7043429150
- Backend: Go API server with Gin framework - Frontend: React setup (placeholder) - ML Service: Python FastAPI skeleton - Docker Compose: Full stack configuration - Database: PostgreSQL schema with migrations - Documentation: Implementation plan and README Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
123 lines
2.9 KiB
Python
123 lines
2.9 KiB
Python
from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional
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import logging
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = FastAPI(
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title="VictoriaLogs ML Service",
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description="Machine Learning service for anomaly detection in logs",
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version="1.0.0"
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)
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# CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Request/Response models
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class TrainRequest(BaseModel):
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model_name: str
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query: str
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start_time: str
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end_time: str
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contamination: float = 0.1
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class DetectRequest(BaseModel):
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model_name: str
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logs: List[Dict[str, Any]]
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class TrainResponse(BaseModel):
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status: str
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model_name: str
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model_path: str
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message: str
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class DetectResponse(BaseModel):
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status: str
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anomalies: List[Dict[str, Any]]
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anomaly_count: int
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@app.get("/")
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async def root():
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return {
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"service": "VictoriaLogs ML Service",
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"status": "running",
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"version": "1.0.0"
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}
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@app.get("/health")
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async def health_check():
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return {"status": "healthy"}
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@app.post("/api/ml/train", response_model=TrainResponse)
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async def train_model(request: TrainRequest):
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"""
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Train an anomaly detection model on historical logs
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"""
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logger.info(f"Training model: {request.model_name}")
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# TODO: Implement model training
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# 1. Fetch logs from VictoriaLogs
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# 2. Extract features
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# 3. Train Isolation Forest model
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# 4. Save model to disk
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return TrainResponse(
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status="success",
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model_name=request.model_name,
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model_path=f"/app/models/{request.model_name}.pkl",
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message="Model training not yet implemented"
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)
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@app.post("/api/ml/detect", response_model=DetectResponse)
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async def detect_anomalies(request: DetectRequest):
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"""
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Detect anomalies in a batch of logs using a trained model
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"""
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logger.info(f"Detecting anomalies with model: {request.model_name}")
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# TODO: Implement anomaly detection
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# 1. Load trained model
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# 2. Extract features from logs
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# 3. Predict anomalies
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# 4. Return flagged logs with scores
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return DetectResponse(
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status="success",
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anomalies=[],
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anomaly_count=0
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)
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@app.get("/api/ml/models")
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async def list_models():
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"""
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List all trained models
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"""
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# TODO: List model files from disk
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return {
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"models": [],
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"count": 0
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}
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@app.delete("/api/ml/models/{model_name}")
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async def delete_model(model_name: str):
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"""
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Delete a trained model
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"""
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logger.info(f"Deleting model: {model_name}")
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# TODO: Delete model file
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return {
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"status": "success",
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"message": f"Model {model_name} deleted"
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}
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