SurfSense/surfsense_backend/app/config/__init__.py

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"

    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")

    # Google Drive redirect URI
    GOOGLE_DRIVE_REDIRECT_URI = os.getenv("GOOGLE_DRIVE_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")

    # Notion OAuth
    NOTION_CLIENT_ID = os.getenv("NOTION_CLIENT_ID")
    NOTION_CLIENT_SECRET = os.getenv("NOTION_CLIENT_SECRET")
    NOTION_REDIRECT_URI = os.getenv("NOTION_REDIRECT_URI")

    # Atlassian OAuth (shared for Jira and Confluence)
    ATLASSIAN_CLIENT_ID = os.getenv("ATLASSIAN_CLIENT_ID")
    ATLASSIAN_CLIENT_SECRET = os.getenv("ATLASSIAN_CLIENT_SECRET")
    JIRA_REDIRECT_URI = os.getenv("JIRA_REDIRECT_URI")
    CONFLUENCE_REDIRECT_URI = os.getenv("CONFLUENCE_REDIRECT_URI")

    # Linear OAuth
    LINEAR_CLIENT_ID = os.getenv("LINEAR_CLIENT_ID")
    LINEAR_CLIENT_SECRET = os.getenv("LINEAR_CLIENT_SECRET")
    LINEAR_REDIRECT_URI = os.getenv("LINEAR_REDIRECT_URI")

    # Slack OAuth
    SLACK_CLIENT_ID = os.getenv("SLACK_CLIENT_ID")
    SLACK_CLIENT_SECRET = os.getenv("SLACK_CLIENT_SECRET")
    SLACK_REDIRECT_URI = os.getenv("SLACK_REDIRECT_URI")

    # Discord OAuth
    DISCORD_CLIENT_ID = os.getenv("DISCORD_CLIENT_ID")
    DISCORD_CLIENT_SECRET = os.getenv("DISCORD_CLIENT_SECRET")
    DISCORD_REDIRECT_URI = os.getenv("DISCORD_REDIRECT_URI")
    DISCORD_BOT_TOKEN = os.getenv("DISCORD_BOT_TOKEN")

    # Microsoft Teams OAuth
    TEAMS_CLIENT_ID = os.getenv("TEAMS_CLIENT_ID")
    TEAMS_CLIENT_SECRET = os.getenv("TEAMS_CLIENT_SECRET")
    TEAMS_REDIRECT_URI = os.getenv("TEAMS_REDIRECT_URI")

    # ClickUp OAuth
    CLICKUP_CLIENT_ID = os.getenv("CLICKUP_CLIENT_ID")
    CLICKUP_CLIENT_SECRET = os.getenv("CLICKUP_CLIENT_SECRET")
    CLICKUP_REDIRECT_URI = os.getenv("CLICKUP_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()