mirror of
https://github.com/zvx-echo6/meshai.git
synced 2026-05-21 15:14:45 +02:00
fix: Override LLM brevity for mesh questions — give detailed data responses
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
parent
69f3d5d284
commit
45630f2cc6
2 changed files with 359 additions and 353 deletions
689
meshai/config.py
689
meshai/config.py
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@ -1,344 +1,345 @@
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"""Configuration management for MeshAI."""
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import logging
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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import yaml
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_config_logger = logging.getLogger(__name__)
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@dataclass
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class BotConfig:
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"""Bot identity and trigger settings."""
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name: str = "ai"
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owner: str = ""
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respond_to_dms: bool = True
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filter_bbs_protocols: bool = True
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@dataclass
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class ConnectionConfig:
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"""Meshtastic connection settings."""
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type: str = "serial" # serial or tcp
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serial_port: str = "/dev/ttyUSB0"
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tcp_host: str = "192.168.1.100"
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tcp_port: int = 4403
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@dataclass
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class ResponseConfig:
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"""Response behavior settings."""
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delay_min: float = 2.2
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delay_max: float = 3.0
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max_length: int = 150
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max_messages: int = 2
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@dataclass
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class HistoryConfig:
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"""Conversation history settings."""
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database: str = "conversations.db"
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max_messages_per_user: int = 50
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conversation_timeout: int = 86400 # 24 hours
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# Cleanup settings
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auto_cleanup: bool = True
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cleanup_interval_hours: int = 24
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max_age_days: int = 30 # Delete conversations older than this
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@dataclass
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class MemoryConfig:
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"""Rolling summary memory settings."""
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enabled: bool = True # Enable memory optimization
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window_size: int = 4 # Recent message pairs to keep in full
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summarize_threshold: int = 8 # Messages before re-summarizing
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@dataclass
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class ContextConfig:
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"""Passive mesh context settings."""
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enabled: bool = True
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observe_channels: list[int] = field(default_factory=list) # Empty = all channels
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ignore_nodes: list[str] = field(default_factory=list) # Node IDs to ignore
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max_age: int = 2_592_000 # 30 days in seconds
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max_context_items: int = 20 # Max observations injected into LLM context
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@dataclass
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class CommandsConfig:
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"""Command settings."""
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enabled: bool = True
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prefix: str = "!"
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disabled_commands: list[str] = field(default_factory=list)
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custom_commands: dict = field(default_factory=dict)
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@dataclass
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class LLMConfig:
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"""LLM backend settings."""
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backend: str = "openai" # openai, anthropic, google
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api_key: str = ""
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base_url: str = "https://api.openai.com/v1"
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model: str = "gpt-4o-mini"
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timeout: int = 30
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system_prompt: str = (
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"YOUR COMMANDS (handled directly by you via DM):\n"
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"!help — List available commands.\n"
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"!ping — Connectivity test, responds with pong.\n"
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"!status — Shows your version, uptime, user count, and message count.\n"
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"!weather [location] — Weather lookup using Open-Meteo API.\n"
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"!reset — Clears conversation history and memory.\n"
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"!clear — Same as !reset.\n\n"
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"YOUR ARCHITECTURE: Modular Python — pluggable LLM backends (OpenAI, Anthropic, "
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"Google, local), per-user SQLite conversation history, rolling summary memory, "
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"passive mesh context buffer (observes channel traffic), smart chunking for LoRa "
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"message limits, prompt injection defense, advBBS filtering.\n\n"
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"RESPONSE RULES:\n"
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"- Keep responses very brief — 1-2 short sentences, under 300 characters. Only give longer answers if the user explicitly asks for detail or explanation.\n"
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"- Be concise but friendly. No markdown formatting.\n"
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"- If asked about mesh activity and no recent traffic is shown, say you haven't "
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"observed any yet.\n"
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"- When asked about yourself or commands, answer conversationally. Don't dump lists.\n"
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"- You are part of the freq51 mesh in the Twin Falls, Idaho area."
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)
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use_system_prompt: bool = True # Toggle to disable sending system prompt
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web_search: bool = False # Enable web search (Open WebUI feature)
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google_grounding: bool = False # Enable Google Search grounding (Gemini only)
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@dataclass
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class OpenMeteoConfig:
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"""Open-Meteo weather provider settings."""
