Delete config.py

config.py

@@ -1,78 +0,0 @@
-import os
-from pydantic_settings import BaseSettings, SettingsConfigDict
-from functools import lru_cache
-
-class Settings(BaseSettings):
-    #openai llm service
-    openai_api_key: str
-    chat_model: str = "gpt-4o-mini"
-    llm_temperature: float = 0.1
-    llm_max_tokens: int = 1024
-
-    #bge embeddings 
-    embedding_model: str = "BAAI/bge-large-en-v1.5"
-    embedding_dimensions: int = 1024
-    embedding_device: str = "cuda"
-    embedding_batch_size: int = 32
-    embedding_normalize: bool = True
-
-    #FAISS
-    faiss_index_path: str = "./faiss_indexes"
-    faiss_index_name: str = "prod_rag"
-
-    #Chunking
-    chunk_size: int = 800
-    chunk_overlap: int = 150
-    min_chunk_size: int = 100
-
-    #retrieval
-    top_k_retrieval: int = 20
-    top_k_rerank: int = 6
-    mmr_lambda: float = 0.6
-    bm25_weight: float = 0.4
-    vector_weight: float = 0.6
-
-    #memory
-    max_history_turns: int = 10
-    context_window_tokens: int = 8000
-
-    #cache
-    cache_enabled: bool = False
-    redis_url: str = "redis://localhost:6379"
-    cache_ttl_seconds: int = 3600
-    semantic_cache_threshold: float = 0.95
-
-    #api
-    api_title: str = "Production RAG API"
-    api_version: str = "1.0.0"
-    cors_origins: list[str] = ["*"]
-    rate_limit_per_minute: int = 60
-
-    #guardrails
-    guardrails_use_llama_guard: bool = True
-    guardrails_model_id: str = "meta-llama/Llama-Guard-3-1B"
-    guardrails_max_new_tokens: int = 32
-    guardrails_local_model_path: str | None = None
-    guardrails_local_files_only: bool = True
-    guardrails_download_if_missing: bool = True
-    guardrails_require_harm_intent_for_llama_unsafe: bool = True
-    guardrails_risk_block_threshold: float = 0.50
-    guardrails_unsafe_base_score: float = 0.20
-    hf_token: str | None = None
-
-    #evaluation
-    faithfullness_threshold: float = 0.7
-    answer_relevance_threshold: float = 0.7
-
-    model_config = SettingsConfigDict(
-        env_file=os.path.join(os.path.dirname(__file__), ".env"),
-        env_file_encoding="utf-8",
-        case_sensitive=False
-    )
-
-@lru_cache(maxsize=1)
-def get_settings() -> Settings:
-    return Settings()
-
-settings = get_settings()
-print(f"[Config] Loaded. Model: {settings.chat_model}, Embedding Model: {settings.embedding_model},EmbedDim: {settings.embedding_dimensions}")