self-review

server/reflector/hatchet/run_workers.py

@@ -37,6 +37,8 @@ def main() -> None:
     from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
     from reflector.hatchet.workflows import (  # noqa: PLC0415
         diarization_pipeline,
+        subject_workflow,
+        topic_chunk_workflow,
         track_workflow,
     )
 
@@ -44,7 +46,12 @@ def main() -> None:
 
     worker = hatchet.worker(
         "reflector-diarization-worker",
-        workflows=[diarization_pipeline, track_workflow],
+        workflows=[
+            diarization_pipeline,
+            subject_workflow,
+            topic_chunk_workflow,
+            track_workflow,
+        ],
     )
 
     def shutdown_handler(signum: int, frame) -> None:

server/reflector/hatchet/workflows/__init__.py

@@ -4,11 +4,23 @@ from reflector.hatchet.workflows.diarization_pipeline import (
     PipelineInput,
     diarization_pipeline,
 )
+from reflector.hatchet.workflows.subject_processing import (
+    SubjectInput,
+    subject_workflow,
+)
+from reflector.hatchet.workflows.topic_chunk_processing import (
+    TopicChunkInput,
+    topic_chunk_workflow,
+)
 from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
 
 __all__ = [
     "diarization_pipeline",
+    "subject_workflow",
+    "topic_chunk_workflow",
     "track_workflow",
     "PipelineInput",
+    "SubjectInput",
+    "TopicChunkInput",
     "TrackInput",
 ]

server/reflector/hatchet/workflows/diarization_pipeline.py

@@ -29,32 +29,42 @@ from reflector.hatchet.broadcast import (
 )
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.models import (
+    ActionItemsResult,
     ConsentResult,
     FinalizeResult,
     MixdownResult,
     PaddedTrackInfo,
+    PadTrackResult,
+    ParticipantInfo,
     ParticipantsResult,
+    ProcessSubjectsResult,
     ProcessTracksResult,
+    RecapResult,
     RecordingResult,
-    SummaryResult,
+    SubjectsResult,
+    SubjectSummaryResult,
     TitleResult,
+    TopicChunkResult,
     TopicsResult,
+    TranscribeTrackResult,
     WaveformResult,
     WebhookResult,
     ZulipResult,
 )
+from reflector.hatchet.workflows.subject_processing import (
+    SubjectInput,
+    subject_workflow,
+)
+from reflector.hatchet.workflows.topic_chunk_processing import (
+    TopicChunkInput,
+    topic_chunk_workflow,
+)
 from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
 from reflector.logger import logger
 from reflector.pipelines import topic_processing
 from reflector.processors import AudioFileWriterProcessor
-from reflector.processors.types import (
+from reflector.processors.types import TitleSummary, Word
-    TitleSummary,
+from reflector.processors.types import Transcript as TranscriptType
-    TitleSummaryWithId,
-    Word,
-)
-from reflector.processors.types import (
-    Transcript as TranscriptType,
-)
 from reflector.settings import settings
 from reflector.storage.storage_aws import AwsStorage
 from reflector.utils.audio_constants import (
@@ -71,6 +81,7 @@ from reflector.utils.daily import (
     parse_daily_recording_filename,
 )
 from reflector.utils.string import NonEmptyString, assert_non_none_and_non_empty
+from reflector.utils.transcript_constants import TOPIC_CHUNK_WORD_COUNT
 from reflector.zulip import post_transcript_notification
 
 
@@ -274,7 +285,7 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
         track_keys = [t["s3_key"] for t in input.tracks]
         cam_audio_keys = filter_cam_audio_tracks(track_keys)
 
-        participants_list = []
+        participants_list: list[ParticipantInfo] = []
         for idx, key in enumerate(cam_audio_keys):
             try:
                 parsed = parse_daily_recording_filename(key)
@@ -296,11 +307,11 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
             )
             await transcripts_controller.upsert_participant(transcript, participant)
             participants_list.append(
-                {
+                ParticipantInfo(
-                    "participant_id": participant_id,
+                    participant_id=participant_id,
-                    "user_name": name,
+                    user_name=name,
-                    "speaker": idx,
+                    speaker=idx,
-                }
+                )
             )
 
         ctx.log(f"get_participants complete: {len(participants_list)} participants")
@@ -324,9 +335,9 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
     participants_result = ctx.task_output(get_participants)
     source_language = participants_result.source_language
 
-    child_coroutines = [
+    bulk_runs = [
-        track_workflow.aio_run(
+        track_workflow.create_bulk_run_item(
-            TrackInput(
+            input=TrackInput(
                 track_index=i,
                 s3_key=track["s3_key"],
                 bucket_name=input.bucket_name,
@@ -337,35 +348,34 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
         for i, track in enumerate(input.tracks)
     ]
 
