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"""
System Configuration
------------------
Central configuration for the Agentic System including:
1. Local Model Settings
2. Team Settings
3. System Parameters
4. Resource Limits
5. Free API Configurations
"""
import os
from typing import Dict, Any
from pathlib import Path
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
class SystemConfig:
"""System-wide configuration."""
# Base Paths
BASE_DIR = Path(__file__).parent.absolute()
CACHE_DIR = BASE_DIR / "cache"
LOG_DIR = BASE_DIR / "logs"
DATA_DIR = BASE_DIR / "data"
MODEL_DIR = BASE_DIR / "models"
# System Parameters
DEBUG_MODE = os.getenv("DEBUG_MODE", "False").lower() == "true"
LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
MAX_WORKERS = int(os.getenv("MAX_WORKERS", "4"))
ASYNC_TIMEOUT = int(os.getenv("ASYNC_TIMEOUT", "30"))
# Local Model Configurations
MODEL_CONFIG = {
"quick_coder": {
"name": "tugstugi/Qwen2.5-Coder-0.5B-QwQ-draft",
"type": "transformers",
"description": "Fast code completion and simple tasks",
"temperature": 0.2,
"max_tokens": 1000,
"timeout": 30
},
"deep_coder": {
"name": "YorkieOH10/deepseek-coder-6.7B-kexer-Q8_0-GGUF",
"type": "gguf",
"description": "Complex code generation and refactoring",
"temperature": 0.3,
"max_tokens": 2000,
"timeout": 45
},
"text_gen": {
"name": "Orenguteng/Llama-3-8B-Lexi-Uncensored",
"type": "transformers",
"description": "General text generation and reasoning",
"temperature": 0.7,
"max_tokens": 1500,
"timeout": 40
},
"workflow": {
"name": "deepseek-ai/JanusFlow-1.3B",
"type": "transformers",
"description": "Task planning and workflow management",
"temperature": 0.5,
"max_tokens": 1000,
"timeout": 30
}
}
# Team Configurations
TEAM_CONFIG = {
"coders": {
"min_agents": 3,
"max_agents": 7,
"capabilities": [
"full_stack_development",
"cloud_architecture",
"ai_ml",
"blockchain",
"mobile_development"
],
"resource_limits": {
"cpu_percent": 80,
"memory_mb": 4096,
"gpu_memory_mb": 2048
}
},
"business": {
"min_agents": 2,
"max_agents": 5,
"capabilities": [
"market_analysis",
"business_strategy",
"digital_transformation",
"startup_innovation",
"product_management"
],
"resource_limits": {
"cpu_percent": 60,
"memory_mb": 2048,
"api_calls_per_minute": 100
}
},
"research": {
"min_agents": 2,
"max_agents": 6,
"capabilities": [
"deep_research",
"data_analysis",
"trend_forecasting",
"competitive_analysis",
"technology_assessment"
],
"resource_limits": {
"cpu_percent": 70,
"memory_mb": 3072,
"api_calls_per_minute": 150
}
},
"traders": {
"min_agents": 2,
"max_agents": 5,
"capabilities": [
"crypto_trading",
"sports_betting",
"risk_management",
"market_timing",
"portfolio_optimization"
],
"resource_limits": {
"cpu_percent": 60,
"memory_mb": 2048,
"api_calls_per_minute": 200
}
}
}
# Resource Management
RESOURCE_LIMITS = {
"total_cpu_percent": 90,
"total_memory_mb": 8192,
"total_gpu_memory_mb": 4096,
"max_api_calls_per_minute": 500,
"max_concurrent_tasks": 20
}
# Collaboration Settings
COLLABORATION_CONFIG = {
"min_confidence_threshold": 0.6,
"max_team_size": 10,
"max_concurrent_objectives": 5,
"objective_timeout_minutes": 60,
"team_sync_interval_seconds": 30
}
# Error Recovery
ERROR_RECOVERY = {
"max_retries": 3,
"retry_delay_seconds": 5,
