context7.json

{
  "url": "https://context7.com/mrveiss/autobot-ai",
  "public_key": "pk_2lkpZukfMqQ54FxypyPxu",
  "projectTitle": "AutoBot AI Platform",
  "description": "Autonomous AI-powered Linux administration platform with multi-modal AI, 20+ specialized agents, distributed fleet management via SLM, NPU hardware acceleration, RAG knowledge pipelines, and RBAC security across a 6-VM architecture",
  "folders": [
    "docs"
  ],
  "excludeFolders": [
    "docs/archives",
    "docs/fixes",
    "docs/screenshots",
    "docs/plans",
    "docs/reports/legacy"
  ],
  "rules": [
    "Always use autobot_shared.redis_client.get_redis_client() for Redis connections, never direct redis.Redis()",
    "Always use autobot_shared.ssot_config.config for configuration values, never hardcode IPs",
    "The SLM (Service Lifecycle Manager) on 172.16.168.19 manages the fleet via Ansible playbooks",
    "AutoBot uses Ollama for LLM inference - configure via AUTOBOT_OLLAMA_HOST environment variable",
    "The chat pipeline flows through ChatWorkflowManager -> LLMHandlerMixin._prepare_llm_request_params()",
    "Use Python logging.getLogger(__name__) for backend, createLogger() for frontend - never print() or console.log()",
    "Backend HTTPS on port 8443, Redis on 172.16.168.23:6379 with 4 databases (main, knowledge, prompts, analytics)"
  ]
}