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rml_ai/server.py

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+"""
+FastAPI Server for RML System
+"""
+
+import time
+from typing import Dict, Any
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import uvicorn
+
+from .core import RMLSystem, RMLResponse
+from .config import RMLConfig
+
+
+# Request/Response models
+class ChatRequest(BaseModel):
+    message: str
+
+class ChatResponse(BaseModel):
+    answer: str
+    response_ms: float
+
+class HealthResponse(BaseModel):
+    status: str
+    timestamp: float
+
+class ReadyResponse(BaseModel):
+    ready: bool
+    entries: int
+    device: str
+
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="RML-AI API",
+    description="Resonant Memory Learning AI System API",
+    version="0.1.0",
+    docs_url="/docs",
+    redoc_url="/redoc"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Global RML system instance
+rml_system: RMLSystem = None
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Initialize RML system on startup"""
+    global rml_system
+    
+    print("Initializing RML system...")
+    config = RMLConfig()
+    print(f"Configuration: {config}")
+    
+    try:
+        rml_system = RMLSystem(config)
+        print("RML system initialized successfully!")
+    except Exception as e:
+        print(f"Error initializing RML system: {e}")
+        raise e
+
+
+@app.get("/", response_model=Dict[str, str])
+async def root():
+    """Root endpoint"""
+    return {
+        "message": "RML-AI API",
+        "description": "Resonant Memory Learning AI System",
+        "version": "0.1.0",
+        "docs": "/docs"
+    }
+
+
+@app.get("/health", response_model=HealthResponse)
+async def health_check():
+    """Health check endpoint"""
+    return HealthResponse(
+        status="healthy",
+        timestamp=time.time()
+    )
+
+
+@app.get("/ready", response_model=ReadyResponse)
+async def ready_check():
+    """Ready check endpoint"""
+    if rml_system is None:
+        raise HTTPException(status_code=503, detail="RML system not initialized")
+    
+    stats = rml_system.memory.get_stats()
+    return ReadyResponse(
+        ready=True,
+        entries=stats['total_entries'],
+        device=rml_system.config.device
+    )
+
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat(request: ChatRequest):
+    """Chat endpoint for RML queries"""
+    if rml_system is None:
+        raise HTTPException(status_code=503, detail="RML system not initialized")
+    
+    try:
+        response = rml_system.query(request.message)
+        return ChatResponse(
+            answer=response.answer,
+            response_ms=response.response_ms
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")
+
+
+@app.get("/config")
+async def get_config():
+    """Get current configuration"""
+    if rml_system is None:
+        raise HTTPException(status_code=503, detail="RML system not initialized")
+    
+    return rml_system.config.to_dict()
+
+
+def main():
+    """Main function to run the server"""
+    uvicorn.run(
+        "rml_ai.server:app",
+        host="127.0.0.1",
+        port=8000,
+        reload=False,
+        log_level="info"
+    )
+
+
+if __name__ == "__main__":
+    main()