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| """Configuration settings for the RAG engine. | |
| Loads environment variables from .env or uses sensible defaults. | |
| All settings can be customized via environment variables. | |
| """ | |
| import os | |
| from pathlib import Path | |
| from dotenv import load_dotenv | |
| ROOT = Path(__file__).resolve().parent.parent | |
| load_dotenv(ROOT / ".env") | |
| class Settings: | |
| """Central configuration for paths, models, and retrieval hyperparameters.""" | |
| def __init__(self): | |
| # LLM configuration | |
| self.user_llm_api_key = os.getenv("USER_LLM_API_KEY", "").strip() | |
| self.user_llm_base_url = os.getenv("USER_LLM_BASE_URL", "") | |
| self.user_llm_model = os.getenv("USER_LLM_MODEL", "gpt-4o-mini") | |
| self.llm_temperature = float(os.getenv("LLM_TEMPERATURE", "0.1")) | |
| self.llm_max_tokens = int(os.getenv("LLM_MAX_TOKENS", "1000")) | |
| # Embeddings & Vector Store | |
| # HashEmbeddings = 100% offline, zero-dependency token hashing for demo/testing | |
| self.use_hash_embeddings = os.getenv("USE_HASH_EMBEDDINGS", "true").lower() == "true" | |
| self.embedding_model = os.getenv("EMBEDDING_MODEL", "all-MiniLM-L6-v2") | |
| self.collection_name = "rag_engine_docs" | |
| # Chunking parameters | |
| self.chunk_size = int(os.getenv("CHUNK_SIZE", "400")) | |
| self.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "50")) | |
| self.parent_chunk_size = int(os.getenv("PARENT_CHUNK_SIZE", "1500")) | |
| # Retrieval hyperparameters | |
| self.dense_top_k = int(os.getenv("DENSE_TOP_K", "10")) | |
| self.bm25_top_k = int(os.getenv("BM25_TOP_K", "10")) | |
| self.final_top_k = int(os.getenv("FINAL_TOP_K", "4")) | |
| self.dense_weight = float(os.getenv("DENSE_WEIGHT", "0.6")) | |
| self.bm25_weight = float(os.getenv("BM25_WEIGHT", "0.4")) | |
| # Storage paths | |
| self.data_dir = ROOT / "data" | |
| self.documents_file = self.data_dir / "documents.jsonl" | |
| self.chroma_dir = self.data_dir / "chroma" | |
| self.bm25_file = self.data_dir / "bm25_index.pkl" | |
| self.parent_store_file = self.data_dir / "parents.jsonl" | |
| # Ensure base directories exist | |
| self.ensure_dirs() | |
| def ensure_dirs(self): | |
| """Create necessary data storage directories.""" | |
| self.data_dir.mkdir(parents=True, exist_ok=True) | |
| self.chroma_dir.mkdir(parents=True, exist_ok=True) | |
| def has_llm_key(self): | |
| """Check if a valid LLM API key has been provided.""" | |
| return bool(self.user_llm_api_key) and self.user_llm_api_key != "your-api-key-here" | |
| _cached_settings = None | |
| def get_settings(): | |
| """Returns a singleton Settings instance.""" | |
| global _cached_settings | |
| if _cached_settings is None: | |
| _cached_settings = Settings() | |
| return _cached_settings | |