perf: concurrent summarize-all (parallel LLM, serial DB)

Refactor summarize_all to run LLM summarization across a thread pool (default 8
workers) while keeping all SQLite reads/writes on the main thread (the single
connection is never shared across threads). Extract _summarize_transcript
(transcript -> gist, no DB) for the worker.

The MI50 proved far too slow for the large-transcript backfill (~29 summaries in
9h due to gfx906 prefill); on cloud gpt-4o-mini with concurrency this runs at
~30 summaries/minute (~17 min for the full backfill, ~$2). MI50 stays the chat
backend where small prompts make it snappy.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lyra/summary.py

@@ -11,6 +11,7 @@ from __future__ import annotations
 
 import sys
 import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from lyra import config, llm, logbus, memory
 from lyra.llm import Backend, Message
@@ -64,25 +65,23 @@ def _summarize_text(text: str, backend: Backend) -> str:
     raise RuntimeError("unreachable")
 
 
+def _summarize_transcript(transcript: str, backend: Backend) -> str:
+    """Transcript -> gist (LLM only, no DB). Chunks + merges if oversized."""
+    if len(transcript) <= MAX_TRANSCRIPT_CHARS:
+        return _summarize_text(transcript, backend)
+    partials = [_summarize_text(c, backend) for c in _chunk(transcript, MAX_TRANSCRIPT_CHARS)]
+    return _summarize_text("Partial summaries to merge:\n\n" + "\n\n".join(partials), backend)
+
+
 def summarize_session(session_id: str, backend: Backend | None = None) -> str | None:
     """(Re)generate and store the gist for a session. Returns the summary text."""
     exchanges = memory.history(session_id)
     if not exchanges:
         return None
-
     backend = backend or config.load().summary_backend
-    transcript = _transcript(exchanges)
-    if len(transcript) <= MAX_TRANSCRIPT_CHARS:
-        gist = _summarize_text(transcript, backend)
-    else:
-        partials = [_summarize_text(c, backend) for c in _chunk(transcript, MAX_TRANSCRIPT_CHARS)]
-        gist = _summarize_text("Partial summaries to merge:\n\n" + "\n\n".join(partials), backend)
-
+    gist = _summarize_transcript(_transcript(exchanges), backend)
     memory.store_summary(session_id, gist, exchanges[-1].id)
-    logbus.log(
-        "info", "summarized session", session=session_id,
-        exchanges=len(exchanges), backend=backend,
-    )
+    logbus.log("info", "summarized session", session=session_id, exchanges=len(exchanges))
     return gist
 
 
@@ -92,31 +91,53 @@ def maybe_summarize(session_id: str, backend: Backend | None = None) -> None:
         summarize_session(session_id, backend=backend)
 
 
-def summarize_all(backend: Backend | None = None, limit: int | None = None) -> dict:
-    """Summarize every session that needs it. Idempotent and resumable: sessions
-    with an up-to-date summary are skipped, so re-running continues where it left off.
+def summarize_all(
+    backend: Backend | None = None, limit: int | None = None, workers: int = 8
+) -> dict:
+    """Summarize every session that needs it. Idempotent and resumable.
+
+    LLM summarization runs concurrently across `workers` threads (great for a
+    cloud backend). DB reads (loading transcripts) and writes (store_summary,
+    which also embeds) happen on the main thread, so the single SQLite
+    connection is never touched from multiple threads.
     """
-    sessions = memory.list_sessions()
-    done, skipped, failed = 0, 0, 0
-    for s in sessions:
+    backend = backend or config.load().summary_backend
+
+    # Main thread: collect the work (transcripts) for sessions needing a summary.
+    todo: list[tuple[str, str, int]] = []
+    for s in memory.list_sessions():
         sid = s["id"]
         if memory.get_summary(sid) and memory.unsummarized_count(sid) == 0:
-            skipped += 1
             continue
+        exchanges = memory.history(sid)
+        if not exchanges:
+            continue
+        todo.append((sid, _transcript(exchanges), exchanges[-1].id))
+        if limit is not None and len(todo) >= limit:
+            break
+
+    done, failed = 0, 0
+    logbus.log("info", "summarize-all starting", todo=len(todo), backend=backend, workers=workers)
+
+    def work(item: tuple[str, str, int]) -> tuple[str, str, int]:
+        sid, transcript, last_id = item
+        return sid, _summarize_transcript(transcript, backend), last_id
+
+    with ThreadPoolExecutor(max_workers=workers) as pool:
+        futures = {pool.submit(work, item): item for item in todo}
+        for fut in as_completed(futures):
+            sid = futures[fut][0]
             try:
-            summarize_session(sid, backend=backend)
+                _, gist, last_id = fut.result()
+                memory.store_summary(sid, gist, last_id)  # main thread: embed + write
+                done += 1
             except Exception as exc:
-            # Don't let one bad session kill the batch; log and move on (it'll
-            # be retried on the next run, since it stays unsummarized).
                 failed += 1
                 logbus.log("error", "summarize failed", session=sid, error=str(exc)[:120])
-            continue
-        done += 1
-        if done % 25 == 0:
-            logbus.log("info", "summarize-all progress", summarized=done, skipped=skipped, failed=failed)
-        if limit is not None and done >= limit:
-            break
-    report = {"summarized": done, "skipped": skipped, "failed": failed, "total": len(sessions)}
+            if (done + failed) % 25 == 0:
+                logbus.log("info", "summarize-all progress", done=done, failed=failed, total=len(todo))
+
+    report = {"summarized": done, "failed": failed, "total": len(todo)}
     logbus.log("info", "summarize-all complete", **report)
     return report