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Merge remote-tracking branch 'origin/main' into branding-upd
# Conflicts: # docsgpt/core/models/anthropic.yaml # docsgpt/core/models/openai.yaml # frontend/src/admin/AdminUI.tsx
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@@ -15,7 +15,7 @@ have to know which driver a given field feeds. Each normalizer also
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silently upgrades the legacy ``postgresql+psycopg2://`` prefix since
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psycopg2 is no longer in the project.
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This module is deliberately separate from ``docsgpt/core/settings.py``
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This module is deliberately separate from ``docsgpt/core/settings``
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so the Settings class stays focused on field declarations, and the
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URI-rewriting logic can be unit-tested without triggering ``.env``
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file loading from importing Settings.
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@@ -140,7 +140,7 @@ class ModelRegistry:
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from docsgpt.llm.providers import ALL_PROVIDERS
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directories = [BUILTIN_MODELS_DIR]
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operator_dir = getattr(settings, "MODELS_CONFIG_DIR", None)
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operator_dir = settings.MODELS_CONFIG_DIR
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if operator_dir:
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op_path = Path(operator_dir)
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if not op_path.exists():
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@@ -32,8 +32,15 @@ class ModelCapabilities:
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supports_streaming: bool = True
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supported_attachment_types: List[str] = field(default_factory=list)
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context_window: int = 128000
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input_cost_per_token: Optional[float] = None
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output_cost_per_token: Optional[float] = None
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# USD per 1M tokens; consumed by ``docsgpt/pricing.py``. ``None`` means
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# "not declared": the call is recorded at $0 unless
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# ``QUOTA_UNPRICED_RATE_PER_MILLION`` is set.
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input_cost_per_million: Optional[float] = None
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output_cost_per_million: Optional[float] = None
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# Rates for the prompt-cache sub-bins of the prompt total. ``None`` bills
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# those tokens at ``input_cost_per_million``.
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cached_input_cost_per_million: Optional[float] = None
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cache_write_cost_per_million: Optional[float] = None
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# OpenAI reasoning-model effort hint (none/minimal/low/medium/high/xhigh;
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# the accepted subset is model-dependent). Consumed by OpenAILLM — sent
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# top-level on Chat Completions and nested under ``reasoning`` on the
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@@ -18,7 +18,7 @@ from pathlib import Path
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from typing import Dict, List, Optional, Sequence
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import yaml
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from pydantic import BaseModel, ConfigDict, Field, field_validator
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from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
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from docsgpt.core.model_settings import (
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AvailableModel,
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@@ -65,11 +65,33 @@ class _CapabilityFields(BaseModel):
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supports_streaming: Optional[bool] = None
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attachments: Optional[List[str]] = None
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context_window: Optional[int] = None
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input_cost_per_token: Optional[float] = None
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output_cost_per_token: Optional[float] = None
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input_cost_per_million: Optional[float] = Field(default=None, ge=0)
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output_cost_per_million: Optional[float] = Field(default=None, ge=0)
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cached_input_cost_per_million: Optional[float] = Field(default=None, ge=0)
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cache_write_cost_per_million: Optional[float] = Field(default=None, ge=0)
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reasoning_effort: Optional[str] = None
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api_flavor: Optional[str] = None
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@model_validator(mode="before")
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@classmethod
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def _per_token_alias(cls, data):
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"""Accept the deprecated ``*_cost_per_token`` keys, scaled to per-1M."""
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if not isinstance(data, dict):
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return data
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data = dict(data)
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for side in ("input", "output"):
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old, new = f"{side}_cost_per_token", f"{side}_cost_per_million"
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if old not in data:
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continue
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value = data.pop(old)
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if new in data:
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raise ValueError(f"set only one of {old} and {new}")
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if isinstance(value, bool) or not isinstance(value, (int, float)):
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raise ValueError(f"{old} must be a number")
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logger.warning("%s is deprecated; use %s (USD per 1M tokens)", old, new)
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data[new] = value * 1_000_000
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return data
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@field_validator("reasoning_effort")
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@classmethod
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def _valid_reasoning_effort(cls, v: Optional[str]) -> Optional[str]:
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@@ -237,8 +259,10 @@ def _build_model(
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supports_streaming=pick("supports_streaming", True),
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supported_attachment_types=expanded,
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context_window=pick("context_window", 128000),
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input_cost_per_token=pick("input_cost_per_token", None),
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output_cost_per_token=pick("output_cost_per_token", None),
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input_cost_per_million=pick("input_cost_per_million", None),
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output_cost_per_million=pick("output_cost_per_million", None),
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cached_input_cost_per_million=pick("cached_input_cost_per_million", None),
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cache_write_cost_per_million=pick("cache_write_cost_per_million", None),
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reasoning_effort=pick("reasoning_effort", None),
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api_flavor=pick("api_flavor", "chat_completions"),
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)
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@@ -108,8 +108,10 @@ defaults: # optional, applied to every model below
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supports_streaming: bool # default true
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attachments: [<alias-or-mime>, ...] # default []
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context_window: int # default 128000
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input_cost_per_token: float # default null
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output_cost_per_token: float # default null
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input_cost_per_million: float # USD per 1M prompt tokens; default null (unpriced)
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output_cost_per_million: float # USD per 1M generated tokens; default null
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cached_input_cost_per_million: float # prompt-cache reads; default: the input rate
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cache_write_cost_per_million: float # prompt-cache writes; default: the input rate
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reasoning_effort: <string> # default null; none|minimal|low|medium|high|xhigh (subset is model-dependent)
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api_flavor: <string> # chat_completions (default) or responses
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@@ -10,17 +10,29 @@ models:
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description: Most capable Claude model for complex reasoning and agentic coding
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context_window: 1000000
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supports_structured_output: true
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input_cost_per_million: 5.0
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output_cost_per_million: 25.0
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cached_input_cost_per_million: 0.5
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cache_write_cost_per_million: 6.25
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- id: claude-sonnet-4-6
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display_name: Claude Sonnet 4.6
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description: Best balance of speed and intelligence with extended thinking
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context_window: 1000000
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supports_structured_output: true
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input_cost_per_million: 3.0
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output_cost_per_million: 15.0
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cached_input_cost_per_million: 0.3
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cache_write_cost_per_million: 3.75
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- id: claude-haiku-4-5
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display_name: Claude Haiku 4.5
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description: Fastest Claude model with near-frontier intelligence
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supports_structured_output: true
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input_cost_per_million: 1.0
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output_cost_per_million: 5.0
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cached_input_cost_per_million: 0.1
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cache_write_cost_per_million: 1.25
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- id: claude-fable-5
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display_name: Claude Fable 5
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@@ -12,7 +12,11 @@ models:
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- id: deepseek-v4-flash
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display_name: DeepSeek V4 Flash
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description: Cost-efficient 1M-context model with hybrid thinking / non-thinking modes, tool calling and FIM completion
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input_cost_per_million: 0.14
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output_cost_per_million: 0.28
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- id: deepseek-v4-pro
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display_name: DeepSeek V4 Pro
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description: Frontier 1M-context model with hybrid thinking / non-thinking modes for advanced reasoning and agentic coding
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input_cost_per_million: 0.435
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output_cost_per_million: 0.87
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@@ -7,3 +7,6 @@ models:
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supports_tools: true
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attachments: [image]
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context_window: 1048576
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input_cost_per_million: 0.15
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output_cost_per_million: 0.5
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cached_input_cost_per_million: 0.03
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@@ -9,9 +9,16 @@ models:
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- id: gemini-3.1-pro-preview
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display_name: Gemini 3.1 Pro (preview)
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description: Most capable Gemini 3 model with advanced reasoning and agentic coding (preview)
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# Priced at the >200k-token tier; long prompts are common with attachments.
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input_cost_per_million: 4.0
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output_cost_per_million: 18.0
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- id: gemini-3.5-flash
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display_name: Gemini 3.5 Flash
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description: Frontier-class Flash for sustained performance on agentic and coding tasks
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input_cost_per_million: 1.5
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output_cost_per_million: 9
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- id: gemini-3.1-flash-lite
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display_name: Gemini 3.1 Flash-Lite
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description: Cost-efficient frontier-class multimodal model for high-throughput workloads
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input_cost_per_million: 0.25
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output_cost_per_million: 1.5
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@@ -8,9 +8,16 @@ models:
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display_name: GPT-OSS 120B
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description: OpenAI's open-weight 120B flagship served on Groq's LPU hardware; strong general reasoning with strict structured output support
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supports_structured_output: true
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input_cost_per_million: 0.15
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output_cost_per_million: 0.6
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cached_input_cost_per_million: 0.075
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- id: llama-3.3-70b-versatile
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display_name: Llama 3.3 70B Versatile
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description: Meta's Llama 3.3 70B for general-purpose chat with parallel tool use
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input_cost_per_million: 0.59
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output_cost_per_million: 0.79
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- id: llama-3.1-8b-instant
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display_name: Llama 3.1 8B Instant
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description: Small, very low-latency Llama model (~560 tok/s) with parallel tool use
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input_cost_per_million: 0.05
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output_cost_per_million: 0.08
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@@ -8,14 +8,20 @@ models:
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display_name: DeepSeek V4 Pro
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description: 1.6T MoE (49B active) with 1M context, hybrid CSA/HCA attention, top-tier reasoning and agentic coding
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context_window: 1048576
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input_cost_per_million: 1.6
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output_cost_per_million: 3.2
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- id: moonshotai/kimi-k2.6
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display_name: Kimi K2.6
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description: 1T-parameter open-weight MoE with native vision/video, multi-step tool calling, and agentic long-horizon execution
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attachments: [image]
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context_window: 262144
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input_cost_per_million: 0.8
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output_cost_per_million: 3.4
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- id: zai-org/glm-5
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display_name: GLM-5
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description: Z.AI 754B-parameter MoE with strong general reasoning, function calling, and structured output
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context_window: 202800
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input_cost_per_million: 1.0
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output_cost_per_million: 3.2
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@@ -12,12 +12,22 @@ models:
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context_window: 1050000
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api_flavor: responses
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reasoning_effort: medium
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# Short-context rates. Prompts over 272K tokens bill at $10 / $45 (cached $1).
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input_cost_per_million: 5.0
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output_cost_per_million: 30.0
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cached_input_cost_per_million: 0.5
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- id: gpt-5.4-mini
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display_name: GPT-5.4 Mini
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description: Cost-efficient GPT-5.4-class model for high-volume coding, computer use, and subagent workloads
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input_cost_per_million: 0.75
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output_cost_per_million: 4.5
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cached_input_cost_per_million: 0.075
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- id: gpt-5.4-nano
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display_name: GPT-5.4 Nano
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description: Cheapest GPT-5.4-class model, optimized for simple high-volume tasks where speed and cost matter most
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input_cost_per_million: 0.2
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output_cost_per_million: 1.25
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cached_input_cost_per_million: 0.02
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- id: gpt-5.6-sol
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display_name: GPT-5.6 Sol
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description: Latest GPT-5.6 generation flagship for long-horizon reasoning and agentic work
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@@ -10,16 +10,25 @@ models:
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description: Free-tier 480B MoE coder model with strong agentic tool use; rate-limited
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context_window: 262000
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attachments: []
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input_cost_per_million: 0.0
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output_cost_per_million: 0.0
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- id: deepseek/deepseek-v3.2
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display_name: DeepSeek V3.2
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description: Open-weights reasoning model, very low cost (~$0.25 in / $0.38 out per 1M)
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description: Open-weights reasoning model, very low cost (~$0.27 in / $0.40 out per 1M)
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context_window: 131072
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attachments: []
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supports_structured_output: true
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input_cost_per_million: 0.269
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output_cost_per_million: 0.4
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cached_input_cost_per_million: 0.1345
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- id: anthropic/claude-sonnet-4.6
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display_name: Claude Sonnet 4.6 (via OpenRouter)
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description: Frontier Sonnet-class model with 1M context, vision, and extended thinking
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context_window: 1000000
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supports_structured_output: true
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input_cost_per_million: 3.0
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output_cost_per_million: 15.0
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cached_input_cost_per_million: 0.3
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cache_write_cost_per_million: 3.75
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+16
-6
@@ -5,15 +5,18 @@ ships with it (prompts, model catalogs, migrations). A checkout, when the
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package is imported from one, is the directory holding ``pyproject.toml``.
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The data home is where runtime data lives: ``.env``, ``inputs``, ``indexes``.
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The home is ``DOCSGPT_HOME`` when set, else the checkout, else the current
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directory. That keeps a source checkout and the Docker image (which runs from
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``/app`` with the package beside it) behaving as before, and gives a
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``pip install docsgpt`` user a home that is not ``site-packages``.
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The home is ``DOCSGPT_HOME`` when set, else the checkout, else the default
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home (``~/.docsgpt/server``, or ``/opt/docsgpt`` for root on Linux). That keeps
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a source checkout and the Docker image (which pins ``DOCSGPT_HOME=/app``)
|
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behaving as before, and gives an installed package one home no matter which
|
||||
directory the command runs from. ``docsgpt up`` keeps its stack in the same
|
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place.
