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af8b301 | 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 | """
Lightweight observability layer.
Tracks per-request metrics and persists them to logs/logs.json.
Metrics collected:
- timestamp
- model_used
- backend
- prompt_length (chars)
- response_length (chars)
- estimated_prompt_tokens
- estimated_response_tokens
- latency_ms
- was_refused (safety refusal)
- violation_type (if refused)
- session_id
"""
from __future__ import annotations
import json
import logging
import os
import time
import uuid
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import List, Optional
logger = logging.getLogger(__name__)
LOGS_PATH = Path("logs/logs.json")
# ---------------------------------------------------------------------------
# Data model
# ---------------------------------------------------------------------------
@dataclass
class RequestLog:
session_id: str
model_used: str
backend: str
prompt_length: int
response_length: int
estimated_prompt_tokens: int
estimated_response_tokens: int
latency_ms: float
was_refused: bool
violation_type: Optional[str]
timestamp: str = field(default_factory=lambda: time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()))
request_id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
def to_dict(self) -> dict:
return asdict(self)
# ---------------------------------------------------------------------------
# Token estimation
# ---------------------------------------------------------------------------
def estimate_tokens(text: str) -> int:
"""
Rough token estimate: ~4 characters per token (standard approximation).
Good enough for observability; not a replacement for a proper tokenizer.
"""
return max(1, len(text) // 4)
# ---------------------------------------------------------------------------
# Observability manager
# ---------------------------------------------------------------------------
class ObservabilityManager:
"""
Collects and persists request logs.
Thread-safe for single-process Streamlit apps.
"""
def __init__(self, log_path: Path = LOGS_PATH):
self.log_path = log_path
self._logs: List[RequestLog] = []
self._session_id: str = str(uuid.uuid4())[:8]
self._ensure_log_dir()
self._load_existing_logs()
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def log_request(
self,
model_used: str,
backend: str,
prompt: str,
response: str,
latency_ms: float,
was_refused: bool = False,
violation_type: Optional[str] = None,
) -> RequestLog:
"""Record a single request/response pair."""
log = RequestLog(
session_id=self._session_id,
model_used=model_used,
backend=backend,
prompt_length=len(prompt),
response_length=len(response),
estimated_prompt_tokens=estimate_tokens(prompt),
estimated_response_tokens=estimate_tokens(response),
latency_ms=round(latency_ms, 2),
was_refused=was_refused,
violation_type=violation_type,
)
self._logs.append(log)
self._persist(log)
return log
def get_all_logs(self) -> List[dict]:
return [log.to_dict() for log in self._logs]
def get_summary(self) -> dict:
"""Aggregate statistics across all logged requests."""
if not self._logs:
return {}
total = len(self._logs)
refused = sum(1 for l in self._logs if l.was_refused)
latencies = [l.latency_ms for l in self._logs if not l.was_refused]
avg_latency = sum(latencies) / len(latencies) if latencies else 0.0
total_tokens = sum(
l.estimated_prompt_tokens + l.estimated_response_tokens
for l in self._logs
)
by_backend: dict = {}
for log in self._logs:
b = log.backend
if b not in by_backend:
by_backend[b] = {"count": 0, "latencies": [], "refused": 0}
by_backend[b]["count"] += 1
by_backend[b]["latencies"].append(log.latency_ms)
if log.was_refused:
by_backend[b]["refused"] += 1
backend_summary = {}
for b, data in by_backend.items():
lats = data["latencies"]
backend_summary[b] = {
"requests": data["count"],
"avg_latency_ms": round(sum(lats) / len(lats), 2) if lats else 0,
"refusals": data["refused"],
}
return {
"total_requests": total,
"total_refusals": refused,
"refusal_rate": round(refused / total, 3) if total else 0,
"avg_latency_ms": round(avg_latency, 2),
"total_estimated_tokens": total_tokens,
"by_backend": backend_summary,
}
def new_session(self) -> None:
"""Start a new session (new session_id)."""
self._session_id = str(uuid.uuid4())[:8]
# ------------------------------------------------------------------
# Internal
# ------------------------------------------------------------------
def _ensure_log_dir(self) -> None:
self.log_path.parent.mkdir(parents=True, exist_ok=True)
def _load_existing_logs(self) -> None:
"""Load existing logs from disk on startup."""
if not self.log_path.exists():
return
try:
with open(self.log_path, "r") as f:
raw = json.load(f)
for entry in raw:
try:
self._logs.append(RequestLog(**entry))
except Exception:
pass # Skip malformed entries
except Exception as exc:
logger.warning("Could not load existing logs: %s", exc)
def _persist(self, log: RequestLog) -> None:
"""Append a single log entry to the JSON file."""
try:
existing: List[dict] = []
if self.log_path.exists():
with open(self.log_path, "r") as f:
existing = json.load(f)
existing.append(log.to_dict())
with open(self.log_path, "w") as f:
json.dump(existing, f, indent=2)
except Exception as exc:
logger.error("Failed to persist log: %s", exc)
# ---------------------------------------------------------------------------
# Context manager for timing
# ---------------------------------------------------------------------------
class Timer:
"""Simple context manager for measuring elapsed time in milliseconds."""
def __init__(self):
self.elapsed_ms: float = 0.0
self._start: float = 0.0
def __enter__(self) -> "Timer":
self._start = time.perf_counter()
return self
def __exit__(self, *_) -> None:
self.elapsed_ms = (time.perf_counter() - self._start) * 1000
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