-    results = await asyncio.gather(*child_coroutines)
+    results = await track_workflow.aio_run_many(bulk_runs)
 
     target_language = participants_result.target_language
 
-    track_words = []
+    track_words: list[list[Word]] = []
     padded_tracks = []
     created_padded_files = set()
 
     for result in results:
-        transcribe_result = result.get("transcribe_track", {})
+        transcribe_result = TranscribeTrackResult(**result["transcribe_track"])
-        track_words.append(transcribe_result.get("words", []))
+        track_words.append(transcribe_result.words)
 
-        pad_result = result.get("pad_track", {})
+        pad_result = PadTrackResult(**result["pad_track"])
-        padded_key = pad_result.get("padded_key")
-        bucket_name = pad_result.get("bucket_name")
 
         # Store S3 key info (not presigned URL) - consumer tasks presign on demand
-        if padded_key:
+        if pad_result.padded_key:
             padded_tracks.append(
-                PaddedTrackInfo(key=padded_key, bucket_name=bucket_name)
+                PaddedTrackInfo(
+                    key=pad_result.padded_key, bucket_name=pad_result.bucket_name
+                )
             )
 
-        track_index = pad_result.get("track_index")
+        if pad_result.size > 0:
-        if pad_result.get("size", 0) > 0 and track_index is not None:
+            storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{pad_result.track_index}.webm"
-            storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{track_index}.webm"
             created_padded_files.add(storage_path)
 
     all_words = [word for words in track_words for word in words]
-    all_words.sort(key=lambda w: w.get("start", 0))
+    all_words.sort(key=lambda w: w.start)
 
     ctx.log(
         f"process_tracks complete: {len(all_words)} words from {len(input.tracks)} tracks"
@@ -529,55 +539,100 @@ async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResul
 
 
 @diarization_pipeline.task(
-    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=300), retries=3
+    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=600), retries=3
 )
 @with_error_handling("detect_topics")
 async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
-    """Detect topics using LLM and save to database (matches Celery on_topic callback)."""
+    """Detect topics using parallel child workflows (one per chunk)."""
     ctx.log("detect_topics: analyzing transcript for topics")
 
     track_result = ctx.task_output(process_tracks)
     words = track_result.all_words
-    target_language = track_result.target_language
 
+    if not words:
+        ctx.log("detect_topics: no words, returning empty topics")
+        return TopicsResult(topics=[])
+
+    # Deferred imports: Hatchet workers fork processes
     from reflector.db.transcripts import (  # noqa: PLC0415
         TranscriptTopic,
         transcripts_controller,
     )
 
-    word_objects = [Word(**w) for w in words]
+    chunk_size = TOPIC_CHUNK_WORD_COUNT
-    transcript_type = TranscriptType(words=word_objects)
+    chunks = []
+    for i in range(0, len(words), chunk_size):
+        chunk_words = words[i : i + chunk_size]
+        if not chunk_words:
+            continue
 
-    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+        first_word = chunk_words[0]
+        last_word = chunk_words[-1]
+        timestamp = first_word.start
+        duration = last_word.end - timestamp
+        chunk_text = " ".join(w.text for w in chunk_words)
+
+        chunks.append(
+            {
+                "index": len(chunks),
+                "text": chunk_text,
+                "timestamp": timestamp,
+                "duration": duration,
+                "words": chunk_words,
+            }
+        )
+
+    if not chunks:
+        ctx.log("detect_topics: no chunks generated, returning empty topics")
+        return TopicsResult(topics=[])
+
+    ctx.log(f"detect_topics: spawning {len(chunks)} topic chunk workflows in parallel")
+
+    bulk_runs = [
+        topic_chunk_workflow.create_bulk_run_item(
+            input=TopicChunkInput(
+                chunk_index=chunk["index"],
+                chunk_text=chunk["text"],
+                timestamp=chunk["timestamp"],
+                duration=chunk["duration"],
+                words=chunk["words"],
+            )
+        )
+        for chunk in chunks
+    ]
+
+    results = await topic_chunk_workflow.aio_run_many(bulk_runs)
+
+    topic_chunks = [
+        TopicChunkResult(**result["detect_chunk_topic"]) for result in results
+    ]
 
     async with fresh_db_connection():
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
 
