Spaces:
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File size: 1,986 Bytes
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Loads config.yaml and .env into a single typed config object.
Everything in the codebase imports from here — no hardcoded values elsewhere.
"""
import os
from pathlib import Path
import yaml
from dotenv import load_dotenv
load_dotenv()
_CONFIG_PATH = Path(__file__).parent.parent.parent / "configs" / "config.yaml"
def _load_yaml() -> dict:
with open(_CONFIG_PATH, "r") as f:
return yaml.safe_load(f)
class Config:
"""Single source of truth for all runtime settings."""
def __init__(self):
cfg = _load_yaml()
# Ingestion
self.chunk_size: int = cfg["ingestion"]["chunk_size"]
self.chunk_overlap: int = cfg["ingestion"]["chunk_overlap"]
# Retrieval
self.embedding_model: str = cfg["retrieval"]["embedding_model"]
self.top_k: int = cfg["retrieval"]["top_k"]
self.collection_name: str = cfg["retrieval"]["collection_name"]
self.chroma_persist_dir: str = cfg["retrieval"]["chroma_persist_dir"]
# Generation
self.llm_backend: str = cfg["generation"]["backend"]
self.groq_model: str = cfg["generation"]["groq_model"]
self.max_new_tokens: int = cfg["generation"]["max_new_tokens"]
self.temperature: float = cfg["generation"]["temperature"]
self.groq_model: str = cfg["generation"]["groq_model"]
# Secrets from .env (never from yaml)
self.hf_api_token: str = os.getenv("HF_API_TOKEN", "")
self.ollama_base_url: str = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
# Evaluation
self.faithfulness_threshold: float = cfg["evaluation"]["faithfulness_threshold"]
def __repr__(self):
return (
f"Config(backend={self.llm_backend}, "
f"embedding={self.embedding_model}, "
f"chunk_size={self.chunk_size})"
)
# Module-level singleton — import this everywhere
config = Config()
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