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"""
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||||
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||||
from __future__ import annotations
|
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import os
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import sys
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from pathlib import Path
|
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HOME_ENV = "DOCSGPT_HOME"
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@@ -31,12 +34,19 @@ def checkout_root() -> Path | None:
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return root if (root / "pyproject.toml").is_file() else None
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|
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def default_home() -> Path:
|
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"""The home outside a checkout: ``/opt/docsgpt`` for root on Linux, else ``~/.docsgpt/server``."""
|
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if sys.platform.startswith("linux") and hasattr(os, "geteuid") and os.geteuid() == 0:
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return Path("/opt/docsgpt")
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return Path.home() / ".docsgpt" / "server"
|
||||
|
||||
|
||||
def home_dir() -> Path:
|
||||
"""Directory for runtime data: ``DOCSGPT_HOME``, else the checkout, else cwd."""
|
||||
"""Directory for runtime data: ``DOCSGPT_HOME``, else the checkout, else the default home."""
|
||||
configured = os.environ.get(HOME_ENV)
|
||||
if configured:
|
||||
return Path(configured).expanduser().resolve()
|
||||
return checkout_root() or Path.cwd()
|
||||
return checkout_root() or default_home()
|
||||
|
||||
|
||||
def env_file() -> Path:
|
||||
|
||||
@@ -1,596 +0,0 @@
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import AliasChoices, Field, field_validator
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
from docsgpt.core.db_uri import (
|
||||
normalize_pgvector_connection_string,
|
||||
normalize_postgres_uri,
|
||||
)
|
||||
from docsgpt.core.paths import env_file, home_dir
|
||||
|
||||
# Runtime data home (DOCSGPT_HOME, the checkout, or cwd); see docsgpt.core.paths.
|
||||
current_dir = str(home_dir())
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
model_config = SettingsConfigDict(extra="ignore")
|
||||
|
||||
AUTH_TYPE: Optional[str] = None # simple_jwt, session_jwt, oidc, or None
|
||||
|
||||
# OIDC SSO (AUTH_TYPE=oidc) — any OpenID Connect IdP with discovery (Authentik, Keycloak, ...)
|
||||
OIDC_ISSUER: Optional[str] = None # e.g. https://auth.example.com/application/o/docsgpt/
|
||||
OIDC_CLIENT_ID: Optional[str] = None
|
||||
OIDC_CLIENT_SECRET: Optional[str] = None # optional; PKCE is always used
|
||||
OIDC_SCOPES: str = "openid profile email"
|
||||
OIDC_USER_ID_CLAIM: str = "sub" # ID-token claim mapped to the DocsGPT user id
|
||||
OIDC_FRONTEND_URL: Optional[str] = None # browser-facing app origin, e.g. http://localhost:5173
|
||||
OIDC_REDIRECT_URI: Optional[str] = None # override; default <request host>/api/auth/oidc/callback
|
||||
OIDC_SESSION_LIFETIME_SECONDS: int = 28800 # minted session JWT lifetime (8h)
|
||||
OIDC_PROVIDER_NAME: Optional[str] = None # sign-in button label, e.g. "Acme SSO"
|
||||
OIDC_ALLOWED_GROUPS: Optional[str] = None # comma-separated allowlist; unset = any authenticated user
|
||||
OIDC_GROUPS_CLAIM: str = "groups" # ID-token/userinfo claim carrying group membership
|
||||
OIDC_ADMIN_GROUPS: Optional[str] = None # comma-separated groups granted admin; unset = no OIDC admin mapping
|
||||
|
||||
# RBAC: persisted admin grants live in user_roles (AUTH_TYPE=oidc only). This is the
|
||||
# only non-DB admin path, for AUTH_TYPE=None self-host. MUST stay False if networked.
|
||||
LOCAL_MODE_ADMIN: bool = False
|
||||
|
||||
# SCIM 2.0 provisioning (IdP-driven user create/deactivate at /scim/v2)
|
||||
SCIM_ENABLED: bool = False
|
||||
SCIM_TOKEN: Optional[str] = None # bearer token for IdP SCIM clients (required when enabled)
|
||||
|
||||
LLM_PROVIDER: str = "docsgpt"
|
||||
LLM_NAME: Optional[str] = None # if LLM_PROVIDER is openai, LLM_NAME can be gpt-4 or gpt-3.5-turbo
|
||||
# Legacy model on purpose: an install that never pinned this has vectors from it, and
|
||||
# granite is the same width so a swap would fail silently. New installs get granite from
|
||||
# .env-template; existing ones switch by setting this and running docsgpt.scripts.reembed.
|
||||
EMBEDDINGS_NAME: str = "huggingface_sentence-transformers/all-mpnet-base-v2"
|
||||
EMBEDDINGS_BASE_URL: Optional[str] = None # Remote embeddings API URL (OpenAI-compatible)
|
||||
EMBEDDINGS_KEY: Optional[str] = None # api key for embeddings (if using openai, just copy API_KEY)
|
||||
EMBEDDINGS_MAX_INPUT_TOKENS: Optional[int] = None # truncate each remote embed input to N tokens (overflow lost)
|
||||
EMBEDDINGS_BATCH_SIZE: int = 32 # chunks per store transaction / remote embed request
|
||||
# Documents per local ONNX forward pass. Each pass pads to its longest input, and that
|
||||
# waste grows with the square of chunk length: at 1250 tokens, 32 peaked at 6.6 GB, 1 at 2.9 GB.
|
||||
EMBEDDINGS_MODEL_BATCH_SIZE: int = 1
|
||||
# Intra-op threads for the local ONNX runner; None = every core. It scales sub-linearly,
|
||||
# so several single-threaded workers beat one many-threaded process on the same cores.
|
||||
EMBEDDINGS_THREADS: Optional[int] = None
|
||||
# Embedding models and their tokenizers. Persistent by default: FastEmbed's own default is the temp dir.
|
||||
EMBEDDINGS_CACHE_DIR: Optional[str] = Field(default_factory=lambda: str(home_dir() / "models"))
|
||||
# Pooling ("cls"/"mean") and L2 normalisation. Read from the model's own repository;
|
||||
# set these only for a repository that declares neither, or to override what it declares.
|
||||
EMBEDDINGS_POOLING: Optional[str] = None
|
||||
EMBEDDINGS_NORMALIZE: Optional[bool] = None
|
||||
# Embed on the worker so the API holds no model (~890 MB), at one broker round trip per
|
||||
# query. Ignored when EMBEDDINGS_BASE_URL is set, which is the better answer for production.
|
||||
EMBEDDINGS_DELEGATE_TO_WORKER: bool = True
|
||||
EMBEDDINGS_QUEUE: str = "embeddings" # queue the embed task is routed to
|
||||
EMBEDDINGS_DELEGATE_TIMEOUT: int = 60 # seconds to wait for the worker
|
||||
GITHUB_INGEST_MAX_FILE_BYTES: int = 1048576 # skip repo blobs larger than this (0 = no cap)
|
||||
GITHUB_INGEST_MAX_WORKERS: int = 8 # parallel file fetches per GitHub repo ingest
|
||||
# Operator-supplied model YAMLs, loaded after the built-in catalog; later wins on
|
||||
# duplicate model id. See docsgpt/core/models/README.md.
|
||||
MODELS_CONFIG_DIR: Optional[str] = None
|
||||
|
||||
CELERY_BROKER_URL: str = "redis://localhost:6379/0"
|
||||
CELERY_RESULT_BACKEND: str = "redis://localhost:6379/1"
|
||||
# Prefetch=1 caps SIGKILL loss to one task. Visibility timeout must exceed the longest
|
||||
# legitimate task runtime but stay short enough that SIGKILLed tasks redeliver promptly.
|
||||
CELERY_WORKER_PREFETCH_MULTIPLIER: int = 1
|
||||
CELERY_VISIBILITY_TIMEOUT: int = 3600
|
||||
# Recycle a prefork child past this resident size in KB; backstops docling/torch heap growth.
|
||||
# Checked between tasks, so it does not bound the peak within one. 0 disables.
|
||||
CELERY_WORKER_MAX_MEMORY_PER_CHILD: int = 4194304
|
||||
CELERY_WORKER_MAX_TASKS_PER_CHILD: int = 0 # recycle after N tasks; 0 disables
|
||||
# Only consulted when VECTOR_STORE=mongodb or when running scripts/db/backfill.py; user data lives in Postgres.
|
||||
MONGO_URI: Optional[str] = None
|
||||
# User-data Postgres DB.
|
||||
POSTGRES_URI: Optional[str] = None
|
||||
# On startup, apply pending Alembic migrations. Disable if you manage schema out-of-band.
|
||||
AUTO_MIGRATE: bool = True
|
||||
# On startup, create the target Postgres database if missing (needs CREATEDB privilege).
|
||||
AUTO_CREATE_DB: bool = True
|
||||
# On startup, create the pgvector/graph tables and verify the embedding dimension. No Alembic
|
||||
# migration covers the vector DB (it may be a separate cluster); set False to manage it yourself.
|
||||
AUTO_VECTOR_SCHEMA: bool = True
|
||||
LLM_PATH: str = os.path.join(current_dir, "models/docsgpt-7b-f16.gguf")
|
||||
DEFAULT_MAX_HISTORY: int = 150
|
||||
DEFAULT_LLM_TOKEN_LIMIT: int = 128000 # Fallback when model not found in registry
|
||||
RESERVED_TOKENS: dict = {
|
||||
"system_prompt": 500,
|
||||
"current_query": 500,
|
||||
"safety_buffer": 1000,
|
||||
}
|
||||
DEFAULT_AGENT_LIMITS: dict = {
|
||||
"token_limit": 50000,
|
||||
"request_limit": 500,
|
||||
}
|
||||
UPLOAD_FOLDER: str = "inputs"
|
||||
# Serve the web UI shipped in the package (docsgpt/static) from the API process.
|
||||
SERVE_UI: bool = True
|
||||
# Request cap is applied by Flask before multipart parsing; the per-file cap also while copying.
|
||||
UPLOAD_MAX_REQUEST_BYTES: int = Field(default=256 * 1024 * 1024, gt=0)
|
||||
UPLOAD_MAX_FILE_BYTES: int = Field(default=100 * 1024 * 1024, gt=0)
|
||||
PARSE_SPEC_MAX_BYTES: int = Field(default=10 * 1024 * 1024, gt=0)
|
||||
# ZIP limits apply cumulatively across nested archives in one extraction.
|
||||
UPLOAD_MAX_ARCHIVE_BYTES: int = Field(default=250 * 1024 * 1024, gt=0)
|
||||
UPLOAD_MAX_ARCHIVE_FILES: int = Field(default=10_000, gt=0)
|
||||
UPLOAD_MAX_ARCHIVE_RATIO: int = Field(default=1000, gt=0)
|
||||
UPLOAD_MAX_ARCHIVE_DEPTH: int = Field(default=3, ge=0)
|
||||
PARSE_PDF_AS_IMAGE: bool = False
|
||||
PARSE_IMAGE_REMOTE: bool = False
|
||||
# Document parser for source ingestion, chat attachments and the
|
||||
# read_document tool. "anydoc" (default): firecrawl-anydoc, a Rust
|
||||
# converter with no ML models — milliseconds per file, ~100 MB peak RSS.
|
||||
# "docling": the layout/table-model pipeline (optional install; needed
|
||||
# for read_document's structured output and the docling OCR backend).
|
||||
# Files anydoc cannot convert (scanned PDFs, malformed input) fall back to
|
||||
# docling when it is installed, otherwise to the native OCR parsers (OCR
|
||||
# on) or the legacy parsers. Rollback to the previous behaviour is this
|
||||
# one variable.
|
||||
DOC_PARSER_ENGINE: str = "anydoc"
|
||||
# OCR for scanned PDFs and images. OCR_ENABLED covers source ingestion,
|
||||
# OCR_ATTACHMENTS_ENABLED chat attachments. Which stack performs it is
|
||||
# OCR_BACKEND; which engine, OCR_ENGINE. The DOCLING_OCR_* names are the
|
||||
# pre-2026-09 spellings and stay accepted as aliases.
|
||||
OCR_ENABLED: bool = Field(
|
||||
default=False, validation_alias=AliasChoices("OCR_ENABLED", "DOCLING_OCR_ENABLED")
|
||||
)
|
||||
OCR_ATTACHMENTS_ENABLED: bool = Field(
|
||||
default=False,
|
||||
validation_alias=AliasChoices("OCR_ATTACHMENTS_ENABLED", "DOCLING_OCR_ATTACHMENTS_ENABLED"),
|
||||
)
|
||||
# Which stack runs OCR when it is on:
|
||||
# auto — docling when installed, otherwise native.
|
||||
# docling — the layout-model pipeline (hybrid region OCR, reading order,
|
||||
# table structure); needs the optional docling extra.
|
||||
# native — pypdfium2/Pillow page rendering straight into tesseract or a
|
||||
# DeepSeek-OCR endpoint (docsgpt/parser/file/ocr_parser.py).
|
||||
# No ML models in the worker; tables come out as text lines
|
||||
# under tesseract.
|
||||
OCR_BACKEND: str = "auto"
