feat: add typed configuration loader with tilde expansion
This commit is contained in:
212
src/companion/config.py
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212
src/companion/config.py
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import json
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import os
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from pathlib import Path
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from typing import Any, Dict, List
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from pydantic import BaseModel, Field
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class PersonaConfig(BaseModel):
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role: str
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tone: str
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style: str
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boundaries: List[str] = []
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class MemoryConfig(BaseModel):
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session_turns: int
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persistent_store: str
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summarize_after: int
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class ChatConfig(BaseModel):
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streaming: bool
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max_response_tokens: int
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default_temperature: float
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allow_temperature_override: bool
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class CompanionConfig(BaseModel):
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name: str
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persona: PersonaConfig
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memory: MemoryConfig
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chat: ChatConfig
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class IndexingConfig(BaseModel):
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auto_sync: bool
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auto_sync_interval_minutes: int
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watch_fs_events: bool
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file_patterns: List[str]
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deny_dirs: List[str]
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deny_patterns: List[str]
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class ChunkingRule(BaseModel):
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strategy: str
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chunk_size: int
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chunk_overlap: int
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section_tags: List[str] = []
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class VaultConfig(BaseModel):
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path: str
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indexing: IndexingConfig
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chunking_rules: Dict[str, ChunkingRule] = {}
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class EmbeddingConfig(BaseModel):
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provider: str
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model: str
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base_url: str
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dimensions: int
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batch_size: int
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class VectorStoreConfig(BaseModel):
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type: str
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path: str
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class HybridSearchConfig(BaseModel):
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enabled: bool
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keyword_weight: float
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semantic_weight: float
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class FiltersConfig(BaseModel):
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date_range_enabled: bool
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tag_filter_enabled: bool
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directory_filter_enabled: bool
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class SearchConfig(BaseModel):
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default_top_k: int
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max_top_k: int
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similarity_threshold: float
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hybrid_search: HybridSearchConfig
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filters: FiltersConfig
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class RagConfig(BaseModel):
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embedding: EmbeddingConfig
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vector_store: VectorStoreConfig
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search: SearchConfig
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class InferenceConfig(BaseModel):
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backend: str
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model_path: str
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context_length: int
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gpu_layers: int
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batch_size: int
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threads: int
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class FineTuningConfig(BaseModel):
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base_model: str
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output_dir: str
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lora_rank: int
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lora_alpha: int
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learning_rate: float
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batch_size: int
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gradient_accumulation_steps: int
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num_epochs: int
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warmup_steps: int
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save_steps: int
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eval_steps: int
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training_data_path: str
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validation_split: float
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class RetrainScheduleConfig(BaseModel):
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auto_reminder: bool
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default_interval_days: int
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reminder_channels: List[str] = []
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class ModelConfig(BaseModel):
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inference: InferenceConfig
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fine_tuning: FineTuningConfig
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retrain_schedule: RetrainScheduleConfig
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class AuthConfig(BaseModel):
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enabled: bool
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class ApiConfig(BaseModel):
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host: str
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port: int
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cors_origins: List[str] = []
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auth: AuthConfig
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class WebFeaturesConfig(BaseModel):
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streaming: bool
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citations: bool
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source_preview: bool
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class WebConfig(BaseModel):
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enabled: bool
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theme: str
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features: WebFeaturesConfig
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class CliConfig(BaseModel):
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enabled: bool
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rich_output: bool
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class UiConfig(BaseModel):
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web: WebConfig
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cli: CliConfig
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class LoggingConfig(BaseModel):
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level: str
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file: str
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max_size_mb: int
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backup_count: int
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class SecurityConfig(BaseModel):
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local_only: bool
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vault_path_traversal_check: bool
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sensitive_content_detection: bool
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sensitive_patterns: List[str] = []
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require_confirmation_for_external_apis: bool
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class Config(BaseModel):
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companion: CompanionConfig
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vault: VaultConfig
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rag: RagConfig
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model: ModelConfig
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api: ApiConfig
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ui: UiConfig
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logging: LoggingConfig
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security: SecurityConfig
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def expand_tilde_recursive(obj: Any) -> Any:
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"""Recursively expand ~/ in string values."""