-        async def on_topic_callback(data):
+        for chunk in topic_chunks:
             topic = TranscriptTopic(
-                title=data.title,
+                title=chunk.title,
-                summary=data.summary,
+                summary=chunk.summary,
-                timestamp=data.timestamp,
+                timestamp=chunk.timestamp,
-                transcript=data.transcript.text,
+                transcript=" ".join(w.text for w in chunk.words),
-                words=data.transcript.words,
+                words=[w.model_dump() for w in chunk.words],
             )
-            if isinstance(
-                data, TitleSummaryWithId
-            ):  # Celery parity: main_live_pipeline.py
-                topic.id = data.id
             await transcripts_controller.upsert_topic(transcript, topic)
             await append_event_and_broadcast(
                 input.transcript_id, transcript, "TOPIC", topic, logger=logger
             )
 
-        topics = await topic_processing.detect_topics(
+    topics_list = [
-            transcript_type,
+        TitleSummary(
-            target_language,
+            title=chunk.title,
-            on_topic_callback=on_topic_callback,
+            summary=chunk.summary,
-            empty_pipeline=empty_pipeline,
+            timestamp=chunk.timestamp,
+            duration=chunk.duration,
+            transcript=TranscriptType(words=chunk.words),
         )
-
+        for chunk in topic_chunks
-    topics_list = [t.model_dump() for t in topics]
+    ]
 
     ctx.log(f"detect_topics complete: found {len(topics_list)} topics")
 
@@ -601,8 +656,7 @@ async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
         transcripts_controller,
     )
 
-    topic_objects = [TitleSummary(**t) for t in topics]
+    ctx.log(f"generate_title: received {len(topics)} TitleSummary objects")
-    ctx.log(f"generate_title: created {len(topic_objects)} TitleSummary objects")
 
     empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
     title_result = None
@@ -634,7 +688,7 @@ async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
 
         ctx.log("generate_title: calling topic_processing.generate_title (LLM call)...")
         await topic_processing.generate_title(
-            topic_objects,
+            topics,
             on_title_callback=on_title_callback,
             empty_pipeline=empty_pipeline,
             logger=logger,
@@ -647,97 +701,269 @@ async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
 
 
 @diarization_pipeline.task(
-    parents=[detect_topics], execution_timeout=timedelta(seconds=600), retries=3
+    parents=[detect_topics], execution_timeout=timedelta(seconds=120), retries=3
 )
-@with_error_handling("generate_summary")
+@with_error_handling("extract_subjects")
-async def generate_summary(input: PipelineInput, ctx: Context) -> SummaryResult:
+async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult:
-    """Generate meeting summary using LLM and save to database (matches Celery callbacks)."""
+    """Extract main subjects/topics from transcript for parallel processing."""
-    ctx.log(f"generate_summary: starting for transcript_id={input.transcript_id}")
+    ctx.log(f"extract_subjects: starting for transcript_id={input.transcript_id}")
 
     topics_result = ctx.task_output(detect_topics)
     topics = topics_result.topics
-    ctx.log(f"generate_summary: received {len(topics)} topics from detect_topics")
 