|
||||
# Pages docling's threaded pipeline buffers in flight; the library
|
||||
# default (100) drives worker RSS to ~3 GB on a mid-size PDF.
|
||||
DOCLING_PIPELINE_QUEUE_MAX_SIZE: int = 2
|
||||
DOCLING_COMPILE_TORCH_MODELS: bool = False
|
||||
DOCLING_TABULAR_MAX_BYTES: int = 2_000_000
|
||||
DOCLING_MARKUP_MAX_BYTES: int = 8_000_000
|
||||
# HTML/XHTML larger than this (bytes) are head-truncated before the
|
||||
# markdownify parser runs (the anydoc engine's HTML path). The tree that
|
||||
# path builds costs ~50x the input — 30 MB of HTML measured at 1.6 GB RSS —
|
||||
# and the upload cap is 100 MB, so the gate is what keeps one upload from
|
||||
# taking the ingest worker down. 0 disables it.
|
||||
MARKUP_MAX_BYTES: int = 8_000_000
|
||||
# Trust-check anydoc's PDF output (docsgpt/parser/file/pdf_trust.py):
|
||||
# flag composite (Type0) fonts without a ToUnicode map, and CJK-declaring
|
||||
# PDFs whose extracted text has almost no CJK — the two classes where
|
||||
# anydoc drops text silently. A flagged file re-parses on the docling
|
||||
# fallback when docling is installed; otherwise the anydoc output is kept
|
||||
# and the document gets extra_info["parse_warnings"]. ~30 ms per scanned MB.
|
||||
PDF_TRUST_CHECK: bool = True
|
||||
# Rewrite dot-leader / whitespace-aligned table runs in anydoc's PDF
|
||||
# markdown into GFM tables (docsgpt/parser/file/tableize.py). Off by
|
||||
# default: it rewrites content on a heuristic (>=3 uniform label+numbers
|
||||
# lines) validated only on a small corpus so far.
|
||||
ANYDOC_TABLEIZE: bool = False
|
||||
# OCR engine used when OCR is on (OCR_ENABLED / OCR_ATTACHMENTS_ENABLED).
|
||||
# Benched 2026-08 on EN/ZH/table/degraded scans (docs/Guides/ocr has the
|
||||
# menu):
|
||||
# tesseract — recommended: best classic-engine accuracy (perfect EN word
|
||||
# recall, 0.000 bilingual CER, 100% table cells), ~35 MB, CPU-only.
|
||||
# Needs the system binary + language packs: an optional install like
|
||||
# every OCR dependency (build with INSTALL_TESSERACT=true, or apt/brew
|
||||
# install tesseract-ocr for a local run). Both backends.
|
||||
# deepseek — DeepSeek-OCR against an Ollama/vLLM endpoint
|
||||
# (OCR_DEEPSEEK_*). Best table/CJK quality; the worker stays light
|
||||
# (no layout models) but each page costs seconds on the model server.
|
||||
# Both backends.
|
||||
# auto — docling's pick: ocrmac on macOS (excellent), rapidocr on Linux
|
||||
# (silently shreds some long text lines — avoid as a server default).
|
||||
# ocrmac | rapidocr — force one of those.
|
||||
# auto/ocrmac/rapidocr exist only inside docling; the native backend runs
|
||||
# tesseract for them. An engine that is not installed degrades (docling:
|
||||
# to "auto") with a warning instead of failing the parse.
|
||||
OCR_ENGINE: str = "tesseract"
|
||||
# Tesseract language packs, "+"-separated (e.g. "eng+chi_sim+deu"). Other
|
||||
# engines keep their own defaults — their language codes differ.
|
||||
OCR_LANGS: str = "eng"
|
||||
OCR_DEEPSEEK_URL: str = "http://localhost:11434/v1/chat/completions"
|
||||
OCR_DEEPSEEK_MODEL: str = "deepseek-ocr:3b"
|
||||
# Seconds allowed per page request to the DeepSeek endpoint, on both
|
||||
# backends (native sends pages one at a time; docling's VLM pipeline
|
||||
# keeps its own concurrency). A 3B model on a laptop needs minutes; a
|
||||
# vLLM GPU deployment, seconds.
|
||||
OCR_DEEPSEEK_TIMEOUT: float = 300.0
|
||||
# Native backend only: resolution at which pages without a text layer are
|
||||
# rendered before OCR. 200 suits tesseract; clamped to 72-600.
|
||||
OCR_RENDER_DPI: int = 200
|
||||
# Chars-per-page floor below which an OCR'd PDF/image parse is treated as an OCR
|
||||
# dropout (long-running docling workers were observed returning zero characters for
|
||||
# every scanned page after a long scanned PDF, with no error) rather than as content.
|
||||
# docling retries once on a fresh full-page-OCR converter; both backends then fail
|
||||
# loudly instead of indexing an empty document. 0 disables the guard.
|
||||
OCR_MIN_CHARS_PER_PAGE: int = Field(
|
||||
default=20, validation_alias=AliasChoices("OCR_MIN_CHARS_PER_PAGE", "DOCLING_OCR_MIN_CHARS_PER_PAGE")
|
||||
)
|
||||
# Read PDF *attachments* via their embedded text layer (pypdfium2) instead
|
||||
# of docling, falling back to docling when there is no text layer to read.
|
||||
# Attachments go into a prompt, so docling's structural markdown earns far
|
||||
# less than the tens of seconds per file it costs; source ingestion is
|
||||
# unaffected and always uses docling, because chunking and retrieval do
|
||||
# depend on that structure.
|
||||
ATTACHMENT_PDF_TEXT_FAST_PATH: bool = True
|
||||
# Median chars per sampled page below which a PDF is treated as a scan and handed to docling.
|
||||
# Measured on real uploads: scans at 0-17 chars/page, text-layer documents at 433-6834.
|
||||
ATTACHMENT_PDF_TEXT_MIN_MEDIAN_CHARS: int = 32
|
||||
ATTACHMENT_TEXT_MAX_BYTES: int = 5_000_000
|
||||
AGENT_IMAGE_MAX_BYTES: int = 5_000_000
|
||||
AGENT_IMAGE_MAX_PIXELS: int = 16_777_216
|
||||
VECTOR_STORE: str = "faiss" # "faiss" or "elasticsearch" or "qdrant" or "milvus" or "lancedb" or "pgvector"
|
||||
# Retriever keys an agent may use; must match RetrieverCreator.retrievers registry keys,
|
||||
# NOT the legacy ``classic_rag`` label which never matched the registry.
|
||||
RETRIEVERS_ENABLED: list = ["classic", "default"]
|
||||
# Concurrent per-source searches in one retrieval; the query is embedded once and shared.
|
||||
RETRIEVAL_MAX_PARALLEL_SOURCES: int = 4
|
||||
# Kill-switch for per-source retrieval dispatch; False collapses to a single retriever.
|
||||
PER_SOURCE_RETRIEVAL_ENABLED: bool = True
|
||||
GRAPHRAG_ENABLED: bool = False # gates graph-aware ingestion/retrieval
|
||||
# Model for ingest-time graph extraction; None reuses LLM_PROVIDER/LLM_NAME.
|
||||
GRAPHRAG_EXTRACTION_MODEL: Optional[str] = None
|
||||
# Hard cap on chunks extracted per source (cost control).
|
||||
GRAPHRAG_MAX_CHUNKS_FOR_EXTRACTION: int = 2000
|
||||
AGENT_NAME: str = "classic"
|
||||
FALLBACK_LLM_PROVIDER: Optional[str] = None # provider for fallback llm
|
||||
FALLBACK_LLM_NAME: Optional[str] = None # model name for fallback llm
|
||||
FALLBACK_LLM_API_KEY: Optional[str] = None # api key for fallback llm
|
||||
|
||||
# Google Drive integration
|
||||
GOOGLE_CLIENT_ID: Optional[str] = None # Replace with your actual Google OAuth client ID
|
||||
GOOGLE_CLIENT_SECRET: Optional[str] = None # Replace with your actual Google OAuth client secret
|
||||
CONNECTOR_REDIRECT_BASE_URI: Optional[str] = (
|
||||
"http://127.0.0.1:7091/api/connectors/callback" ##add redirect url as it is to your provider's console(gcp)
|
||||
)
|
||||
# Comma-separated frontend origins allowed to receive connector OAuth results, e.g. https://docsgpt.example.com.
|
||||
# The callback origin and OIDC_FRONTEND_URL are always allowed; a loopback callback also allows localhost:5173.
|
||||
CONNECTOR_ALLOWED_ORIGINS: Optional[str] = None
|
||||
|
||||
# Microsoft Entra ID (Azure AD) integration
|
||||
MICROSOFT_CLIENT_ID: Optional[str] = None # Azure AD Application (client) ID
|
||||
MICROSOFT_CLIENT_SECRET: Optional[str] = None # Azure AD Application client secret
|
||||
MICROSOFT_TENANT_ID: Optional[str] = "common" # Azure AD Tenant ID (or 'common' for multi-tenant)
|
||||
MICROSOFT_AUTHORITY: Optional[str] = None # e.g., "https://login.microsoftonline.com/{tenant_id}"
|
||||
|
||||
# Confluence Cloud integration
|
||||
CONFLUENCE_CLIENT_ID: Optional[str] = None
|
||||
CONFLUENCE_CLIENT_SECRET: Optional[str] = None
|
||||
|
||||
# GitHub source
|
||||
GITHUB_ACCESS_TOKEN: Optional[str] = None # PAT token with read repo access
|
||||
|
||||
# LLM Cache
|
||||
CACHE_REDIS_URL: str = "redis://localhost:6379/2"
|
||||
|
||||
API_URL: str = "http://localhost:7091" # backend url for celery worker
|
||||
|
||||
# Public base URL for user-facing endpoint references in prompts
|
||||
PUBLIC_API_BASE_URL: Optional[str] = None
|
||||
MCP_OAUTH_REDIRECT_URI: Optional[str] = None # public callback URL for MCP OAuth
|
||||
INTERNAL_KEY: Optional[str] = None # internal api key for worker-to-backend auth
|
||||
|
||||
API_KEY: Optional[str] = None # LLM api key (used by LLM_PROVIDER)
|
||||
|
||||
# Provider-specific API keys (for multi-model support)
|
||||
OPENAI_API_KEY: Optional[str] = None
|
||||
ANTHROPIC_API_KEY: Optional[str] = None
|
||||
GOOGLE_API_KEY: Optional[str] = None
|
||||
GROQ_API_KEY: Optional[str] = None
|
||||
HUGGINGFACE_API_KEY: Optional[str] = None
|
||||
OPEN_ROUTER_API_KEY: Optional[str] = None
|
||||
NOVITA_API_KEY: Optional[str] = None
|
||||
|
||||
OPENAI_API_BASE: Optional[str] = None # azure openai api base url
|
||||
OPENAI_API_VERSION: Optional[str] = None # azure openai api version
|
||||
AZURE_DEPLOYMENT_NAME: Optional[str] = None # azure deployment name for answering
|
||||
AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = None # azure deployment name for embeddings
|
||||
OPENAI_BASE_URL: Optional[str] = None # openai base url for open ai compatable models
|
||||
|
||||
# elasticsearch
|
||||
ELASTIC_CLOUD_ID: Optional[str] = None # cloud id for elasticsearch
|
||||
ELASTIC_USERNAME: Optional[str] = None # username for elasticsearch
|
||||
ELASTIC_PASSWORD: Optional[str] = None # password for elasticsearch
|
||||
ELASTIC_URL: Optional[str] = None # url for elasticsearch
|
||||
ELASTIC_INDEX: Optional[str] = "docsgpt" # index name for elasticsearch
|
||||
|
||||
# Legacy AWS credentials from the retired SageMaker provider. Still read as a deprecated
|
||||
# fallback by S3 storage; do not use for new deployments.
|
||||
SAGEMAKER_REGION: Optional[str] = None
|
||||
SAGEMAKER_ACCESS_KEY: Optional[str] = None
|
||||
SAGEMAKER_SECRET_KEY: Optional[str] = None
|
||||
|
||||
# Qdrant vectorstore config
|
||||
QDRANT_COLLECTION_NAME: Optional[str] = "docsgpt"
|
||||
QDRANT_LOCATION: Optional[str] = None
|
||||
QDRANT_URL: Optional[str] = None
|
||||
QDRANT_PORT: Optional[int] = 6333
|
||||
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")
|
||||
@@ -0,0 +1,79 @@
|
||||
"""Application settings.
|
||||
|
||||
``settings`` is the process-wide instance, loaded from the environment and the
|
||||
``.env`` file in the data home (see ``docsgpt.core.paths``). Every setting is a
|
||||
flat attribute, ``settings.NAME``, matching the environment variable of the
|
||||
same name.
|
||||
|
||||
The definitions are split by domain into the modules of this package; each
|
||||
module owns one ``SettingsGroup`` and ``Settings`` composes them all. Add a new
|
||||
setting to the group it belongs to (or add a group and list it in
|
||||
``SETTINGS_GROUPS``), with a ``description`` -- the settings reference in the
|
||||
docs is generated from these definitions.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from docsgpt.core.paths import env_file, home_dir
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_secret
|
||||
from docsgpt.core.settings.agents import AgentSettings
|
||||
from docsgpt.core.settings.auth import AuthSettings
|
||||
from docsgpt.core.settings.connectors import ConnectorSettings
|
||||
from docsgpt.core.settings.database import DatabaseSettings
|
||||
from docsgpt.core.settings.embeddings import EmbeddingsSettings
|
||||
from docsgpt.core.settings.events import EventsSettings
|
||||
from docsgpt.core.settings.guardrails import GuardrailSettings
|
||||
from docsgpt.core.settings.ingestion import IngestionSettings
|
||||
from docsgpt.core.settings.llm import LLMSettings
|
||||
from docsgpt.core.settings.ocr import OCRSettings
|
||||
from docsgpt.core.settings.quotas import QuotaSettings
|
||||
from docsgpt.core.settings.retrieval import RetrievalSettings
|
||||
from docsgpt.core.settings.sandbox import SandboxSettings
|
||||
from docsgpt.core.settings.scheduler import SchedulerSettings
|
||||
from docsgpt.core.settings.server import ServerSettings
|
||||
from docsgpt.core.settings.speech import SpeechSettings
|
||||
from docsgpt.core.settings.storage import StorageSettings
|
||||
from docsgpt.core.settings.vectorstores import VectorStoreSettings
|
||||
from docsgpt.core.settings.workers import WorkerSettings
|
||||
|
||||
#: Every settings group, in the order the generated reference lists them.
|
||||
SETTINGS_GROUPS: tuple[tuple[str, type[SettingsGroup]], ...] = (
|
||||
("Authentication", AuthSettings),
|
||||
("LLM providers", LLMSettings),
|
||||
("Embeddings", EmbeddingsSettings),
|
||||
("Retrieval", RetrievalSettings),
|
||||
("Vector stores", VectorStoreSettings),
|
||||
("User-data database", DatabaseSettings),
|
||||
("Workers", WorkerSettings),
|
||||
("Ingestion and parsing", IngestionSettings),
|
||||
("OCR", OCRSettings),
|
||||
("File storage", StorageSettings),
|
||||
("Connectors", ConnectorSettings),
|
||||
("Server", ServerSettings),
|
||||
("Events and devices", EventsSettings),
|
||||
("Agents", AgentSettings),
|
||||
("Guardrails", GuardrailSettings),
|
||||
("Quotas", QuotaSettings),
|
||||
("Scheduler", SchedulerSettings),
|
||||
("Sandbox", SandboxSettings),
|
||||
("Speech", SpeechSettings),
|
||||
)
|
||||
|
||||
# Runtime data home (DOCSGPT_HOME, the checkout, or cwd); see docsgpt.core.paths.
|
||||
current_dir = str(home_dir())
|
||||
|
||||
|
||||
class Settings(*(group for _, group in SETTINGS_GROUPS)):
|
||||
"""All settings, composed from the per-domain groups in this package."""
|
||||
|
||||
@classmethod
|
||||
def normalize_api_key(cls, v: Optional[str]) -> Optional[str]:
|
||||
"""Normalize a secret the way the per-field validators do; kept for callers that reuse it."""
|
||||
return normalize_secret(v)
|
||||
|
||||
|
||||
settings = Settings(_env_file=env_file(), _env_file_encoding="utf-8")
|
||||
|
||||
__all__ = ["SETTINGS_GROUPS", "Settings", "SettingsGroup", "current_dir", "settings"]
|
||||
@@ -0,0 +1,80 @@
|
||||
"""Building blocks shared by the settings groups.
|
||||
|
||||
Every group in this package is a :class:`SettingsGroup`: a ``BaseSettings``
|
||||
subclass that owns one domain's fields. ``docsgpt.core.settings.Settings``
|
||||
inherits from all of them, so the composed class keeps the flat
|
||||
``settings.NAME`` attributes the rest of the codebase reads while each
|
||||
domain's definitions live in their own module.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import types
|
||||
import typing
|
||||
from typing import Any, Optional
|
||||
|
||||
from pydantic import model_validator
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
|
||||
def _is_optional_str(annotation: Any) -> bool:
|
||||
"""``Optional[str]`` or ``Optional[Literal[...]]`` whose choices are all strings."""
|
||||
if typing.get_origin(annotation) not in (typing.Union, types.UnionType):
|
||||
return False
|
||||
members = set(typing.get_args(annotation))
|
||||
if type(None) not in members or len(members) != 2:
|
||||
return False
|
||||
(member,) = members - {type(None)}
|
||||
if member is str:
|
||||
return True
|
||||
return typing.get_origin(member) is typing.Literal and all(
|
||||
isinstance(choice, str) for choice in typing.get_args(member)
|
||||
)
|
||||
|
||||
|
||||
class SettingsGroup(BaseSettings):
|
||||
"""Base for one domain's settings; groups are composed into ``Settings``.
