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feat: add typed configuration loader with tilde expansion

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This commit is contained in:
Santhosh Janardhanan
2026-04-13 13:25:58 -04:00
parent bc8e23cae7
commit 828f707178
2 changed files with 356 additions and 0 deletions
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212
src/companion/config.py Normal file
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import json
import os
from pathlib import Path
from typing import Any, Dict, List
from pydantic import BaseModel, Field
class PersonaConfig(BaseModel):
role: str
tone: str
style: str
boundaries: List[str] = []
class MemoryConfig(BaseModel):
session_turns: int
persistent_store: str
summarize_after: int
class ChatConfig(BaseModel):
streaming: bool
max_response_tokens: int
default_temperature: float
allow_temperature_override: bool
class CompanionConfig(BaseModel):
name: str
persona: PersonaConfig
memory: MemoryConfig
chat: ChatConfig
class IndexingConfig(BaseModel):
auto_sync: bool
auto_sync_interval_minutes: int
watch_fs_events: bool
file_patterns: List[str]
deny_dirs: List[str]
deny_patterns: List[str]
class ChunkingRule(BaseModel):
strategy: str
chunk_size: int
chunk_overlap: int
section_tags: List[str] = []
class VaultConfig(BaseModel):
path: str
indexing: IndexingConfig
chunking_rules: Dict[str, ChunkingRule] = {}
class EmbeddingConfig(BaseModel):
provider: str
model: str
base_url: str
dimensions: int
batch_size: int
class VectorStoreConfig(BaseModel):
type: str
path: str
class HybridSearchConfig(BaseModel):
enabled: bool
keyword_weight: float
semantic_weight: float
class FiltersConfig(BaseModel):
date_range_enabled: bool
tag_filter_enabled: bool
directory_filter_enabled: bool
class SearchConfig(BaseModel):
default_top_k: int
max_top_k: int
similarity_threshold: float
hybrid_search: HybridSearchConfig
filters: FiltersConfig
class RagConfig(BaseModel):
embedding: EmbeddingConfig
vector_store: VectorStoreConfig
search: SearchConfig
class InferenceConfig(BaseModel):
backend: str
model_path: str
context_length: int
gpu_layers: int
batch_size: int
threads: int
class FineTuningConfig(BaseModel):
base_model: str
output_dir: str
lora_rank: int
lora_alpha: int
learning_rate: float
batch_size: int
gradient_accumulation_steps: int
num_epochs: int
warmup_steps: int
save_steps: int
eval_steps: int
training_data_path: str
validation_split: float
class RetrainScheduleConfig(BaseModel):
auto_reminder: bool
default_interval_days: int
reminder_channels: List[str] = []
class ModelConfig(BaseModel):
inference: InferenceConfig
fine_tuning: FineTuningConfig
retrain_schedule: RetrainScheduleConfig
class AuthConfig(BaseModel):
enabled: bool
class ApiConfig(BaseModel):
host: str
port: int
cors_origins: List[str] = []
auth: AuthConfig
class WebFeaturesConfig(BaseModel):
streaming: bool
citations: bool
source_preview: bool
class WebConfig(BaseModel):
enabled: bool
theme: str
features: WebFeaturesConfig
class CliConfig(BaseModel):
enabled: bool
rich_output: bool
class UiConfig(BaseModel):
web: WebConfig
cli: CliConfig
class LoggingConfig(BaseModel):
level: str
file: str
max_size_mb: int
backup_count: int
class SecurityConfig(BaseModel):
local_only: bool
vault_path_traversal_check: bool
sensitive_content_detection: bool
sensitive_patterns: List[str] = []
require_confirmation_for_external_apis: bool
class Config(BaseModel):
companion: CompanionConfig
vault: VaultConfig
rag: RagConfig
model: ModelConfig
api: ApiConfig
ui: UiConfig
logging: LoggingConfig
security: SecurityConfig
def expand_tilde_recursive(obj: Any) -> Any:
"""Recursively expand ~/ in string values."""
