Pablan, as it stands

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

backend/app/metrics.py

@@ -0,0 +1,92 @@
+"""In-process metrics registry — no dependencies, single event loop.
+
+Counters, gauges and histogram summaries (count/sum/min/max), labeled.
+Exposed as JSON via GET /api/admin/metrics; a Prometheus text exporter would
+sit on top of this registry rather than replace it.
+"""
+
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any
+
+LabelKey = tuple[tuple[str, str], ...]
+
+
+def _key(labels: dict[str, str] | None) -> LabelKey:
+    return tuple(sorted((labels or {}).items()))
+
+
+@dataclass
+class HistogramData:
+    count: int = 0
+    total: float = 0.0
+    minimum: float | None = None
+    maximum: float | None = None
+
+
+class MetricsRegistry:
+    def __init__(self) -> None:
+        self._counters: dict[str, dict[LabelKey, float]] = defaultdict(
+            lambda: defaultdict(float)
+        )
+        self._gauges: dict[str, dict[LabelKey, float]] = defaultdict(dict)
+        self._histograms: dict[str, dict[LabelKey, HistogramData]] = defaultdict(dict)
+
+    def inc(
+        self, name: str, labels: dict[str, str] | None = None, value: float = 1.0
+    ) -> None:
+        self._counters[name][_key(labels)] += value
+
+    def set_gauge(
+        self, name: str, value: float, labels: dict[str, str] | None = None
+    ) -> None:
+        self._gauges[name][_key(labels)] = value
+
+    def observe(
+        self, name: str, value: float, labels: dict[str, str] | None = None
+    ) -> None:
+        data = self._histograms[name].setdefault(_key(labels), HistogramData())
+        data.count += 1
+        data.total += value
+        data.minimum = value if data.minimum is None else min(data.minimum, value)
+        data.maximum = value if data.maximum is None else max(data.maximum, value)
+
+    def snapshot(self) -> dict[str, Any]:
+        return {
+            "counters": {
+                name: [
+                    {"labels": dict(key), "value": value}
+                    for key, value in sorted(series.items())
+                ]
+                for name, series in sorted(self._counters.items())
+            },
+            "gauges": {
+                name: [
+                    {"labels": dict(key), "value": value}
+                    for key, value in sorted(series.items())
+                ]
+                for name, series in sorted(self._gauges.items())
+            },
+            "histograms": {
+                name: [
+                    {
+                        "labels": dict(key),
+                        "count": data.count,
+                        "sum": data.total,
+                        "min": data.minimum,
+                        "max": data.maximum,
+                        "avg": data.total / data.count if data.count else None,
+                    }
+                    for key, data in sorted(series.items())
+                ]
+                for name, series in sorted(self._histograms.items())
+            },
+        }
+
+    def reset(self) -> None:
+        self._counters.clear()
+        self._gauges.clear()
+        self._histograms.clear()
+
+
+metrics = MetricsRegistry()