mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-03 09:12:55 +00:00
Alex
7da46c2bea
- Ship tiktoken's cl100k_base inside the package and build the encoding from it, so token counting never downloads anything. - Default EMBEDDINGS_CACHE_DIR to <data home>/models instead of FastEmbed's temp dir, and read tokenizer.json and repo metadata from that cache, so a model downloads once and survives reboots. - TTS_PROVIDER=none and STT_PROVIDER=none switch the speech features off: the endpoints return 404, audio files fail to ingest with a clear message, /api/config reports tts_available/stt_available, and the UI hides the Speak and microphone buttons. - Drop the Google Fonts Roboto import from the web UI. - prefetch-models fills the cache the app reads; verify-offline checks the packaged encoding. - Docs: new Air-Gapped Deployment guide, settings and cache notes.
597 lines
34 KiB
Python
597 lines
34 KiB
Python
import os
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from typing import Optional
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from pydantic import AliasChoices, Field, field_validator
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from pydantic_settings import BaseSettings, SettingsConfigDict
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from docsgpt.core.db_uri import (
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normalize_pgvector_connection_string,
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normalize_postgres_uri,
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)
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from docsgpt.core.paths import env_file, home_dir
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# Runtime data home (DOCSGPT_HOME, the checkout, or cwd); see docsgpt.core.paths.
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current_dir = str(home_dir())
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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: persisted admin grants live in user_roles (AUTH_TYPE=oidc only). This is the
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# only non-DB admin path, for AUTH_TYPE=None self-host. MUST stay False if networked.
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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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# Legacy model on purpose: an install that never pinned this has vectors from it, and
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# granite is the same width so a swap would fail silently. New installs get granite from
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# .env-template; existing ones switch by setting this and running docsgpt.scripts.reembed.
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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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EMBEDDINGS_BATCH_SIZE: int = 32 # chunks per store transaction / remote embed request
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# Documents per local ONNX forward pass. Each pass pads to its longest input, and that
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# waste grows with the square of chunk length: at 1250 tokens, 32 peaked at 6.6 GB, 1 at 2.9 GB.
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EMBEDDINGS_MODEL_BATCH_SIZE: int = 1
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# Intra-op threads for the local ONNX runner; None = every core. It scales sub-linearly,
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# so several single-threaded workers beat one many-threaded process on the same cores.
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EMBEDDINGS_THREADS: Optional[int] = None
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# Embedding models and their tokenizers. Persistent by default: FastEmbed's own default is the temp dir.
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EMBEDDINGS_CACHE_DIR: Optional[str] = Field(default_factory=lambda: str(home_dir() / "models"))
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# Pooling ("cls"/"mean") and L2 normalisation. Read from the model's own repository;
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# set these only for a repository that declares neither, or to override what it declares.
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EMBEDDINGS_POOLING: Optional[str] = None
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EMBEDDINGS_NORMALIZE: Optional[bool] = None
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# Embed on the worker so the API holds no model (~890 MB), at one broker round trip per
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# query. Ignored when EMBEDDINGS_BASE_URL is set, which is the better answer for production.
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EMBEDDINGS_DELEGATE_TO_WORKER: bool = True
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EMBEDDINGS_QUEUE: str = "embeddings" # queue the embed task is routed to
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EMBEDDINGS_DELEGATE_TIMEOUT: int = 60 # seconds to wait for the worker
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GITHUB_INGEST_MAX_FILE_BYTES: int = 1048576 # skip repo blobs larger than this (0 = no cap)
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GITHUB_INGEST_MAX_WORKERS: int = 8 # parallel file fetches per GitHub repo ingest
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# Operator-supplied model YAMLs, loaded after the built-in catalog; later wins on
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# duplicate model id. See docsgpt/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 the longest
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# legitimate task runtime but stay short enough that SIGKILLed tasks redeliver promptly.
