Files
agenticSystem/src/config.py
2026-04-10 16:13:35 +00:00

105 lines
3.5 KiB
Python

"""Application configuration via environment variables."""
from __future__ import annotations
from pydantic_settings import BaseSettings
from pydantic import Field
class Settings(BaseSettings):
# --- Service ---
service_name: str = "agentic-microservice"
service_version: str = "1.0.0"
host: str = "0.0.0.0"
port: int = 8000
debug: bool = False
# --- Redis ---
redis_host: str = "localhost"
redis_port: int = 6379
redis_db: int = 0
redis_password: str = ""
redis_key_prefix: str = "agentic"
session_ttl_seconds: int = 86400 # 24h
@property
def redis_url(self) -> str:
auth = f":{self.redis_password}@" if self.redis_password else ""
return f"redis://{auth}{self.redis_host}:{self.redis_port}/{self.redis_db}"
# --- Model providers ---
anthropic_api_key: str = ""
anthropic_base_url: str = "" # Custom base URL (for MiniMax Anthropic-compatible, etc.)
openai_api_key: str = ""
openai_base_url: str = "" # Custom base URL (for MiniMax, DeepInfra, etc.)
default_model_provider: str = "claude"
default_model_id: str = "claude-sonnet-4-20250514"
max_tokens: int = 4096
temperature: float = 0.3
# --- Context engine ---
model_context_window: int = 0 # 0 = use legacy fixed budget / explicit override
model_max_output_tokens: int = 4096
context_max_tokens: int = 0 # 0 = auto-budget from model window, fallback legacy 120k
compaction_threshold_tokens: int = 0 # 0 = derive from ratio
compaction_threshold_ratio: float = 0.80
context_reserve_ratio: float = 0.10
artifact_summary_max_chars: int = 2000
knowledge_base_max_tokens: int = 30_000
working_context_max_items: int = 20
tool_raw_output_max_chars: int = 2000
conversation_recent_raw_limit: int = 2
task_history_max_entries: int = 20
task_history_max_tokens: int = 1500
# --- MCP ---
mcp_config_path: str = "" # Path to mcp.json; empty = legacy single-server mode
mcp_server_command: str = "" # Legacy: single server command
mcp_server_args: list[str] = Field(default_factory=list)
mcp_timeout_seconds: float = 30.0
mcp_startup_timeout_seconds: float = 10.0
# --- Pricing (per 1M tokens) ---
cost_per_1m_input: float = 2.50
cost_per_1m_output: float = 15.00
# --- Orchestrator ---
max_execution_steps: int = 25
subagent_max_steps: int = 30
max_execution_timeout_seconds: float = 300.0 # 5 min global timeout
# --- SSE ---
sse_keepalive_seconds: float = 15.0
model_config = {"env_prefix": "AGENTIC_", "env_file": ".env", "extra": "ignore"}
@property
def reserve_tokens(self) -> int:
if self.model_context_window <= 0:
return 0
return max(0, int(self.model_context_window * self.context_reserve_ratio))
@property
def effective_context_budget(self) -> int:
if self.context_max_tokens > 0:
return self.context_max_tokens
if self.model_context_window > 0:
budget = (
self.model_context_window
- max(0, self.model_max_output_tokens)
- self.reserve_tokens
)
return max(1, budget)
return 120_000
@property
def effective_compaction_threshold(self) -> int:
if self.compaction_threshold_tokens > 0:
return min(self.compaction_threshold_tokens, self.effective_context_budget)
return max(1, int(self.effective_context_budget * self.compaction_threshold_ratio))
settings = Settings()