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443 lines
23 KiB
Python
443 lines
23 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(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from application.core.db_uri import ( # noqa: E402
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normalize_pgvector_connection_string,
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normalize_postgres_uri,
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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, oidc, or None
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# OIDC SSO (AUTH_TYPE=oidc) — any OpenID Connect IdP with discovery (Authentik, Keycloak, ...)
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OIDC_ISSUER: Optional[str] = None # e.g. https://auth.example.com/application/o/docsgpt/
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OIDC_CLIENT_ID: Optional[str] = None
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OIDC_CLIENT_SECRET: Optional[str] = None # optional; PKCE is always used
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OIDC_SCOPES: str = "openid profile email"
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OIDC_USER_ID_CLAIM: str = "sub" # ID-token claim mapped to the DocsGPT user id
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OIDC_FRONTEND_URL: Optional[str] = None # browser-facing app origin, e.g. http://localhost:5173
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OIDC_REDIRECT_URI: Optional[str] = None # override; default <request host>/api/auth/oidc/callback
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OIDC_SESSION_LIFETIME_SECONDS: int = 28800 # minted session JWT lifetime (8h)
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OIDC_PROVIDER_NAME: Optional[str] = None # sign-in button label, e.g. "Acme SSO"
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OIDC_ALLOWED_GROUPS: Optional[str] = None # comma-separated allowlist; unset = any authenticated user
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OIDC_GROUPS_CLAIM: str = "groups" # ID-token/userinfo claim carrying group membership
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OIDC_ADMIN_GROUPS: Optional[str] = None # comma-separated groups granted admin; unset = no OIDC admin mapping
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# RBAC (admin/user roles). Persisted admin grants live in the user_roles
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# table and apply only under AUTH_TYPE=oidc. LOCAL_MODE_ADMIN is the only
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# non-DB admin path and applies only to AUTH_TYPE=None (no-auth self-host).
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# It MUST stay False in any networked deployment.
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LOCAL_MODE_ADMIN: bool = False
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# SCIM 2.0 provisioning (IdP-driven user create/deactivate at /scim/v2)
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SCIM_ENABLED: bool = False
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SCIM_TOKEN: Optional[str] = None # bearer token for IdP SCIM clients (required when enabled)
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LLM_PROVIDER: str = "docsgpt"
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LLM_NAME: Optional[str] = None # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo
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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] = None # api key for embeddings (if using openai, just copy API_KEY)
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EMBEDDINGS_MAX_INPUT_TOKENS: Optional[int] = None # truncate each remote embed input to N tokens (overflow lost)
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# Optional directory of operator-supplied model YAMLs, loaded after the
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# built-in catalog under application/core/models/. Later wins on
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# duplicate model id. See application/core/models/README.md.
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MODELS_CONFIG_DIR: Optional[str] = None
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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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# Prefetch=1 caps SIGKILL loss to one task. Visibility timeout must exceed
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# the longest legitimate task runtime (ingest, agent webhook) but stay
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# short enough that SIGKILLed tasks redeliver promptly. 1h matches Onyx
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# and Dify defaults; long ingests can override via env.
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CELERY_WORKER_PREFETCH_MULTIPLIER: int = 1
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CELERY_VISIBILITY_TIMEOUT: int = 3600
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# Recycle the prefork worker child once its resident size crosses this many
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# kilobytes — backstops native-heap growth from docling/torch parsing. 0 disables.
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CELERY_WORKER_MAX_MEMORY_PER_CHILD: int = 4194304
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# Recycle the child after this many tasks; 0 disables (memory cap is the primary knob).
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CELERY_WORKER_MAX_TASKS_PER_CHILD: int = 0
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# Only consulted when VECTOR_STORE=mongodb or when running scripts/db/backfill.py; user data lives in Postgres.
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MONGO_URI: Optional[str] = None
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# User-data Postgres DB.
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POSTGRES_URI: Optional[str] = None
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# On app startup, apply pending Alembic migrations. Default ON for dev; disable in prod if you manage schema out-of-band.
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AUTO_MIGRATE: bool = True
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# On app startup, create the target Postgres database if it's missing (requires CREATEDB privilege). Dev-friendly default.