+    if not topics:
+        ctx.log("extract_subjects: no topics, returning empty subjects")
+        return SubjectsResult(
+            subjects=[],
+            transcript_text="",
+            participant_names=[],
+            participant_name_to_id={},
+        )
+
+    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections and LLM HTTP pools across forks
+    from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.processors.summary.summary_builder import (  # noqa: PLC0415
+        SummaryBuilder,
+    )
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        # Build transcript text from topics (same logic as TranscriptFinalSummaryProcessor)
+        speakermap = {}
+        if transcript and transcript.participants:
+            speakermap = {
+                p.speaker: p.name
+                for p in transcript.participants
+                if p.speaker is not None and p.name
+            }
+
+        text_lines = []
+        for topic in topics:
+            for segment in topic.transcript.as_segments():
+                name = speakermap.get(segment.speaker, f"Speaker {segment.speaker}")
+                text_lines.append(f"{name}: {segment.text}")
+
+        transcript_text = "\n".join(text_lines)
+
+        participant_names = []
+        participant_name_to_id = {}
+        if transcript and transcript.participants:
+            participant_names = [p.name for p in transcript.participants if p.name]
+            participant_name_to_id = {
+                p.name: p.id for p in transcript.participants if p.name and p.id
+            }
+
+        # TODO: refactor SummaryBuilder methods into standalone functions
+        llm = LLM(settings=settings)
+        builder = SummaryBuilder(llm, logger=logger)
+        builder.set_transcript(transcript_text)
+
+        if participant_names:
+            builder.set_known_participants(
+                participant_names, participant_name_to_id=participant_name_to_id
+            )
+
+        ctx.log("extract_subjects: calling LLM to extract subjects")
+        await builder.extract_subjects()
+
+        ctx.log(f"extract_subjects complete: {len(builder.subjects)} subjects")
+
+    return SubjectsResult(
+        subjects=builder.subjects,
+        transcript_text=transcript_text,
+        participant_names=participant_names,
+        participant_name_to_id=participant_name_to_id,
+    )
+
+
+@diarization_pipeline.task(
+    parents=[extract_subjects], execution_timeout=timedelta(seconds=600), retries=3
+)
+@with_error_handling("process_subjects")
+async def process_subjects(input: PipelineInput, ctx: Context) -> ProcessSubjectsResult:
+    """Spawn child workflows for each subject (dynamic fan-out, parallel LLM calls)."""
+    subjects_result = ctx.task_output(extract_subjects)
+    subjects = subjects_result.subjects
+
+    if not subjects:
+        ctx.log("process_subjects: no subjects to process")
+        return ProcessSubjectsResult(subject_summaries=[])
+
+    ctx.log(f"process_subjects: spawning {len(subjects)} subject workflows in parallel")
+
+    bulk_runs = [
+        subject_workflow.create_bulk_run_item(
+            input=SubjectInput(
+                subject=subject,
+                subject_index=i,
+                transcript_text=subjects_result.transcript_text,
+                participant_names=subjects_result.participant_names,
+                participant_name_to_id=subjects_result.participant_name_to_id,
+            )
+        )
+        for i, subject in enumerate(subjects)
+    ]
+
+    results = await subject_workflow.aio_run_many(bulk_runs)
+
+    subject_summaries = [
+        SubjectSummaryResult(**result["generate_detailed_summary"])
+        for result in results
+    ]
+
+    ctx.log(f"process_subjects complete: {len(subject_summaries)} summaries")
+
+    return ProcessSubjectsResult(subject_summaries=subject_summaries)
+
+
+@diarization_pipeline.task(
+    parents=[process_subjects], execution_timeout=timedelta(seconds=120), retries=3
+)
+@with_error_handling("generate_recap")
+async def generate_recap(input: PipelineInput, ctx: Context) -> RecapResult:
+    """Generate recap and long summary from subject summaries, save to database."""
+    ctx.log(f"generate_recap: starting for transcript_id={input.transcript_id}")
+
+    subjects_result = ctx.task_output(extract_subjects)
+    process_result = ctx.task_output(process_subjects)
+
+    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections and LLM HTTP pools across forks
     from reflector.db.transcripts import (  # noqa: PLC0415
-        TranscriptActionItems,
         TranscriptFinalLongSummary,
         TranscriptFinalShortSummary,
         transcripts_controller,
     )
+    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.processors.summary.prompts import (  # noqa: PLC0415
+        RECAP_PROMPT,
+        build_participant_instructions,
+        build_summary_markdown,
+    )
 
-    topic_objects = [TitleSummary(**t) for t in topics]
+    subject_summaries = process_result.subject_summaries
-    ctx.log(f"generate_summary: created {len(topic_objects)} TitleSummary objects")
 
-    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    if not subject_summaries:
-    summary_result = None
+        ctx.log("generate_recap: no subject summaries, returning empty")
-    short_summary_result = None
+        return RecapResult(short_summary="", long_summary="")
-    action_items_result = None
+
+    summaries = [
+        {"subject": s.subject, "summary": s.paragraph_summary}
+        for s in subject_summaries
+    ]
+
+    summaries_text = "\n\n".join([f"{s['subject']}: {s['summary']}" for s in summaries])
+
+    llm = LLM(settings=settings)
+
+    participant_instructions = build_participant_instructions(
+        subjects_result.participant_names
+    )
+
+    recap_prompt = RECAP_PROMPT
+    if participant_instructions:
+        recap_prompt = f"{recap_prompt}\n\n{participant_instructions}"
+
+    ctx.log("generate_recap: calling LLM for recap")
+    recap_response = await llm.get_response(
+        recap_prompt,
+        [summaries_text],
+        tone_name="Recap summarizer",
+    )
+    short_summary = str(recap_response)
+
+    long_summary = build_summary_markdown(short_summary, summaries)
 
     async with fresh_db_connection():
-        ctx.log("generate_summary: DB connection established")
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
-        ctx.log(
+        if transcript:
-            f"generate_summary: fetched transcript, exists={transcript is not None}"
-        )
-
-        async def on_long_summary_callback(data):
-            nonlocal summary_result
-            ctx.log(
-                f"generate_summary: on_long_summary_callback received ({len(data.long_summary)} chars)"
-            )
-            summary_result = data.long_summary
-            final_long_summary = TranscriptFinalLongSummary(
-                long_summary=data.long_summary
-            )
             await transcripts_controller.update(
                 transcript,
-                {"long_summary": final_long_summary.long_summary},
+                {
+                    "short_summary": short_summary,
+                    "long_summary": long_summary,
+                },
             )
-            ctx.log("generate_summary: saved long_summary to DB")
-            await append_event_and_broadcast(
-                input.transcript_id,
-                transcript,
-                "FINAL_LONG_SUMMARY",
-                final_long_summary,
-                logger=logger,
-            )
-            ctx.log("generate_summary: broadcasted FINAL_LONG_SUMMARY event")
 