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CELERY_WORKER_PREFETCH_MULTIPLIER: int = 1
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CELERY_VISIBILITY_TIMEOUT: int = 3600
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# Recycle a prefork child past this resident size in KB; backstops docling/torch heap growth.
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# Checked between tasks, so it does not bound the peak within one. 0 disables.
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CELERY_WORKER_MAX_MEMORY_PER_CHILD: int = 4194304
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CELERY_WORKER_MAX_TASKS_PER_CHILD: int = 0 # recycle after N tasks; 0 disables
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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 startup, apply pending Alembic migrations. Disable if you manage schema out-of-band.
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AUTO_MIGRATE: bool = True
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# On startup, create the target Postgres database if missing (needs CREATEDB privilege).
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AUTO_CREATE_DB: bool = True
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# On startup, create the pgvector/graph tables and verify the embedding dimension. No Alembic
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# migration covers the vector DB (it may be a separate cluster); set False to manage it yourself.
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AUTO_VECTOR_SCHEMA: 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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# Serve the web UI shipped in the package (docsgpt/static) from the API process.
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SERVE_UI: bool = True
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# Request cap is applied by Flask before multipart parsing; the per-file cap also while copying.
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UPLOAD_MAX_REQUEST_BYTES: int = Field(default=256 * 1024 * 1024, gt=0)
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UPLOAD_MAX_FILE_BYTES: int = Field(default=100 * 1024 * 1024, gt=0)
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PARSE_SPEC_MAX_BYTES: int = Field(default=10 * 1024 * 1024, gt=0)
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# ZIP limits apply cumulatively across nested archives in one extraction.
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UPLOAD_MAX_ARCHIVE_BYTES: int = Field(default=250 * 1024 * 1024, gt=0)
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UPLOAD_MAX_ARCHIVE_FILES: int = Field(default=10_000, gt=0)
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UPLOAD_MAX_ARCHIVE_RATIO: int = Field(default=1000, gt=0)
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UPLOAD_MAX_ARCHIVE_DEPTH: int = Field(default=3, ge=0)
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PARSE_PDF_AS_IMAGE: bool = False
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PARSE_IMAGE_REMOTE: bool = False
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# Document parser for source ingestion, chat attachments and the
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# read_document tool. "anydoc" (default): firecrawl-anydoc, a Rust
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# converter with no ML models — milliseconds per file, ~100 MB peak RSS.
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# "docling": the layout/table-model pipeline (optional install; needed
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# for read_document's structured output and the docling OCR backend).
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# Files anydoc cannot convert (scanned PDFs, malformed input) fall back to
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# docling when it is installed, otherwise to the native OCR parsers (OCR
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# on) or the legacy parsers. Rollback to the previous behaviour is this
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# one variable.
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DOC_PARSER_ENGINE: str = "anydoc"
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# OCR for scanned PDFs and images. OCR_ENABLED covers source ingestion,
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# OCR_ATTACHMENTS_ENABLED chat attachments. Which stack performs it is
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# OCR_BACKEND; which engine, OCR_ENGINE. The DOCLING_OCR_* names are the
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# pre-2026-09 spellings and stay accepted as aliases.
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OCR_ENABLED: bool = Field(
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default=False, validation_alias=AliasChoices("OCR_ENABLED", "DOCLING_OCR_ENABLED")
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)
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OCR_ATTACHMENTS_ENABLED: bool = Field(
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default=False,
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validation_alias=AliasChoices("OCR_ATTACHMENTS_ENABLED", "DOCLING_OCR_ATTACHMENTS_ENABLED"),
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)
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# Which stack runs OCR when it is on:
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# auto — docling when installed, otherwise native.
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# docling — the layout-model pipeline (hybrid region OCR, reading order,
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# table structure); needs the optional docling extra.
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# native — pypdfium2/Pillow page rendering straight into tesseract or a
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# DeepSeek-OCR endpoint (docsgpt/parser/file/ocr_parser.py).