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AUTO_CREATE_DB: bool = True
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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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# Pages docling's threaded pipeline buffers in flight; the library
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# default (100) drives worker RSS to ~3 GB on a mid-size PDF.
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DOCLING_PIPELINE_QUEUE_MAX_SIZE: int = 2
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VECTOR_STORE: str = "faiss" # "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb" or "pgvector"
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# Allow-list of retriever keys an agent may use. Values must match the
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# ``RetrieverCreator.retrievers`` registry keys (``classic`` / ``default``),
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# NOT the legacy ``classic_rag`` label which never matched the registry.
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RETRIEVERS_ENABLED: list = ["classic", "default"]
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# Kill-switch for per-source retrieval dispatch. When False the retrieval
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# path collapses to today's single-retriever behavior (consumed by the
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# Dispatcher in a later change; defined here so the flag exists up front).
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PER_SOURCE_RETRIEVAL_ENABLED: bool = True
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# Flagship GraphRAG flag. Reserved and unused for now; gates graph-aware
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# ingestion/retrieval when that feature lands.
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GRAPHRAG_ENABLED: bool = False
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# Model for ingest-time graph extraction; None reuses the instance default
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# model (LLM_PROVIDER/LLM_NAME). Operator-overridable (e.g. a cheaper model).
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GRAPHRAG_EXTRACTION_MODEL: Optional[str] = None
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# Hard cap on chunks extracted per source (cost control).
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GRAPHRAG_MAX_CHUNKS_FOR_EXTRACTION: int = 2000
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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] = (
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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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# 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_AUTHORITY: Optional[str] = None # e.g., "https://login.microsoftonline.com/{tenant_id}"
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# Confluence Cloud integration
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CONFLUENCE_CLIENT_ID: Optional[str] = None
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CONFLUENCE_CLIENT_SECRET: Optional[str] = None
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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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# 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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MCP_OAUTH_REDIRECT_URI: Optional[str] = None # public callback URL for MCP OAuth
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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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NOVITA_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] = None # azure deployment name for embeddings
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OPENAI_BASE_URL: Optional[str] = None # openai base url for open ai compatable models
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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. Write the URI in whichever form you
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# prefer — ``postgres://``, ``postgresql://``, or even the SQLAlchemy
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# dialect form (``postgresql+psycopg://``) are all accepted and
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# normalized internally for ``psycopg.connect()``.
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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] = "docsgpts" # Name of the table to use for storing vectors
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FLASK_DEBUG_MODE: bool = False
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STORAGE_TYPE: str = "local" # local or s3
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# S3-compatible object storage (used when STORAGE_TYPE=s3). Works with AWS
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# S3 and any S3-compatible service (MinIO, Cloudflare R2, Backblaze B2,
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# DigitalOcean Spaces, ...). For non-AWS services, set S3_ENDPOINT_URL and
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# usually S3_PATH_STYLE=true. The SAGEMAKER_* credentials are still read as
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# a deprecated fallback for backward compatibility.
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S3_BUCKET_NAME: str = "docsgpt-test-bucket"
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S3_ENDPOINT_URL: Optional[str] = None # custom endpoint for S3-compatible services; omit for AWS
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S3_ACCESS_KEY_ID: Optional[str] = None
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S3_SECRET_ACCESS_KEY: Optional[str] = None
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S3_REGION: Optional[str] = None # AWS region; use "auto" for Cloudflare R2
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S3_PATH_STYLE: bool = False # path-style addressing (required by most non-AWS services)
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# Anonymous startup version check for security issues.
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VERSION_CHECK: bool = True
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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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STT_PROVIDER: str = "openai" # openai or faster_whisper
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OPENAI_STT_MODEL: str = "gpt-4o-mini-transcribe"
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STT_LANGUAGE: Optional[str] = None
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STT_MAX_FILE_SIZE_MB: int = 50
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STT_ENABLE_TIMESTAMPS: bool = False
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STT_ENABLE_DIARIZATION: bool = False
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# Tool pre-fetch settings
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ENABLE_TOOL_PREFETCH: bool = True
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# When True, OpenAI Responses API calls are persisted server-side
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# (store=true) so a previous_response_id can chain turns. When False
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# (the default) Responses calls are stateless (store=false) and any
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# reasoning is carried across the in-turn tool loop via encrypted
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# reasoning items instead.