|
||||
|
||||
Every ``Optional[str]`` field (and optional string ``Literal``) treats the spellings an unset value has in a
|
||||
``.env`` file (``KEY=``, ``KEY=None``, whitespace) as ``None``, so a check
|
||||
like ``if settings.OIDC_ISSUER`` or a fallback like ``settings.X or default``
|
||||
sees "unset" rather than a truthy placeholder string. Real values are
|
||||
stripped. Fields typed ``str`` keep whatever they are given.
|
||||
"""
|
||||
|
||||
model_config = SettingsConfigDict(extra="ignore")
|
||||
|
||||
@model_validator(mode="before")
|
||||
@classmethod
|
||||
def _unset_optional_strings(cls, data: Any) -> Any:
|
||||
if not isinstance(data, dict):
|
||||
return data
|
||||
data = dict(data)
|
||||
for name, field in cls.model_fields.items():
|
||||
if name in data and _is_optional_str(field.annotation):
|
||||
data[name] = normalize_secret(data[name])
|
||||
return data
|
||||
|
||||
|
||||
def normalize_choice(value: Any) -> Any:
|
||||
"""Case-fold a closed-choice setting so ``PGVector`` and ``pgvector`` are the same choice."""
|
||||
if isinstance(value, str):
|
||||
return value.strip().lower()
|
||||
return value
|
||||
|
||||
|
||||
def normalize_secret(value: Optional[str]) -> Optional[str]:
|
||||
"""Map the ways an unset secret reaches us from ``.env`` to ``None``.
|
||||
|
||||
``.env`` files carry ``KEY=None`` and ``KEY=`` for "not set", and pydantic
|
||||
would otherwise keep those as the strings ``"None"`` and ``""``. Whitespace
|
||||
around a real value is stripped.
|
||||
"""
|
||||
if value is None:
|
||||
return None
|
||||
if not isinstance(value, str):
|
||||
return value
|
||||
stripped = value.strip()
|
||||
if stripped == "" or stripped.lower() == "none":
|
||||
return None
|
||||
return stripped
|
||||
@@ -0,0 +1,91 @@
|
||||
"""Agent runtime: default tools, context management, workflows and artifacts."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class AgentSettings(SettingsGroup):
|
||||
"""What an agent may do per turn and how its context is kept within budget."""
|
||||
|
||||
AGENT_NAME: str = Field(default="classic", description="Default agent type for agentless chats.")
|
||||
DEFAULT_AGENT_LIMITS: dict[str, int] = Field(
|
||||
default={"token_limit": 50000, "request_limit": 500},
|
||||
description="Per-agent default quotas: tokens and requests.",
|
||||
)
|
||||
DEFAULT_CHAT_TOOLS: list[str] = Field(
|
||||
default=["memory", "read_webpage", "scheduler"],
|
||||
description=(
|
||||
"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."
|
||||
),
|
||||
)
|
||||
ENABLE_TOOL_PREFETCH: bool = Field(default=True, description="Pre-fetch retrieval before the agent's first turn.")
|
||||
TOOL_RESULT_MAX_TOKENS: int = Field(
|
||||
default=20000,
|
||||
ge=0,
|
||||
description="Cap on one tool result entering the LLM context (0 disables); journal and DB keep it whole.",
|
||||
)
|
||||
|
||||
# Conversation compression.
|
||||
ENABLE_CONVERSATION_COMPRESSION: bool = Field(
|
||||
default=True, description="Compress long conversations once they approach the context window."
|
||||
)
|
||||
COMPRESSION_THRESHOLD_PERCENTAGE: float = Field(
|
||||
default=0.8, gt=0, le=1, description="Fraction of the context window at which compression triggers."
|
||||
)
|
||||
COMPRESSION_MODEL_OVERRIDE: Optional[str] = Field(
|
||||
default=None, description="Use a different model for compression; unset reuses the answer model."
|
||||
)
|
||||
COMPRESSION_PROMPT_VERSION: str = Field(default="v1.0", description="Tracks compression prompt iterations.")
|
||||
COMPRESSION_MAX_HISTORY_POINTS: int = Field(
|
||||
default=3, description="Keep only the last N compression points to prevent DB bloat."
|
||||
)
|
||||
COMPRESSION_RECENT_FIELD_MAX_TOKENS: int = Field(
|
||||
default=8000, ge=0, description="Per-field cap on the verbatim tail kept after a compression point (0 disables)."
|
||||
)
|
||||
|
||||
# Workflows.
|
||||
WORKFLOW_NODE_NATIVE_MAX_FILES: int = Field(
|
||||
default=5,
|
||||
description=(
|
||||
"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_EXTRACT_MAX_FILES: int = Field(
|
||||
default=5,
|
||||
description=(
|
||||
"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_BUDGET_SECONDS: int = Field(
|
||||
default=900,
|
||||
description=(
|
||||
"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_RUN_STALE_SECONDS: int = Field(
|
||||
default=3600,
|
||||
description=(
|
||||
"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."
|
||||
),
|
||||
)
|
||||
|
||||
# Per-user artifact quotas, enforced at persistence time. 0 or negative disables a quota.
|
||||
ARTIFACT_MAX_BYTES: int = Field(
|
||||
default=50 * 1024 * 1024, description="Cap on a single stored artifact version's bytes (0 disables)."
|
||||
)
|
||||
ARTIFACT_MAX_COUNT_PER_USER: int = Field(default=5000, description="Cap on artifacts a user may own (0 disables).")
|
||||
ARTIFACT_MAX_TOTAL_BYTES_PER_USER: int = Field(
|
||||
default=5 * 1024 * 1024 * 1024, description="Cap on a user's total stored artifact bytes (0 disables)."
|
||||
)
|
||||
@@ -0,0 +1,128 @@
|
||||
"""Authentication, SSO and provisioning."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator, model_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
#: Settings an OIDC deployment cannot run without; checked when AUTH_TYPE=oidc.
|
||||
OIDC_REQUIRED = ("OIDC_ISSUER", "OIDC_CLIENT_ID", "OIDC_FRONTEND_URL")
|
||||
|
||||
|
||||
class AuthSettings(SettingsGroup):
|
||||
"""How users authenticate: none, a shared token, per-session JWTs, or OIDC SSO."""
|
||||
|
||||
AUTH_TYPE: Optional[Literal["simple_jwt", "session_jwt", "oidc"]] = Field(
|
||||
default=None,
|
||||
description="Authentication mode: simple_jwt, session_jwt, oidc, or unset (None) for no authentication.",
|
||||
)
|
||||
JWT_SECRET_KEY: str = Field(
|
||||
default="",
|
||||
description=(
|
||||
"Signing key for session tokens and other signed capabilities. Required on every replica in "
|
||||
"production; local development may fall back to a key generated on disk."
|
||||
),
|
||||
)
|
||||
ENCRYPTION_SECRET_KEY: str = Field(
|
||||
default="default-docsgpt-encryption-key",
|
||||
description="Key used to encrypt stored credentials such as tool and connector secrets.",
|
||||
)
|
||||
INTERNAL_KEY: Optional[str] = Field(
|
||||
default=None, description="Internal API key for worker-to-backend authentication."
|
||||
)
|
||||
|
||||
# OIDC SSO (AUTH_TYPE=oidc): any OpenID Connect IdP with discovery (Authentik, Keycloak, ...).
|
||||
OIDC_ISSUER: Optional[str] = Field(
|
||||
default=None,
|
||||
description="OIDC issuer URL with discovery, e.g. https://auth.example.com/application/o/docsgpt/.",
|
||||
)
|
||||
OIDC_CLIENT_ID: Optional[str] = Field(default=None, description="OIDC client id.")
|
||||
OIDC_CLIENT_SECRET: Optional[str] = Field(
|
||||
default=None, description="OIDC client secret. Optional; PKCE is always used."
|
||||
)
|
||||
OIDC_SCOPES: str = Field(default="openid profile email", description="Scopes requested from the IdP.")
|
||||
OIDC_USER_ID_CLAIM: str = Field(
|
||||
default="sub", description="ID-token claim mapped to the DocsGPT user id."
|
||||
)
|
||||
OIDC_FRONTEND_URL: Optional[str] = Field(
|
||||
default=None, description="Browser-facing app origin, e.g. http://localhost:5173."
|
||||
)
|
||||
OIDC_REDIRECT_URI: Optional[str] = Field(
|
||||
default=None, description="Override for the callback URL; default is <request host>/api/auth/oidc/callback."
|
||||
)
|
||||
OIDC_SESSION_LIFETIME_SECONDS: int = Field(
|
||||
default=28800, gt=0, description="Lifetime of the minted session JWT in seconds (8h)."
|
||||
)
|
||||
OIDC_PROVIDER_NAME: Optional[str] = Field(
|
||||
default=None, description='Sign-in button label, e.g. "Acme SSO".'
|
||||
)
|
||||
OIDC_ALLOWED_GROUPS: Optional[str] = Field(
|
||||
default=None, description="Comma-separated group allowlist; unset admits any authenticated user."
|
||||
)
|
||||
OIDC_GROUPS_CLAIM: str = Field(
|
||||
default="groups", description="ID-token/userinfo claim carrying group membership."
|
||||
)
|
||||
OIDC_ADMIN_GROUPS: Optional[str] = Field(
|
||||
default=None, description="Comma-separated groups granted admin; unset means no OIDC admin mapping."
|
||||
)
|
||||
|
||||
LOCAL_MODE_ADMIN: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
"Grant admin without a database role. Persisted admin grants live in user_roles (AUTH_TYPE=oidc "
|
||||
"only); this is the only non-DB admin path, for AUTH_TYPE=None self-host. MUST stay False if "
|
||||
"networked."
|
||||
),
|
||||
)
|
||||
|
||||
# SCIM 2.0 provisioning (IdP-driven user create/deactivate at /scim/v2).
|
||||
SCIM_ENABLED: bool = Field(default=False, description="Enable SCIM 2.0 provisioning at /scim/v2.")
|
||||
SCIM_TOKEN: Optional[str] = Field(
|
||||
default=None, description="Bearer token for IdP SCIM clients (required when SCIM is enabled)."
|
||||
)
|
||||
|
||||
# Personal access tokens: scoped user-level API credentials for CLI and CI/CD use.
|
||||
PAT_ENABLED: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Master switch for personal access tokens. When false, no token can be created AND every existing "
|
||||
"token stops authenticating immediately (pipelines using them get 401); tokens are kept and work "
|
||||
"again when re-enabled. Tokens are only available under AUTH_TYPE=oidc or unset (None); switching "
|
||||
"to simple_jwt or session_jwt disables them the same way."
|
||||
),
|
||||
)
|
||||
PAT_DEFAULT_LIFETIME_DAYS: int = Field(
|
||||
default=90, gt=0, description="Lifetime of a personal access token created without an explicit expiry."
|
||||
)
|
||||
PAT_MAX_LIFETIME_DAYS: int = Field(
|
||||
default=365, gt=0, description="Longest lifetime a user may request for a personal access token."
|
||||
)
|
||||
PAT_ALLOW_NON_EXPIRING: bool = Field(
|
||||
default=False,
|
||||
description="Let users create personal access tokens that never expire. Off by default.",
|
||||
)
|
||||
PAT_MAX_PER_USER: int = Field(
|
||||
default=25, gt=0, description="Maximum number of live personal access tokens per user."
|
||||
)
|
||||
|
||||
@field_validator("AUTH_TYPE", mode="before")
|
||||
@classmethod
|
||||
def _normalize_auth_type(cls, v):
|
||||
# Unset spellings ("None", "") became None on the group base; this only case-folds a value.
|
||||
return normalize_choice(v)
|
||||
|
||||
@model_validator(mode="after")
|
||||
def _require_dependent_settings(self):
|
||||
if self.AUTH_TYPE == "oidc":
|
||||
missing = [name for name in OIDC_REQUIRED if not getattr(self, name)]
|
||||
if missing:
|
||||
raise ValueError(f"AUTH_TYPE=oidc requires settings: {', '.join(missing)}")
|
||||
if self.SCIM_ENABLED and not self.SCIM_TOKEN:
|
||||
raise ValueError("SCIM_ENABLED requires settings: SCIM_TOKEN")
|
||||
if self.PAT_DEFAULT_LIFETIME_DAYS > self.PAT_MAX_LIFETIME_DAYS:
|
||||
raise ValueError("PAT_DEFAULT_LIFETIME_DAYS must not exceed PAT_MAX_LIFETIME_DAYS")
|
||||
return self
|
||||
@@ -0,0 +1,51 @@
|
||||
"""OAuth credentials for external source connectors."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class ConnectorSettings(SettingsGroup):
|
||||
"""Client credentials and callback URLs for Google Drive, Microsoft, Confluence, GitHub and MCP."""
|
||||
|
||||
# Google Drive integration.
|
||||
GOOGLE_CLIENT_ID: Optional[str] = Field(default=None, description="Google OAuth client id.")
|
||||
GOOGLE_CLIENT_SECRET: Optional[str] = Field(default=None, description="Google OAuth client secret.")
|
||||
CONNECTOR_REDIRECT_BASE_URI: str = Field(
|
||||
default="http://127.0.0.1:7091/api/connectors/callback",
|
||||
description="OAuth callback URL; register it as-is in your provider's console (e.g. GCP).",
|
||||
)
|
||||
CONNECTOR_ALLOWED_ORIGINS: Optional[str] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Comma-separated frontend origins allowed to receive connector OAuth results, e.g. "
|
||||
"https://docsgpt.example.com. The callback origin and OIDC_FRONTEND_URL are always allowed; a "
|
||||
"loopback callback also allows localhost:5173."
|
||||
),
|
||||
)
|
||||
|
||||
# Microsoft Entra ID (Azure AD) integration.
|
||||
MICROSOFT_CLIENT_ID: Optional[str] = Field(default=None, description="Azure AD application (client) id.")
|
||||
MICROSOFT_CLIENT_SECRET: Optional[str] = Field(default=None, description="Azure AD application client secret.")
|
||||
MICROSOFT_TENANT_ID: str = Field(
|
||||
default="common", description="Azure AD tenant id, or 'common' for multi-tenant."
|
||||
)
|
||||
MICROSOFT_AUTHORITY: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Authority URL override; unset derives https://login.microsoftonline.com/<MICROSOFT_TENANT_ID>.",
|
||||
)
|
||||
|
||||
# Confluence Cloud integration.
|
||||
CONFLUENCE_CLIENT_ID: Optional[str] = Field(default=None, description="Confluence Cloud OAuth client id.")
|
||||
CONFLUENCE_CLIENT_SECRET: Optional[str] = Field(default=None, description="Confluence Cloud OAuth client secret.")
|
||||
|
||||
# GitHub source.
|
||||
GITHUB_ACCESS_TOKEN: Optional[str] = Field(default=None, description="GitHub PAT with read access to repositories.")