"error_threshold": 0.2,
"recovery_timeout": 300
}
# Monitoring
MONITORING = {
"metrics_interval_seconds": 60,
"health_check_interval": 30,
"performance_log_retention_days": 7,
"alert_threshold": {
"cpu": 85,
"memory": 90,
"error_rate": 0.1
}
}
# Free API Configurations (No API Keys Required)
API_CONFIG = {
"search": {
"duckduckgo": {
"base_url": "https://api.duckduckgo.com",
"rate_limit": 100,
"requires_auth": False,
"method": "GET"
},
"wikipedia": {
"base_url": "https://en.wikipedia.org/w/api.php",
"rate_limit": 200,
"requires_auth": False,
"method": "GET"
},
"arxiv": {
"base_url": "http://export.arxiv.org/api/query",
"rate_limit": 60,
"requires_auth": False,
"method": "GET"
},
"crossref": {
"base_url": "https://api.crossref.org/works",
"rate_limit": 50,
"requires_auth": False,
"method": "GET"
},
"unpaywall": {
"base_url": "https://api.unpaywall.org/v2",
"rate_limit": 100,
"requires_auth": False,
"method": "GET"
}
},
"crypto": {
"coincap": {
"base_url": "https://api.coincap.io/v2",
"rate_limit": 200,
"requires_auth": False,
"method": "GET",
"endpoints": {
"assets": "/assets",
"rates": "/rates",
"markets": "/markets"
}
},
"blockchair": {
"base_url": "https://api.blockchair.com",
"rate_limit": 30,
"requires_auth": False,
"method": "GET"
}
},
"news": {
"wikinews": {
"base_url": "https://en.wikinews.org/w/api.php",
"rate_limit": 200,
"requires_auth": False,
"method": "GET"
},
"reddit": {
"base_url": "https://www.reddit.com/r/news/.json",
"rate_limit": 60,
"requires_auth": False,
"method": "GET"
},
"hackernews": {
"base_url": "https://hacker-news.firebaseio.com/v0",
"rate_limit": 100,
"requires_auth": False,
"method": "GET"
}
},
"market_data": {
"yahoo_finance": {
"base_url": "https://query1.finance.yahoo.com/v8/finance",
"rate_limit": 100,
"requires_auth": False,
"method": "GET"
},
"marketstack_free": {
"base_url": "https://api.marketstack.com/v1",
"rate_limit": 100,
"requires_auth": False,
"method": "GET"
}
},
"sports": {
"football_data": {
"base_url": "https://www.football-data.org/v4",
"rate_limit": 10,
"requires_auth": False,
"method": "GET",
"free_endpoints": [
"/competitions",
"/matches"
]
},
"nhl": {
"base_url": "https://statsapi.web.nhl.com/api/v1",
"rate_limit": 50,
"requires_auth": False,
"method": "GET"
},
"mlb": {
"base_url": "https://statsapi.mlb.com/api/v1",
"rate_limit": 50,
"requires_auth": False,
"method": "GET"
}
},
"web_scraping": {
"web_archive": {
"base_url": "https://archive.org/wayback/available",
"rate_limit": 40,
"requires_auth": False,
"method": "GET"
},
"metahtml": {
"base_url": "https://html.spec.whatwg.org/multipage",
"rate_limit": 30,
"requires_auth": False,
"method": "GET"
}
}
}
@classmethod
def get_team_config(cls, team_name: str) -> Dict[str, Any]:
"""Get configuration for a specific team."""
return cls.TEAM_CONFIG.get(team_name, {})
@classmethod
def get_model_config(cls, model_type: str) -> Dict[str, Any]:
"""Get configuration for a specific model type."""
return cls.MODEL_CONFIG.get(model_type, {})
@classmethod
def get_api_config(cls, api_name: str) -> Dict[str, Any]:
"""Get configuration for a specific API."""
for category in cls.API_CONFIG.values():
if api_name in category:
return category[api_name]
return {}
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