-        async def on_short_summary_callback(data):
+            final_short = TranscriptFinalShortSummary(short_summary=short_summary)
-            nonlocal short_summary_result
-            ctx.log(
-                f"generate_summary: on_short_summary_callback received ({len(data.short_summary)} chars)"
-            )
-            short_summary_result = data.short_summary
-            final_short_summary = TranscriptFinalShortSummary(
-                short_summary=data.short_summary
-            )
-            await transcripts_controller.update(
-                transcript,
-                {"short_summary": final_short_summary.short_summary},
-            )
-            ctx.log("generate_summary: saved short_summary to DB")
             await append_event_and_broadcast(
                 input.transcript_id,
                 transcript,
                 "FINAL_SHORT_SUMMARY",
-                final_short_summary,
+                final_short,
                 logger=logger,
             )
-            ctx.log("generate_summary: broadcasted FINAL_SHORT_SUMMARY event")
 
-        async def on_action_items_callback(data):
+            final_long = TranscriptFinalLongSummary(long_summary=long_summary)
-            nonlocal action_items_result
+            await append_event_and_broadcast(
-            ctx.log(
+                input.transcript_id,
-                f"generate_summary: on_action_items_callback received ({len(data.action_items)} items)"
-            )
-            action_items_result = data.action_items
-            action_items = TranscriptActionItems(action_items=data.action_items)
-            await transcripts_controller.update(
                 transcript,
-                {"action_items": action_items.action_items},
+                "FINAL_LONG_SUMMARY",
+                final_long,
+                logger=logger,
+            )
+
+    ctx.log("generate_recap complete")
+
+    return RecapResult(short_summary=short_summary, long_summary=long_summary)
+
+
+@diarization_pipeline.task(
+    parents=[extract_subjects], execution_timeout=timedelta(seconds=180), retries=3
+)
+@with_error_handling("identify_action_items")
+async def identify_action_items(
+    input: PipelineInput, ctx: Context
+) -> ActionItemsResult:
+    """Identify action items from transcript (parallel with subject processing)."""
+    ctx.log(f"identify_action_items: starting for transcript_id={input.transcript_id}")
+
+    subjects_result = ctx.task_output(extract_subjects)
+
+    if not subjects_result.transcript_text:
+        ctx.log("identify_action_items: no transcript text, returning empty")
+        return ActionItemsResult(action_items={"decisions": [], "next_steps": []})
+
+    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections and LLM HTTP pools across forks
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptActionItems,
+        transcripts_controller,
+    )
+    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.processors.summary.summary_builder import (  # noqa: PLC0415
+        SummaryBuilder,
+    )
+
+    # TODO: refactor SummaryBuilder methods into standalone functions
+    llm = LLM(settings=settings)
+    builder = SummaryBuilder(llm, logger=logger)
+    builder.set_transcript(subjects_result.transcript_text)
+
+    if subjects_result.participant_names:
+        builder.set_known_participants(
+            subjects_result.participant_names,
+            participant_name_to_id=subjects_result.participant_name_to_id,
+        )
+
+    ctx.log("identify_action_items: calling LLM")
+    action_items_response = await builder.identify_action_items()
+
+    if action_items_response is None:
+        raise RuntimeError("Failed to identify action items - LLM call failed")
+
+    action_items_dict = action_items_response.model_dump()
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            action_items = TranscriptActionItems(action_items=action_items_dict)
+            await transcripts_controller.update(
+                transcript, {"action_items": action_items.action_items}
             )
-            ctx.log("generate_summary: saved action_items to DB")
             await append_event_and_broadcast(
                 input.transcript_id,
                 transcript,
@@ -745,33 +971,17 @@ async def generate_summary(input: PipelineInput, ctx: Context) -> SummaryResult:
                 action_items,
                 logger=logger,
             )
-            ctx.log("generate_summary: broadcasted ACTION_ITEMS event")
 