if isinstance(obj, str) and obj.startswith("~/"):
return os.path.expanduser(obj)
elif isinstance(obj, dict):
return {k: expand_tilde_recursive(v) for k, v in obj.items()}
elif isinstance(obj, list):
return [expand_tilde_recursive(item) for item in obj]
return obj
def load_config(path: str) -> Config:
"""Load configuration from a JSON file with tilde expansion."""
with open(path, "r", encoding="utf-8") as f:
raw_data = json.load(f)
# Recursively expand tilde paths in strings
expanded_data = expand_tilde_recursive(raw_data)
return Config.model_validate(expanded_data)

144
tests/test_config.py Normal file
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import json
import os
import tempfile
from companion.config import load_config
def test_load_config_reads_json_and_expands_tilde():
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
json.dump(
{
"companion": {
"name": "SAN",
"persona": {
"role": "companion",
"tone": "reflective",
"style": "questioning",
"boundaries": [],
},
"memory": {
"session_turns": 20,
"persistent_store": "~/mem.db",
"summarize_after": 10,
},
"chat": {
"streaming": True,
"max_response_tokens": 2048,
"default_temperature": 0.7,
"allow_temperature_override": True,
},
},
"vault": {
"path": "~/test-vault",
"indexing": {
"auto_sync": False,
"auto_sync_interval_minutes": 1440,
"watch_fs_events": False,
"file_patterns": ["*.md"],
"deny_dirs": [".git"],
"deny_patterns": [".*"],
},
"chunking_rules": {},
},
"rag": {
"embedding": {
"provider": "ollama",
"model": "dummy",
"base_url": "http://localhost:11434",
"dimensions": 4,
"batch_size": 2,
},
"vector_store": {
"type": "lancedb",
"path": "~/.companion/vectors.lance",
},
"search": {
"default_top_k": 8,
"max_top_k": 20,
"similarity_threshold": 0.75,
"hybrid_search": {
"enabled": False,
"keyword_weight": 0.3,
"semantic_weight": 0.7,
},
"filters": {
"date_range_enabled": True,
"tag_filter_enabled": True,
"directory_filter_enabled": True,
},
},
},
"model": {
"inference": {
"backend": "llama.cpp",
"model_path": "",
"context_length": 8192,
"gpu_layers": 35,
"batch_size": 512,
"threads": 8,
},
"fine_tuning": {
"base_model": "",
"output_dir": "",
"lora_rank": 16,
"lora_alpha": 32,
"learning_rate": 0.0002,
"batch_size": 4,
"gradient_accumulation_steps": 4,
"num_epochs": 3,
"warmup_steps": 100,
"save_steps": 500,
"eval_steps": 250,
"training_data_path": "",
"validation_split": 0.1,
},
"retrain_schedule": {
"auto_reminder": True,
"default_interval_days": 90,
"reminder_channels": [],
},
},
"api": {
"host": "127.0.0.1",
"port": 7373,
"cors_origins": [],
"auth": {"enabled": False},
},
"ui": {
"web": {
"enabled": True,
"theme": "obsidian",
"features": {
"streaming": True,
"citations": True,
"source_preview": True,
},
},
"cli": {"enabled": True, "rich_output": True},
},
"logging": {
"level": "INFO",
"file": "",
"max_size_mb": 100,
"backup_count": 5,
},
"security": {
"local_only": True,
"vault_path_traversal_check": True,
"sensitive_content_detection": True,
"sensitive_patterns": [],
"require_confirmation_for_external_apis": True,
},
},
f,
)
path = f.name
try:
config = load_config(path)
assert config.vault.path == os.path.expanduser("~/test-vault")
assert config.rag.vector_store.path == os.path.expanduser(
"~/.companion/vectors.lance"
)
finally:
os.unlink(path)