mirror of
https://github.com/arc53/DocsGPT.git
synced 2026-03-02 00:02:22 +00:00
1e0eaefba2
- Updated .env-template and settings.py for Microsoft Entra ID configuration. - Enhanced ConnectorsCallback to support SharePoint authentication. - Introduced SharePointAuth and SharePointLoader classes. - Added required dependencies in requirements.txt.
204 lines
8.0 KiB
Python
204 lines
8.0 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 import field_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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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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model_config = SettingsConfigDict(extra="ignore")
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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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EMBEDDINGS_BASE_URL: Optional[str] = None # Remote embeddings API URL (OpenAI-compatible)
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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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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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DEFAULT_LLM_TOKEN_LIMIT: int = 128000 # Fallback when model not found in registry
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RESERVED_TOKENS: dict = {
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"system_prompt": 500,
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"current_query": 500,
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"safety_buffer": 1000,
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}
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DEFAULT_AGENT_LIMITS: dict = {
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"token_limit": 50000,
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"request_limit": 500,
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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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DOCLING_OCR_ENABLED: bool = False # Enable OCR for docling parsers (PDF, images)
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DOCLING_OCR_ATTACHMENTS_ENABLED: bool = False # Enable OCR for docling when parsing attachments
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VECTOR_STORE: str = (
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"faiss" # "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb" or "pgvector"
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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] = (
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None # Replace with your actual Google OAuth client ID
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)
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GOOGLE_CLIENT_SECRET: Optional[str] = (
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None # Replace with your actual Google OAuth client secret
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)
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CONNECTOR_REDIRECT_BASE_URI: Optional[str] = (
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"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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)
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# GitHub source
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GITHUB_ACCESS_TOKEN: Optional[str] = None # PAT token with read repo access
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# Microsoft Entra ID (Azure AD) integration
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MICROSOFT_CLIENT_ID: Optional[str] = None # Azure AD Application (client) ID
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MICROSOFT_CLIENT_SECRET: Optional[str] = None # Azure AD Application client secret
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MICROSOFT_TENANT_ID: Optional[str] = "common" # Azure AD Tenant ID (or 'common' for multi-tenant)
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MICROSOFT_REDIRECT_URI: Optional[str] = "http://localhost:7091/api/connectors/callback" # Your project's redirect URI that you registered in Azure Portal.
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MICROSOFT_AUTHORITY: Optional[str] = None # e.g., "https://login.microsoftonline.com/{tenant_id}"
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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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INTERNAL_KEY: Optional[str] = None # internal api key for worker-to-backend auth
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API_KEY: Optional[str] = None # LLM api key (used by LLM_PROVIDER)
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# Provider-specific API keys (for multi-model support)
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OPENAI_API_KEY: Optional[str] = None
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ANTHROPIC_API_KEY: Optional[str] = None
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GOOGLE_API_KEY: Optional[str] = None
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GROQ_API_KEY: Optional[str] = None
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HUGGINGFACE_API_KEY: Optional[str] = None
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OPEN_ROUTER_API_KEY: Optional[str] = None
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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 = "./data/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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TTS_PROVIDER: str = "google_tts" # google_tts or elevenlabs
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ELEVENLABS_API_KEY: Optional[str] = None
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# Tool pre-fetch settings
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ENABLE_TOOL_PREFETCH: bool = True
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# Conversation Compression Settings
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ENABLE_CONVERSATION_COMPRESSION: bool = True
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COMPRESSION_THRESHOLD_PERCENTAGE: float = 0.8 # Trigger at 80% of context
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COMPRESSION_MODEL_OVERRIDE: Optional[str] = None # Use different model for compression
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COMPRESSION_PROMPT_VERSION: str = "v1.0" # Track prompt iterations
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COMPRESSION_MAX_HISTORY_POINTS: int = 3 # Keep only last N compression points to prevent DB bloat
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@field_validator(
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"API_KEY",
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"OPENAI_API_KEY",
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"ANTHROPIC_API_KEY",
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"GOOGLE_API_KEY",
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"GROQ_API_KEY",
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"HUGGINGFACE_API_KEY",
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"EMBEDDINGS_KEY",
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"FALLBACK_LLM_API_KEY",
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"QDRANT_API_KEY",
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"ELEVENLABS_API_KEY",
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"INTERNAL_KEY",
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mode="before",
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)
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@classmethod
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def normalize_api_key(cls, v: Optional[str]) -> Optional[str]:
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"""
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Normalize API keys: convert 'None', 'none', empty strings,
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and whitespace-only strings to actual None.
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Handles Pydantic loading 'None' from .env as string "None".
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"""
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if v is None:
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return None
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if not isinstance(v, str):
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return v
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stripped = v.strip()
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if stripped == "" or stripped.lower() == "none":
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return None
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return stripped
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# Project root is one level above application/
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path = Path(__file__).parent.parent.parent.absolute()
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settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")
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