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2129c29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | """Server configuration for NLProxy
Author: IntelliDeep Labs Team
License: BSL 1.1
"""
from __future__ import annotations
from typing import Any, Literal
from pydantic import BaseModel, ConfigDict, field_validator
from nlproxy.llm.client import LLMProvider
class Settings(BaseModel):
"""Runtime settings loaded from environment variables."""
model_config = ConfigDict(env_prefix="NLPROXY_", case_sensitive=False)
host: str = "0.0.0.0"
port: int = 8000
workers: int = 4
log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO"
redis_url: str = "redis://localhost:6379"
redis_max_connections: int = 50
redis_socket_timeout: float = 5.0
enable_semantic_cache: bool = True
cache_similarity_threshold: float = 0.92
cache_default_ttl: int = 3600
cache_embedding_dim: int = 384
default_aggressiveness: float = 0.2
max_compression_timeout: float = 60.0
compression_max_retries: int = 3
default_llm_provider: str = "openai"
default_llm_model: str = "gpt-4"
llm_request_timeout: float = 30.0
llm_max_retries: int = 3
enable_llm_fallback: bool = True
min_confidence_threshold: float = 0.6
max_regeneration_attempts: int = 3
enable_auto_correction: bool = True
privacy_mode_default: bool = False
enable_metrics: bool = True
metrics_path: str = "/metrics"
enable_tracing: bool = False
trace_sample_rate: float = 0.1
enable_nli_verification: bool = True
enable_perplexity_check: bool = False
enable_semantic_drift: bool = True
@field_validator("log_level")
@classmethod
def validate_log_level(cls, value: str) -> str:
allowed = {"DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"}
normalized = value.upper().strip()
if normalized not in allowed:
raise ValueError(f"log_level must be one of {sorted(allowed)}")
return normalized
@field_validator("default_llm_provider")
@classmethod
def validate_llm_provider(cls, value: str) -> str:
allowed = {p.value for p in LLMProvider}
normalized = value.lower().strip()
if normalized not in allowed:
raise ValueError(
f"Invalid LLM provider: {value}. Must be one of {sorted(allowed)}"
)
return normalized
@field_validator(
"enable_semantic_cache",
"enable_llm_fallback",
"enable_metrics",
"enable_tracing",
"enable_nli_verification",
"enable_perplexity_check",
"enable_semantic_drift",
"enable_auto_correction",
"privacy_mode_default",
)
@classmethod
def parse_boolean_flags(cls, value: Any) -> bool:
if isinstance(value, str):
normalized = value.strip().lower()
return normalized in {"1", "true", "yes", "on"}
return bool(value)
settings = Settings()
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