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OPENAI_RESPONSES_STORE: bool = False
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# Config-free tools on by default in agentless chats. ``scheduler`` is
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# dual-registered (also in ``BUILTIN_AGENT_TOOLS``) so the same synthetic id
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DEFAULT_CHAT_TOOLS: list = [
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"memory",
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"read_webpage",
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"scheduler",
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]
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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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# Internal SSE push channel (notifications + durable replay journal)
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# Master switch — when False, /api/events emits a "push_disabled" comment
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# and returns; clients fall back to polling. Publisher becomes a no-op.
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ENABLE_SSE_PUSH: bool = True
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# Per-user durable backlog cap (~entries). At typical event rates this
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# gives ~24h of replay; tune up for verbose feeds, down for memory.
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EVENTS_STREAM_MAXLEN: int = 1000
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# Bounds uvicorn's graceful-shutdown drain (uvicorn_worker doesn't forward
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# --graceful-timeout). Keep below the gunicorn --timeout (180) watchdog.
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# Used by gunicorn_worker.BoundedDrainUvicornWorker.
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GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS: int = 30
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WSGI_THREADPOOL_WORKERS: int = 96
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# SSE keepalive comment cadence. Must sit under Cloudflare's 100s idle
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# close and iOS Safari's ~60s — 15s gives generous headroom.
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SSE_KEEPALIVE_SECONDS: int = 15
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# Cap on simultaneous SSE connections per user. Each connection holds
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# one WSGI thread (32 per gunicorn worker) and one Redis pub/sub
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# connection. 8 covers normal multi-tab use without letting one user
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# starve the pool. Set to 0 to disable the cap.
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SSE_MAX_CONCURRENT_PER_USER: int = 8
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# Per-request cap on the number of backlog entries XRANGE returns
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# for ``/api/events`` snapshots. Bounds the bytes a single replay
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# can move from Redis to the wire — a malicious client looping
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# ``Last-Event-ID=<oldest>`` reconnects can only enumerate this
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# many entries per round-trip. Combined with the per-user
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# connection cap above and the windowed budget below, total
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# enumeration throughput is bounded.
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EVENTS_REPLAY_MAX_PER_REQUEST: int = 200
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EVENTS_REPLAY_MAX_AGE_HOURS: int = 48
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# Sliding-window cap on snapshot replays per user. Once the budget
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# is exhausted the route returns HTTP 429 with the cursor pinned;
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# the client backs off and retries after the window rolls over.
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EVENTS_REPLAY_BUDGET_REQUESTS_PER_WINDOW: int = 30
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EVENTS_REPLAY_BUDGET_WINDOW_SECONDS: int = 60
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# Retention for the ``message_events`` journal. The ``cleanup_message_events``
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# beat task deletes rows older than this. Reconnect-replay only
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# needs the journal for streams a client could still be tailing,
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# so 14 days is a generous default that covers paused/tool-action
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# flows without unbounded table growth.
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MESSAGE_EVENTS_RETENTION_DAYS: int = 14
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# Remote Device feature.
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REMOTE_DEVICE_SESSION_IDLE_SECONDS: int = 60
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REMOTE_DEVICE_REQUIRE_SIGNATURE: bool = False
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REMOTE_DEVICE_PAIRING_TTL_SECONDS: int = 600
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# Redis-backed broker tunables (route invocations cross-process so a
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# scheduled/Celery run reaches the web-held device session). The command
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# queue TTL must exceed the max command drain deadline (the tool caps
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# timeout_ms at 600s, drained with a +5s margin = 605s) so a queued command
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# for a briefly-offline device isn't evicted before its own drain gives up.
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REMOTE_DEVICE_CMD_QUEUE_TTL_SECONDS: int = 900
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REMOTE_DEVICE_INVOCATION_TTL_SECONDS: int = 900
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REMOTE_DEVICE_OUTPUT_STREAM_MAXLEN: int = 10_000
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# Scheduler (see scheduler.md).
