""" GT 2.0 Resource Cluster - Resource Management API with CB-REST Standards This module handles non-AI endpoints using CB-REST standard. AI inference endpoints maintain OpenAI compatibility. """ from typing import List, Optional, Dict, Any from fastapi import APIRouter, Depends, Query, Request, BackgroundTasks from pydantic import BaseModel, Field import logging import uuid from datetime import datetime, timedelta from app.core.api_standards import ( format_response, format_error, ErrorCode, APIError ) logger = logging.getLogger(__name__) router = APIRouter(prefix="/resources", tags=["Resource Management"]) # Request/Response Models class HealthCheckRequest(BaseModel): resource_id: str = Field(..., description="Resource identifier") deep_check: bool = Field(False, description="Perform deep health check") class RAGProcessRequest(BaseModel): document_content: str = Field(..., description="Document content to process") chunking_strategy: str = Field("semantic", description="Chunking strategy") chunk_size: int = Field(1000, ge=100, le=10000) chunk_overlap: int = Field(100, ge=0, le=500) embedding_model: str = Field("text-embedding-3-small") class SemanticSearchRequest(BaseModel): query: str = Field(..., description="Search query") collection_id: str = Field(..., description="Vector collection ID") top_k: int = Field(10, ge=1, le=100) relevance_threshold: float = Field(0.7, ge=0.0, le=1.0) filters: Optional[Dict[str, Any]] = None class AgentExecutionRequest(BaseModel): agent_type: str = Field(..., description="Agent type") task: Dict[str, Any] = Field(..., description="Task configuration") timeout: int = Field(300, ge=10, le=3600, description="Timeout in seconds") execution_context: Optional[Dict[str, Any]] = None @router.get("/health/system") async def system_health(request: Request): """ Get overall system health status CB-REST Capability Required: health:system:read """ try: health_status = { "overall_health": "healthy", "service_statuses": [ {"service": "ai_inference", "status": "healthy", "latency_ms": 45}, {"service": "rag_processing", "status": "healthy", "latency_ms": 120}, {"service": "vector_storage", "status": "healthy", "latency_ms": 30}, {"service": "agent_orchestration", "status": "healthy", "latency_ms": 85} ], "resource_utilization": { "cpu_percent": 42.5, "memory_percent": 68.3, "gpu_percent": 35.0, "disk_percent": 55.2 }, "performance_metrics": { "requests_per_second": 145, "average_latency_ms": 95, "error_rate_percent": 0.02, "active_connections": 234 }, "timestamp": datetime.utcnow().isoformat() } return format_response( data=health_status, capability_used="health:system:read", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to get system health: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="health:system:read", request_id=getattr(request.state, 'request_id', None) ) @router.post("/health/check") async def check_resource_health( request: Request, health_req: HealthCheckRequest, background_tasks: BackgroundTasks ): """ Perform health check on a specific resource CB-REST Capability Required: health:resource:check """ try: # Mock health check result health_result = { "resource_id": health_req.resource_id, "status": "healthy", "latency_ms": 87, "last_successful_request": datetime.utcnow().isoformat(), "error_count_24h": 3, "success_rate_24h": 99.97, "details": { "endpoint_reachable": True, "authentication_valid": True, "rate_limit_ok": True, "response_time_acceptable": True } } if health_req.deep_check: health_result["deep_check_results"] = { "model_loaded": True, "memory_usage_mb": 2048, "inference_test_passed": True, "test_latency_ms": 145 } return format_response( data=health_result, capability_used="health:resource:check", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to check resource health: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="health:resource:check", request_id=getattr(request.state, 'request_id', None) ) @router.post("/rag/process-document") async def process_document( request: Request, rag_req: RAGProcessRequest, background_tasks: BackgroundTasks ): """ Process document for RAG pipeline CB-REST Capability Required: rag:document:process """ try: processing_id = str(uuid.uuid4()) # Start async processing background_tasks.add_task( process_document_async, processing_id, rag_req ) return format_response( data={ "processing_id": processing_id, "status": "processing", "chunk_preview": [ { "chunk_id": f"chunk_{i}", "text": f"Sample chunk {i} from document...", "metadata": {"position": i, "size": rag_req.chunk_size} } for i in range(3) ], "estimated_completion": (datetime.utcnow() + timedelta(seconds=30)).isoformat() }, capability_used="rag:document:process", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to