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# No ML models in the worker; tables come out as text lines
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# under tesseract.
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OCR_BACKEND: str = "auto"
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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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DOCLING_COMPILE_TORCH_MODELS: bool = False
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DOCLING_TABULAR_MAX_BYTES: int = 2_000_000
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DOCLING_MARKUP_MAX_BYTES: int = 8_000_000
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# HTML/XHTML larger than this (bytes) are head-truncated before the
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# markdownify parser runs (the anydoc engine's HTML path). The tree that
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# path builds costs ~50x the input — 30 MB of HTML measured at 1.6 GB RSS —
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# and the upload cap is 100 MB, so the gate is what keeps one upload from
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# taking the ingest worker down. 0 disables it.
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MARKUP_MAX_BYTES: int = 8_000_000
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# Trust-check anydoc's PDF output (docsgpt/parser/file/pdf_trust.py):
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# flag composite (Type0) fonts without a ToUnicode map, and CJK-declaring
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# PDFs whose extracted text has almost no CJK — the two classes where
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# anydoc drops text silently. A flagged file re-parses on the docling
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# fallback when docling is installed; otherwise the anydoc output is kept
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# and the document gets extra_info["parse_warnings"]. ~30 ms per scanned MB.
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PDF_TRUST_CHECK: bool = True
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# Rewrite dot-leader / whitespace-aligned table runs in anydoc's PDF
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# markdown into GFM tables (docsgpt/parser/file/tableize.py). Off by
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# default: it rewrites content on a heuristic (>=3 uniform label+numbers
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# lines) validated only on a small corpus so far.
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ANYDOC_TABLEIZE: bool = False
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# OCR engine used when OCR is on (OCR_ENABLED / OCR_ATTACHMENTS_ENABLED).
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# Benched 2026-08 on EN/ZH/table/degraded scans (docs/Guides/ocr has the
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# menu):
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# tesseract — recommended: best classic-engine accuracy (perfect EN word
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# recall, 0.000 bilingual CER, 100% table cells), ~35 MB, CPU-only.
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# Needs the system binary + language packs: an optional install like
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# every OCR dependency (build with INSTALL_TESSERACT=true, or apt/brew
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# install tesseract-ocr for a local run). Both backends.
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# deepseek — DeepSeek-OCR against an Ollama/vLLM endpoint
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# (OCR_DEEPSEEK_*). Best table/CJK quality; the worker stays light
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# (no layout models) but each page costs seconds on the model server.
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# Both backends.
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# auto — docling's pick: ocrmac on macOS (excellent), rapidocr on Linux
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# (silently shreds some long text lines — avoid as a server default).
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# ocrmac | rapidocr — force one of those.
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# auto/ocrmac/rapidocr exist only inside docling; the native backend runs
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# tesseract for them. An engine that is not installed degrades (docling:
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# to "auto") with a warning instead of failing the parse.
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OCR_ENGINE: str = "tesseract"
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# Tesseract language packs, "+"-separated (e.g. "eng+chi_sim+deu"). Other
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# engines keep their own defaults — their language codes differ.
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OCR_LANGS: str = "eng"
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OCR_DEEPSEEK_URL: str = "http://localhost:11434/v1/chat/completions"
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OCR_DEEPSEEK_MODEL: str = "deepseek-ocr:3b"
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# Seconds allowed per page request to the DeepSeek endpoint, on both
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# backends (native sends pages one at a time; docling's VLM pipeline
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# keeps its own concurrency). A 3B model on a laptop needs minutes; a
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# vLLM GPU deployment, seconds.
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OCR_DEEPSEEK_TIMEOUT: float = 300.0
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# Native backend only: resolution at which pages without a text layer are
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# rendered before OCR. 200 suits tesseract; clamped to 72-600.
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OCR_RENDER_DPI: int = 200
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# Chars-per-page floor below which an OCR'd PDF/image parse is treated as an OCR
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# dropout (long-running docling workers were observed returning zero characters for
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# every scanned page after a long scanned PDF, with no error) rather than as content.