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url: str = "https://api.open-meteo.com/v1"
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@dataclass
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class WttrConfig:
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"""wttr.in weather provider settings."""
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url: str = "https://wttr.in"
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@dataclass
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class WeatherConfig:
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"""Weather command settings."""
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primary: str = "openmeteo" # openmeteo, wttr, llm
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fallback: str = "llm" # openmeteo, wttr, llm, none
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default_location: str = ""
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openmeteo: OpenMeteoConfig = field(default_factory=OpenMeteoConfig)
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wttr: WttrConfig = field(default_factory=WttrConfig)
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@dataclass
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class MeshMonitorConfig:
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"""MeshMonitor trigger sync settings."""
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enabled: bool = False
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url: str = "" # e.g., http://100.64.0.11:3333
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inject_into_prompt: bool = True # Tell LLM about MeshMonitor commands
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refresh_interval: int = 300 # Seconds between refreshes
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@dataclass
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class KnowledgeConfig:
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"""FTS5 knowledge base settings."""
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enabled: bool = False
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db_path: str = ""
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top_k: int = 5
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@dataclass
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class MeshSourceConfig:
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"""Configuration for a mesh data source."""
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name: str = ""
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type: str = "" # "meshview" or "meshmonitor"
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url: str = ""
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api_token: str = "" # MeshMonitor only, supports ${ENV_VAR}
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refresh_interval: int = 300
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enabled: bool = True
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@dataclass
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class RegionAnchor:
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"""A fixed region anchor point."""
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name: str = ""
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lat: float = 0.0
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lon: float = 0.0
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@dataclass
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class MeshIntelligenceConfig:
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"""Mesh intelligence and health scoring settings."""
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enabled: bool = False
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regions: list[RegionAnchor] = field(default_factory=list) # Fixed region anchors
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locality_radius_miles: float = 8.0 # Radius for locality clustering within regions
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offline_threshold_hours: int = 24 # Hours before node considered offline
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packet_threshold: int = 500 # Non-text packets per 24h to flag
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battery_warning_percent: int = 20 # Battery level for warnings
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@dataclass
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class Config:
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"""Main configuration container."""
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bot: BotConfig = field(default_factory=BotConfig)
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connection: ConnectionConfig = field(default_factory=ConnectionConfig)
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response: ResponseConfig = field(default_factory=ResponseConfig)
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history: HistoryConfig = field(default_factory=HistoryConfig)
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memory: MemoryConfig = field(default_factory=MemoryConfig)
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context: ContextConfig = field(default_factory=ContextConfig)
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commands: CommandsConfig = field(default_factory=CommandsConfig)
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llm: LLMConfig = field(default_factory=LLMConfig)
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weather: WeatherConfig = field(default_factory=WeatherConfig)
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meshmonitor: MeshMonitorConfig = field(default_factory=MeshMonitorConfig)
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knowledge: KnowledgeConfig = field(default_factory=KnowledgeConfig)
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mesh_sources: list[MeshSourceConfig] = field(default_factory=list)
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mesh_intelligence: MeshIntelligenceConfig = field(default_factory=MeshIntelligenceConfig)
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_config_path: Optional[Path] = field(default=None, repr=False)
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def resolve_api_key(self) -> str:
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"""Resolve API key from config or environment."""
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if self.llm.api_key:
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# Check if it's an env var reference like ${LLM_API_KEY}
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if self.llm.api_key.startswith("${") and self.llm.api_key.endswith("}"):
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env_var = self.llm.api_key[2:-1]
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return os.environ.get(env_var, "")
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return self.llm.api_key
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# Fall back to common env vars
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for env_var in ["LLM_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"]:
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if value := os.environ.get(env_var):
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return value
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return ""
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def _dict_to_dataclass(cls, data: dict):
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"""Recursively convert dict to dataclass, handling nested structures."""
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if data is None:
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return cls()
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field_types = {f.name: f.type for f in cls.__dataclass_fields__.values()}
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kwargs = {}
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for key, value in data.items():
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if key.startswith("_"):
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continue
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if key not in field_types:
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continue
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field_type = field_types[key]
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# Handle nested dataclasses
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if hasattr(field_type, "__dataclass_fields__") and isinstance(value, dict):
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kwargs[key] = _dict_to_dataclass(field_type, value)
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# Handle list of MeshSourceConfig
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elif key == "mesh_sources" and isinstance(value, list):
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kwargs[key] = [
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_dict_to_dataclass(MeshSourceConfig, item)
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if isinstance(item, dict) else item
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for item in value
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]
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# Handle list of RegionAnchor
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elif key == "regions" and isinstance(value, list):
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kwargs[key] = [
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_dict_to_dataclass(RegionAnchor, item)
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if isinstance(item, dict) else item
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for item in value
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]
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else:
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kwargs[key] = value
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return cls(**kwargs)
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def _dataclass_to_dict(obj) -> dict:
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"""Recursively convert dataclass to dict for YAML serialization."""
