3d7c619461
A source PDF is chunked, vectorised and then read by a model a chunk at a time, so a 500 MB upload is not a big file — it is an hour of work nobody asked for and a bucket that grows for ever. The cap is 20 MB, in the config default, in backend/.env which was overriding it at 524288000, and in nginx, which was letting 500M through to be refused by the application afterwards. A backup of .env is beside it. And a document is deleted where the documents are listed. The endpoint has always existed and removes the file, the vector collection and the row — but reaching it meant opening the document first, which is a page you go to in order to extract from it, not somewhere you visit to tidy up. Delete, then Delete it or Keep, in the workbench list. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
113 lines
4.9 KiB
Python
113 lines
4.9 KiB
Python
from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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model_config = SettingsConfigDict(env_file=".env", extra="ignore")
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DATABASE_URL: str = "sqlite:///./quiz.db"
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SECRET_KEY: str = "change-me-to-a-random-secret-key-in-production"
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# Guessing a password: how many tries from one address, over how long.
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# Cleared by a correct password, so this counts guesses rather than people.
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LOGIN_MAX_ATTEMPTS: int = 10
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LOGIN_WINDOW_MINUTES: int = 15
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# Refresh is flood-protected rather than rate-limited; see auth.refresh.
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REFRESH_MAX_PER_HOUR: int = 600
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# Where the indexed clinical library answers, when there is one. The admin
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# page can override it; this is the default a deployment ships with.
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CLINICAL_MCP_URL: str = ""
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ALGORITHM: str = "HS256"
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ACCESS_TOKEN_EXPIRE_MINUTES: int = 1440
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REDIS_URL: str = "redis://localhost:6379/0"
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LITELLM_MODEL: str = "gpt-4o-mini"
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LITELLM_API_KEY: str = ""
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LITELLM_API_BASE: str = ""
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LITELLM_EMBEDDING_MODEL: str = ""
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# Cross-encoder reranker, named as the proxy serves it. Blank turns
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# reranking off and leaves every result list in the order rank fusion
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# produced, which is what a deployment without this proxy gets.
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LITELLM_RERANK_MODEL: str = "cohere-rerank-v4.0-pro"
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OPENAI_API_KEY: str = ""
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ELEVENLABS_API_KEY: str = ""
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GOOGLE_TTS_API_KEY: str = ""
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LOCAL_SPEECH_GATEWAY_URL: str = "http://127.0.0.1:8110"
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AWS_ACCESS_KEY_ID: str = ""
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AWS_SECRET_ACCESS_KEY: str = ""
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AWS_REGION: str = "us-east-1"
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AWS_BEDROCK_REGION: str = "us-east-1"
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EMBEDDING_DIMENSIONS: int = 1024
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APP_URL: str = "https://quiz.danvics.com"
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CHROMA_PERSIST_DIR: str = "./chroma_data"
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MAIL_USERNAME: str = ""
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MAIL_PASSWORD: str = ""
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MAIL_FROM: str = ""
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MAIL_PORT: int = 587
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MAIL_SERVER: str = "smtp.gmail.com"
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MAIL_STARTTLS: bool = True
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MAIL_SSL_TLS: bool = False
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UPLOAD_DIR: str = "./uploads"
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# local | s3. Reads fall back to the volume either way, so existing uploads
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# keep working and files can migrate gradually.
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STORAGE_BACKEND: str = "local"
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S3_ENDPOINT_URL: str = "http://minio:9000"
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S3_ACCESS_KEY: str = ""
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S3_SECRET_KEY: str = ""
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S3_BUCKET: str = "pedshub-media"
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S3_REGION: str = "us-east-1"
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# 20 MB. A source PDF is chunked, vectorised and then read by a model a
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# chunk at a time, so a 500 MB upload is not a big file — it is an hour of
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# work nobody asked for and a bucket that grows for ever. Anything larger
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# is split before it comes in.
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MAX_UPLOAD_SIZE: int = 20971520 # 20MB
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# hCaptcha. Leave the secret blank to disable the challenge entirely.
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# Cap, self-hosted beside us. The secret verifies a solve and never leaves
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# the server; the site key is public and names the widget's endpoint.
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CAP_API_URL: str = "http://cap:3000"
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CAP_SECRET_KEY: str = ""
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# The browser gets its own copy of the site key from the frontend
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# container, which is a separate image with a separate .env. This one is
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# here so an operator can keep both halves of the pair together and see
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# at a glance which widget the secret belongs to.
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CAP_SITE_KEY: str = ""
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ADMIN_EMAIL: str = "" # Where contact form submissions are emailed
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DEFAULT_ADMIN_EMAIL: str = "" # Set it and a first admin is created with this address
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# Leave blank and one is generated and printed to the log at first start:
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# docker compose logs backend | grep -A3 "FIRST ADMIN"
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DEFAULT_ADMIN_PASSWORD: str = ""
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BBB_SERVER_URL: str = "" # BigBlueButton server URL (e.g. https://bbb.example.com/bigbluebutton)
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BBB_SECRET: str = "" # BigBlueButton shared secret
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# OIDC / SSO — leave blank to disable
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OIDC_PROVIDER_URL: str = "" # e.g. https://accounts.google.com, https://login.microsoftonline.com/{tenant}/v2.0
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OIDC_CLIENT_ID: str = ""
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OIDC_CLIENT_SECRET: str = ""
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OIDC_SCOPES: str = "openid email profile" # space-separated
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OIDC_PROVIDER_NAME: str = "SSO" # Display name on login button
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# Which claim carries the groups or roles a person belongs to at the
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# provider — "groups" for most, "roles" for Entra app roles. Blank means
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# no mapping: everybody who signs in through SSO is a learner, which is
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# the safe default and was the only behaviour there was.
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OIDC_ROLE_CLAIM: str = ""
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# Values in that claim which mean admin, and which mean moderator.
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# Comma-separated, matched case-insensitively. Admin wins over moderator.
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OIDC_ADMIN_GROUPS: str = ""
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OIDC_MODERATOR_GROUPS: str = ""
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LOG_LEVEL: str = "INFO" # DEBUG, INFO, WARNING, ERROR
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# Clinical library index (Milvus on the ped-ai stack), read-only. Articles
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# are grounded in what it retrieves; nothing here writes to it.
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CLINICAL_MILVUS_URI: str = ""
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CLINICAL_MILVUS_TOKEN: str = ""
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CLINICAL_MILVUS_COLLECTION: str = "mcp_bge_m3_1024"
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settings = Settings()
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