-        ctx.log(
+    ctx.log(
-            "generate_summary: calling topic_processing.generate_summaries (LLM calls)..."
+        f"identify_action_items complete: {len(action_items_dict.get('decisions', []))} decisions, "
-        )
+        f"{len(action_items_dict.get('next_steps', []))} next steps"
-        await topic_processing.generate_summaries(
-            topic_objects,
-            transcript,
-            on_long_summary_callback=on_long_summary_callback,
-            on_short_summary_callback=on_short_summary_callback,
-            on_action_items_callback=on_action_items_callback,
-            empty_pipeline=empty_pipeline,
-            logger=logger,
-        )
-        ctx.log("generate_summary: topic_processing.generate_summaries returned")
-
-    ctx.log("generate_summary complete")
-
-    return SummaryResult(
-        summary=summary_result,
-        short_summary=short_summary_result,
-        action_items=action_items_result,
     )
 
+    return ActionItemsResult(action_items=action_items_dict)
+
 
 @diarization_pipeline.task(
-    parents=[generate_waveform, generate_title, generate_summary],
+    parents=[generate_waveform, generate_title, generate_recap, identify_action_items],
     execution_timeout=timedelta(seconds=60),
     retries=3,
 )
@@ -818,8 +1028,7 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
         if transcript is None:
             raise ValueError(f"Transcript {input.transcript_id} not found in database")
 
-        word_objects = [Word(**w) for w in all_words]
+        merged_transcript = TranscriptType(words=all_words, translation=None)
-        merged_transcript = TranscriptType(words=word_objects, translation=None)
 
         await append_event_and_broadcast(
             input.transcript_id,

server/reflector/hatchet/workflows/models.py

@@ -5,13 +5,20 @@ Provides static typing for all task outputs, enabling type checking
 and better IDE support.
 """
 
-from typing import Any
-
 from pydantic import BaseModel
 
+from reflector.processors.types import TitleSummary, Word
 from reflector.utils.string import NonEmptyString
 
 
+class ParticipantInfo(BaseModel):
+    """Participant info with speaker index for workflow result."""
+
+    participant_id: NonEmptyString
+    user_name: NonEmptyString
+    speaker: int
+
+
 class PadTrackResult(BaseModel):
     """Result from pad_track task."""
 
@@ -26,7 +33,7 @@ class PadTrackResult(BaseModel):
 class TranscribeTrackResult(BaseModel):
     """Result from transcribe_track task."""
 
-    words: list[dict[str, Any]]
+    words: list[Word]
     track_index: int
 
 
@@ -41,7 +48,7 @@ class RecordingResult(BaseModel):
 class ParticipantsResult(BaseModel):
     """Result from get_participants task."""
 
-    participants: list[dict[str, Any]]
+    participants: list[ParticipantInfo]
     num_tracks: int
     source_language: NonEmptyString
     target_language: NonEmptyString
@@ -57,7 +64,7 @@ class PaddedTrackInfo(BaseModel):
 class ProcessTracksResult(BaseModel):
     """Result from process_tracks task."""
 
-    all_words: list[dict[str, Any]]
+    all_words: list[Word]
     padded_tracks: list[PaddedTrackInfo]  # S3 keys, not presigned URLs
     word_count: int
     num_tracks: int
@@ -79,10 +86,21 @@ class WaveformResult(BaseModel):
     waveform_generated: bool
 
 
+class TopicChunkResult(BaseModel):
+    """Result from topic chunk child workflow."""
+
+    chunk_index: int
+    title: str
+    summary: str
+    timestamp: float
+    duration: float
+    words: list[Word]
+
+
 class TopicsResult(BaseModel):
     """Result from detect_topics task."""
 
-    topics: list[dict[str, Any]]
+    topics: list[TitleSummary]
 
 
 class TitleResult(BaseModel):
@@ -91,12 +109,41 @@ class TitleResult(BaseModel):
     title: str | None
 
 
-class SummaryResult(BaseModel):
+class SubjectsResult(BaseModel):
-    """Result from generate_summary task."""
+    """Result from extract_subjects task."""
 
-    summary: str | None
+    subjects: list[str]
-    short_summary: str | None
+    transcript_text: str  # Formatted transcript for LLM consumption
-    action_items: dict | None = None
+    participant_names: list[str]
+    participant_name_to_id: dict[str, str]
+
+
+class SubjectSummaryResult(BaseModel):
+    """Result from subject summary child workflow."""
+
+    subject: str
+    subject_index: int
+    detailed_summary: str
+    paragraph_summary: str
+
+
+class ProcessSubjectsResult(BaseModel):
+    """Result from process_subjects fan-out task."""
+
+    subject_summaries: list[SubjectSummaryResult]
+
+
+class RecapResult(BaseModel):
+    """Result from generate_recap task."""
+
+    short_summary: str  # Recap paragraph
+    long_summary: str  # Full markdown summary
+
+
+class ActionItemsResult(BaseModel):
+    """Result from identify_action_items task."""
+
+    action_items: dict  # ActionItemsResponse as dict (may have empty lists)
 
