Spaces:
Sleeping
Sleeping
File size: 8,917 Bytes
c4f5f25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 | """
Prometheus metrics collection for MediGuard AI.
"""
import logging
import time
from functools import wraps
from fastapi import Request, Response
from prometheus_client import CONTENT_TYPE_LATEST, Counter, Gauge, Histogram, generate_latest
logger = logging.getLogger(__name__)
# HTTP metrics
http_requests_total = Counter(
'http_requests_total',
'Total HTTP requests',
['method', 'endpoint', 'status']
)
http_request_duration = Histogram(
'http_request_duration_seconds',
'HTTP request duration in seconds',
['method', 'endpoint'],
buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
# Workflow metrics
workflow_duration = Histogram(
'workflow_duration_seconds',
'Workflow execution duration in seconds',
['workflow_type'],
buckets=[1.0, 2.5, 5.0, 10.0, 25.0, 50.0, 100.0]
)
workflow_total = Counter(
'workflow_total',
'Total workflow executions',
['workflow_type', 'status']
)
# Agent metrics
agent_execution_duration = Histogram(
'agent_execution_duration_seconds',
'Agent execution duration in seconds',
['agent_name'],
buckets=[0.1, 0.5, 1.0, 2.5, 5.0, 10.0]
)
agent_total = Counter(
'agent_total',
'Total agent executions',
['agent_name', 'status']
)
# Database metrics
opensearch_connections_active = Gauge(
'opensearch_connections_active',
'Active OpenSearch connections'
)
redis_connections_active = Gauge(
'redis_connections_active',
'Active Redis connections'
)
# Cache metrics
cache_hits_total = Counter(
'cache_hits_total',
'Total cache hits',
['cache_type']
)
cache_misses_total = Counter(
'cache_misses_total',
'Total cache misses',
['cache_type']
)
# LLM metrics
llm_requests_total = Counter(
'llm_requests_total',
'Total LLM requests',
['provider', 'model']
)
llm_request_duration = Histogram(
'llm_request_duration_seconds',
'LLM request duration in seconds',
['provider', 'model'],
buckets=[0.5, 1.0, 2.5, 5.0, 10.0, 30.0, 60.0]
)
llm_tokens_total = Counter(
'llm_tokens_total',
'Total LLM tokens',
['provider', 'model', 'type'] # type: input, output
)
# System metrics
active_users = Gauge(
'active_users_total',
'Number of active users'
)
memory_usage_bytes = Gauge(
'process_resident_memory_bytes',
'Process resident memory in bytes'
)
cpu_usage = Gauge(
'process_cpu_seconds_total',
'Total process CPU time in seconds'
)
def track_http_requests(func):
"""Decorator to track HTTP request metrics."""
@wraps(func)
async def wrapper(request: Request, *args, **kwargs):
start_time = time.time()
try:
response = await func(request, *args, **kwargs)
status = str(response.status_code)
except Exception as e:
status = "500"
logger.error(f"HTTP request error: {e}")
raise
finally:
duration = time.time() - start_time
# Record metrics
http_requests_total.labels(
method=request.method,
endpoint=request.url.path,
status=status
).inc()
http_request_duration.labels(
method=request.method,
endpoint=request.url.path
).observe(duration)
return response
return wrapper
def track_workflow(workflow_type: str):
"""Decorator to track workflow execution metrics."""
def decorator(func):
@wraps(func)
async def wrapper(*args, **kwargs):
start_time = time.time()
status = "success"
try:
result = await func(*args, **kwargs)
return result
except Exception as e:
status = "error"
logger.error(f"Workflow {workflow_type} error: {e}")
raise
finally:
duration = time.time() - start_time
workflow_total.labels(
workflow_type=workflow_type,
status=status
).inc()
workflow_duration.labels(
workflow_type=workflow_type
).observe(duration)
return wrapper
return decorator
def track_agent(agent_name: str):
"""Decorator to track agent execution metrics."""
def decorator(func):
@wraps(func)
async def wrapper(*args, **kwargs):
start_time = time.time()
status = "success"
try:
result = await func(*args, **kwargs)
return result
except Exception as e:
status = "error"
logger.error(f"Agent {agent_name} error: {e}")
raise
finally:
duration = time.time() - start_time
agent_total.labels(
agent_name=agent_name,
status=status
).inc()
agent_execution_duration.labels(
agent_name=agent_name
).observe(duration)
return wrapper
return decorator
def track_llm_request(provider: str, model: str):
"""Decorator to track LLM request metrics."""
def decorator(func):
@wraps(func)
async def wrapper(*args, **kwargs):
start_time = time.time()
try:
result = await func(*args, **kwargs)
# Track tokens if available
if hasattr(result, 'usage'):
if hasattr(result.usage, 'prompt_tokens'):
llm_tokens_total.labels(
provider=provider,
model=model,
type="input"
).inc(result.usage.prompt_tokens)
if hasattr(result.usage, 'completion_tokens'):
llm_tokens_total.labels(
provider=provider,
model=model,
type="output"
).inc(result.usage.completion_tokens)
return result
except Exception as e:
logger.error(f"LLM request error: {e}")
raise
finally:
duration = time.time() - start_time
llm_requests_total.labels(
provider=provider,
model=model
).inc()
llm_request_duration.labels(
provider=provider,
model=model
).observe(duration)
return wrapper
return decorator
def track_cache_operation(cache_type: str):
"""Track cache operations."""
def record_hit():
cache_hits_total.labels(cache_type=cache_type).inc()
def record_miss():
cache_misses_total.labels(cache_type=cache_type).inc()
return record_hit, record_miss
def update_system_metrics():
"""Update system-level metrics."""
import os
import psutil
process = psutil.Process(os.getpid())
# Memory usage
memory_usage_bytes.set(process.memory_info().rss)
# CPU usage
cpu_usage.set(process.cpu_times().user)
def metrics_endpoint():
"""FastAPI endpoint to serve Prometheus metrics."""
def metrics():
update_system_metrics()
return Response(generate_latest(), media_type=CONTENT_TYPE_LATEST)
return metrics
class MetricsCollector:
"""Central metrics collector for the application."""
def __init__(self):
self.start_time = time.time()
self.request_counts: dict[str, int] = {}
self.error_counts: dict[str, int] = {}
def increment_request_count(self, endpoint: str):
"""Increment request count for an endpoint."""
self.request_counts[endpoint] = self.request_counts.get(endpoint, 0) + 1
def increment_error_count(self, error_type: str):
"""Increment error count for an error type."""
self.error_counts[error_type] = self.error_counts.get(error_type, 0) + 1
def get_uptime_seconds(self) -> float:
"""Get application uptime in seconds."""
return time.time() - self.start_time
def get_request_rate(self) -> float:
"""Get current request rate per second."""
uptime = self.get_uptime_seconds()
if uptime > 0:
total_requests = sum(self.request_counts.values())
return total_requests / uptime
return 0.0
def get_error_rate(self) -> float:
"""Get current error rate."""
total_requests = sum(self.request_counts.values())
total_errors = sum(self.error_counts.values())
if total_requests > 0:
return total_errors / total_requests
return 0.0
# Global metrics collector instance
metrics_collector = MetricsCollector()
|