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3c25c17 | 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 | import os
import logging
import warnings
from dataclasses import dataclass, field
from dotenv import load_dotenv
load_dotenv()
# Suppress noisy third-party logs
os.environ["TOKENIZERS_PARALLELISM"] = "false"
logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
logging.getLogger("transformers").setLevel(logging.WARNING)
logging.getLogger("huggingface_hub").setLevel(logging.WARNING)
warnings.filterwarnings("ignore", message=".*Pydantic V1.*")
warnings.filterwarnings("ignore", message=".*urllib3.*")
warnings.filterwarnings("ignore", message=".*HuggingFaceEmbeddings.*")
warnings.filterwarnings("ignore", category=DeprecationWarning)
@dataclass
class Settings:
llm_base_url: str = field(
default_factory=lambda: os.getenv("LLM_BASE_URL", "")
)
llm_model: str = field(
default_factory=lambda: os.getenv("LLM_MODEL", "")
)
llm_api_key: str = field(
default_factory=lambda: os.getenv("LLM_API_KEY", "")
)
@property
def is_llm_configured(self) -> bool:
return bool(self.llm_base_url and self.llm_model)
embedding_model: str = field(
default_factory=lambda: os.getenv(
"EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2"
)
)
faiss_index_path: str = field(
default_factory=lambda: os.getenv("FAISS_INDEX_PATH", "rag/faiss_index")
)
memory_dir: str = field(
default_factory=lambda: os.getenv("MEMORY_DIR", "memory/data")
)
ocr_confidence_threshold: float = 0.6
asr_confidence_threshold: float = 0.6
verifier_confidence_threshold: float = 0.7
rag_top_k: int = 5
max_solver_retries: int = 2
settings = Settings()
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