ProfessorNovaandClaude Opus 5
784b76baf7
Self-hosted knowledge management for SMEs: a split-screen Markdown editor whose sections an LLM refines while you write, and RAG question answering over the documents that result. FastAPI + Postgres/pgvector on the back, SvelteKit on the front, everything OpenAI-compatible and self-hostable. Squashed into a single commit; the development history stays local. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CA43ZJda8Rbp2hKXNy8f6b
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
"""In-process metrics registry — no dependencies, single event loop.
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Counters, gauges and histogram summaries (count/sum/min/max), labeled.
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Exposed as JSON via GET /api/admin/metrics; a Prometheus text exporter would
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sit on top of this registry rather than replace it.
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"""
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from collections import defaultdict
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from dataclasses import dataclass
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from typing import Any
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LabelKey = tuple[tuple[str, str], ...]
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def _key(labels: dict[str, str] | None) -> LabelKey:
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return tuple(sorted((labels or {}).items()))
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@dataclass
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class HistogramData:
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count: int = 0
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total: float = 0.0
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minimum: float | None = None
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maximum: float | None = None
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class MetricsRegistry:
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def __init__(self) -> None:
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self._counters: dict[str, dict[LabelKey, float]] = defaultdict(
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lambda: defaultdict(float)
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)
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self._gauges: dict[str, dict[LabelKey, float]] = defaultdict(dict)
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self._histograms: dict[str, dict[LabelKey, HistogramData]] = defaultdict(dict)
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def inc(
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self, name: str, labels: dict[str, str] | None = None, value: float = 1.0
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) -> None:
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self._counters[name][_key(labels)] += value
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def set_gauge(
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self, name: str, value: float, labels: dict[str, str] | None = None
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) -> None:
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self._gauges[name][_key(labels)] = value
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def observe(
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self, name: str, value: float, labels: dict[str, str] | None = None
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) -> None:
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data = self._histograms[name].setdefault(_key(labels), HistogramData())
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data.count += 1
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data.total += value
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data.minimum = value if data.minimum is None else min(data.minimum, value)
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data.maximum = value if data.maximum is None else max(data.maximum, value)
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def snapshot(self) -> dict[str, Any]:
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return {
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"counters": {
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name: [
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{"labels": dict(key), "value": value}
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for key, value in sorted(series.items())
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]
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for name, series in sorted(self._counters.items())
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},
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"gauges": {
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name: [
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{"labels": dict(key), "value": value}
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for key, value in sorted(series.items())
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]
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for name, series in sorted(self._gauges.items())
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},
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"histograms": {
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name: [
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{
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"labels": dict(key),
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"count": data.count,
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"sum": data.total,
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"min": data.minimum,
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"max": data.maximum,
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"avg": data.total / data.count if data.count else None,
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}
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for key, data in sorted(series.items())
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]
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for name, series in sorted(self._histograms.items())
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},
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}
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def reset(self) -> None:
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self._counters.clear()
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self._gauges.clear()
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self._histograms.clear()
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metrics = MetricsRegistry()
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