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SCHEDULE_DISPATCHER_INTERVAL: int = 30
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SCHEDULE_MIN_INTERVAL: int = 900
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SCHEDULE_MAX_PER_USER: int = 50
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SCHEDULE_RUN_TIMEOUT: int = 600
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SCHEDULE_MISFIRE_GRACE: int = 60
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SCHEDULE_AUTOPAUSE_FAILURES: int = 3
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SCHEDULE_ONCE_MAX_HORIZON: int = 31_536_000
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SCHEDULE_RUN_OUTPUT_RETENTION_DAYS: int = 90
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# Code-execution sandbox (see artifacts-code-execution-spec.md §4 C2).
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# The app is a CLIENT of an always-on runner; defaults are safe so app
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# import never fails when the sandbox is unconfigured.
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SANDBOX_BACKEND: str = "jupyter" # "jupyter" (self-host) | "daytona" (Daytona Cloud)
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# URL of the Jupyter Kernel Gateway runner (the docsgpt-sandbox service).
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SANDBOX_GATEWAY_URL: str = "http://localhost:8888"
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SANDBOX_GATEWAY_AUTH_TOKEN: Optional[str] = None # gateway auth token, if set
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# Kernelspec launched per session. Defaults to the env-scrubbing "docsgpt-python"
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# spec (shipped by the docsgpt-sandbox runner) so kernel code cannot read the
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# gateway auth token or operator secrets from os.environ. The stock "python3"
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# spec inherits the gateway env verbatim and must not be used with untrusted code.
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SANDBOX_KERNEL_NAME: str = "docsgpt-python"
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SANDBOX_MAX_TTL: int = 1200 # hard cap (s) on agent-selectable keep-alive TTL
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# Per-process/worker cap on concurrent live sandbox sessions. Backend-agnostic
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# (complements DAYTONA_MAX_SANDBOXES); when reached, an LRU-idle session is
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|
# evicted to make room. This bound is local to each app/worker process.
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# 0 (or any non-positive value) disables the cap (unlimited sessions).
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SANDBOX_MAX_SESSIONS: int = 32
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SANDBOX_EXEC_TIMEOUT: int = 60 # default wall-clock cap (s) per exec call
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SANDBOX_HTTP_TIMEOUT: int = 10 # fixed cap (s) for REST control calls (create/delete/alive/interrupt)
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|
SANDBOX_MAX_OUTPUT_BYTES: int = 8 * 1024 * 1024 # cap on buffered stdout+stderr per exec
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|
SANDBOX_MAX_FILE_BYTES: int = 10 * 1024 * 1024 # cap on get_file size routed through stdout
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SANDBOX_MAX_INPUT_BYTES: int = 25 * 1024 * 1024 # cap on an input document staged into a sandbox session
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|
# ``read_document`` parsing on a dedicated Celery ``parsing`` queue (backend parser).
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DOCUMENT_PARSE_QUEUE: str = "parsing" # queue the parse_document task is routed to
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DOCUMENT_PARSE_TIMEOUT: int = 120 # seconds the tool awaits the enqueued parse before degrading
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DOCUMENT_PARSE_MAX_BYTES: int = 0 # cap on a parsed document's bytes (0 = reuse SANDBOX_MAX_INPUT_BYTES)
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DOCUMENT_MAX_DECOMPRESSED_BYTES: int = 300 * 1024 * 1024
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DOCUMENT_MAX_ARCHIVE_ENTRIES: int = 10000
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|
# Per-agent-node cap on files passed natively to the node's LLM (vision/doc
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# inputs). Files past the cap are extracted to text or dropped, not attached
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# natively, to bound context/cost. Re-uses SANDBOX_MAX_INPUT_BYTES per file.
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WORKFLOW_NODE_NATIVE_MAX_FILES: int = 5
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# Per-agent-node cap on documents extracted to text via the parsing worker.