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# docling retries once on a fresh full-page-OCR converter; both backends then fail
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# loudly instead of indexing an empty document. 0 disables the guard.
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OCR_MIN_CHARS_PER_PAGE: int = Field(
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default=20, validation_alias=AliasChoices("OCR_MIN_CHARS_PER_PAGE", "DOCLING_OCR_MIN_CHARS_PER_PAGE")
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)
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# Read PDF *attachments* via their embedded text layer (pypdfium2) instead
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# of docling, falling back to docling when there is no text layer to read.
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# Attachments go into a prompt, so docling's structural markdown earns far
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# less than the tens of seconds per file it costs; source ingestion is
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# unaffected and always uses docling, because chunking and retrieval do
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# depend on that structure.
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ATTACHMENT_PDF_TEXT_FAST_PATH: bool = True
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# Median chars per sampled page below which a PDF is treated as a scan and handed to docling.
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# Measured on real uploads: scans at 0-17 chars/page, text-layer documents at 433-6834.
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ATTACHMENT_PDF_TEXT_MIN_MEDIAN_CHARS: int = 32
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ATTACHMENT_TEXT_MAX_BYTES: int = 5_000_000
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AGENT_IMAGE_MAX_BYTES: int = 5_000_000
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AGENT_IMAGE_MAX_PIXELS: int = 16_777_216
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VECTOR_STORE: str = "faiss" # "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb" or "pgvector"
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# Retriever keys an agent may use; must match RetrieverCreator.retrievers registry keys,
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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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# Concurrent per-source searches in one retrieval; the query is embedded once and shared.
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RETRIEVAL_MAX_PARALLEL_SOURCES: int = 4
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# Kill-switch for per-source retrieval dispatch; False collapses to a single retriever.
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PER_SOURCE_RETRIEVAL_ENABLED: bool = True
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GRAPHRAG_ENABLED: bool = False # gates graph-aware ingestion/retrieval
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# Model for ingest-time graph extraction; None reuses LLM_PROVIDER/LLM_NAME.
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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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# Comma-separated frontend origins allowed to receive connector OAuth results, e.g. https://docsgpt.example.com.
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# The callback origin and OIDC_FRONTEND_URL are always allowed; a loopback callback also allows localhost:5173.
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CONNECTOR_ALLOWED_ORIGINS: Optional[str] = None
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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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# Public base URL for user-facing endpoint references in prompts
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PUBLIC_API_BASE_URL: Optional[str] = None
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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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# Legacy AWS credentials from the retired SageMaker provider. Still read as a deprecated
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# fallback by S3 storage; do not use for new deployments.
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SAGEMAKER_REGION: Optional[str] = None
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SAGEMAKER_ACCESS_KEY: Optional[str] = None
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SAGEMAKER_SECRET_KEY: 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
|
|
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"
|
|
|
|
# PGVector config. postgres://, postgresql:// and postgresql+psycopg:// are all accepted
|
|
# and normalized internally for psycopg.connect().
|
|
PGVECTOR_CONNECTION_STRING: Optional[str] = None
|
|
PGVECTOR_POOL_MAX_SIZE: int = 8 # per-process pool; 0 = one direct connection per store
|
|
# IVFFlat probes; None derives sqrt(lists) from the index. Higher = better recall, more scan.
|
|
PGVECTOR_IVFFLAT_PROBES: Optional[int] = None
|
|
# Milvus vectorstore config
|
|
MILVUS_COLLECTION_NAME: Optional[str] = "docsgpt"
|
|
# milvus-lite (embedded) database file, under the data home like the other local stores
|
|
MILVUS_URI: Optional[str] = Field(default_factory=lambda: str(home_dir() / "milvus_local.db"))
|
|
MILVUS_TOKEN: Optional[str] = ""
|
|
|
|
# LanceDB vectorstore config
|
|
LANCEDB_PATH: str = Field(default_factory=lambda: str(home_dir() / "data" / "lancedb")) # LanceDB local data
|
|
LANCEDB_TABLE_NAME: Optional[str] = "docsgpts" # Name of the table to use for storing vectors
