settings.py

from pathlib import Path
from typing import Optional
import os

from pydantic_settings import BaseSettings

current_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))


class Settings(BaseSettings):
    LLM_NAME: str = "docsgpt"
    MODEL_NAME: Optional[str] = None # if LLM_NAME is openai, MODEL_NAME can be gpt-4 or gpt-3.5-turbo
    EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
    CELERY_BROKER_URL: str = "redis://localhost:6379/0"
    CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
    MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
    MODEL_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
    DEFAULT_MAX_HISTORY: int = 150
    MODEL_TOKEN_LIMITS: dict = {"gpt-3.5-turbo": 4096, "claude-2": 1e5}
    UPLOAD_FOLDER: str = "inputs"
    VECTOR_STORE: str = "faiss" #  "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
    RETRIEVERS_ENABLED: list = ["classic_rag", "duckduck_search"] # also brave_search

    # LLM Cache
    CACHE_REDIS_URL: str = "redis://localhost:6379/2"

    API_URL: str = "http://localhost:7091"  # backend url for celery worker

    API_KEY: Optional[str] = None  # LLM api key
    EMBEDDINGS_KEY: Optional[str] = None  # api key for embeddings (if using openai, just copy API_KEY)
    OPENAI_API_BASE: Optional[str] = None  # azure openai api base url
    OPENAI_API_VERSION: Optional[str] = None  # azure openai api version
    AZURE_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for answering
    AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None  # azure deployment name for embeddings
    OPENAI_BASE_URL: Optional[str] = None # openai base url for open ai compatable models

    # elasticsearch
    ELASTIC_CLOUD_ID: Optional[str] = None  # cloud id for elasticsearch
    ELASTIC_USERNAME: Optional[str] = None  # username for elasticsearch
    ELASTIC_PASSWORD: Optional[str] = None  # password for elasticsearch
    ELASTIC_URL: Optional[str] = None  # url for elasticsearch
    ELASTIC_INDEX: Optional[str] = "docsgpt"  # index name for elasticsearch

    # SageMaker config
    SAGEMAKER_ENDPOINT: Optional[str] = None  # SageMaker endpoint name
    SAGEMAKER_REGION: Optional[str] = None  # SageMaker region name
    SAGEMAKER_ACCESS_KEY: Optional[str] = None  # SageMaker access key
    SAGEMAKER_SECRET_KEY: Optional[str] = None  # SageMaker secret key

    # prem ai project id
    PREMAI_PROJECT_ID: Optional[str] = None

    # Qdrant vectorstore config
    QDRANT_COLLECTION_NAME: Optional[str] = "docsgpt"
    QDRANT_LOCATION: Optional[str] = None
    QDRANT_URL: Optional[str] = None
    QDRANT_PORT: Optional[int] = 6333
    QDRANT_GRPC_PORT: int = 6334
    QDRANT_PREFER_GRPC: bool = False
    QDRANT_HTTPS: Optional[bool] = None
    QDRANT_API_KEY: Optional[str] = None
    QDRANT_PREFIX: Optional[str] = None
    QDRANT_TIMEOUT: Optional[float] = None
    QDRANT_HOST: Optional[str] = None
    QDRANT_PATH: Optional[str] = None
    QDRANT_DISTANCE_FUNC: str = "Cosine"

    # Milvus vectorstore config
    MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
    MILVUS_URI: Optional[str] = "./milvus_local.db"   # milvus lite version as default
    MILVUS_TOKEN: Optional[str] = ""

    # LanceDB vectorstore config
    LANCEDB_PATH: str = "/tmp/lancedb"  # Path where LanceDB stores its local data
    LANCEDB_TABLE_NAME: Optional[str] = "docsgpts"  # Name of the table to use for storing vectors
    BRAVE_SEARCH_API_KEY: Optional[str] = None

    FLASK_DEBUG_MODE: bool = False


path = Path(__file__).parent.parent.absolute()
settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")