truetype
9a113002d4
- Add SemanticSearchConfig to settings.py with embedding model options - Update WorldMapper to accept embedding_model parameter - Update main.py to pass model from config to WorldMapper - Add documentation for available embedding models: - sentence-transformers/all-MiniLM-L6-v2 (default, fast) - sentence-transformers/all-mpnet-base-v2 (better quality) - google/embeddinggemma-300m (state-of-the-art) - Update config.yaml and config.example.yaml with new options Users can now choose the best embedding model for their needs.
129 lines
3.9 KiB
Python
129 lines
3.9 KiB
Python
"""Configuration loading and validation"""
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import yaml
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from pydantic import BaseModel, Field
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from typing import List, Optional
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class MudConfig(BaseModel):
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"""MUD server configuration"""
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host: str
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port: int
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mud_type: str = "dead_souls"
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username: str = ""
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password: str = ""
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keep_alive_interval: int = 60
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keep_alive_command: str = ""
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class CharacterConfig(BaseModel):
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"""Character preferences"""
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name: str = "AIPlayer"
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preferred_class: str = "warrior"
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preferred_race: str = "human"
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play_style: str = "balanced"
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class LLMProviderConfig(BaseModel):
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"""LLM provider configuration (Ollama / OpenAI-compatible)"""
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enabled: bool = True
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base_url: str = "http://localhost:11434"
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model: str = "llama3.2"
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temperature: float = 0.3
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max_tokens: int = 512
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timeout_seconds: float = 30.0
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max_retries: int = 2
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system_prompt: str = (
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"You are an AI playing a text-based MUD game. "
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"You must respond ONLY with the requested format. "
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"Be concise and precise. No explanations unless asked."
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)
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memory_db_path: str = "data/llm_memory.db"
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"""Path to SQLite database for LLM long-term memory."""
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class AIConfig(BaseModel):
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"""AI engine configuration"""
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engine: str = "hybrid"
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llm_model: str = "llama3.2"
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decision_delay_ms: List[int] = Field(default=[200, 2000])
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exploration_strategy: str = "depth_first"
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llm: LLMProviderConfig = Field(default_factory=LLMProviderConfig)
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class MapperConfig(BaseModel):
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"""Mapper configuration"""
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class ActiveStorageConfig(BaseModel):
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db_path: str = "data/maps/active/world.db"
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graph_cache_size: int = 10000
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class HistoricalStorageConfig(BaseModel):
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archive_dir: str = "data/maps/archive"
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class SnapshotTriggers(BaseModel):
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room_count: int = 500
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session_end: bool = True
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time_interval_hours: int = 24
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snapshot_triggers: SnapshotTriggers = Field(default_factory=SnapshotTriggers)
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class SemanticSearchConfig(BaseModel):
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model: str = "sentence-transformers/all-MiniLM-L6-v2"
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top_k: int = 5
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similarity_threshold: float = 0.7
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active_storage: ActiveStorageConfig = Field(default_factory=ActiveStorageConfig)
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historical_storage: HistoricalStorageConfig = Field(default_factory=HistoricalStorageConfig)
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semantic_search: SemanticSearchConfig = Field(default_factory=SemanticSearchConfig)
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class BehaviorConfig(BaseModel):
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"""Behavior simulation configuration"""
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reaction_time_min_ms: int = 200
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reaction_time_max_ms: int = 2000
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pause_probability: float = 0.05
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typo_probability: float = 0.0
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class LoggingConfig(BaseModel):
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"""Logging configuration"""
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level: str = "INFO"
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file: str = "data/logs/game.log"
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console: bool = True
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max_file_size_mb: int = 10
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backup_count: int = 5
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class MonitoringConfig(BaseModel):
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"""Web monitoring dashboard configuration"""
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enabled: bool = True
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host: str = "127.0.0.1"
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port: int = 8080
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class Config(BaseModel):
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"""Main configuration"""
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mud: MudConfig
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character: CharacterConfig = Field(default_factory=CharacterConfig)
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ai: AIConfig = Field(default_factory=AIConfig)
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mapper: MapperConfig = Field(default_factory=MapperConfig)
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behavior: BehaviorConfig = Field(default_factory=BehaviorConfig)
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logging: LoggingConfig = Field(default_factory=LoggingConfig)
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monitoring: MonitoringConfig = Field(default_factory=MonitoringConfig)
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def load_config(config_path: str = "config.yaml") -> Config:
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"""
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Load configuration from YAML file
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Args:
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config_path: Path to config file
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Returns:
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Validated Config object
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"""
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with open(config_path, 'r') as f:
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config_dict = yaml.safe_load(f)
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return Config(**config_dict)
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