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| from pydantic_settings import BaseSettings, SettingsConfigDict | |
| class Settings(BaseSettings): | |
| # --- LLM (Groq free tier) --- | |
| groq_api_key: str = "" | |
| llm_model: str = "openai/gpt-oss-120b" | |
| use_query_rewrite: bool = True # LLM-based question understanding (pages, doc names, Hinglish) | |
| # --- retrieval models (run locally, free) --- | |
| embed_model: str = "sentence-transformers/all-MiniLM-L6-v2" | |
| use_reranker: bool = True | |
| rerank_model: str = "cross-encoder/ms-marco-MiniLM-L-6-v2" | |
| # --- storage --- | |
| data_dir: str = "./data" # holds data/qdrant (vectors) and data/registry.json (document list) | |
| collection: str = "documents" | |
| # --- ingestion --- | |
| chunk_size: int = 1000 | |
| chunk_overlap: int = 150 | |
| embed_batch: int = 64 | |
| # --- retrieval --- | |
| top_k: int = 6 # chunks sent to the LLM | |
| candidate_k: int = 30 # candidates per search (single group) | |
| per_doc_k: int = 10 # candidates per document when searching several documents | |
| max_doc_groups: int = 10 # above this many documents, search all at once | |
| max_lookup_pages: int = 8 # max pages read directly for "what is on page N" | |
| max_context_chars: int = 14000 | |
| cors_origins: str = "http://localhost:3000,http://127.0.0.1:3000" | |
| model_config = SettingsConfigDict(env_file=".env", extra="ignore") | |
| settings = Settings() | |