process document: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="rag:document:process", request_id=getattr(request.state, 'request_id', None) ) @router.post("/rag/semantic-search") async def semantic_search( request: Request, search_req: SemanticSearchRequest ): """ Perform semantic search in vector database CB-REST Capability Required: rag:search:execute """ try: # Mock search results results = [ { "document_id": f"doc_{i}", "chunk_id": f"chunk_{i}", "text": f"Relevant text snippet {i} matching query: {search_req.query[:50]}...", "relevance_score": 0.95 - (i * 0.05), "metadata": { "source": f"document_{i}.pdf", "page": i + 1, "timestamp": datetime.utcnow().isoformat() } } for i in range(min(search_req.top_k, 5)) ] return format_response( data={ "results": results, "query_embedding": [0.1] * 10, # Truncated for brevity "search_metadata": { "collection_id": search_req.collection_id, "documents_searched": 1500, "search_time_ms": 145, "model_used": "text-embedding-3-small" } }, capability_used="rag:search:execute", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to perform semantic search: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="rag:search:execute", request_id=getattr(request.state, 'request_id', None) ) @router.post("/agents/execute") async def execute_agent( request: Request, agent_req: AgentExecutionRequest, background_tasks: BackgroundTasks ): """ Execute an agentic workflow CB-REST Capability Required: agent:*:execute """ try: execution_id = str(uuid.uuid4()) # Start async agent execution background_tasks.add_task( execute_agent_async, execution_id, agent_req ) return format_response( data={ "execution_id": execution_id, "status": "queued", "estimated_duration": agent_req.timeout // 2, "resource_allocation": { "cpu_cores": 2, "memory_mb": 4096, "gpu_allocation": 0.25 } }, capability_used="agent:*:execute", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to execute agent: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="agent:*:execute", request_id=getattr(request.state, 'request_id', None) ) @router.get("/agents/{execution_id}/status") async def get_agent_status( request: Request, execution_id: str ): """ Get agent execution status CB-REST Capability Required: agent:{execution_id}:status """ try: # Mock status status = { "execution_id": execution_id, "status": "running", "progress_percent": 65, "current_task": { "name": "data_analysis", "status": "in_progress", "started_at": datetime.utcnow().isoformat() }, "memory_usage": { "working_memory_mb": 512, "context_size": 8192, "tool_calls_made": 12 }, "performance_metrics": { "steps_completed": 8, "total_steps": 12, "average_step_time_ms": 2500, "errors_encountered": 0 } } return format_response( data=status, capability_used=f"agent:{execution_id}:status", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to get agent status: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used=f"agent:{execution_id}:status", request_id=getattr(request.state, 'request_id', None) ) @router.post("/usage/record") async def record_usage( request: Request, operation_type: str, resource_id: str, usage_metrics: Dict[str, Any] ): """ Record resource usage for billing and analytics CB-REST Capability Required: usage:*:write """ try: usage_record = { "record_id": str(uuid.uuid4()), "recorded": True, "updated_quotas": { "tokens_remaining": 950000, "requests_remaining": 9500, "cost_accumulated_cents": 125 }, "warnings": [] } # Check for quota warnings if usage_metrics.get("tokens_used", 0) > 10000: usage_record["warnings"].append({ "type": "high_token_usage", "message": "High token usage detected", "threshold": 10000, "actual": usage_metrics.get("tokens_used", 0) }) return format_response( data=usage_record, capability_used="usage:*:write", request_id=getattr(request.state, 'request_id', None) ) except Exception as e: logger.error(f"Failed to record usage: {e}") return format_error( code=ErrorCode.SYSTEM_ERROR, message="Internal server error", capability_used="usage:*:write", request_id=getattr(request.state, 'request_id', None) ) # Async helper functions async def process_document_async(processing_id: str, rag_req: RAGProcessRequest): """Background task for document processing""" # Implement actual document processing logic here await asyncio.sleep(30) # Simulate processing logger.info(f"Document processing completed: {processing_id}") async def execute_agent_async(execution_id: str, agent_req: AgentExecutionRequest): """Background task for agent execution""" # Implement actual agent execution logic here await asyncio.sleep(agent_req.timeout // 2) # Simulate execution logger.info(f"Agent execution completed: {execution_id}")