 
 class FinalizeResult(BaseModel):

server/reflector/hatchet/workflows/subject_processing.py

@@ -0,0 +1,101 @@
+"""
+Hatchet child workflow: SubjectProcessing
+
+Handles individual subject/topic summary generation.
+Spawned dynamically by the main diarization pipeline for each extracted subject
+via aio_run_many() for parallel processing.
+"""
+
+from datetime import timedelta
+
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.workflows.models import SubjectSummaryResult
+from reflector.logger import logger
+from reflector.processors.summary.prompts import (
+    DETAILED_SUBJECT_PROMPT_TEMPLATE,
+    PARAGRAPH_SUMMARY_PROMPT,
+    build_participant_instructions,
+)
+
+
+class SubjectInput(BaseModel):
+    """Input for individual subject processing."""
+
+    subject: str
+    subject_index: int
+    transcript_text: str
+    participant_names: list[str]
+    participant_name_to_id: dict[str, str]
+
+
+hatchet = HatchetClientManager.get_client()
+
+subject_workflow = hatchet.workflow(
+    name="SubjectProcessing", input_validator=SubjectInput
+)
+
+
+@subject_workflow.task(execution_timeout=timedelta(seconds=120), retries=3)
+async def generate_detailed_summary(
+    input: SubjectInput, ctx: Context
+) -> SubjectSummaryResult:
+    """Generate detailed analysis for a single subject, then condense to paragraph."""
+    ctx.log(
+        f"generate_detailed_summary: subject '{input.subject}' (index {input.subject_index})"
+    )
+    logger.info(
+        "[Hatchet] generate_detailed_summary",
+        subject=input.subject,
+        subject_index=input.subject_index,
+    )
+
+    # Deferred imports: Hatchet workers fork processes, fresh imports ensure
+    # LLM HTTP connection pools aren't shared across forks
+    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.settings import settings  # noqa: PLC0415
+
+    llm = LLM(settings=settings)
+
+    participant_instructions = build_participant_instructions(input.participant_names)
+    detailed_prompt = DETAILED_SUBJECT_PROMPT_TEMPLATE.format(subject=input.subject)
+    if participant_instructions:
+        detailed_prompt = f"{detailed_prompt}\n\n{participant_instructions}"
+
+    ctx.log("generate_detailed_summary: calling LLM for detailed analysis")
+    detailed_response = await llm.get_response(
+        detailed_prompt,
+        [input.transcript_text],
+        tone_name="Topic assistant",
+    )
+    detailed_summary = str(detailed_response)
+
+    paragraph_prompt = PARAGRAPH_SUMMARY_PROMPT
+    if participant_instructions:
+        paragraph_prompt = f"{paragraph_prompt}\n\n{participant_instructions}"
+
+    ctx.log("generate_detailed_summary: calling LLM for paragraph summary")
+    paragraph_response = await llm.get_response(
+        paragraph_prompt,
+        [detailed_summary],
+        tone_name="Topic summarizer",
+    )
+    paragraph_summary = str(paragraph_response)
+
+    ctx.log(f"generate_detailed_summary complete: subject '{input.subject}'")
+    logger.info(
+        "[Hatchet] generate_detailed_summary complete",
+        subject=input.subject,
+        subject_index=input.subject_index,
+        detailed_len=len(detailed_summary),
+        paragraph_len=len(paragraph_summary),
+    )
+
+    return SubjectSummaryResult(
+        subject=input.subject,
+        subject_index=input.subject_index,
+        detailed_summary=detailed_summary,
+        paragraph_summary=paragraph_summary,
+    )

