forked from Rockachopa/Timmy-time-dashboard
15596ca325
Introduces a vendor-agnostic chat platform architecture: - chat_bridge/base.py: ChatPlatform ABC, ChatMessage, ChatThread - chat_bridge/registry.py: PlatformRegistry singleton - chat_bridge/invite_parser.py: QR + Ollama vision invite extraction - chat_bridge/vendors/discord.py: DiscordVendor with native threads Workflow: paste a screenshot of a Discord invite or QR code at POST /discord/join → Timmy extracts the invite automatically. Every Discord conversation gets its own thread, keeping channels clean. Bot responds to @mentions and DMs, routes through Timmy agent. 43 new tests (base classes, registry, invite parser, vendor, routes). https://claude.ai/code/session_01WU4h3cQQiouMwmgYmAgkMM
70 lines
3.2 KiB
Python
70 lines
3.2 KiB
Python
from typing import Literal
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from pydantic_settings import BaseSettings, SettingsConfigDict
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class Settings(BaseSettings):
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# Ollama host — override with OLLAMA_URL env var or .env file
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ollama_url: str = "http://localhost:11434"
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# LLM model passed to Agno/Ollama — override with OLLAMA_MODEL
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ollama_model: str = "llama3.2"
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# Set DEBUG=true to enable /docs and /redoc (disabled by default)
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debug: bool = False
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# Telegram bot token — set via TELEGRAM_TOKEN env var or the /telegram/setup endpoint
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telegram_token: str = ""
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# Discord bot token — set via DISCORD_TOKEN env var or the /discord/setup endpoint
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discord_token: str = ""
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# ── AirLLM / backend selection ───────────────────────────────────────────
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# "ollama" — always use Ollama (default, safe everywhere)
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# "airllm" — always use AirLLM (requires pip install ".[bigbrain]")
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# "auto" — use AirLLM on Apple Silicon if airllm is installed,
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# fall back to Ollama otherwise
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timmy_model_backend: Literal["ollama", "airllm", "auto"] = "ollama"
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# AirLLM model size when backend is airllm or auto.
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# Larger = smarter, but needs more RAM / disk.
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# 8b ~16 GB | 70b ~140 GB | 405b ~810 GB
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airllm_model_size: Literal["8b", "70b", "405b"] = "70b"
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# ── Spark Intelligence ────────────────────────────────────────────────
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# Enable/disable the Spark cognitive layer.
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# When enabled, Spark captures swarm events, runs EIDOS predictions,
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# consolidates memories, and generates advisory recommendations.
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spark_enabled: bool = True
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# ── Git / DevOps ──────────────────────────────────────────────────────
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git_default_repo_dir: str = "~/repos"
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# ── Creative — Image Generation (Pixel) ───────────────────────────────
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flux_model_id: str = "black-forest-labs/FLUX.1-schnell"
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image_output_dir: str = "data/images"
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image_default_steps: int = 4
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# ── Creative — Music Generation (Lyra) ────────────────────────────────
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music_output_dir: str = "data/music"
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ace_step_model: str = "ace-step/ACE-Step-v1.5"
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# ── Creative — Video Generation (Reel) ────────────────────────────────
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video_output_dir: str = "data/video"
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wan_model_id: str = "Wan-AI/Wan2.1-T2V-1.3B"
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video_default_resolution: str = "480p"
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# ── Creative — Pipeline / Assembly ────────────────────────────────────
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creative_output_dir: str = "data/creative"
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video_transition_duration: float = 1.0
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default_video_codec: str = "libx264"
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model_config = SettingsConfigDict(
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env_file=".env",
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env_file_encoding="utf-8",
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extra="ignore",
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)
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settings = Settings()
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