When diagnose() auto-detects a source name, FTS keyword scoring can
bury real errors whose text doesn't match the symptom query. Add
recent_source_errors() — a plain-SQL scan ordered by timestamp — so
the most recent errors from a known service always surface regardless
of keyword overlap.
- Add `incidents` table to SQLite schema (id, label, started_at, ended_at,
notes, created_at, severity)
- Extract `ensure_schema()` from ingest pipeline so tables are always
created at startup, not only during ingest
- New `app/services/incidents.py`: create/list/get/delete + time-window
entry association (FTS keyword search + raw window fallback)
- New `entries_in_window()` in search.py: plain SQL scan for incident
detail when keyword FTS returns nothing
- REST endpoints: POST/GET /api/incidents, GET/DELETE /api/incidents/{id}
- Incident detail returns up to 100 associated log entries sorted by
timestamp, prioritising FTS keyword hits then ERROR/CRITICAL then all
Ingest pipeline (journald / Caddy / Docker-wrapped formats) with
per-source state tracking (repeat dedup, out-of-order detection),
named pattern tagging at ingest time, and idempotent SHA1-keyed writes.
FTS5 search layer with porter stemmer, severity/source/pattern/time
filters, and BM25 ranking. MCP server (FastMCP stdio) with three tools:
search_logs, diagnose, list_log_sources — compatible with both
Claude Code and Copilot CLI.
WAL mode enabled on all connections. FTS index auto-built after ingest.
MCP configs included for Claude Code (.mcp.json) and Copilot CLI
(.github/copilot/mcp.json).