|
|
|
|
FLASK_DEBUG_MODE: bool = False
|
|
STORAGE_TYPE: str = "local" # local or s3
|
|
|
|
# S3-compatible object storage (STORAGE_TYPE=s3): AWS S3, MinIO, R2, B2, Spaces, ...
|
|
# For non-AWS, set S3_ENDPOINT_URL and usually S3_PATH_STYLE=true.
|
|
S3_BUCKET_NAME: str = "docsgpt-test-bucket"
|
|
S3_ENDPOINT_URL: Optional[str] = None # custom endpoint for S3-compatible services; omit for AWS
|
|
S3_ACCESS_KEY_ID: Optional[str] = None
|
|
S3_SECRET_ACCESS_KEY: Optional[str] = None
|
|
S3_REGION: Optional[str] = None # AWS region; use "auto" for Cloudflare R2
|
|
S3_PATH_STYLE: bool = False # path-style addressing (required by most non-AWS services)
|
|
|
|
# Anonymous startup version check for security issues.
|
|
VERSION_CHECK: bool = True
|
|
URL_STRATEGY: str = "backend" # backend or s3
|
|
|
|
JWT_SECRET_KEY: str = ""
|
|
|
|
# Encryption settings
|
|
ENCRYPTION_SECRET_KEY: str = "default-docsgpt-encryption-key"
|
|
|
|
TTS_PROVIDER: str = "google_tts" # google_tts, elevenlabs, or none to switch text-to-speech off
|
|
ELEVENLABS_API_KEY: Optional[str] = None
|
|
STT_PROVIDER: str = "openai" # openai, faster_whisper, or none to switch speech-to-text off
|
|
OPENAI_STT_MODEL: str = "gpt-4o-mini-transcribe"
|
|
STT_LANGUAGE: Optional[str] = None
|
|
STT_MAX_FILE_SIZE_MB: int = 50
|
|
STT_ENABLE_TIMESTAMPS: bool = False
|
|
STT_ENABLE_DIARIZATION: bool = False
|
|
|
|
# Tool pre-fetch settings
|
|
ENABLE_TOOL_PREFETCH: bool = True
|
|
|
|
# True persists Responses API calls server-side so previous_response_id can chain turns.
|
|
# False keeps them stateless, carrying reasoning across the tool loop as encrypted items.
|
|
OPENAI_RESPONSES_STORE: bool = False
|
|
# Cross-turn ``previous_response_id`` chaining (store mode only). The
|
|
# chained transcript lives on the provider and is invisible to every
|
|
# local guard, so it is bounded: a turn starts from the local history
|
|
# when the previous turn's reported prompt already reached the budget
|
|
# (default: the model's context window) or when the conversation was
|
|
# compressed after that turn was produced.
|
|
OPENAI_RESPONSES_CHAIN_ACROSS_TURNS: bool = True
|
|
OPENAI_RESPONSES_CHAIN_BUDGET_TOKENS: Optional[int] = None
|
|
# ``truncation: "auto"`` lets the provider drop the oldest input items
|
|
# instead of failing every request once a chain exceeds the model's window.
|
|
OPENAI_RESPONSES_TRUNCATION_AUTO: bool = False
|
|
# Prompt-cache hints on the Responses API: route a user's calls to the
|
|
# same cache shard (opaque per-user key), and request extended retention
|
|
# where offered.
|
|
OPENAI_PROMPT_CACHE_KEY: bool = True
|
|
OPENAI_PROMPT_CACHE_RETENTION: Optional[str] = None
|
|
OPENAI_REASONING_SUMMARY: str = "auto"
