Surface per-instance scores (SampleScore), structured feedback
(TrialFeedback), Pareto frontier tracking, pillar-targeted mutation,
and config merge strategies. Store migration adds new columns with
backward-compatible deserialization.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add the orchestration layer that ties together the LLM optimizer, trial
runner, and persistence into a propose-evaluate-analyze loop with early
stopping and recipe export. 36 new tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement LLMOptimizer that uses a cloud LLM to propose and analyze
OpenJarvis configurations, inspired by DSPy's GEPA approach with
textual trace feedback rather than just scalar rewards. 46 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add TrialRunner to bridge TrialConfig to the eval framework (EvalRunner),
TraceJudge for LLM-as-judge scoring of agent traces, and FeedbackCollector
for aggregating explicit, thumbs, and judge-driven feedback signals.
68 tests pass covering all three modules.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Synthesize reusable benchmarks from interaction traces with feedback
scores. Mines high-quality traces, groups by query class, picks the
best reference per class, and exposes results through DatasetProvider
and LLM-judge Scorer so EvalRunner can evaluate against personal
workflow patterns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add the optimize module with core data types (SearchDimension, SearchSpace,
TrialConfig, TrialResult, OptimizationRun) and search space builder
(build_search_space, DEFAULT_SEARCH_SPACE) covering all 5 pillars. Includes
TrialConfig.to_recipe() mapping and SearchSpace.to_prompt_description()
for LLM-readable rendering. 66 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- .gitignore: keep both Rust target/ and Claude plan artifacts/Tauri schemas sections
- uv.lock: keep main's provides-extras with speech, eval-wandb, eval-sheets extras
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Introduce ResultTracker ABC with pluggable experiment tracking for the
eval framework, enabling per-sample streaming to W&B and summary-row
appending to Google Sheets — critical for the NeurIPS paper sweep.
- ResultTracker ABC: on_run_start/on_result/on_summary/on_run_end
- WandbTracker: per-sample wandb.log with sample/ prefix, flattened
MetricStats in run summary, reinit=True for suite mode
- SheetsTracker: summary-only (no per-sample API calls), 29-column
canonical row, idempotent header, service account + ADC auth
- EvalRunner: trackers wired into lifecycle, all calls try/except
wrapped so tracker failures never abort an eval run
- CLI: 7 new flags (--wandb-project/entity/tags/group,
--sheets-id/worksheet/creds) on both jarvis eval run and
python -m openjarvis.evals run
- Config: tracker fields parsed from TOML [run] section, propagated
through expand_suite() for suite mode
- pyproject.toml: eval-wandb and eval-sheets optional dependency groups
- 9 new tests (lifecycle, crash resilience, mock W&B/Sheets)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The transcribe_audio command uses reqwest::multipart which requires
the "multipart" feature flag on the reqwest crate.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Updates test count to ~3295, adds Speech subsystem section, API
endpoints, Phase 24 row, and speech extras to the docs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds openjarvis[speech] (faster-whisper) and openjarvis[speech-deepgram]
(deepgram-sdk) extras to pyproject.toml.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Proxies multipart audio upload and health check to the Python backend
for the desktop app's speech-to-text integration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds transcribeAudio() and fetchSpeechHealth() to the API client,
and the useSpeech React hook for managing mic recording lifecycle.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Define the abstract interface for the speech subsystem: SpeechBackend ABC
with transcribe/health/supported_formats methods, TranscriptionResult and
Segment dataclasses for structured transcription output.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add SpeechConfig dataclass with backend, model, language, device, and
compute_type fields. Wire it into JarvisConfig and load_config() so
[speech] TOML sections are loaded automatically.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
