
# Service ports
task_a_api_port: int
task_a_ui_port: int
task_b_api_port: int
task_b_ui_port: int
def _build() -> Settings:
data_dir = Path(os.environ.get("DATA_DIR", "./data")).resolve()
provider = os.environ.get("LLM_PROVIDER", "openai").lower()
openai_reasoning = os.environ.get("OPENAI_REASONING_MODEL", "gpt-4o")
openai_bulk = os.environ.get("OPENAI_BULK_MODEL", "gpt-4o-mini")
gemini_reasoning = os.environ.get("GEMINI_REASONING_MODEL", "gemini-2.5-flash")
gemini_bulk = os.environ.get("GEMINI_BULK_MODEL", "gemini-2.5-flash-lite")
# Convenience aliases point to the active provider's models
if provider == "gemini":
active_reasoning, active_bulk = gemini_reasoning, gemini_bulk
else:
active_reasoning, active_bulk = openai_reasoning, openai_bulk
return Settings(
llm_provider=provider,
openai_api_key=os.environ.get("OPENAI_API_KEY", ""),
openai_reasoning_model=openai_reasoning,
openai_bulk_model=openai_bulk,
gemini_api_key=os.environ.get("GEMINI_API_KEY", ""),
gemini_reasoning_model=gemini_reasoning,
gemini_bulk_model=gemini_bulk,
reasoning_model=active_reasoning,
bulk_model=active_bulk,
sbert_model=os.environ.get("SBERT_MODEL", "sentence-transformers/all-MiniLM-L6-v2"),
data_dir=data_dir,
processed_dir=data_dir / "processed",
chroma_persist_dir=Path(os.environ.get("CHROMA_PERSIST_DIR", data_dir / "chroma")),
task_a_api_port=int(os.environ.get("TASK_A_API_PORT", 8001)),
task_a_ui_port=int(os.environ.get("TASK_A_UI_PORT", 8501)),
task_b_api_port=int(os.environ.get("TASK_B_API_PORT", 8002)),
task_b_ui_port=int(os.environ.get("TASK_B_UI_PORT", 8502)),
)
settings = _build()
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