import os import shutil from pathlib import Path import yaml from chonkie import AutoEmbeddings, CodeChunker, RecursiveChunker from dotenv import load_dotenv from rerankers import Reranker # Get the base directory of the project BASE_DIR = Path(__file__).resolve().parent.parent.parent env_file = BASE_DIR / ".env" load_dotenv(env_file) def is_ffmpeg_installed(): """ Check if ffmpeg is installed on the current system. Returns: bool: True if ffmpeg is installed, False otherwise. """ return shutil.which("ffmpeg") is not None def load_global_llm_configs(): """ Load global LLM configurations from YAML file. Falls back to example file if main file doesn't exist. Returns: list: List of global LLM config dictionaries, or empty list if file doesn't exist """ # Try main config file first global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.yaml" # Fall back to example file for testing # if not global_config_file.exists(): # global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.example.yaml" # if global_config_file.exists(): # print("Info: Using global_llm_config.example.yaml (copy to global_llm_config.yaml for production)") if not global_config_file.exists(): # No global configs available return [] try: with open(global_config_file, encoding="utf-8") as f: data = yaml.safe_load(f) return data.get("global_llm_configs", []) except Exception as e: print(f"Warning: Failed to load global LLM configs: {e}") return [] class Config: # Check if ffmpeg is installed if not is_ffmpeg_installed(): import static_ffmpeg # ffmpeg installed on first call to add_paths(), threadsafe. static_ffmpeg.add_paths() # check if ffmpeg is installed again if not is_ffmpeg_installed(): raise ValueError( "FFmpeg is not installed on the system. Please install it to use the Surfsense Podcaster." ) # Database DATABASE_URL = os.getenv("DATABASE_URL") NEXT_FRONTEND_URL = os.getenv("NEXT_FRONTEND_URL") # Backend URL to override the http to https in the OAuth redirect URI BACKEND_URL = os.getenv("BACKEND_URL") # Auth AUTH_TYPE = os.getenv("AUTH_TYPE") REGISTRATION_ENABLED = os.getenv("REGISTRATION_ENABLED", "TRUE").upper() == "TRUE" # Google OAuth GOOGLE_OAUTH_CLIENT_ID = os.getenv("GOOGLE_OAUTH_CLIENT_ID") GOOGLE_OAUTH_CLIENT_SECRET = os.getenv("GOOGLE_OAUTH_CLIENT_SECRET") # Google Calendar redirect URI GOOGLE_CALENDAR_REDIRECT_URI = os.getenv("GOOGLE_CALENDAR_REDIRECT_URI") # Google Gmail redirect URI GOOGLE_GMAIL_REDIRECT_URI = os.getenv("GOOGLE_GMAIL_REDIRECT_URI") # Airtable OAuth AIRTABLE_CLIENT_ID = os.getenv("AIRTABLE_CLIENT_ID") AIRTABLE_CLIENT_SECRET = os.getenv("AIRTABLE_CLIENT_SECRET") AIRTABLE_REDIRECT_URI = os.getenv("AIRTABLE_REDIRECT_URI") # LLM instances are now managed per-user through the LLMConfig system # Legacy environment variables removed in favor of user-specific configurations # Global LLM Configurations (optional) # Load from global_llm_config.yaml if available # These can be used as default options for users GLOBAL_LLM_CONFIGS = load_global_llm_configs() # Chonkie Configuration | Edit this to your needs EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL") # Azure OpenAI credentials from environment variables AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT") AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY") # Pass Azure credentials to embeddings when using Azure OpenAI embedding_kwargs = {} if AZURE_OPENAI_ENDPOINT: embedding_kwargs["azure_endpoint"] = AZURE_OPENAI_ENDPOINT if AZURE_OPENAI_API_KEY: embedding_kwargs["azure_api_key"] = AZURE_OPENAI_API_KEY embedding_model_instance = AutoEmbeddings.get_embeddings( EMBEDDING_MODEL, **embedding_kwargs, ) chunker_instance = RecursiveChunker( chunk_size=getattr(embedding_model_instance, "max_seq_length", 512) ) code_chunker_instance = CodeChunker( chunk_size=getattr(embedding_model_instance, "max_seq_length", 512) ) # Reranker's Configuration | Pinecode, Cohere etc. Read more at https://github.com/AnswerDotAI/rerankers?tab=readme-ov-file#usage RERANKERS_ENABLED = os.getenv("RERANKERS_ENABLED", "FALSE").upper() == "TRUE" if RERANKERS_ENABLED: RERANKERS_MODEL_NAME = os.getenv("RERANKERS_MODEL_NAME") RERANKERS_MODEL_TYPE = os.getenv("RERANKERS_MODEL_TYPE") reranker_instance = Reranker( model_name=RERANKERS_MODEL_NAME, model_type=RERANKERS_MODEL_TYPE, ) else: reranker_instance = None # OAuth JWT SECRET_KEY = os.getenv("SECRET_KEY") # ETL Service ETL_SERVICE = os.getenv("ETL_SERVICE") # Pages limit for ETL services (default to very high number for OSS unlimited usage) PAGES_LIMIT = int(os.getenv("PAGES_LIMIT", "999999999")) if ETL_SERVICE == "UNSTRUCTURED": # Unstructured API Key UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY") elif ETL_SERVICE == "LLAMACLOUD": # LlamaCloud API Key LLAMA_CLOUD_API_KEY = os.getenv("LLAMA_CLOUD_API_KEY") # Litellm TTS Configuration TTS_SERVICE = os.getenv("TTS_SERVICE") TTS_SERVICE_API_BASE = os.getenv("TTS_SERVICE_API_BASE") TTS_SERVICE_API_KEY = os.getenv("TTS_SERVICE_API_KEY") # STT Configuration STT_SERVICE = os.getenv("STT_SERVICE") STT_SERVICE_API_BASE = os.getenv("STT_SERVICE_API_BASE") STT_SERVICE_API_KEY = os.getenv("STT_SERVICE_API_KEY") # Validation Checks # Check embedding dimension if ( hasattr(embedding_model_instance, "dimension") and embedding_model_instance.dimension > 2000 ): raise ValueError( f"Embedding dimension for Model: {EMBEDDING_MODEL} " f"has {embedding_model_instance.dimension} dimensions, which " f"exceeds the maximum of 2000 allowed by PGVector." ) @classmethod def get_settings(cls): """Get all settings as a dictionary.""" return { key: value for key, value in cls.__dict__.items() if not key.startswith("_") and not callable(value) } # Create a config instance config = Config()