|
|
|
|
# Lets OpenAI-compatible clients identify a logical chat by session header, which
|
|
# chat-completions itself has no field for.
|
|
V1_SESSION_TTL_SECONDS: int = 24 * 60 * 60
|
|
# Optional cheaper model for conversation titles; unset reuses the answer model.
|
|
TITLE_MODEL_ID: Optional[str] = None
|
|
|
|
# Config-free tools on by default in agentless chats. ``scheduler`` is dual-registered in
|
|
# BUILTIN_AGENT_TOOLS so one synthetic id resolves via defaults or the agent picker.
|
|
# Add "code_executor" and "artifact_generator" once a sandbox runner is configured — both
|
|
# execute through it and would fail on every call without one.
|
|
DEFAULT_CHAT_TOOLS: list = [
|
|
"memory",
|
|
"read_webpage",
|
|
"scheduler",
|
|
]
|
|
|
|
# Conversation Compression Settings
|
|
ENABLE_CONVERSATION_COMPRESSION: bool = True
|
|
COMPRESSION_THRESHOLD_PERCENTAGE: float = 0.8 # Trigger at 80% of context
|
|
COMPRESSION_MODEL_OVERRIDE: Optional[str] = None # Use different model for compression
|
|
COMPRESSION_PROMPT_VERSION: str = "v1.0" # Track prompt iterations
|
|
COMPRESSION_MAX_HISTORY_POINTS: int = 3 # Keep only last N compression points to prevent DB bloat
|
|
# Per-field cap on the verbatim tail kept after a compression point (0 disables).
|
|
COMPRESSION_RECENT_FIELD_MAX_TOKENS: int = 8000
|
|
# Cap on one tool result entering the LLM context (0 disables); journal/DB keep it whole.
|
|
TOOL_RESULT_MAX_TOKENS: int = 20000
|
|
|
|
# Agent Guardrails
|
|
GUARDRAILS_ENABLED: bool = True # master switch; False disables every stage
|
|
# Allowlist of GuardrailCreator.checks keys; empty means every registered check.
|
|
GUARDRAILS_CHECKS_ENABLED: list = []
|
|
# A GuardrailsConfig fragment every agent inherits and cannot weaken; agents may add
|
|
# controls or make an action stricter, never looser. "enabled" is required — without it
|
|
# the floor parses but applies to nothing. Example:
|
|
# {"enabled": true, "mode": "scan_all",
|
|
# "controls": [{"check": "secrets", "stage": "output", "action": "redact"}]}
|
|
GUARDRAILS_FLOOR: dict = {}
|
|
# Judge model for the topic/policy checks; None reuses the request's model.
|
|
GUARDRAILS_JUDGE_MODEL: Optional[str] = None
|
|
# Persist scanned text alongside guardrail_events. Off by default: pre-redaction text is
|
|
# exactly the material a PII control exists to keep out of storage.
|
|
GUARDRAILS_STORE_SCANNED_TEXT: bool = False
|
|
GUARDRAILS_EVENTS_RETENTION_DAYS: int = Field(default=30, ge=1)
|
|
|
|
# Internal SSE push channel (notifications + durable replay journal).
|
|
# False makes /api/events emit "push_disabled" and return; clients fall back to polling.
|
|
ENABLE_SSE_PUSH: bool = True
|
|
# Per-user durable backlog cap in entries; ~24h of replay at typical rates.
|
|
EVENTS_STREAM_MAXLEN: int = 1000
|
|
# Bounds uvicorn's shutdown drain (uvicorn_worker doesn't forward --graceful-timeout).
|
|
# Keep below the gunicorn --timeout (180) watchdog. Used by BoundedDrainUvicornWorker.
|
|
GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS: int = 30
|
|
WSGI_THREADPOOL_WORKERS: int = 96
|
|
SSE_KEEPALIVE_SECONDS: int = Field(default=15, ge=1)
