Commit graph

7 commits

Author SHA1 Message Date
73a14bd782 fix(diagnose): add max_tokens to all LLM calls; fix reasoning card contrast
Truncation fix: call_llm() in _llm_client.py now accepts max_tokens (default
2048) and passes it in both the cf-orch task payload and the OpenAI-compat
fallback body. Hypothesizer uses max_tokens=1024 (JSON array output);
synthesizer and legacy summarize use 2048 (structured 5-section narrative).
Without this, backends use their own default (often 512 tokens), causing
mid-sentence truncation of the diagnosis output.

UI fix: reasoning card changed from bg-accent/5 border-accent/30 (opacity
modifiers on CSS variables don't compose reliably across themes) to the
callout pattern: bg-surface-raised with a solid border-l-4 border-accent.
Header label changed from text-text-dim to text-accent for visual anchoring.
Text remains text-text-primary for guaranteed contrast on both light and dark
themes.

Tracks: #56 (technical-level post-processor, filed as follow-on feature)
2026-05-27 22:23:36 -07:00
b5ce0a24b2 feat: inject environment context into diagnose pipeline and LLM prompt
- 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)
2026-05-13 16:29:26 -07:00
784a4072b4 feat: SSE streaming diagnose, severity filter pills, per-source-cap search
- diagnose_stream() async generator: status/summary/entries/reasoning/done events
- POST /api/diagnose/stream SSE endpoint wired in rest.py
- entries_in_window() gains per_source_cap to prevent high-volume sources crowding results
- QuickCapture: severity filter pills, filtered entries view, pipeline status spinner
- llm.py: remove overly broad HTTPStatusError re-raise
2026-05-13 15:45:35 -07:00
b70c89e7b5 feat: try cf-orch task endpoint first; fall back to direct model call
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).
2026-05-13 08:20:29 -07:00
a21c158917 fix: increase LLM summarize timeout to 120s for remote cf-orch routing
20s was too tight for first-request model swaps in Ollama (model cold load
can take 30-60s). 120s matches coordinator inference timeout.
2026-05-12 18:27:52 -07:00
7d46314e86 feat: switch LLM backend to OpenAI-compat; add cf-orch remote inference support
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 Contributor2'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
2026-05-12 12:58:38 -07:00
0882083755 feat: LLM reasoning layer — Ollama summarization on diagnose results 2026-05-11 11:35:07 -07:00