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|
# Each non-native, non-text document issues a separate blocking parse, so a
|
|
# node referencing many documents (e.g. the ``*`` token) is bounded here to
|
|
# avoid serializing dozens of parses; documents past the cap are skipped with
|
|
# a truncation note instead of extracted.
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|
WORKFLOW_NODE_EXTRACT_MAX_FILES: int = 5
|
|
# A workflow run row is pre-created as ``running`` and finalized when its
|
|
# generator completes; a client disconnect or worker crash can strand it in
|
|
# ``running`` forever. The beat reaper fails runs still ``running`` past this
|
|
# many seconds. Generous so a legitimately long run is never cut off.
|
|
WORKFLOW_RUN_STALE_SECONDS: int = 3600
|
|
# Runner container resource caps — consumed by the docsgpt-sandbox compose
|
|
# service (deployment/sandbox), not by the app client. cgroup CPU/mem caps
|
|
# are part of the untrusted-code security boundary.
|
|
SANDBOX_MEMORY: str = "1g" # docker mem_limit for the runner container
|
|
SANDBOX_CPUS: str = "1.0" # docker cpu quota for the runner container
|
|
# Daytona Cloud managed backend (used only when SANDBOX_BACKEND="daytona").
|
|
# The app is a REST client of Daytona Cloud authenticated by DAYTONA_API_KEY;
|
|
# all knobs are optional so app import never fails when the backend is unused.
|
|
DAYTONA_API_KEY: Optional[str] = None # Daytona Cloud API key (secret)
|
|
DAYTONA_API_URL: Optional[str] = None # override Daytona API base URL, if self-targeting
|
|
DAYTONA_TARGET: Optional[str] = None # Daytona region/target, e.g. "us"
|
|
DAYTONA_SNAPSHOT: Optional[str] = None # image for new sandboxes; render libs via scripts/build_daytona_snapshot.py
|
|
DAYTONA_LANGUAGE: str = "python" # default runtime language for created sandboxes
|
|
DAYTONA_AUTO_STOP_INTERVAL: int = 15 # minutes idle before Daytona auto-stops a sandbox (0 disables)
|
|
DAYTONA_AUTO_DELETE_INTERVAL: int = 60 # minutes after stop before Daytona auto-deletes (-1 disables)
|
|
DAYTONA_MAX_SANDBOXES: int = 50 # cap on concurrent live Daytona sandboxes (cost-DoS guard)
|
|
# Per-user artifact quotas (generous defaults; enforced at persistence time).
|
|
# For all three, 0 (or any non-positive value) disables that quota (unlimited).
|
|
ARTIFACT_MAX_BYTES: int = 50 * 1024 * 1024 # cap on a single stored artifact version's bytes
|
|
ARTIFACT_MAX_COUNT_PER_USER: int = 5000 # cap on artifacts a user may own
|
|
ARTIFACT_MAX_TOTAL_BYTES_PER_USER: int = 5 * 1024 * 1024 * 1024 # cap on a user's total stored bytes
|
|
|
|
@field_validator("POSTGRES_URI", mode="before")
|
|
@classmethod
|
|
def _normalize_postgres_uri_validator(cls, v):
|
|
return normalize_postgres_uri(v)
|
|
|
|
@field_validator("PGVECTOR_CONNECTION_STRING", mode="before")
|
|
@classmethod
|
|
def _normalize_pgvector_connection_string_validator(cls, v):
|
|
return normalize_pgvector_connection_string(v)
|
|
|
|
@field_validator(
|
|
"API_KEY",
|
|
"OPENAI_API_KEY",
|
|
"ANTHROPIC_API_KEY",
|
|
"GOOGLE_API_KEY",
|
|
"GROQ_API_KEY",
|
|
"HUGGINGFACE_API_KEY",
|
|
"NOVITA_API_KEY",
|
|
"EMBEDDINGS_KEY",
|
|
"FALLBACK_LLM_API_KEY",
|
|
"QDRANT_API_KEY",
|
|
"ELEVENLABS_API_KEY",
|
|
"INTERNAL_KEY",
|
|
mode="before",
|
|
)
|
|
@classmethod
|
|
def normalize_api_key(cls, v: Optional[str]) -> Optional[str]:
|
|
"""
|
|
Normalize API keys: convert 'None', 'none', empty strings,
|
|
and whitespace-only strings to actual None.
|
|
Handles Pydantic loading 'None' from .env as string "None".
|
|
"""
|
|
if v is None:
|
|
return None
|
|
if not isinstance(v, str):
|
|
return v
|
|
stripped = v.strip()
|
|
if stripped == "" or stripped.lower() == "none":
|
|
return None
|
|
return stripped
|
|
|
|
|
|
# Project root is one level above application/
|
|
path = Path(__file__).parent.parent.parent.absolute()
|
|
settings = Settings(_env_file=path.joinpath(".env"), _env_file_encoding="utf-8")
|