|
|
# Simultaneous SSE connections per user; each holds a pooled async Redis connection for
|
|
# its lifetime. 8 covers multi-tab use without one user starving the pool. 0 disables.
|
|
SSE_MAX_CONCURRENT_PER_USER: int = 8
|
|
# Pool size of the async Redis client behind the event-loop routes, per process. Every
|
|
# open notification tab, chat reconnect and device session holds one connection, so this
|
|
# caps concurrent streams per worker (redis-py's own default is 100). Keep the total
|
|
# across workers below the Redis server's maxclients (10000 by default).
|
|
ASYNC_REDIS_MAX_CONNECTIONS: int = Field(default=2000, ge=1)
|
|
# Backlog entries XRANGE returns per /api/events snapshot. Bounds what one replay moves
|
|
# from Redis to the wire: a client looping Last-Event-ID reconnects enumerates at most
|
|
# this many per round-trip, and the budget below bounds total throughput.
|
|
EVENTS_REPLAY_MAX_PER_REQUEST: int = 200
|
|
EVENTS_REPLAY_MAX_AGE_HOURS: int = 48
|
|
# Sliding-window cap on snapshot replays per user; exhausting it returns 429 with the
|
|
# cursor pinned so the client backs off until the window rolls over.
|
|
EVENTS_REPLAY_BUDGET_REQUESTS_PER_WINDOW: int = 30
|
|
EVENTS_REPLAY_BUDGET_WINDOW_SECONDS: int = 60
|
|
|
|
# Retention for the message_events journal, enforced by the cleanup_message_events beat
|
|
# task. Replay only needs streams a client could still be tailing.
|
|
MESSAGE_EVENTS_RETENTION_DAYS: int = 14
|
|
|
|
# Remote Device feature.
|
|
REMOTE_DEVICE_SESSION_IDLE_SECONDS: int = 60
|
|
REMOTE_DEVICE_REQUIRE_SIGNATURE: bool = False
|
|
REMOTE_DEVICE_PAIRING_TTL_SECONDS: int = 600
|
|
# Redis broker tunables, routing invocations cross-process so a scheduled run reaches the
|
|
# web-held device session. The queue TTL must exceed the max drain deadline (605s) so a
|
|
# command for a briefly-offline device isn't evicted before its own drain gives up.
|
|
REMOTE_DEVICE_CMD_QUEUE_TTL_SECONDS: int = 900
|
|
REMOTE_DEVICE_INVOCATION_TTL_SECONDS: int = 900
|
|
REMOTE_DEVICE_OUTPUT_STREAM_MAXLEN: int = 10_000
|
|
|
|
# Scheduler (see scheduler.md).
|
|
SCHEDULE_DISPATCHER_INTERVAL: int = 30
|
|
SCHEDULE_MIN_INTERVAL: int = 900
|
|
SCHEDULE_MAX_PER_USER: int = 50
|
|
SCHEDULE_RUN_TIMEOUT: int = 600
|
|
SCHEDULE_MISFIRE_GRACE: int = 60
|
|
SCHEDULE_AUTOPAUSE_FAILURES: int = 3
|
|
SCHEDULE_ONCE_MAX_HORIZON: int = 31_536_000
|
|
SCHEDULE_RUN_OUTPUT_RETENTION_DAYS: int = 90
|
|
|
|
# Code-execution sandbox. The app is a CLIENT of an always-on runner; defaults are safe so
|
|
# app import never fails when the sandbox is unconfigured.
|
|
SANDBOX_BACKEND: str = "jupyter" # "jupyter" (self-host) | "daytona" (Daytona Cloud)
|
|
# URL of the Jupyter Kernel Gateway runner (the docsgpt-sandbox service).
|
|
SANDBOX_GATEWAY_URL: str = "http://localhost:8888"