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if not hasattr(obj, "__dataclass_fields__"):
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return obj
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result = {}
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for field_name in obj.__dataclass_fields__:
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if field_name.startswith("_"):
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continue
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value = getattr(obj, field_name)
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if hasattr(value, "__dataclass_fields__"):
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result[field_name] = _dataclass_to_dict(value)
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elif isinstance(value, list):
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# Handle list of dataclasses (like mesh_sources)
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result[field_name] = [
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_dataclass_to_dict(item) if hasattr(item, "__dataclass_fields__") else item
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for item in value
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]
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else:
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result[field_name] = value
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return result
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def load_config(config_path: Optional[Path] = None) -> Config:
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"""Load configuration from YAML file.
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Args:
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config_path: Path to config file. Defaults to ./config.yaml
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Returns:
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Config object with loaded settings
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"""
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if config_path is None:
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config_path = Path("config.yaml")
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config_path = Path(config_path)
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if not config_path.exists():
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# Return default config if file doesn't exist
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config = Config()
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config._config_path = config_path
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return config
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with open(config_path, "r") as f:
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data = yaml.safe_load(f) or {}
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config = _dict_to_dataclass(Config, data)
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config._config_path = config_path
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return config
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def save_config(config: Config, config_path: Optional[Path] = None) -> None:
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"""Save configuration to YAML file.
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Args:
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config: Config object to save
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config_path: Path to save to. Uses config._config_path if not specified
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"""
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if config_path is None:
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config_path = config._config_path or Path("config.yaml")
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config_path = Path(config_path)
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data = _dataclass_to_dict(config)
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# Add header comment
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header = "# MeshAI Configuration\n# Generated by meshai --config\n\n"
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with open(config_path, "w") as f:
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f.write(header)
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yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
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"""Configuration management for MeshAI."""
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import logging
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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import yaml
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_config_logger = logging.getLogger(__name__)
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@dataclass
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class BotConfig:
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"""Bot identity and trigger settings."""
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name: str = "ai"
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owner: str = ""
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respond_to_dms: bool = True
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filter_bbs_protocols: bool = True
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@dataclass
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class ConnectionConfig:
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"""Meshtastic connection settings."""
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type: str = "serial" # serial or tcp
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serial_port: str = "/dev/ttyUSB0"
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tcp_host: str = "192.168.1.100"
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tcp_port: int = 4403
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@dataclass
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class ResponseConfig:
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"""Response behavior settings."""
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delay_min: float = 2.2
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delay_max: float = 3.0
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max_length: int = 150
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max_messages: int = 2
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@dataclass
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class HistoryConfig:
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"""Conversation history settings."""
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database: str = "conversations.db"
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max_messages_per_user: int = 50
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conversation_timeout: int = 86400 # 24 hours
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# Cleanup settings
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auto_cleanup: bool = True
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cleanup_interval_hours: int = 24
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max_age_days: int = 30 # Delete conversations older than this
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@dataclass
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class MemoryConfig:
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"""Rolling summary memory settings."""
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enabled: bool = True # Enable memory optimization
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window_size: int = 4 # Recent message pairs to keep in full
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summarize_threshold: int = 8 # Messages before re-summarizing
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|
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@dataclass
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class ContextConfig:
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"""Passive mesh context settings."""
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|
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enabled: bool = True
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observe_channels: list[int] = field(default_factory=list) # Empty = all channels
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ignore_nodes: list[str] = field(default_factory=list) # Node IDs to ignore
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max_age: int = 2_592_000 # 30 days in seconds
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max_context_items: int = 20 # Max observations injected into LLM context
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|
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|
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@dataclass
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class CommandsConfig:
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"""Command settings."""
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enabled: bool = True
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prefix: str = "!"
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disabled_commands: list[str] = field(default_factory=list)
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custom_commands: dict = field(default_factory=dict)
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|
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|
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@dataclass
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class LLMConfig:
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"""LLM backend settings."""
