- Add context_block param to summarize() and thread it into _PROMPT_TEMPLATE
- Wire retrieve_context/format_context_block into diagnose_stream() before
log search; emit context SSE event (facts + chunks) to the client
- 3 new tests covering prompt injection and SSE event emission (155 total, all pass)
POST /api/inference/task with product=turnstone task=log_analysis routes to
the security reasoning model assigned in cf-orch. Falls back to the OpenAI-
compat /v1/chat/completions path on 404 (no assignment) or if the task
endpoint is absent (local instances, example-node).
- Diagnose: add source_filter param threaded through entries_in_window,
search, _diagnose, and DiagnoseRequest — clicking diagnose on a
dashboard source now scopes both keyword and window hits to that source
- QuickCapture: read route.query.source; show scope badge with clear ✕;
auto-run when source param is present without a query
- DashboardView: pass source= (not q=) when navigating to diagnose
- collect_cluster_logs.sh: auto-discover Docker containers on all nodes
(Heimdall non-watched, Navi, Strahl via SSH); collect Cass Plex logs
via SSH; write to per-node dirs for directory-mode ingest
- turnstone-cluster.service: add --reload for hot-reload during dev
Turnstone now calls /v1/chat/completions instead of Ollama's /api/generate.
This format works with both local Ollama (>=0.1.24) and a remote cf-orch
coordinator, enabling GPU-less nodes like Xander's to route diagnoses through
the cluster without any local model.
- llm.py: OpenAI-compat messages format, optional Bearer auth header
- diagnose.py: thread llm_api_key through the call chain
- rest.py: llm_api_key pref (default empty), SettingsBody field, passed to diagnose
- SettingsView.vue: API Key field, label updated from "Ollama URL" to "LLM Endpoint URL"
- tests: updated mocks for new response shape; added bearer token assertion test
Turnstone incidents now carry an issue_type tag (free-text with datalist
suggestions) used to categorize patterns for signature building.
Backend:
- Incident model gains issue_type; additive ALTER TABLE migration keeps
existing DBs working without a full schema rebuild
- New received_bundles table stores incoming JSON bundles with indexes on
bundled_at and issue_type
- build_bundle() assembles incident + related log entries into a versioned
bundle dict; store_bundle()/list_bundles()/get_bundle() for the receiver
- POST /api/incidents/{id}/send — pushes bundle to TURNSTONE_BUNDLE_ENDPOINT
- GET /api/incidents/{id}/bundle — export without sending
- POST /api/bundles — receive and store an incoming bundle
- GET /api/bundles — list all received bundles
- TURNSTONE_SOURCE_HOST and TURNSTONE_BUNDLE_ENDPOINT env vars; auto-set
source host from hostname in podman-standalone.sh
Frontend:
- Incidents form: issue_type field with datalist suggestions; Type column
in the table; Send Bundle button + status feedback in the detail drawer
- New BundlesView: collapsible bundle rows, inline JSON parse (no extra
round-trip), Export JSON download button
- Router and nav updated with /bundles route
- Add GET /api/stats endpoint with 24h windowed aggregation (criticals,
errors, per-source health, recent criticals list)
- Fix timestamp format bug: strftime('%Y-%m-%dT%H:%M:%S', ...) to match
stored ISO-8601 T-separated timestamps (datetime('now') uses space)
- Add composite index idx_ts_repeat(timestamp_iso, repeat_count) — drops
stats query from 3.5 s to <1 ms by resolving both WHERE conditions
from the index without table row fetches
- New DashboardView: 3 stat cards, source health table with health dots,
diagnose-per-source button, recent criticals panel, zero-state card
- Router default / → /dashboard; Dashboard first in nav
- DiagnoseView: reads ?q= query param on mount and auto-runs; shows
formatted LLM summary block
- LogEntryRow: expand/collapse for long entries (>200 chars or multiline)
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).
