mirror of
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124 lines
4.8 KiB
Python
124 lines
4.8 KiB
Python
import os
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from pathlib import Path
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from typing import Optional
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from pydantic_settings import BaseSettings
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current_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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class Settings(BaseSettings):
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AUTH_TYPE: Optional[str] = None # simple_jwt, session_jwt, or None
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LLM_PROVIDER: str = "docsgpt"
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LLM_NAME: Optional[str] = (
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None # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo
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)
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EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
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CELERY_BROKER_URL: str = "redis://localhost:6379/0"
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CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
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MONGO_URI: str = "mongodb://localhost:27017/docsgpt"
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MONGO_DB_NAME: str = "docsgpt"
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LLM_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
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DEFAULT_MAX_HISTORY: int = 150
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LLM_TOKEN_LIMITS: dict = {
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"gpt-4o-mini": 128000,
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"gpt-3.5-turbo": 4096,
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"claude-2": 1e5,
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"gemini-2.5-flash": 1e6,
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}
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UPLOAD_FOLDER: str = "inputs"
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PARSE_PDF_AS_IMAGE: bool = False
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PARSE_IMAGE_REMOTE: bool = False
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VECTOR_STORE: str = (
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"faiss" # "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb"
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)
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RETRIEVERS_ENABLED: list = ["classic_rag"]
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AGENT_NAME: str = "classic"
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FALLBACK_LLM_PROVIDER: Optional[str] = None # provider for fallback llm
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FALLBACK_LLM_NAME: Optional[str] = None # model name for fallback llm
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FALLBACK_LLM_API_KEY: Optional[str] = None # api key for fallback llm
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# Google Drive integration
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GOOGLE_CLIENT_ID: Optional[str] = None # Replace with your actual Google OAuth client ID
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GOOGLE_CLIENT_SECRET: Optional[str] = None# Replace with your actual Google OAuth client secret
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CONNECTOR_REDIRECT_BASE_URI: Optional[str] = "http://127.0.0.1:7091/api/connectors/callback" ##add redirect url as it is to your provider's console(gcp)
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# LLM Cache
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CACHE_REDIS_URL: str = "redis://localhost:6379/2"
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API_URL: str = "http://localhost:7091" # backend url for celery worker
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API_KEY: Optional[str] = None # LLM api key
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EMBEDDINGS_KEY: Optional[str] = (
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None # api key for embeddings (if using openai, just copy API_KEY)
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)
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OPENAI_API_BASE: Optional[str] = None # azure openai api base url
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OPENAI_API_VERSION: Optional[str] = None # azure openai api version
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AZURE_DEPLOYMENT_NAME: Optional[str] = None # azure deployment name for answering
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AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = (
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None # azure deployment name for embeddings
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)
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OPENAI_BASE_URL: Optional[str] = (
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None # openai base url for open ai compatable models
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)
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# elasticsearch
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ELASTIC_CLOUD_ID: Optional[str] = None # cloud id for elasticsearch
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ELASTIC_USERNAME: Optional[str] = None # username for elasticsearch
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ELASTIC_PASSWORD: Optional[str] = None # password for elasticsearch
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ELASTIC_URL: Optional[str] = None # url for elasticsearch
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ELASTIC_INDEX: Optional[str] = "docsgpt" # index name for elasticsearch
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# SageMaker config
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SAGEMAKER_ENDPOINT: Optional[str] = None # SageMaker endpoint name
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SAGEMAKER_REGION: Optional[str] = None # SageMaker region name
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SAGEMAKER_ACCESS_KEY: Optional[str] = None # SageMaker access key
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SAGEMAKER_SECRET_KEY: Optional[str] = None # SageMaker secret key
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# prem ai project id
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PREMAI_PROJECT_ID: Optional[str] = None
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# Qdrant vectorstore config
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QDRANT_COLLECTION_NAME: Optional[str] = "docsgpt"
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QDRANT_LOCATION: Optional[str] = None
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QDRANT_URL: Optional[str] = None
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QDRANT_PORT: Optional[int] = 6333
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QDRANT_GRPC_PORT: int = 6334
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QDRANT_PREFER_GRPC: bool = False
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QDRANT_HTTPS: Optional[bool] = None
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QDRANT_API_KEY: Optional[str] = None
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QDRANT_PREFIX: Optional[str] = None
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QDRANT_TIMEOUT: Optional[float] = None
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QDRANT_HOST: Optional[str] = None
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QDRANT_PATH: Optional[str] = None
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QDRANT_DISTANCE_FUNC: str = "Cosine"
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# PGVector vectorstore config
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PGVECTOR_CONNECTION_STRING: Optional[str] = None
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# Milvus vectorstore config
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MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
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MILVUS_URI: Optional[str] = "./milvus_local.db" # milvus lite version as default
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MILVUS_TOKEN: Optional[str] = ""
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# LanceDB vectorstore config
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LANCEDB_PATH: str = "/tmp/lancedb" # Path where LanceDB stores its local data
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LANCEDB_TABLE_NAME: Optional[str] = (
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"docsgpts" # Name of the table to use for storing vectors
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)
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FLASK_DEBUG_MODE: bool = False
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STORAGE_TYPE: str = "local" # local or s3
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URL_STRATEGY: str = "backend" # backend or s3
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JWT_SECRET_KEY: str = ""
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# Encryption settings
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ENCRYPTION_SECRET_KEY: str = "default-docsgpt-encryption-key"
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path = Path(__file__).parent.parent.absolute()
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settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")
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