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backend: str = "openai" # openai, anthropic, google
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api_key: str = ""
|
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base_url: str = "https://api.openai.com/v1"
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model: str = "gpt-4o-mini"
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timeout: int = 30
|
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|
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system_prompt: str = (
|
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"YOUR COMMANDS (handled directly by you via DM):\n"
|
||||
"!help — List available commands.\n"
|
||||
"!ping — Connectivity test, responds with pong.\n"
|
||||
"!status — Shows your version, uptime, user count, and message count.\n"
|
||||
"!weather [location] — Weather lookup using Open-Meteo API.\n"
|
||||
"!reset — Clears conversation history and memory.\n"
|
||||
"!clear — Same as !reset.\n\n"
|
||||
"YOUR ARCHITECTURE: Modular Python — pluggable LLM backends (OpenAI, Anthropic, "
|
||||
"Google, local), per-user SQLite conversation history, rolling summary memory, "
|
||||
"passive mesh context buffer (observes channel traffic), smart chunking for LoRa "
|
||||
"message limits, prompt injection defense, advBBS filtering.\n\n"
|
||||
"RESPONSE RULES:\n"
|
||||
"- For casual conversation, keep responses brief (1-2 sentences).\n"
|
||||
"- For mesh health questions, give detailed data-driven responses.\n"
|
||||
"- Be concise but friendly. No markdown formatting.\n"
|
||||
"- If asked about mesh activity and no recent traffic is shown, say you haven't "
|
||||
"observed any yet.\n"
|
||||
"- When asked about yourself or commands, answer conversationally. Don't dump lists.\n"
|
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"- You are part of the freq51 mesh in the Twin Falls, Idaho area."
|
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)
|
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use_system_prompt: bool = True # Toggle to disable sending system prompt
|
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web_search: bool = False # Enable web search (Open WebUI feature)
|
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google_grounding: bool = False # Enable Google Search grounding (Gemini only)
|
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|
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@dataclass
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class OpenMeteoConfig:
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"""Open-Meteo weather provider settings."""
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url: str = "https://api.open-meteo.com/v1"
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|
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@dataclass
|
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class WttrConfig:
|
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"""wttr.in weather provider settings."""
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|
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url: str = "https://wttr.in"
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@dataclass
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class WeatherConfig:
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"""Weather command settings."""
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primary: str = "openmeteo" # openmeteo, wttr, llm
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fallback: str = "llm" # openmeteo, wttr, llm, none
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default_location: str = ""
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openmeteo: OpenMeteoConfig = field(default_factory=OpenMeteoConfig)
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wttr: WttrConfig = field(default_factory=WttrConfig)
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@dataclass
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class MeshMonitorConfig:
|
||||
"""MeshMonitor trigger sync settings."""
|
||||
|
||||
enabled: bool = False
|
||||
url: str = "" # e.g., http://100.64.0.11:3333
|
||||
inject_into_prompt: bool = True # Tell LLM about MeshMonitor commands
|
||||
refresh_interval: int = 300 # Seconds between refreshes
|
||||
|
||||
|
||||
@dataclass
|
||||
class KnowledgeConfig:
|
||||
"""FTS5 knowledge base settings."""
|
||||
|
||||
enabled: bool = False
|
||||
db_path: str = ""
|
||||
top_k: int = 5
|
||||
|
||||
|
||||
@dataclass
|
||||
class MeshSourceConfig:
|
||||
"""Configuration for a mesh data source."""
|
||||
|
||||
name: str = ""
|
||||
type: str = "" # "meshview" or "meshmonitor"
|
||||
url: str = ""
|
||||
api_token: str = "" # MeshMonitor only, supports ${ENV_VAR}
|
||||
refresh_interval: int = 300
|
||||
enabled: bool = True
|
||||
|
||||
|
||||
@dataclass
|
||||
class RegionAnchor:
|
||||
"""A fixed region anchor point."""
|
||||
|
||||
name: str = ""
|
||||
lat: float = 0.0
|
||||
lon: float = 0.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class MeshIntelligenceConfig:
|
||||
"""Mesh intelligence and health scoring settings."""