|
|
SANDBOX_GATEWAY_AUTH_TOKEN: Optional[str] = None # gateway auth token, if set
|
|
# Kernelspec per session. The env-scrubbing "docsgpt-python" spec keeps kernel code from
|
|
# reading the gateway token or operator secrets from os.environ; the stock "python3" spec
|
|
# inherits the gateway env verbatim and must not be used with untrusted code.
|
|
SANDBOX_KERNEL_NAME: str = "docsgpt-python"
|
|
SANDBOX_MAX_TTL: int = 1200 # hard cap (s) on agent-selectable keep-alive TTL
|
|
# Concurrent live sessions per process, backend-agnostic; at the cap an LRU-idle session is
|
|
# evicted. 0 or negative disables the cap.
|
|
SANDBOX_MAX_SESSIONS: int = 32
|
|
SANDBOX_EXEC_TIMEOUT: int = 60 # default wall-clock cap (s) per exec call
|
|
SANDBOX_HTTP_TIMEOUT: int = 10 # fixed cap (s) for REST control calls (create/delete/alive/interrupt)
|
|
SANDBOX_MAX_OUTPUT_BYTES: int = 8 * 1024 * 1024 # cap on buffered stdout+stderr per exec
|
|
SANDBOX_MAX_FILE_BYTES: int = 10 * 1024 * 1024 # cap on get_file size routed through stdout
|
|
SANDBOX_MAX_INPUT_BYTES: int = 25 * 1024 * 1024 # cap on an input document staged into a sandbox session
|
|
# ``read_document`` parsing on a dedicated Celery ``parsing`` queue (backend parser).
|
|
DOCUMENT_PARSE_QUEUE: str = "parsing" # queue the parse_document task is routed to
|
|
DOCUMENT_PARSE_TIMEOUT: int = 120 # seconds the tool awaits the enqueued parse before degrading
|
|
# The base timeout is a FLOOR: the window grows with document size, because OCR cost scales
|
|
# with pages. Without this a large scan is silently dropped at the base window.
|
|
DOCUMENT_PARSE_TIMEOUT_PER_MB: int = 60 # extra seconds of parse window per MiB of input
|
|
DOCUMENT_PARSE_TIMEOUT_MAX: int = 900 # absolute ceiling on the size-scaled parse window
|
|
DOCUMENT_PARSE_MAX_BYTES: int = 0 # cap on a parsed document's bytes (0 = reuse SANDBOX_MAX_INPUT_BYTES)
|
|
DOCUMENT_MAX_DECOMPRESSED_BYTES: int = 300 * 1024 * 1024
|
|
DOCUMENT_MAX_ARCHIVE_ENTRIES: int = 10000
|
|
# Files per node passed natively to the LLM; past the cap they are extracted to text or
|
|
# dropped, to bound context and cost. Re-uses SANDBOX_MAX_INPUT_BYTES per file.
|
|
WORKFLOW_NODE_NATIVE_MAX_FILES: int = 5
|
|
# Documents per node extracted via the parsing worker. Each issues a separate blocking
|
|
# parse; past the cap they are skipped with a truncation note.
|
|
WORKFLOW_NODE_EXTRACT_MAX_FILES: int = 5
|
|
# Wall clock one node may spend on blocking parses, shared across all of them. Without it a
|
|
# node could serialize WORKFLOW_NODE_EXTRACT_MAX_FILES full windows on a web threadpool slot.
|
|
WORKFLOW_NODE_EXTRACT_BUDGET_SECONDS: int = 900
|
|
# A run row is pre-created as ``running``; a disconnect or crash can strand it there. The
|
|
# beat reaper fails runs still ``running`` past this. Generous so a long run is never cut off.
|
|
WORKFLOW_RUN_STALE_SECONDS: int = 3600
|
|
# Runner container caps, consumed by the docsgpt-sandbox compose service, not the app.
|
|
# These cgroup limits 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 backend (SANDBOX_BACKEND="daytona"). 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, enforced at persistence time. 0 or negative disables a quota.
|
|
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
|
|
|
|
|
|
settings = Settings(_env_file=env_file(), _env_file_encoding="utf-8")
|