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if isinstance(obj, str) and obj.startswith("~/"):
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return os.path.expanduser(obj)
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elif isinstance(obj, dict):
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return {k: expand_tilde_recursive(v) for k, v in obj.items()}
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elif isinstance(obj, list):
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return [expand_tilde_recursive(item) for item in obj]
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return obj
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def load_config(path: str) -> Config:
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"""Load configuration from a JSON file with tilde expansion."""
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with open(path, "r", encoding="utf-8") as f:
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raw_data = json.load(f)
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# Recursively expand tilde paths in strings
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expanded_data = expand_tilde_recursive(raw_data)
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return Config.model_validate(expanded_data)
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144
tests/test_config.py
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144
tests/test_config.py
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@@ -0,0 +1,144 @@
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import json
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import os
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import tempfile
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from companion.config import load_config
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def test_load_config_reads_json_and_expands_tilde():
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with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
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json.dump(
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{
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"companion": {
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"name": "SAN",
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"persona": {
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"role": "companion",
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"tone": "reflective",
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"style": "questioning",
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"boundaries": [],
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},
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"memory": {
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"session_turns": 20,
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"persistent_store": "~/mem.db",
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"summarize_after": 10,
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},
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"chat": {
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"streaming": True,
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"max_response_tokens": 2048,
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"default_temperature": 0.7,
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"allow_temperature_override": True,
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},
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},
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"vault": {
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"path": "~/test-vault",
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"indexing": {
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"auto_sync": False,
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"auto_sync_interval_minutes": 1440,
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"watch_fs_events": False,
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"file_patterns": ["*.md"],
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"deny_dirs": [".git"],
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"deny_patterns": [".*"],
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},
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"chunking_rules": {},
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},
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"rag": {
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"embedding": {
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"provider": "ollama",
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"model": "dummy",
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"base_url": "http://localhost:11434",
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"dimensions": 4,
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"batch_size": 2,
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},
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"vector_store": {
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"type": "lancedb",
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"path": "~/.companion/vectors.lance",
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},
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"search": {
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"default_top_k": 8,
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"max_top_k": 20,
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"similarity_threshold": 0.75,
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"hybrid_search": {
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"enabled": False,
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"keyword_weight": 0.3,
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"semantic_weight": 0.7,
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},
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"filters": {
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"date_range_enabled": True,
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"tag_filter_enabled": True,
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"directory_filter_enabled": True,
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},
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},
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},
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"model": {
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"inference": {
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"backend": "llama.cpp",
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"model_path": "",
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"context_length": 8192,
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"gpu_layers": 35,
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"batch_size": 512,
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"threads": 8,
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},
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"fine_tuning": {
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"base_model": "",
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"output_dir": "",
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"lora_rank": 16,
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"lora_alpha": 32,
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"learning_rate": 0.0002,
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"batch_size": 4,
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"gradient_accumulation_steps": 4,
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"num_epochs": 3,
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"warmup_steps": 100,
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"save_steps": 500,
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"eval_steps": 250,
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"training_data_path": "",
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"validation_split": 0.1,
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},
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"retrain_schedule": {
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"auto_reminder": True,
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"default_interval_days": 90,
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"reminder_channels": [],
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},
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},
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"api": {
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"host": "127.0.0.1",
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"port": 7373,
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"cors_origins": [],
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"auth": {"enabled": False},
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},
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"ui": {
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"web": {
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"enabled": True,
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"theme": "obsidian",
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"features": {
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"streaming": True,
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"citations": True,
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"source_preview": True,
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},
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},
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"cli": {"enabled": True, "rich_output": True},
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},
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"logging": {
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"level": "INFO",
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"file": "",
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"max_size_mb": 100,
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"backup_count": 5,
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},
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"security": {
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"local_only": True,
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"vault_path_traversal_check": True,
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"sensitive_content_detection": True,
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"sensitive_patterns": [],
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"require_confirmation_for_external_apis": True,
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},
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},
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f,
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)
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path = f.name
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try:
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config = load_config(path)
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assert config.vault.path == os.path.expanduser("~/test-vault")
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assert config.rag.vector_store.path == os.path.expanduser(
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"~/.companion/vectors.lance"
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)
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finally:
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os.unlink(path)
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