|
||||
|
||||
enabled: bool = False
|
||||
regions: list[RegionAnchor] = field(default_factory=list) # Fixed region anchors
|
||||
locality_radius_miles: float = 8.0 # Radius for locality clustering within regions
|
||||
offline_threshold_hours: int = 24 # Hours before node considered offline
|
||||
packet_threshold: int = 500 # Non-text packets per 24h to flag
|
||||
battery_warning_percent: int = 20 # Battery level for warnings
|
||||
|
||||
|
||||
@dataclass
|
||||
class Config:
|
||||
"""Main configuration container."""
|
||||
|
||||
bot: BotConfig = field(default_factory=BotConfig)
|
||||
connection: ConnectionConfig = field(default_factory=ConnectionConfig)
|
||||
response: ResponseConfig = field(default_factory=ResponseConfig)
|
||||
history: HistoryConfig = field(default_factory=HistoryConfig)
|
||||
memory: MemoryConfig = field(default_factory=MemoryConfig)
|
||||
context: ContextConfig = field(default_factory=ContextConfig)
|
||||
commands: CommandsConfig = field(default_factory=CommandsConfig)
|
||||
llm: LLMConfig = field(default_factory=LLMConfig)
|
||||
weather: WeatherConfig = field(default_factory=WeatherConfig)
|
||||
meshmonitor: MeshMonitorConfig = field(default_factory=MeshMonitorConfig)
|
||||
knowledge: KnowledgeConfig = field(default_factory=KnowledgeConfig)
|
||||
mesh_sources: list[MeshSourceConfig] = field(default_factory=list)
|
||||
mesh_intelligence: MeshIntelligenceConfig = field(default_factory=MeshIntelligenceConfig)
|
||||
|
||||
_config_path: Optional[Path] = field(default=None, repr=False)
|
||||
|
||||
def resolve_api_key(self) -> str:
|
||||
"""Resolve API key from config or environment."""
|
||||
if self.llm.api_key:
|
||||
# Check if it's an env var reference like ${LLM_API_KEY}
|
||||
if self.llm.api_key.startswith("${") and self.llm.api_key.endswith("}"):
|
||||
env_var = self.llm.api_key[2:-1]
|
||||
return os.environ.get(env_var, "")
|
||||
return self.llm.api_key
|
||||
# Fall back to common env vars
|
||||
for env_var in ["LLM_API_KEY", "OPENAI_API_KEY", "ANTHROPIC_API_KEY"]:
|
||||
if value := os.environ.get(env_var):
|
||||
return value
|
||||
return ""
|
||||
|
||||
|
||||
def _dict_to_dataclass(cls, data: dict):
|
||||
"""Recursively convert dict to dataclass, handling nested structures."""
|
||||
if data is None:
|
||||
return cls()
|
||||
|
||||
field_types = {f.name: f.type for f in cls.__dataclass_fields__.values()}
|
||||
kwargs = {}
|
||||
|
||||
for key, value in data.items():
|
||||
if key.startswith("_"):
|
||||
continue
|
||||
if key not in field_types:
|
||||
continue
|
||||
|
||||
field_type = field_types[key]
|
||||
|
||||
# Handle nested dataclasses
|
||||
if hasattr(field_type, "__dataclass_fields__") and isinstance(value, dict):
|
||||
kwargs[key] = _dict_to_dataclass(field_type, value)
|
||||
# Handle list of MeshSourceConfig
|
||||
elif key == "mesh_sources" and isinstance(value, list):
|
||||
kwargs[key] = [
|
||||
_dict_to_dataclass(MeshSourceConfig, item)
|
||||
if isinstance(item, dict) else item
|
||||
for item in value
|
||||
]
|
||||
# Handle list of RegionAnchor
|
||||
elif key == "regions" and isinstance(value, list):
|
||||
kwargs[key] = [
|
||||
_dict_to_dataclass(RegionAnchor, item)
|
||||
if isinstance(item, dict) else item
|
||||
for item in value
|
||||
]
|
||||
else:
|
||||
kwargs[key] = value
|
||||
|
||||
return cls(**kwargs)
|
||||
|
||||
|
||||
def _dataclass_to_dict(obj) -> dict:
|
||||
"""Recursively convert dataclass to dict for YAML serialization."""