|
||||
|
||||
MCP_OAUTH_REDIRECT_URI: Optional[str] = Field(
|
||||
default=None, description="Public callback URL for MCP OAuth; unset derives it from CONNECTOR_REDIRECT_BASE_URI."
|
||||
)
|
||||
@@ -0,0 +1,36 @@
|
||||
"""User-data Postgres and schema management at startup."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.db_uri import normalize_postgres_uri
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class DatabaseSettings(SettingsGroup):
|
||||
"""The Postgres database holding users, conversations and sources, and what startup may do to it."""
|
||||
|
||||
POSTGRES_URI: Optional[str] = Field(default=None, description="User-data Postgres connection URI.")
|
||||
AUTO_MIGRATE: bool = Field(
|
||||
default=True,
|
||||
description="On startup, apply pending Alembic migrations. Disable if you manage schema out-of-band.",
|
||||
)
|
||||
AUTO_CREATE_DB: bool = Field(
|
||||
default=True,
|
||||
description="On startup, create the target Postgres database if missing (needs CREATEDB privilege).",
|
||||
)
|
||||
AUTO_VECTOR_SCHEMA: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"On startup, create the pgvector/graph tables and verify the embedding dimension. No Alembic "
|
||||
"migration covers the vector DB (it may be a separate cluster); set False to manage it yourself."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("POSTGRES_URI", mode="before")
|
||||
@classmethod
|
||||
def _normalize_postgres_uri(cls, v):
|
||||
return normalize_postgres_uri(v)
|
||||
@@ -0,0 +1,88 @@
|
||||
"""Embedding model selection and where it runs."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.paths import home_dir
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class EmbeddingsSettings(SettingsGroup):
|
||||
"""The embedding model, remote or local, and the batching around it."""
|
||||
|
||||
EMBEDDINGS_NAME: str = Field(
|
||||
default="huggingface_sentence-transformers/all-mpnet-base-v2",
|
||||
description=(
|
||||
"Embedding model. The legacy model is the default on purpose: an install that never pinned this "
|
||||
"has vectors from it, and granite is the same width so a swap would fail silently. New installs "
|
||||
"get granite from .env-template; existing ones switch by setting this and running "
|
||||
"docsgpt.scripts.reembed."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_BASE_URL: Optional[str] = Field(
|
||||
default=None, description="Remote embeddings API URL (OpenAI-compatible)."
|
||||
)
|
||||
EMBEDDINGS_KEY: Optional[str] = Field(
|
||||
default=None, description="API key for embeddings (with OpenAI, the same value as API_KEY)."
|
||||
)
|
||||
EMBEDDINGS_MAX_INPUT_TOKENS: Optional[int] = Field(
|
||||
default=None, description="Truncate each remote embed input to N tokens (overflow is lost)."
|
||||
)
|
||||
EMBEDDINGS_BATCH_SIZE: int = Field(
|
||||
default=32, ge=1, description="Chunks per store transaction and per remote embed request."
|
||||
)
|
||||
EMBEDDINGS_MODEL_BATCH_SIZE: int = Field(
|
||||
default=1,
|
||||
ge=1,
|
||||
description=(
|
||||
"Documents per local ONNX forward pass. Each pass pads to its longest input, and that waste grows "
|
||||
"with the square of chunk length: at 1250 tokens, 32 peaked at 6.6 GB, 1 at 2.9 GB."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_THREADS: Optional[int] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Intra-op threads for the local ONNX runner; unset uses every core. It scales sub-linearly, so "
|
||||
"several single-threaded workers beat one many-threaded process on the same cores."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_CACHE_DIR: Optional[str] = Field(
|
||||
default_factory=lambda: str(home_dir() / "models"),
|
||||
description=(
|
||||
"Where embedding models and their tokenizers are cached. Persistent by default: FastEmbed's own "
|
||||
"default is the temp dir."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_POOLING: Optional[Literal["cls", "mean"]] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
'Pooling strategy ("cls" or "mean"). Read from the model\'s own repository; set only for a '
|
||||
"repository that declares none, or to override what it declares."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_NORMALIZE: Optional[bool] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"L2-normalise embeddings. Read from the model's own repository; set only for a repository that "
|
||||
"declares nothing, or to override what it declares."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_DELEGATE_TO_WORKER: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Embed on the worker so the API holds no model (~890 MB), at one broker round trip per query. "
|
||||
"Ignored when EMBEDDINGS_BASE_URL is set, which is the better answer for production."
|
||||
),
|
||||
)
|
||||
EMBEDDINGS_QUEUE: str = Field(default="embeddings", description="Celery queue the embed task is routed to.")
|
||||
EMBEDDINGS_DELEGATE_TIMEOUT: int = Field(
|
||||
default=60, gt=0, description="Seconds the API waits for the worker to return an embedding."
|
||||
)
|
||||
|
||||
@field_validator("EMBEDDINGS_POOLING", mode="before")
|
||||
@classmethod
|
||||
def _normalize_pooling(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,90 @@
|
||||
"""Server-sent events, replay journal and remote-device sessions."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class EventsSettings(SettingsGroup):
|
||||
"""The internal push channel (notifications and durable replay) and the Redis pool behind it."""
|
||||
|
||||
ENABLE_SSE_PUSH: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Internal SSE push channel (notifications and durable replay journal). False makes /api/events emit "
|
||||
'"push_disabled" and return; clients fall back to polling.'
|
||||
),
|
||||
)
|
||||
EVENTS_STREAM_MAXLEN: int = Field(
|
||||
default=1000, ge=1, description="Per-user durable backlog cap in entries; ~24h of replay at typical rates."
|
||||
)
|
||||
SSE_KEEPALIVE_SECONDS: int = Field(default=15, ge=1, description="Interval between SSE keepalive comments.")
|
||||
SSE_MAX_CONCURRENT_PER_USER: int = Field(
|
||||
default=8,
|
||||
ge=0,
|
||||
description=(
|
||||
"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."
|
||||
),
|
||||
)
|
||||
ASYNC_REDIS_MAX_CONNECTIONS: int = Field(
|
||||
default=2000,
|
||||
ge=1,
|
||||
description=(
|
||||
"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)."
|
||||
),
|
||||
)
|
||||
EVENTS_REPLAY_MAX_PER_REQUEST: int = Field(
|
||||
default=200,
|
||||
ge=1,
|
||||
description=(
|
||||
"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."
|
||||
),
|
||||
)
|
||||
EVENTS_REPLAY_MAX_AGE_HOURS: int = Field(default=48, description="Oldest backlog entry a replay will return.")
|
||||
EVENTS_REPLAY_BUDGET_REQUESTS_PER_WINDOW: int = Field(
|
||||
default=30,
|
||||
description=(
|
||||
"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_WINDOW_SECONDS: int = Field(default=60, description="Length of the replay budget window.")
|
||||
MESSAGE_EVENTS_RETENTION_DAYS: int = Field(
|
||||
default=14,
|
||||
gt=0,
|
||||
description=(
|
||||
"Retention for the message_events journal, enforced by the cleanup_message_events beat task. Replay "
|
||||
"only needs streams a client could still be tailing."
|
||||
),
|
||||
)
|
||||
|
||||
# Remote Device feature.
|
||||
REMOTE_DEVICE_SESSION_IDLE_SECONDS: int = Field(
|
||||
default=60, gt=0, description="Seconds without a heartbeat before a remote-device session is considered idle."
|
||||
)
|
||||
REMOTE_DEVICE_REQUIRE_SIGNATURE: bool = Field(
|
||||
default=False, description="Require signed commands from remote devices."
|
||||
)
|
||||
REMOTE_DEVICE_PAIRING_TTL_SECONDS: int = Field(default=600, gt=0, description="Lifetime of a pairing code.")
|
||||
REMOTE_DEVICE_CMD_QUEUE_TTL_SECONDS: int = Field(
|
||||
default=900,
|
||||
gt=605,
|
||||
description=(
|
||||
"Redis TTL of the per-device command queue, routing invocations cross-process so a scheduled run "
|
||||
"reaches the web-held device session. 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_INVOCATION_TTL_SECONDS: int = Field(
|
||||
default=900, gt=0, description="Redis TTL of a pending remote-device invocation."
|
||||
)
|
||||
REMOTE_DEVICE_OUTPUT_STREAM_MAXLEN: int = Field(
|
||||
default=10_000, description="Cap on buffered output entries per remote-device invocation stream."
|
||||
)
|
||||
@@ -0,0 +1,40 @@
|
||||
"""Agent guardrails."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class GuardrailSettings(SettingsGroup):
|
||||
"""Input/output checks every agent runs, and the floor no agent may weaken."""
|
||||
|
||||
GUARDRAILS_ENABLED: bool = Field(default=True, description="Master switch; False disables every stage.")
|
||||
GUARDRAILS_CHECKS_ENABLED: list[str] = Field(
|
||||
default=[], description="Allowlist of GuardrailCreator.checks keys; empty means every registered check."
|
||||
)
|
||||
GUARDRAILS_FLOOR: dict[str, Any] = Field(
|
||||
default={},
|
||||
description=(
|
||||
"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_JUDGE_MODEL: Optional[str] = Field(
|
||||
default=None, description="Judge model for the topic/policy checks; unset reuses the request's model."
|
||||
)
|
||||
GUARDRAILS_STORE_SCANNED_TEXT: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
"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_EVENTS_RETENTION_DAYS: int = Field(
|
||||
default=30, ge=1, description="Days guardrail events are kept before the cleanup task removes them."
|
||||
)
|
||||
@@ -0,0 +1,152 @@
|
||||
"""Uploads, document parsing and the size caps that keep one file from taking a worker down."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class IngestionSettings(SettingsGroup):
|
||||
"""Upload limits, the parser engine, and per-format byte caps for ingestion and attachments."""
|
||||
|
||||
UPLOAD_FOLDER: str = Field(default="inputs", description="Directory under the data home for uploaded sources.")
|
||||
UPLOAD_MAX_REQUEST_BYTES: int = Field(
|
||||
default=256 * 1024 * 1024,
|
||||
gt=0,
|
||||
description="Cap on an upload request body; applied by Flask before multipart parsing.",
|
||||
)
|
||||
UPLOAD_MAX_FILE_BYTES: int = Field(
|
||||
default=100 * 1024 * 1024, gt=0, description="Cap on a single uploaded file; also enforced while copying."
|
||||
)
|
||||
PARSE_SPEC_MAX_BYTES: int = Field(
|
||||
default=10 * 1024 * 1024, gt=0, description="Cap on an OpenAPI/tool spec file accepted for parsing."
|
||||
)
|
||||
# ZIP limits apply cumulatively across nested archives in one extraction.
|
||||
UPLOAD_MAX_ARCHIVE_BYTES: int = Field(
|
||||
default=250 * 1024 * 1024, gt=0, description="Cap on total bytes extracted from one uploaded archive."
|
||||
)
|
||||
UPLOAD_MAX_ARCHIVE_FILES: int = Field(
|
||||
default=10_000, gt=0, description="Cap on files extracted from one uploaded archive."
|
||||
)
|
||||
UPLOAD_MAX_ARCHIVE_RATIO: int = Field(
|
||||
default=1000, gt=0, description="Maximum decompressed-to-compressed ratio before an archive is rejected."
|
||||
)
|
||||
UPLOAD_MAX_ARCHIVE_DEPTH: int = Field(
|
||||
default=3, ge=0, description="Maximum nesting depth of archives inside archives."
|
||||
)
|
||||
PARSE_PDF_AS_IMAGE: bool = Field(default=False, description="Render PDF pages to images before parsing.")
|
||||
PARSE_IMAGE_REMOTE: bool = Field(default=False, description="Send images to a remote parser.")
|
||||
DOC_PARSER_ENGINE: Literal["anydoc", "docling"] = Field(
|
||||
default="anydoc",
|
||||
description=(
|
||||
'Document parser for source ingestion, chat attachments and the read_document tool. "anydoc" '
|
||||
"(default): firecrawl-anydoc, a Rust converter with no ML models; milliseconds per file, ~100 MB "
|
||||
'peak RSS. "docling": the layout/table-model pipeline (optional install; needed for '
|
||||
"read_document's structured output and the docling OCR backend). Files anydoc cannot convert "
|
||||
"(scanned PDFs, malformed input) fall back to docling when it is installed, otherwise to the native "
|
||||
"OCR parsers (OCR on) or the legacy parsers. Rollback to the previous behaviour is this one variable."
|
||||
),
|
||||
)
|
||||
DOCLING_PIPELINE_QUEUE_MAX_SIZE: int = Field(
|
||||
default=2,
|
||||
description=(
|
||||
"Pages docling's threaded pipeline buffers in flight; the library default (100) drives worker RSS "
|
||||
"to ~3 GB on a mid-size PDF."
|
||||
),
|
||||
)
|
||||
DOCLING_COMPILE_TORCH_MODELS: bool = Field(
|
||||
default=False, description="Let docling torch.compile its models (slower start, faster pages)."
|
||||
)
|
||||
DOCLING_TABULAR_MAX_BYTES: int = Field(
|
||||
default=2_000_000, description="Largest CSV/XLSX docling will parse, in bytes."
|
||||
)
|
||||
DOCLING_MARKUP_MAX_BYTES: int = Field(
|
||||
default=8_000_000, description="Largest HTML/XML docling will parse, in bytes."
|
||||
)
|
||||
MARKUP_MAX_BYTES: int = Field(
|
||||
default=8_000_000,
|
||||
ge=0,
|
||||
description=(
|
||||
"HTML/XHTML larger than this (bytes) are head-truncated before the markdownify parser runs (the "
|
||||
"anydoc engine's HTML path). The tree that path builds costs ~50x the input (30 MB of HTML measured "
|
||||
"at 1.6 GB RSS) and the upload cap is 100 MB, so the gate is what keeps one upload from taking the "
|
||||
"ingest worker down. 0 disables it."
|
||||
),
|
||||
)
|
||||
PDF_TRUST_CHECK: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Trust-check anydoc's PDF output (docsgpt/parser/file/pdf_trust.py): flag composite (Type0) fonts "
|
||||
"without a ToUnicode map, and CJK-declaring PDFs whose extracted text has almost no CJK, the two "
|
||||
"classes where anydoc drops text silently. A flagged file re-parses on the docling fallback when "
|
||||
"docling is installed; otherwise the anydoc output is kept and the document gets "
|
||||
'extra_info["parse_warnings"]. ~30 ms per scanned MB.'
|
||||
),
|
||||
)
|
||||
ANYDOC_TABLEIZE: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
"Rewrite dot-leader / whitespace-aligned table runs in anydoc's PDF markdown into GFM tables "
|
||||
"(docsgpt/parser/file/tableize.py). Off by default: it rewrites content on a heuristic (>=3 uniform "
|
||||
"label+numbers lines) validated only on a small corpus so far."
|
||||
),
|
||||
)
|
||||
ATTACHMENT_PDF_TEXT_FAST_PATH: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Read PDF attachments via their embedded text layer (pypdfium2) instead of docling, falling back to "
|
||||
"docling when there is no text layer. Attachments go into a prompt, so docling's structural "
|
||||
"markdown earns far less than the tens of seconds per file it costs; source ingestion is "
|
||||
"unaffected because chunking and retrieval do depend on that structure."
|
||||
),
|
||||
)
|
||||
ATTACHMENT_PDF_TEXT_MIN_MEDIAN_CHARS: int = Field(
|
||||
default=32,
|
||||
description=(
|
||||
"Median chars per sampled page below which a PDF attachment is treated as a scan and handed to "
|
||||
"docling. Measured on real uploads: scans at 0-17 chars/page, text-layer documents at 433-6834."
|
||||
),
|
||||
)
|
||||
ATTACHMENT_TEXT_MAX_BYTES: int = Field(default=5_000_000, description="Cap on extracted attachment text.")
|
||||
AGENT_IMAGE_MAX_BYTES: int = Field(default=5_000_000, description="Cap on an image passed to an agent.")
|
||||
AGENT_IMAGE_MAX_PIXELS: int = Field(
|
||||
default=16_777_216, description="Cap on the pixel count of an image passed to an agent."
|
||||
)
|
||||
GITHUB_INGEST_MAX_FILE_BYTES: int = Field(
|
||||
default=1048576, ge=0, description="Skip GitHub repo blobs larger than this (0 = no cap)."
|
||||
)
|
||||
GITHUB_INGEST_MAX_WORKERS: int = Field(default=8, ge=1, description="Parallel file fetches per GitHub repo ingest.")
|
||||
|
||||
# read_document parsing on a dedicated Celery queue (backend parser).
|
||||
DOCUMENT_PARSE_QUEUE: str = Field(default="parsing", description="Celery queue the parse_document task is routed to.")
|
||||
DOCUMENT_PARSE_TIMEOUT: int = Field(
|
||||
default=120, description="Seconds the read_document tool awaits the enqueued parse before degrading."
|
||||
)
|
||||
DOCUMENT_PARSE_TIMEOUT_PER_MB: int = Field(
|
||||
default=60,
|
||||
description=(
|
||||
"Extra seconds of parse window per MiB of input. 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_MAX: int = Field(
|
||||
default=900, description="Absolute ceiling on the size-scaled parse window, in seconds."
|
||||
)
|
||||
DOCUMENT_PARSE_MAX_BYTES: int = Field(
|
||||
default=0, ge=0, description="Cap on a parsed document's bytes (0 = reuse SANDBOX_MAX_INPUT_BYTES)."
|
||||
)
|
||||
DOCUMENT_MAX_DECOMPRESSED_BYTES: int = Field(
|
||||
default=300 * 1024 * 1024, description="Cap on bytes decompressed from an archive handed to read_document."
|
||||
)
|
||||
DOCUMENT_MAX_ARCHIVE_ENTRIES: int = Field(
|
||||
default=10000, description="Cap on entries in an archive handed to read_document."
|
||||
)
|
||||
|
||||
@field_validator("DOC_PARSER_ENGINE", mode="before")
|
||||
@classmethod
|
||||
def _normalize_parser_engine(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,106 @@
|
||||
"""LLM providers, API keys and per-provider tunables."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.paths import home_dir
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class LLMSettings(SettingsGroup):
|
||||
"""Which model answers, how it is reached, and provider-specific behaviour."""
|
||||
|
||||
LLM_PROVIDER: str = Field(default="docsgpt", description="LLM provider key, e.g. openai, anthropic, docsgpt.")
|
||||
LLM_NAME: Optional[str] = Field(
|
||||
default=None, description="Model name for the provider; with openai, e.g. gpt-4 or gpt-3.5-turbo."
|
||||
)
|
||||
API_KEY: Optional[str] = Field(default=None, description="LLM API key used by LLM_PROVIDER.")
|
||||
|
||||
# Provider-specific API keys (for multi-model support).
|
||||
OPENAI_API_KEY: Optional[str] = Field(default=None, description="OpenAI API key.")
|
||||
ANTHROPIC_API_KEY: Optional[str] = Field(default=None, description="Anthropic API key.")
|
||||
GOOGLE_API_KEY: Optional[str] = Field(default=None, description="Google AI API key.")
|
||||
GROQ_API_KEY: Optional[str] = Field(default=None, description="Groq API key.")
|
||||
HUGGINGFACE_API_KEY: Optional[str] = Field(default=None, description="Hugging Face API key.")
|
||||
OPEN_ROUTER_API_KEY: Optional[str] = Field(default=None, description="OpenRouter API key.")
|
||||
NOVITA_API_KEY: Optional[str] = Field(default=None, description="Novita API key.")
|
||||
|
||||
OPENAI_API_BASE: Optional[str] = Field(default=None, description="Azure OpenAI API base URL.")
|
||||
OPENAI_API_VERSION: Optional[str] = Field(default=None, description="Azure OpenAI API version.")
|
||||
AZURE_DEPLOYMENT_NAME: Optional[str] = Field(default=None, description="Azure deployment name for answering.")
|
||||
AZURE_EMBEDDINGS_DEPLOYMENT_NAME: Optional[str] = Field(
|
||||
default=None, description="Azure deployment name for embeddings."
|
||||
)
|
||||
OPENAI_BASE_URL: Optional[str] = Field(
|
||||
default=None, description="Base URL for OpenAI-compatible model servers."
|
||||
)
|
||||
LLM_PATH: str = Field(
|
||||
default=os.path.join(str(home_dir()), "models/docsgpt-7b-f16.gguf"),
|
||||
description="Path to the local GGUF model used by the llama.cpp provider.",
|
||||
)
|
||||
|
||||
FALLBACK_LLM_PROVIDER: Optional[str] = Field(default=None, description="Provider for the fallback LLM.")
|
||||
FALLBACK_LLM_NAME: Optional[str] = Field(default=None, description="Model name for the fallback LLM.")
|
||||
FALLBACK_LLM_API_KEY: Optional[str] = Field(default=None, description="API key for the fallback LLM.")
|
||||
TITLE_MODEL_ID: Optional[str] = Field(
|
||||
default=None, description="Optional cheaper model for conversation titles; unset reuses the answer model."
|
||||
)
|
||||
MODELS_CONFIG_DIR: Optional[str] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Directory of operator-supplied model YAMLs, loaded after the built-in catalog; later wins on "
|
||||
"duplicate model id. See docsgpt/core/models/README.md."
|
||||
),
|
||||
)
|
||||
DEFAULT_LLM_TOKEN_LIMIT: int = Field(
|
||||
default=128000, description="Context window assumed when the model is not found in the registry."
|
||||
)
|
||||
RESERVED_TOKENS: dict[str, int] = Field(
|
||||
default={"system_prompt": 500, "current_query": 500, "safety_buffer": 1000},
|
||||
description="Tokens held back from the context window for the system prompt, the query and a safety buffer.",
|
||||
)
|
||||
CACHE_REDIS_URL: str = Field(default="redis://localhost:6379/2", description="Redis URL for the LLM cache.")
|
||||
|
||||
# OpenAI Responses API.
|
||||
OPENAI_RESPONSES_STORE: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
"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_CHAIN_ACROSS_TURNS: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"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_BUDGET_TOKENS: Optional[int] = Field(
|
||||
default=None, description="Prompt-token budget for cross-turn chaining; unset uses the model's context window."
|
||||
)
|
||||
OPENAI_RESPONSES_TRUNCATION_AUTO: bool = Field(
|
||||
default=False,
|
||||
description=(
|
||||
'Send truncation: "auto" so the provider drops the oldest input items instead of failing every '
|
||||
"request once a chain exceeds the model's window."
|
||||
),
|
||||
)
|
||||
OPENAI_PROMPT_CACHE_KEY: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Route a user's Responses API calls to the same prompt-cache shard with an opaque per-user key."
|
||||
),
|
||||
)
|
||||
OPENAI_PROMPT_CACHE_RETENTION: Optional[str] = Field(
|
||||
default=None, description="Request extended prompt-cache retention where the provider offers it."
|
||||
)
|
||||
OPENAI_REASONING_SUMMARY: str = Field(
|
||||
default="auto", description="Reasoning summary mode requested from the Responses API."
|
||||
)
|
||||
@@ -0,0 +1,99 @@
|
||||
"""OCR for scanned PDFs and images."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import AliasChoices, Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class OCRSettings(SettingsGroup):
|
||||
"""Whether OCR runs, which stack performs it, and which engine it uses.
|
||||
|
||||
OCR_ENABLED covers source ingestion, OCR_ATTACHMENTS_ENABLED chat attachments. Which stack performs
|
||||
it is OCR_BACKEND; which engine, OCR_ENGINE. The DOCLING_OCR_* names are the pre-2026-09 spellings
|
||||
and stay accepted as aliases.
|
||||
"""
|
||||
|
||||
OCR_ENABLED: bool = Field(
|
||||
default=False,
|
||||
validation_alias=AliasChoices("OCR_ENABLED", "DOCLING_OCR_ENABLED"),
|
||||
description="OCR scanned PDFs and images during source ingestion.",
|
||||
)
|
||||
OCR_ATTACHMENTS_ENABLED: bool = Field(
|
||||
default=False,
|
||||
validation_alias=AliasChoices("OCR_ATTACHMENTS_ENABLED", "DOCLING_OCR_ATTACHMENTS_ENABLED"),
|
||||
description="OCR scanned PDFs and images attached to a chat.",
|
||||
)
|
||||
OCR_BACKEND: Literal["auto", "docling", "native"] = Field(
|
||||
default="auto",
|
||||
description=(
|
||||
"Which stack runs OCR when it is on. auto: docling when installed, otherwise native. docling: the "
|
||||
"layout-model pipeline (hybrid region OCR, reading order, table structure); needs the optional "
|
||||
"docling extra. native: pypdfium2/Pillow page rendering straight into tesseract or a DeepSeek-OCR "
|
||||
"endpoint (docsgpt/parser/file/ocr_parser.py); no ML models in the worker, tables come out as text "
|
||||
"lines under tesseract."
|
||||
),
|
||||
)
|
||||
OCR_ENGINE: Literal["tesseract", "deepseek", "auto", "ocrmac", "rapidocr"] = Field(
|
||||
default="tesseract",
|
||||
description=(
|
||||
"OCR engine used when OCR is on. Benched 2026-08 on EN/ZH/table/degraded scans (docs/Guides/ocr has "
|
||||
"the menu). tesseract (recommended): best classic-engine accuracy (perfect EN word recall, 0.000 "
|
||||
"bilingual CER, 100% table cells), ~35 MB, CPU-only; needs the system binary and language packs, an "
|
||||
"optional install like every OCR dependency (build with INSTALL_TESSERACT=true, or apt/brew install "
|
||||
"tesseract-ocr for a local run); both backends. deepseek: DeepSeek-OCR against an Ollama/vLLM "
|
||||
"endpoint (OCR_DEEPSEEK_*); best table/CJK quality, the worker stays light (no layout models) but "
|
||||
"each page costs seconds on the model server; both backends. auto: docling's pick, ocrmac on macOS "
|
||||
"(excellent), rapidocr on Linux (silently shreds some long text lines; avoid as a server default). "
|
||||
"ocrmac | rapidocr: force one of those. auto/ocrmac/rapidocr exist only inside docling; the native "
|
||||
"backend runs tesseract for them. An engine that is not installed degrades (docling: to auto) with "
|
||||
"a warning instead of failing the parse."
|
||||
),
|
||||
)
|
||||
OCR_LANGS: str = Field(
|
||||
default="eng",
|
||||
description=(
|
||||
'Tesseract language packs, "+"-separated (e.g. "eng+chi_sim+deu"). Other engines keep their own '
|
||||
"defaults; their language codes differ."
|
||||
),
|
||||
)
|
||||
OCR_DEEPSEEK_URL: str = Field(
|
||||
default="http://localhost:11434/v1/chat/completions",
|
||||
description="Chat-completions URL of the DeepSeek-OCR endpoint (Ollama or vLLM).",
|
||||
)
|
||||
OCR_DEEPSEEK_MODEL: str = Field(default="deepseek-ocr:3b", description="Model name at the DeepSeek-OCR endpoint.")
|
||||
OCR_DEEPSEEK_TIMEOUT: float = Field(
|
||||
default=300.0,
|
||||
description=(
|
||||
"Seconds allowed per page request to the DeepSeek endpoint, on both backends (native sends pages one "
|
||||
"at a time; docling's VLM pipeline keeps its own concurrency). A 3B model on a laptop needs minutes; "
|
||||
"a vLLM GPU deployment, seconds."
|
||||
),
|
||||
)
|
||||
OCR_RENDER_DPI: int = Field(
|
||||
default=200,
|
||||
description=(
|
||||
"Native backend only: resolution at which pages without a text layer are rendered before OCR. 200 "
|
||||
"suits tesseract; clamped to 72-600."
|
||||
),
|
||||
)
|
||||
OCR_MIN_CHARS_PER_PAGE: int = Field(
|
||||
default=20,
|
||||
ge=0,
|
||||
validation_alias=AliasChoices("OCR_MIN_CHARS_PER_PAGE", "DOCLING_OCR_MIN_CHARS_PER_PAGE"),
|
||||
description=(
|
||||
"Chars-per-page floor below which an OCR'd PDF/image parse is treated as an OCR dropout rather than "
|
||||
"as content (long-running docling workers were observed returning zero characters for every "
|
||||
"scanned page after a long scanned PDF, with no error). docling retries once on a fresh full-page-OCR "
|
||||
"converter; both backends then fail loudly instead of indexing an empty document. 0 disables the "
|
||||
"guard."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("OCR_BACKEND", "OCR_ENGINE", mode="before")
|
||||
@classmethod
|
||||
def _normalize_ocr_choices(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,36 @@
|
||||
"""Admin-set usage quotas and the pricing that feeds their cost budgets."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class QuotaSettings(SettingsGroup):
|
||||
"""Quota window and the treatment of unpriced models."""
|
||||
|
||||
QUOTA_PERIOD: Literal["day", "week", "month"] = Field(
|
||||
default="month",
|
||||
description=(
|
||||
"Window every usage quota is measured over. Windows are calendar-aligned in UTC: "
|
||||
"a day starts at 00:00, a week on Monday, a month on the 1st."
|
||||
),
|
||||
)
|
||||
QUOTA_UNPRICED_RATE_PER_MILLION: Optional[list[float]] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Fallback `[input, output]` USD rates per 1M tokens for models that declare no price, "
|
||||
"e.g. `[0.5, 1.5]`. Unset, such calls are recorded at $0 and only count toward token quotas."
|
||||
),
|
||||
)
|
||||
@field_validator("QUOTA_UNPRICED_RATE_PER_MILLION")
|
||||
@classmethod
|
||||
def _two_non_negative_rates(cls, v: Optional[list[float]]) -> Optional[list[float]]:
|
||||
if v is None:
|
||||
return None
|
||||
if len(v) != 2 or any(rate < 0 for rate in v):
|
||||
raise ValueError("QUOTA_UNPRICED_RATE_PER_MILLION must be two non-negative numbers")
|
||||
return v
|
||||
@@ -0,0 +1,172 @@
|
||||
"""Render the settings reference page from the ``Settings`` definitions.
|
||||
|
||||
The page under ``docs/content/Deploying/Settings-Reference.mdx`` is generated
|
||||
from the field types, defaults and descriptions in this package, so the model
|
||||
is the single source of truth. Regenerate it after changing a setting::
|
||||
|
||||
python -m docsgpt.core.settings.reference --write
|
||||
|
||||
``--check`` exits non-zero when the checked-in page is stale; the test suite
|
||||
runs the same comparison.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import inspect
|
||||
import json
|
||||
import sys
|
||||
import types
|
||||
import typing
|
||||
from pathlib import Path
|
||||
from typing import Any, Literal, Optional, Union
|
||||
|
||||
from pydantic import AliasChoices
|
||||
from pydantic.fields import FieldInfo
|
||||
|
||||
from docsgpt.core.paths import home_dir
|
||||
from docsgpt.core.settings import SETTINGS_GROUPS, Settings
|
||||
|
||||
REFERENCE_PATH = Path("docs") / "content" / "Deploying" / "Settings-Reference.mdx"
|
||||
|
||||
HOME_PLACEHOLDER = "<DOCSGPT_HOME>"
|
||||
|
||||
_HEADER = """\
|
||||
---
|
||||
title: Settings Reference
|
||||
description: Every DocsGPT setting, grouped by domain, with its type, default and purpose.
|
||||
---
|
||||
|
||||
{/* GENERATED FILE. Do not edit by hand: run `python -m docsgpt.core.settings.reference --write`. */}
|
||||
|
||||
# Settings Reference
|
||||
|
||||
Every setting DocsGPT reads, generated from `docsgpt/core/settings/`. Each one is
|
||||
an environment variable of the same name, set in `.env` or the process
|
||||
environment; see [App Configuration](/Deploying/DocsGPT-Settings) for how the
|
||||
file is found and for worked examples. `<DOCSGPT_HOME>` below is the data home
|
||||
described there.
|
||||
"""
|
||||
|
||||
|
||||
def _mdx(text: str) -> str:
|
||||
"""Escape prose for MDX, where braces open expressions and ``<`` opens JSX."""
|
||||
return text.replace("{", "\\{").replace("}", "\\}").replace("<", "<")
|
||||
|
||||
|
||||
def _type_name(annotation: Any) -> str:
|
||||
origin = typing.get_origin(annotation)
|
||||
if origin in (Union, types.UnionType):
|
||||
args = [a for a in typing.get_args(annotation) if a is not type(None)]
|
||||
return " | ".join(_type_name(a) for a in args)
|
||||
if origin is Literal:
|
||||
return " | ".join(json.dumps(v) for v in typing.get_args(annotation))
|
||||
if origin is not None:
|
||||
name = getattr(origin, "__name__", str(origin))
|
||||
args = typing.get_args(annotation)
|
||||
return f"{name}[{', '.join(_type_name(arg) for arg in args)}]" if args else name
|
||||
return getattr(annotation, "__name__", str(annotation))
|
||||
|
||||
|
||||
def _default_text(field: FieldInfo) -> str:
|
||||
value = field.default_factory() if field.default_factory is not None else field.default
|
||||
if value is None:
|
||||
return "unset"
|
||||
if isinstance(value, bool):
|
||||
return "`true`" if value else "`false`"
|
||||
if isinstance(value, str):
|
||||
value = value.replace(str(home_dir()), HOME_PLACEHOLDER)
|
||||
return "`\"\"`" if value == "" else f"`{value}`"
|
||||
if isinstance(value, (list, dict)):
|
||||
return f"`{json.dumps(value)}`"
|
||||
return f"`{value}`"
|
||||
|
||||
|
||||
def _constraints(field: FieldInfo) -> list[str]:
|
||||
out = []
|
||||
for item in field.metadata:
|
||||
for attr, symbol in (("gt", ">"), ("ge", ">="), ("lt", "<"), ("le", "<=")):
|
||||
if hasattr(item, attr):
|
||||
out.append(f"{symbol} {getattr(item, attr)}")
|
||||
return out
|
||||
|
||||
|
||||
def _aliases(name: str, field: FieldInfo) -> list[str]:
|
||||
alias = field.validation_alias
|
||||
if isinstance(alias, AliasChoices):
|
||||
return [str(c) for c in alias.choices if str(c) != name]
|
||||
if isinstance(alias, str) and alias != name:
|
||||
return [alias]
|
||||
return []
|
||||
|
||||
|
||||
def _render_field(name: str, field: FieldInfo) -> str:
|
||||
facts = [f"Type `{_type_name(field.annotation)}`", f"default {_default_text(field)}"]
|
||||
constraints = _constraints(field)
|
||||
if constraints:
|
||||
# Code spans: a bare ``<=`` in MDX prose is parsed as the start of a JSX tag.
|
||||
facts.append("must be " + " and ".join(f"`{c}`" for c in constraints))
|
||||
aliases = _aliases(name, field)
|
||||
if aliases:
|
||||
facts.append("also read from " + ", ".join(f"`{a}`" for a in aliases))
|
||||
# The facts are code spans, which MDX leaves alone; only prose needs escaping.
|
||||
lines = [f"### `{name}`", "", ", ".join(facts) + "."]
|
||||
if field.deprecated:
|
||||
note = field.deprecated if isinstance(field.deprecated, str) else "This setting is deprecated."
|
||||
lines += ["", f"**Deprecated.** {_mdx(str(note))}"]
|
||||
if field.description:
|
||||
lines += ["", _mdx(field.description)]
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _group_intro(group: type) -> str:
|
||||
doc = inspect.getdoc(group) or ""
|
||||
return doc.split("\n\n", 1)[0].replace("\n", " ").strip()
|
||||
|
||||
|
||||
def render_reference() -> str:
|
||||
"""The full reference page as MDX text."""
|
||||
parts = [_HEADER]
|
||||
for title, group in SETTINGS_GROUPS:
|
||||
parts.append(f"\n## {title}\n")
|
||||
intro = _group_intro(group)
|
||||
if intro:
|
||||
parts.append(_mdx(intro) + "\n")
|
||||
for name in group.model_fields:
|
||||
parts.append(_render_field(name, Settings.model_fields[name]) + "\n")
|
||||
return "\n".join(parts).rstrip("\n") + "\n"
|
||||
|
||||
|
||||
def reference_path(root: Optional[Path] = None) -> Path:
|
||||
"""Where the generated page lives in a checkout; ``root`` defaults to the repository root."""
|
||||
if root is None:
|
||||
root = Path(__file__).resolve().parents[3]
|
||||
return root / REFERENCE_PATH
|
||||
|
||||
|
||||
def main(argv: Optional[list[str]] = None) -> int:
|
||||
parser = argparse.ArgumentParser(description=__doc__.split("\n\n", 1)[0])
|
||||
action = parser.add_mutually_exclusive_group()
|
||||
action.add_argument("--write", action="store_true", help="write the page into the docs tree")
|
||||
action.add_argument("--check", action="store_true", help="exit 1 if the checked-in page is stale")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
rendered = render_reference()
|
||||
path = reference_path()
|
||||
if args.write:
|
||||
path.write_text(rendered, encoding="utf-8")
|
||||
print(f"wrote {path}")
|
||||
return 0
|
||||
if args.check:
|
||||
current = path.read_text(encoding="utf-8") if path.exists() else ""
|
||||
if current != rendered:
|
||||
print(f"{path} is stale; run: python -m docsgpt.core.settings.reference --write", file=sys.stderr)
|
||||
return 1
|
||||
print(f"{path} is up to date")
|
||||
return 0
|
||||
sys.stdout.write(rendered)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -0,0 +1,47 @@
|
||||
"""Retrieval strategy and GraphRAG."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class RetrievalSettings(SettingsGroup):
|
||||
"""Which vector store answers searches and how retrieval fans out across sources."""
|
||||
|
||||
VECTOR_STORE: Literal["faiss", "elasticsearch", "mongodb", "qdrant", "milvus", "pgvector"] = Field(
|
||||
default="faiss", description="Vector store backend."
|
||||
)
|
||||
RETRIEVAL_MAX_PARALLEL_SOURCES: int = Field(
|
||||
default=4,
|
||||
ge=1,
|
||||
description="Concurrent per-source searches in one retrieval; the query is embedded once and shared.",
|
||||
)
|
||||
PER_SOURCE_RETRIEVAL_ENABLED: bool = Field(
|
||||
default=True,
|
||||
description="Kill-switch for per-source retrieval dispatch; False collapses to a single retriever.",
|
||||
)
|
||||
GRAPHRAG_ENABLED: bool = Field(default=False, description="Gates graph-aware ingestion and retrieval.")
|
||||
GRAPHRAG_EXTRACTION_MODEL: Optional[str] = Field(
|
||||
default=None, description="Model for ingest-time graph extraction; unset reuses LLM_PROVIDER/LLM_NAME."
|
||||
)
|
||||
GRAPHRAG_MAX_CHUNKS_FOR_EXTRACTION: int = Field(
|
||||
default=2000, ge=0, description="Hard cap on chunks extracted per source (cost control); 0 extracts nothing."
|
||||
)
|
||||
GRAPHRAG_EXTRACTION_WORKERS: int = Field(
|
||||
default=8,
|
||||
ge=1,
|
||||
le=32,
|
||||
description=(
|
||||
"Concurrent extraction calls during ingest. Model calls run in parallel while "
|
||||
"graph writes stay serial, so ordering and idempotency are unchanged; 1 is fully serial."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("VECTOR_STORE", mode="before")
|
||||
@classmethod
|
||||
def _normalize_vector_store(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,93 @@
|
||||
"""Code-execution sandbox: the Jupyter gateway runner or Daytona Cloud."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class SandboxSettings(SettingsGroup):
|
||||
"""The app is a CLIENT of an always-on runner; defaults are safe so app import never fails unconfigured."""
|
||||
|
||||
SANDBOX_BACKEND: Literal["jupyter", "daytona"] = Field(
|
||||
default="jupyter", description="Sandbox backend: jupyter (self-host) or daytona (Daytona Cloud)."
|
||||
)
|
||||
SANDBOX_GATEWAY_URL: str = Field(
|
||||
default="http://localhost:8888",
|
||||
description="URL of the Jupyter Kernel Gateway runner (the docsgpt-sandbox service).",
|
||||
)
|
||||
SANDBOX_GATEWAY_AUTH_TOKEN: Optional[str] = Field(default=None, description="Gateway auth token, if set.")
|
||||
SANDBOX_KERNEL_NAME: str = Field(
|
||||
default="docsgpt-python",
|
||||
description=(
|
||||
"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_MAX_TTL: int = Field(default=1200, description="Hard cap (s) on agent-selectable keep-alive TTL.")
|
||||
SANDBOX_MAX_SESSIONS: int = Field(
|
||||
default=32,
|
||||
description=(
|
||||
"Concurrent live sessions per process, backend-agnostic; at the cap an LRU-idle session is evicted. "
|
||||
"0 or negative disables the cap."
|
||||
),
|
||||
)
|
||||
SANDBOX_EXEC_TIMEOUT: int = Field(default=60, description="Default wall-clock cap (s) per exec call.")
|
||||
SANDBOX_HTTP_TIMEOUT: int = Field(
|
||||
default=10, description="Fixed cap (s) for REST control calls (create/delete/alive/interrupt)."
|
||||
)
|
||||
SANDBOX_MAX_OUTPUT_BYTES: int = Field(
|
||||
default=8 * 1024 * 1024, description="Cap on buffered stdout+stderr per exec."
|
||||
)
|
||||
SANDBOX_MAX_FILE_BYTES: int = Field(
|
||||
default=10 * 1024 * 1024, description="Cap on get_file size routed through stdout."
|
||||
)
|
||||
SANDBOX_MAX_INPUT_BYTES: int = Field(
|
||||
default=25 * 1024 * 1024, description="Cap on an input document staged into a sandbox session."
|
||||
)
|
||||
# Runner container caps, consumed by the docsgpt-sandbox compose service, not the app.
|
||||
SANDBOX_MEMORY: str = Field(
|
||||
default="1g",
|
||||
description=(
|
||||
"Docker mem_limit for the runner container. Consumed by the docsgpt-sandbox compose service, not "
|
||||
"the app; part of the untrusted-code security boundary."
|
||||
),
|
||||
)
|
||||
SANDBOX_CPUS: str = Field(
|
||||
default="1.0",
|
||||
description=(
|
||||
"Docker CPU quota for the runner container. Consumed by the docsgpt-sandbox compose service, not "
|
||||
"the app; part of the untrusted-code security boundary."
|
||||
),
|
||||
)
|
||||
|
||||
# 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] = Field(default=None, description="Daytona Cloud API key (secret).")
|
||||
DAYTONA_API_URL: Optional[str] = Field(
|
||||
default=None, description="Override for the Daytona API base URL, if self-targeting."
|
||||
)
|
||||
DAYTONA_TARGET: Optional[str] = Field(default=None, description='Daytona region/target, e.g. "us".')
|
||||
DAYTONA_SNAPSHOT: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Image for new sandboxes; render libs via scripts/build_daytona_snapshot.py.",
|
||||
)
|
||||
DAYTONA_LANGUAGE: str = Field(default="python", description="Default runtime language for created sandboxes.")
|
||||
DAYTONA_AUTO_STOP_INTERVAL: int = Field(
|
||||
default=15, ge=0, description="Minutes idle before Daytona auto-stops a sandbox (0 disables)."
|
||||
)
|
||||
DAYTONA_AUTO_DELETE_INTERVAL: int = Field(
|
||||
default=60, ge=-1, description="Minutes after stop before Daytona auto-deletes a sandbox (-1 disables)."
|
||||
)
|
||||
DAYTONA_MAX_SANDBOXES: int = Field(
|
||||
default=50, description="Cap on concurrent live Daytona sandboxes (cost-DoS guard)."
