feat: add Settings and Suite pages with comprehensive settings management and question suite builder

- Implemented SettingsPage for configuring default provider, temperature, and model paths.
- Added SuitePage for managing a suite of questions with features to add, edit, duplicate, and delete questions.
- Introduced TypeScript configuration files for app and node environments.
- Set up Vite configuration for development server with API proxying to backend.
This commit is contained in:
Christopher Clendening
2026-07-27 17:41:08 -04:00
parent ce0e022f11
commit adf61ae1a0
50 changed files with 10952 additions and 164 deletions
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"""Background execution of diagnostic runs with server-sent-event streaming.
The engine's ``run_suite`` is synchronous and blocking, so each run executes on
a worker thread. Progress is pushed back onto the asyncio loop through
``loop.call_soon_threadsafe`` and fanned out to any connected SSE subscribers.
Every event is also retained on the run, so a client that connects late (or
reconnects after a dropped connection) replays the full history before it
starts following the live feed.
"""
from __future__ import annotations
import asyncio
import json
import threading
import time
import uuid
from collections import OrderedDict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, AsyncIterator, Dict, List, Optional
from .core_bridge import (
RESULTS_DIR,
TemplateNotFoundError,
TestRunError,
append_sections,
finalize_report_summary,
replace_analysis_section,
run_suite,
sanitize_model_name,
)
from .plugins import (
GradeEntry,
build_run_record,
build_test_record,
collect_report_sections,
get_plugin_manager,
render_grade_section,
)
TERMINAL_EVENTS = {"run.completed", "run.failed", "run.cancelled"}
MAX_RUN_HISTORY = 20
KEEPALIVE_SECONDS = 15.0
class RunCancelled(Exception):
"""Raised inside the engine's progress callback to unwind a running suite.
The engine has no cancellation hook of its own. Raising from the callback
stops the loop cleanly between tests -- results already written to the
report are kept and the summary is finalized with whatever completed.
"""
@dataclass
class TestOutcome:
index: int
title: str
filename: str
status: str
elapsed: float
response: Optional[str] = None
error: Optional[str] = None
metrics: Optional[Dict[str, Any]] = None
grades: List[Dict[str, Any]] = field(default_factory=list)
@property
def score(self) -> Optional[float]:
if not self.grades:
return None
return sum(g["score"] for g in self.grades) / len(self.grades)
def as_event(self, total: int) -> Dict[str, Any]:
return {
"type": "test.completed",
"index": self.index,
"total": total,
"title": self.title,
"filename": self.filename,
"status": self.status,
"elapsed": round(self.elapsed, 2),
"response": self.response,
"error": self.error,
"metrics": self.metrics,
"grades": self.grades,
"score": self.score,
}
@dataclass
class RunState:
id: str
provider: str
model_id: str
model_label: str
temperature: float
total: int
status: str = "running"
started_at: float = field(default_factory=time.time)
finished_at: Optional[float] = None
report_name: Optional[str] = None
error: Optional[str] = None
outcomes: List[TestOutcome] = field(default_factory=list)
events: List[Dict[str, Any]] = field(default_factory=list)
subscribers: List["asyncio.Queue[Dict[str, Any]]"] = field(default_factory=list)
cancel_event: threading.Event = field(default_factory=threading.Event)
grades: Dict[int, List[GradeEntry]] = field(default_factory=dict)
prompts_by_filename: Dict[str, str] = field(default_factory=dict)
@property
def completed(self) -> int:
return len(self.outcomes)
@property
def overall_score(self) -> Optional[float]:
scores = [o.score for o in self.outcomes if o.score is not None]
return sum(scores) / len(scores) if scores else None
def summary(self) -> Dict[str, Any]:
ok = [o for o in self.outcomes if o.status == "ok" and o.metrics]
if ok:
avg_tps = sum(float(o.metrics.get("tokens_per_second", 0)) for o in ok) / len(ok)
avg_ttft = sum(float(o.metrics.get("time_to_first_token", 0)) for o in ok) / len(ok)
total_tokens = sum(int(o.metrics.get("total_tokens", 0)) for o in ok)
else:
avg_tps = avg_ttft = 0.0
total_tokens = 0
overall = self.overall_score
return {
"average_tokens_per_second": round(avg_tps, 2),
"average_time_to_first_token": round(avg_ttft, 2),
"total_tokens": total_tokens,
"passed": len([o for o in self.outcomes if o.status == "ok"]),
"failed": len([o for o in self.outcomes if o.status == "error"]),
"overall_score": round(overall, 4) if overall is not None else None,
"graded": len([o for o in self.outcomes if o.score is not None]),
}
def as_dict(self) -> Dict[str, Any]:
return {
"id": self.id,
"status": self.status,
"provider": self.provider,
"model_id": self.model_id,
"model_label": self.model_label,
"temperature": self.temperature,
"total": self.total,
"completed": self.completed,
"started_at": self.started_at,
"finished_at": self.finished_at,
"report_name": self.report_name,
"error": self.error,
"summary": self.summary(),
}
def _format_sse(event: Dict[str, Any]) -> str:
return f"event: {event['type']}\ndata: {json.dumps(event)}\n\n"
class RunManager:
"""Owns the lifecycle of diagnostic runs.
Only one run may be active at a time: the local engine loads model weights
into memory, so overlapping runs would compete for RAM and produce
meaningless throughput numbers.
"""
def __init__(self) -> None:
self._runs: "OrderedDict[str, RunState]" = OrderedDict()
self._active_id: Optional[str] = None
self._lock = threading.Lock()
# ------------------------------------------------------------------ query
def get(self, run_id: str) -> Optional[RunState]:
with self._lock:
return self._runs.get(run_id)
def active(self) -> Optional[RunState]:
with self._lock:
if self._active_id is None:
return None
return self._runs.get(self._active_id)
def history(self) -> List[RunState]:
with self._lock:
return list(reversed(self._runs.values()))
# ------------------------------------------------------------------ start
def start(
self,
*,
provider_name: str,
model_id: str,
model_label: str,
temperature: float,
prompts: List[Dict[str, str]],
loop: asyncio.AbstractEventLoop,
) -> RunState:
with self._lock:
if self._active_id is not None:
active = self._runs.get(self._active_id)
if active is not None and active.status == "running":
raise RuntimeError(
f"A run is already in progress ({active.model_label}). "
"Cancel it before starting another."
)
run = RunState(
id=uuid.uuid4().hex[:12],
provider=provider_name,
model_id=model_id,
model_label=model_label,
temperature=temperature,
total=len(prompts),
prompts_by_filename={
p.get("filename", ""): p.get("prompt", "") for p in prompts
},
)
self._runs[run.id] = run
self._active_id = run.id
while len(self._runs) > MAX_RUN_HISTORY:
oldest, _ = next(iter(self._runs.items()))
if oldest == self._active_id:
break
self._runs.pop(oldest)
run.events.append(
{
"type": "run.started",
"run": run.as_dict(),
"tests": [
{"index": i, "title": p["title"], "filename": p["filename"]}
for i, p in enumerate(prompts, start=1)
],
}
)
thread = threading.Thread(
target=self._worker,
args=(run, prompts, loop),
name=f"lm-gambit-run-{run.id}",
daemon=True,
)
thread.start()
return run
def cancel(self, run_id: str) -> bool:
run = self.get(run_id)
if run is None or run.status != "running":
return False
run.cancel_event.set()
return True
# ----------------------------------------------------------------- worker
def _worker(
self,
run: RunState,
prompts: List[Dict[str, str]],
loop: asyncio.AbstractEventLoop,
) -> None:
last_tick = time.time()
def progress_callback(
index: int,
total: int,
prompt: Dict[str, str],
result: Dict[str, Any],
) -> None:
nonlocal last_tick
now = time.time()
elapsed = now - last_tick
last_tick = now
if "error" in result:
outcome = TestOutcome(
index=index,
title=prompt.get("title", f"Test {index}"),
filename=prompt.get("filename", f"test{index}"),
status="error",
elapsed=elapsed,
error=str(result["error"]),
)
else:
metrics = result.get("metrics") or {}
outcome = TestOutcome(
index=index,
title=prompt.get("title", f"Test {index}"),
filename=prompt.get("filename", f"test{index}"),
status="ok",
elapsed=elapsed,
response=str(result.get("response", "")),
metrics=dict(metrics),
)
run.outcomes.append(outcome)
self._grade(run, outcome)
# Plugins observe the question after grading, so on_test_complete
# sees the same record the report will.
record = build_test_record(
outcome,
total=run.total,
prompt_text=run.prompts_by_filename.get(outcome.filename, ""),
)
get_plugin_manager().emit("on_test_complete", record)
self._publish(loop, run, outcome.as_event(total))
if run.cancel_event.is_set():
raise RunCancelled
get_plugin_manager().emit(
"on_run_start",
build_run_record(run, prompts_by_filename=run.prompts_by_filename),
)
try:
report_path = run_suite(
provider_name=run.provider,
model_id=run.model_id,
temperature=run.temperature,
progress_callback=progress_callback,
prompts=prompts,
)
except RunCancelled:
self._finalize_partial_report(run)
run.status = "cancelled"
run.finished_at = time.time()
self._apply_plugin_report(run)
self._publish(
loop,
run,
{"type": "run.cancelled", "run": run.as_dict()},
)
except (TestRunError, TemplateNotFoundError) as exc:
run.status = "failed"
run.error = str(exc)
run.finished_at = time.time()
self._publish(
loop,
run,
{"type": "run.failed", "message": str(exc), "run": run.as_dict()},
)
except Exception as exc: # noqa: BLE001 - surface engine faults to the UI
run.status = "failed"
run.error = f"{type(exc).__name__}: {exc}"
run.finished_at = time.time()
self._publish(
loop,
run,
{"type": "run.failed", "message": run.error, "run": run.as_dict()},
)
else:
run.status = "completed"
run.report_name = report_path.name
run.finished_at = time.time()
self._apply_plugin_report(run)
self._publish(
loop,
run,
{"type": "run.completed", "run": run.as_dict()},
)
finally:
with self._lock:
if self._active_id == run.id:
self._active_id = None
get_plugin_manager().emit("on_run_complete", self._record(run))
# ---------------------------------------------------------------- plugins
def _report_path(self, run: RunState) -> Path:
return RESULTS_DIR / f"automated_report_{sanitize_model_name(run.model_label)}.md"
def _record(self, run: RunState):
path = self._report_path(run)
return build_run_record(
run,
prompts_by_filename=run.prompts_by_filename,
report_path=path if path.exists() else None,
)
def _grade(self, run: RunState, outcome: TestOutcome) -> None:
"""Run grader plugins over one answer and attach the results.
Graders run on this worker thread, so a slow grader delays the next
question. That is deliberate: grading is part of the run, and the
report should not be finalized before it lands.
Questions the provider failed are never graded — there is no answer to
judge, and scoring them would let one careless plugin drag down the
overall score. Plugins that want to react to failures use
``on_test_complete``, which fires for every question.
"""
if outcome.status != "ok":
return
record = build_test_record(
outcome,
total=run.total,
prompt_text=run.prompts_by_filename.get(outcome.filename, ""),
)
graded = get_plugin_manager().grade(record)
if not graded:
return
run.grades[outcome.index] = [
GradeEntry(grader=name, grade=grade) for name, grade in graded
]
outcome.grades = [
{
"grader": name,
"score": grade.score,
"label": grade.label,
"notes": grade.notes,
}
for name, grade in graded
]
def _apply_plugin_report(self, run: RunState) -> None:
"""Write grades and plugin sections into the finished report."""
report_path = self._report_path(run)
if not report_path.exists():
return
record = self._record(run)
manager = get_plugin_manager()
grade_markdown = render_grade_section(record)
if grade_markdown:
try:
replace_analysis_section(report_path, grade_markdown)
except OSError:
pass
try:
sections = collect_report_sections(record, manager)
except Exception: # noqa: BLE001 - a plugin fault must not fail the run
sections = []
if sections:
try:
append_sections(report_path, sections)
except OSError:
pass
def _finalize_partial_report(self, run: RunState) -> None:
"""Write the summary block for a run that stopped early."""
report_path = RESULTS_DIR / f"automated_report_{sanitize_model_name(run.model_label)}.md"
if not report_path.exists():
return
results: List[Dict[str, Any]] = []
for outcome in run.outcomes:
if outcome.status == "ok" and outcome.metrics:
results.append({"response": outcome.response, "metrics": outcome.metrics})
else:
results.append({"error": outcome.error or "cancelled"})
try:
finalize_report_summary(report_path, results)
except (OSError, KeyError):
return
run.report_name = report_path.name
# -------------------------------------------------------------- streaming
def _publish(
self,
loop: asyncio.AbstractEventLoop,
run: RunState,
event: Dict[str, Any],
) -> None:
"""Hand an event from the worker thread to the event loop."""
try:
loop.call_soon_threadsafe(self._emit, run, event)
except RuntimeError:
# Loop already closed (server shutting down); keep the history only.
run.events.append(event)
@staticmethod
def _emit(run: RunState, event: Dict[str, Any]) -> None:
run.events.append(event)
for queue in list(run.subscribers):
queue.put_nowait(event)
async def stream(self, run_id: str) -> AsyncIterator[str]:
run = self.get(run_id)
if run is None:
yield _format_sse({"type": "run.failed", "message": "Unknown run id."})
return
queue: "asyncio.Queue[Dict[str, Any]]" = asyncio.Queue()
# Subscribing and snapshotting in the same synchronous block guarantees
# no event is delivered twice or dropped between the two.
run.subscribers.append(queue)
history = list(run.events)
try:
for event in history:
yield _format_sse(event)
if event["type"] in TERMINAL_EVENTS:
return
while True:
try:
event = await asyncio.wait_for(queue.get(), timeout=KEEPALIVE_SECONDS)
except asyncio.TimeoutError:
yield ": keepalive\n\n"
continue
yield _format_sse(event)
if event["type"] in TERMINAL_EVENTS:
return
finally:
if queue in run.subscribers:
run.subscribers.remove(queue)
run_manager = RunManager()