|
||||
if not hasattr(obj, "__dataclass_fields__"):
|
||||
return obj
|
||||
|
||||
result = {}
|
||||
for field_name in obj.__dataclass_fields__:
|
||||
if field_name.startswith("_"):
|
||||
continue
|
||||
value = getattr(obj, field_name)
|
||||
if hasattr(value, "__dataclass_fields__"):
|
||||
result[field_name] = _dataclass_to_dict(value)
|
||||
elif isinstance(value, list):
|
||||
# Handle list of dataclasses (like mesh_sources)
|
||||
result[field_name] = [
|
||||
_dataclass_to_dict(item) if hasattr(item, "__dataclass_fields__") else item
|
||||
for item in value
|
||||
]
|
||||
else:
|
||||
result[field_name] = value
|
||||
return result
|
||||
|
||||
|
||||
def load_config(config_path: Optional[Path] = None) -> Config:
|
||||
"""Load configuration from YAML file.
|
||||
|
||||
Args:
|
||||
config_path: Path to config file. Defaults to ./config.yaml
|
||||
|
||||
Returns:
|
||||
Config object with loaded settings
|
||||
"""
|
||||
if config_path is None:
|
||||
config_path = Path("config.yaml")
|
||||
|
||||
config_path = Path(config_path)
|
||||
|
||||
if not config_path.exists():
|
||||
# Return default config if file doesn't exist
|
||||
config = Config()
|
||||
config._config_path = config_path
|
||||
return config
|
||||
|
||||
with open(config_path, "r") as f:
|
||||
data = yaml.safe_load(f) or {}
|
||||
|
||||
config = _dict_to_dataclass(Config, data)
|
||||
config._config_path = config_path
|
||||
return config
|
||||
|
||||
|
||||
def save_config(config: Config, config_path: Optional[Path] = None) -> None:
|
||||
"""Save configuration to YAML file.
|
||||
|
||||
Args:
|
||||
config: Config object to save
|
||||
config_path: Path to save to. Uses config._config_path if not specified
|
||||
"""
|
||||
if config_path is None:
|
||||
config_path = config._config_path or Path("config.yaml")
|
||||
|
||||
config_path = Path(config_path)
|
||||
|
||||
data = _dataclass_to_dict(config)
|
||||
|
||||
# Add header comment
|
||||
header = "# MeshAI Configuration\n# Generated by meshai --config\n\n"
|
||||
|
||||
with open(config_path, "w") as f:
|
||||
f.write(header)
|
||||
yaml.dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True)
|
||||
|
|
|
|||
|
|
@ -125,14 +125,19 @@ _CITY_TO_REGION = {
|
|||
|
||||
# Mesh awareness instruction for LLM
|
||||
_MESH_AWARENESS_PROMPT = """
|
||||
When the user asks about mesh health, network status, or optimization:
|
||||
- Use the LIVE MESH HEALTH DATA injected above to answer with real numbers
|
||||
- Be specific: name nodes, cite utilization percentages, reference actual scores
|
||||
- Give actionable recommendations based on the data
|
||||
- If asked about a region or node you have detail for, use that detail
|
||||
- If asked about something the data doesn't cover, say so - don't fabricate
|
||||
- Keep responses concise - these go over LoRa with limited message size
|
||||
- Users can run !health for a quick mesh summary or !region [name] for regional info
|
||||
MESH DATA RESPONSE RULES (these OVERRIDE the brevity rules above for mesh questions):
|
||||
- When answering about mesh health, nodes, coverage, or network status: give DETAILED responses
|
||||
- Include actual numbers: scores, percentages, node names, packet counts, battery levels
|
||||
- Use the data injected above — don't summarize it to one sentence
|
||||
- Structure your response with the key data points the user asked about
|
||||
- For node questions: include hardware, region, battery, channel utilization, coverage, neighbors, packets
|
||||
- For region questions: include score, infrastructure status, coverage breakdown, flagged nodes, environment
|
||||
- For mesh questions: include overall score by pillar, regional breakdown, top issues, coverage gaps
|
||||
- For coverage questions: break down by region showing node counts, avg gateways, single-gateway nodes
|
||||
- For "where do we need infrastructure": name specific regions with poor coverage, how many nodes are affected
|
||||
- You CAN use 3-5 messages if needed — LoRa chunking handles splitting
|
||||
- Be specific and data-driven, not vague summaries
|
||||
- Still no markdown formatting — plain text only
|
||||
"""
|
||||
|
||||
|
||||
|
|
@ -673,4 +678,4 @@ class MessageRouter:
|
|||
connector=self.connector,
|
||||
history=self.history,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue