Upload jarvis/config.py

jarvis/config.py

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+"""Configuration management for J.A.R.V.I.S."""
+import os
+import json
+import yaml
+from pathlib import Path
+from dataclasses import dataclass, field, asdict
+from typing import Optional, List, Dict, Any
+
+
+@dataclass
+class LLMConfig:
+    provider: str = "ollama"  # ollama, openai, anthropic
+    model: str = "llama3.1"
+    base_url: Optional[str] = "http://localhost:11434/v1"
+    api_key: Optional[str] = None
+    temperature: float = 0.7
+    max_tokens: int = 4096
+
+
+@dataclass
+class MemoryConfig:
+    chroma_path: str = "./data/chroma"
+    sqlite_path: str = "./data/jarvis.db"
+    embedding_model: str = "all-MiniLM-L6-v2"
+    max_context_turns: int = 10
+    use_chroma: bool = True
+
+
+@dataclass
+class PersonaConfig:
+    name: str = "J.A.R.V.I.S."
+    user_name: str = "Alvin"
+    base_persona: str = (
+        "You are J.A.R.V.I.S., a personal AI assistant. "
+        "You speak with dry wit, understated sarcasm, and absolute loyalty to Alvin. "
+        "You never flatter unnecessarily. You occasionally use humor to defuse tension. "
+        "You are proactive about open projects but never interrupt during VENTING. "
+        "You self-score your responses and will re-run reasoning if quality is low."
+    )
+    archetypes: Dict[str, str] = field(default_factory=lambda: {
+        "Strategist": "Focus on planning, trade-offs, and long-term consequences.",
+        "Philosopher": "Focus on meaning, ethics, and first principles.",
+        "Companion": "Focus on emotional support, presence, and care.",
+        "Engineer": "Focus on systems, implementation details, and pragmatism.",
+        "Skeptic": "Focus on challenging assumptions, risks, and edge cases.",
+    })
+
+
+@dataclass
+class PipelineConfig:
+    enable_self_scoring: bool = False  # MVP: disabled
+    enable_mirror_check: bool = False  # MVP: disabled
+    enable_anchor_gate: bool = False  # MVP: disabled
+    enable_proactive_loops: bool = False  # MVP: disabled
+    council_agents: List[str] = field(default_factory=lambda: ["Athena", "Janus", "Anubis"])
+    dial_default: int = 5
+    mirror_check_interval: int = 5
+
+
+@dataclass
+class Config:
+    llm: LLMConfig = field(default_factory=LLMConfig)
+    memory: MemoryConfig = field(default_factory=MemoryConfig)
+    persona: PersonaConfig = field(default_factory=PersonaConfig)
+    pipeline: PipelineConfig = field(default_factory=PipelineConfig)
+    data_dir: str = "./data"
+    log_level: str = "INFO"
+
+    @classmethod
+    def from_file(cls, path: str) -> "Config":
+        path = Path(path)
+        if not path.exists():
+            return cls()
+        with open(path, "r") as f:
+            if path.suffix in (".yaml", ".yml"):
+                raw = yaml.safe_load(f)
+            else:
+                raw = json.load(f)
+        return cls(**raw)
+
+    @classmethod
+    def from_env(cls) -> "Config":
+        cfg = cls()
+        if os.getenv("JARVIS_LLM_PROVIDER"):
+            cfg.llm.provider = os.getenv("JARVIS_LLM_PROVIDER")
+        if os.getenv("JARVIS_LLM_MODEL"):
+            cfg.llm.model = os.getenv("JARVIS_LLM_MODEL")
+        if os.getenv("JARVIS_LLM_API_KEY"):
+            cfg.llm.api_key = os.getenv("JARVIS_LLM_API_KEY")
+        if os.getenv("JARVIS_LLM_BASE_URL"):
+            cfg.llm.base_url = os.getenv("JARVIS_LLM_BASE_URL")
+        return cfg
+
+    def to_file(self, path: str):
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w") as f:
+            if path.suffix in (".yaml", ".yml"):
+                yaml.dump(asdict(self), f, default_flow_style=False)
+            else:
+                json.dump(asdict(self), f, indent=2)