|
||||
)
|
||||
|
||||
@field_validator("SANDBOX_BACKEND", mode="before")
|
||||
@classmethod
|
||||
def _normalize_sandbox_backend(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,28 @@
|
||||
"""Scheduled agent runs (see scheduler.md)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class SchedulerSettings(SettingsGroup):
|
||||
"""Cadence, quotas and timeouts of scheduled runs."""
|
||||
|
||||
SCHEDULE_DISPATCHER_INTERVAL: int = Field(
|
||||
default=30, description="Seconds between dispatcher passes that enqueue due schedules."
|
||||
)
|
||||
SCHEDULE_MIN_INTERVAL: int = Field(default=900, description="Smallest allowed recurrence interval in seconds.")
|
||||
SCHEDULE_MAX_PER_USER: int = Field(default=50, description="Cap on schedules a user may own.")
|
||||
SCHEDULE_RUN_TIMEOUT: int = Field(default=600, description="Wall-clock cap on one scheduled run, in seconds.")
|
||||
SCHEDULE_MISFIRE_GRACE: int = Field(
|
||||
default=60, description="Seconds past the due time within which a missed run still fires."
|
||||
)
|
||||
SCHEDULE_AUTOPAUSE_FAILURES: int = Field(
|
||||
default=3, description="Consecutive failures after which a schedule is paused automatically."
|
||||
)
|
||||
SCHEDULE_ONCE_MAX_HORIZON: int = Field(
|
||||
default=31_536_000, description="How far ahead a one-off run may be scheduled, in seconds (one year)."
|
||||
)
|
||||
SCHEDULE_RUN_OUTPUT_RETENTION_DAYS: int = Field(default=90, gt=0, description="Days scheduled-run output is kept.")
|
||||
@@ -0,0 +1,46 @@
|
||||
"""The API process itself."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class ServerSettings(SettingsGroup):
|
||||
"""Serving the UI, public URLs, and process-level knobs of the API server."""
|
||||
|
||||
DEPLOYMENT_TYPE: Optional[str] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Deployment class, e.g. cloud or production. A production class refuses to run without a "
|
||||
"configured JWT_SECRET_KEY instead of generating a local one on disk."
|
||||
),
|
||||
)
|
||||
SERVE_UI: bool = Field(
|
||||
default=True, description="Serve the web UI shipped in the package (docsgpt/static) from the API process."
|
||||
)
|
||||
FLASK_DEBUG_MODE: bool = Field(default=False, description="Run Flask in debug mode.")
|
||||
VERSION_CHECK: bool = Field(default=True, description="Anonymous startup version check for security issues.")
|
||||
PUBLIC_API_BASE_URL: Optional[str] = Field(
|
||||
default=None, description="Public base URL for user-facing endpoint references in prompts."
|
||||
)
|
||||
GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS: int = Field(
|
||||
default=30,
|
||||
description=(
|
||||
"Bounds uvicorn's shutdown drain (uvicorn_worker doesn't forward --graceful-timeout). Keep below the "
|
||||
"gunicorn --timeout (180) watchdog. Used by BoundedDrainUvicornWorker."
|
||||
),
|
||||
)
|
||||
WSGI_THREADPOOL_WORKERS: int = Field(
|
||||
default=96, ge=1, description="Threads serving the WSGI (Flask) part of the app under the ASGI server."
|
||||
)
|
||||
V1_SESSION_TTL_SECONDS: int = Field(
|
||||
default=24 * 60 * 60,
|
||||
description=(
|
||||
"Lets OpenAI-compatible clients identify a logical chat by session header, which chat-completions "
|
||||
"itself has no field for; TTL of that session mapping."
|
||||
),
|
||||
)
|
||||
@@ -0,0 +1,33 @@
|
||||
"""Text-to-speech and speech-to-text."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class SpeechSettings(SettingsGroup):
|
||||
"""Voice providers and transcription options."""
|
||||
|
||||
TTS_PROVIDER: Literal["google_tts", "elevenlabs", "none"] = Field(
|
||||
default="google_tts", description="Text-to-speech provider; none switches it off."
|
||||
)
|
||||
ELEVENLABS_API_KEY: Optional[str] = Field(default=None, description="ElevenLabs API key.")
|
||||
STT_PROVIDER: Literal["openai", "faster_whisper", "none"] = Field(
|
||||
default="openai", description="Speech-to-text provider; none switches it off."
|
||||
)
|
||||
OPENAI_STT_MODEL: str = Field(default="gpt-4o-mini-transcribe", description="OpenAI transcription model.")
|
||||
STT_LANGUAGE: Optional[str] = Field(default=None, description="Language hint for transcription; unset auto-detects.")
|
||||
STT_MAX_FILE_SIZE_MB: int = Field(default=50, description="Cap on an audio file accepted for transcription.")
|
||||
STT_ENABLE_TIMESTAMPS: bool = Field(default=False, description="Return word/segment timestamps.")
|
||||
STT_ENABLE_DIARIZATION: bool = Field(default=False, description="Label speakers in the transcript.")
|
||||
|
||||
@field_validator("TTS_PROVIDER", "STT_PROVIDER", mode="before")
|
||||
@classmethod
|
||||
def _normalize_speech_providers(cls, v):
|
||||
# An empty value has always meant "off"; keep that spelling working.
|
||||
v = normalize_choice(v)
|
||||
return "none" if v == "" else v
|
||||
@@ -0,0 +1,57 @@
|
||||
"""Where uploaded files and generated artifacts are stored."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup, normalize_choice
|
||||
|
||||
|
||||
class StorageSettings(SettingsGroup):
|
||||
"""Local disk or an S3-compatible bucket, and how download URLs are produced."""
|
||||
|
||||
STORAGE_TYPE: Literal["local", "s3"] = Field(default="local", description="File storage backend.")
|
||||
URL_STRATEGY: Literal["backend", "s3"] = Field(
|
||||
default="backend",
|
||||
description="How download links are produced: backend (streamed through the API) or s3 (presigned URLs).",
|
||||
)
|
||||
|
||||
# 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 = Field(default="docsgpt-test-bucket", description="Bucket name.")
|
||||
S3_ENDPOINT_URL: Optional[str] = Field(
|
||||
default=None, description="Custom endpoint for S3-compatible services (MinIO, R2, B2, Spaces); omit for AWS."
|
||||
)
|
||||
S3_ACCESS_KEY_ID: Optional[str] = Field(default=None, description="Access key id.")
|
||||
S3_SECRET_ACCESS_KEY: Optional[str] = Field(default=None, description="Secret access key.")
|
||||
S3_REGION: Optional[str] = Field(default=None, description='AWS region; use "auto" for Cloudflare R2.')
|
||||
S3_PATH_STYLE: bool = Field(
|
||||
default=False, description="Path-style addressing (required by most non-AWS services)."
|
||||
)
|
||||
|
||||
# Legacy AWS credentials from the retired SageMaker provider.
|
||||
SAGEMAKER_REGION: Optional[str] = Field(
|
||||
default=None,
|
||||
deprecated="Set S3_REGION instead; the SAGEMAKER_* fallback will be removed.",
|
||||
description="Legacy AWS region from the retired SageMaker provider; deprecated fallback for S3_REGION.",
|
||||
)
|
||||
SAGEMAKER_ACCESS_KEY: Optional[str] = Field(
|
||||
default=None,
|
||||
deprecated="Set S3_ACCESS_KEY_ID instead; the SAGEMAKER_* fallback will be removed.",
|
||||
description="Legacy AWS access key from the retired SageMaker provider; deprecated fallback for S3_ACCESS_KEY_ID.",
|
||||
)
|
||||
SAGEMAKER_SECRET_KEY: Optional[str] = Field(
|
||||
default=None,
|
||||
deprecated="Set S3_SECRET_ACCESS_KEY instead; the SAGEMAKER_* fallback will be removed.",
|
||||
description=(
|
||||
"Legacy AWS secret key from the retired SageMaker provider; deprecated fallback for "
|
||||
"S3_SECRET_ACCESS_KEY."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("STORAGE_TYPE", "URL_STRATEGY", mode="before")
|
||||
@classmethod
|
||||
def _normalize_storage_choices(cls, v):
|
||||
return normalize_choice(v)
|
||||
@@ -0,0 +1,84 @@
|
||||
"""Connection settings for each vector store backend."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
from docsgpt.core.db_uri import normalize_pgvector_connection_string
|
||||
from docsgpt.core.paths import home_dir
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class VectorStoreSettings(SettingsGroup):
|
||||
"""Per-backend connection details; only the backend named by VECTOR_STORE is read."""
|
||||
|
||||
MONGO_URI: Optional[str] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Only consulted when VECTOR_STORE=mongodb or when running scripts/db/backfill.py; user data lives "
|
||||
"in Postgres."
|
||||
),
|
||||
)
|
||||
|
||||
# Elasticsearch.
|
||||
ELASTIC_CLOUD_ID: Optional[str] = Field(default=None, description="Elastic Cloud id.")
|
||||
ELASTIC_USERNAME: Optional[str] = Field(default=None, description="Elasticsearch username.")
|
||||
ELASTIC_PASSWORD: Optional[str] = Field(default=None, description="Elasticsearch password.")
|
||||
ELASTIC_URL: Optional[str] = Field(default=None, description="Elasticsearch URL.")
|
||||
ELASTIC_INDEX: str = Field(default="docsgpt", description="Elasticsearch index name.")
|
||||
|
||||
# Qdrant.
|
||||
QDRANT_COLLECTION_NAME: str = Field(default="docsgpt", description="Qdrant collection name.")
|
||||
QDRANT_LOCATION: Optional[str] = Field(default=None, description="Qdrant location (':memory:' or a URL).")
|
||||
QDRANT_URL: Optional[str] = Field(default=None, description="Qdrant server URL.")
|
||||
QDRANT_PORT: int = Field(default=6333, description="Qdrant REST port.")
|
||||
QDRANT_GRPC_PORT: int = Field(default=6334, description="Qdrant gRPC port.")
|
||||
QDRANT_PREFER_GRPC: bool = Field(default=False, description="Use gRPC instead of REST where possible.")
|
||||
QDRANT_HTTPS: Optional[bool] = Field(default=None, description="Use HTTPS for the Qdrant connection.")
|
||||
QDRANT_API_KEY: Optional[str] = Field(default=None, description="Qdrant API key.")
|
||||
QDRANT_PREFIX: Optional[str] = Field(default=None, description="URL prefix for a Qdrant behind a proxy.")
|
||||
QDRANT_TIMEOUT: Optional[float] = Field(default=None, description="Qdrant request timeout in seconds.")
|
||||
QDRANT_HOST: Optional[str] = Field(default=None, description="Qdrant host (alternative to QDRANT_URL).")
|
||||
QDRANT_PATH: Optional[str] = Field(default=None, description="Path for an embedded on-disk Qdrant.")
|
||||
QDRANT_DISTANCE_FUNC: str = Field(default="Cosine", description="Qdrant distance function.")
|
||||
|
||||
# PGVector.
|
||||
PGVECTOR_CONNECTION_STRING: Optional[str] = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"pgvector connection string. postgres://, postgresql:// and postgresql+psycopg:// are all accepted "
|
||||
"and normalized internally for psycopg.connect(). Unset falls back to POSTGRES_URI."
|
||||
),
|
||||
)
|
||||
PGVECTOR_POOL_MAX_SIZE: int = Field(
|
||||
default=8, ge=0, description="Per-process connection pool size; 0 uses one direct connection per store."
|
||||
)
|
||||
PGVECTOR_IVFFLAT_PROBES: Optional[int] = Field(
|
||||
default=None,
|
||||
description="IVFFlat probes; unset derives sqrt(lists) from the index. Higher means better recall, more scan.",
|
||||
)
|
||||
|
||||
# Milvus.
|
||||
MILVUS_COLLECTION_NAME: str = Field(default="docsgpt", description="Milvus collection name.")
|
||||
MILVUS_URI: Optional[str] = Field(
|
||||
default_factory=lambda: str(home_dir() / "milvus_local.db"),
|
||||
description=(
|
||||
"Milvus server URI. The default is a milvus-lite (embedded) database file under the data home, "
|
||||
"like the other local stores."
|
||||
),
|
||||
)
|
||||
MILVUS_TOKEN: str = Field(default="", description="Milvus auth token.")
|
||||
|
||||
# LanceDB.
|
||||
LANCEDB_PATH: str = Field(
|
||||
default_factory=lambda: str(home_dir() / "data" / "lancedb"),
|
||||
description="LanceDB local data directory.",
|
||||
)
|
||||
LANCEDB_TABLE_NAME: str = Field(default="docsgpts", description="LanceDB table for stored vectors.")
|
||||
|
||||
@field_validator("PGVECTOR_CONNECTION_STRING", mode="before")
|
||||
@classmethod
|
||||
def _normalize_pgvector_connection_string(cls, v):
|
||||
return normalize_pgvector_connection_string(v)
|
||||
@@ -0,0 +1,39 @@
|
||||
"""Celery broker, result backend and worker process limits."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from docsgpt.core.settings._shared import SettingsGroup
|
||||
|
||||
|
||||
class WorkerSettings(SettingsGroup):
|
||||
"""How background tasks are queued and how worker processes are recycled."""
|
||||
|
||||
CELERY_BROKER_URL: str = Field(default="redis://localhost:6379/0", description="Celery broker URL.")
|
||||
CELERY_RESULT_BACKEND: str = Field(default="redis://localhost:6379/1", description="Celery result backend URL.")
|
||||
CELERY_WORKER_PREFETCH_MULTIPLIER: int = Field(
|
||||
default=1, description="Tasks prefetched per worker process; 1 caps SIGKILL loss to one task."
|
||||
)
|
||||
CELERY_VISIBILITY_TIMEOUT: int = Field(
|
||||
default=3600,
|
||||
gt=0,
|
||||
description=(
|
||||
"Broker visibility timeout in seconds. Must exceed the longest legitimate task runtime but stay "
|
||||
"short enough that SIGKILLed tasks redeliver promptly."
|
||||
),
|
||||
)
|
||||
CELERY_WORKER_MAX_MEMORY_PER_CHILD: int = Field(
|
||||
default=4194304,
|
||||
ge=0,
|
||||
description=(
|
||||
"Recycle a prefork child past this resident size in KB; backstops docling/torch heap growth. "
|
||||
"Checked between tasks, so it does not bound the peak within one. 0 disables."
|
||||
),
|
||||
)
|
||||
CELERY_WORKER_MAX_TASKS_PER_CHILD: int = Field(
|
||||
default=0, ge=0, description="Recycle a worker child after N tasks; 0 disables."
|
||||
)
|
||||
API_URL: str = Field(
|
||||
default="http://localhost:7091", description="Backend URL the Celery worker calls back into."
|
||||
)
|
||||
Reference in new issue
Block a user