server/reflector/hatchet/workflows/topic_chunk_processing.py

@@ -0,0 +1,85 @@
+"""
+Hatchet child workflow: TopicChunkProcessing
+
+Handles topic detection for individual transcript chunks.
+Spawned dynamically by detect_topics via aio_run_many() for parallel processing.
+"""
+
+from datetime import timedelta
+
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.workflows.models import TopicChunkResult
+from reflector.logger import logger
+from reflector.processors.prompts import TOPIC_PROMPT
+from reflector.processors.types import Word
+
+
+class TopicChunkInput(BaseModel):
+    """Input for individual topic chunk processing."""
+
+    chunk_index: int
+    chunk_text: str
+    timestamp: float
+    duration: float
+    words: list[Word]
+
+
+hatchet = HatchetClientManager.get_client()
+
+topic_chunk_workflow = hatchet.workflow(
+    name="TopicChunkProcessing", input_validator=TopicChunkInput
+)
+
+
+@topic_chunk_workflow.task(execution_timeout=timedelta(seconds=60), retries=3)
+async def detect_chunk_topic(input: TopicChunkInput, ctx: Context) -> TopicChunkResult:
+    """Detect topic for a single transcript chunk."""
+    ctx.log(f"detect_chunk_topic: chunk {input.chunk_index}")
+    logger.info(
+        "[Hatchet] detect_chunk_topic",
+        chunk_index=input.chunk_index,
+        text_length=len(input.chunk_text),
+    )
+
+    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
+    # sharing LLM HTTP connection pools across forks
+    from reflector.llm import LLM  # noqa: PLC0415
+    from reflector.processors.transcript_topic_detector import (  # noqa: PLC0415
+        TopicResponse,
+    )
+    from reflector.settings import settings  # noqa: PLC0415
+    from reflector.utils.text import clean_title  # noqa: PLC0415
+
+    llm = LLM(settings=settings, temperature=0.9, max_tokens=500)
+
+    prompt = TOPIC_PROMPT.format(text=input.chunk_text)
+    response = await llm.get_structured_response(
+        prompt,
+        [input.chunk_text],
+        TopicResponse,
+        tone_name="Topic analyzer",
+        timeout=settings.LLM_STRUCTURED_RESPONSE_TIMEOUT,
+    )
+
+    title = clean_title(response.title)
+
+    ctx.log(
+        f"detect_chunk_topic complete: chunk {input.chunk_index}, title='{title[:50]}'"
+    )
+    logger.info(
+        "[Hatchet] detect_chunk_topic complete",
+        chunk_index=input.chunk_index,
+        title=title[:50],
+    )
+
+    return TopicChunkResult(
+        chunk_index=input.chunk_index,
+        title=title,
+        summary=response.summary,
+        timestamp=input.timestamp,
+        duration=input.duration,
+        words=input.words,
+    )

server/reflector/hatchet/workflows/track_processing.py

@@ -197,23 +197,20 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
         transcript = await transcribe_file_with_processor(audio_url, input.language)
 
         # Tag all words with speaker index
-        words = []
         for word in transcript.words:
-            word_dict = word.model_dump()
+            word.speaker = input.track_index
-            word_dict["speaker"] = input.track_index
-            words.append(word_dict)
 
         ctx.log(
-            f"transcribe_track complete: track {input.track_index}, {len(words)} words"
+            f"transcribe_track complete: track {input.track_index}, {len(transcript.words)} words"
         )
         logger.info(
             "[Hatchet] transcribe_track complete",
             track_index=input.track_index,
-            word_count=len(words),
+            word_count=len(transcript.words),
         )
 
         return TranscribeTrackResult(
-            words=words,
+            words=transcript.words,
             track_index=input.track_index,
         )
 

server/reflector/pipelines/topic_processing.py

@@ -18,6 +18,7 @@ from reflector.processors import (
 )
 from reflector.processors.types import TitleSummary
 from reflector.processors.types import Transcript as TranscriptType
+from reflector.utils.transcript_constants import TOPIC_CHUNK_WORD_COUNT
 
 
 class EmptyPipeline:
@@ -38,7 +39,7 @@ async def detect_topics(
     on_topic_callback: Callable,
     empty_pipeline: EmptyPipeline,
 ) -> list[TitleSummary]:
-    chunk_size = 300
+    chunk_size = TOPIC_CHUNK_WORD_COUNT
     topics: list[TitleSummary] = []
 
     async def on_topic(topic: TitleSummary):

server/reflector/processors/prompts.py

@@ -0,0 +1,30 @@
+"""
+LLM prompts for transcript processing.
+
+Extracted to a separate module to avoid circular imports when importing
+from processor modules (which import LLM/settings at module level).
+"""
+
+from textwrap import dedent
+
+TOPIC_PROMPT = dedent(
+    """
+    Analyze the following transcript segment and extract the main topic being discussed.
+    Focus on the substantive content and ignore small talk or administrative chatter.
+
+    Create a title that:
+    - Captures the specific subject matter being discussed
+    - Is descriptive and self-explanatory
+    - Uses professional language
+    - Is specific rather than generic
+
+    For the summary:
+    - Summarize the key points in maximum two sentences
+    - Focus on what was discussed, decided, or accomplished
+    - Be concise but informative
+
+    <transcript>
+    {text}
+    </transcript>
+    """
+).strip()

server/reflector/processors/summary/prompts.py

@@ -0,0 +1,91 @@
+"""
+LLM prompts for summary generation.
+
+Extracted to a separate module to avoid circular imports when importing
+from summary_builder.py (which imports LLM/settings at module level).
+"""
+
+from textwrap import dedent
+
+
+def build_participant_instructions(participant_names: list[str]) -> str:
+    """Build participant context instructions for LLM prompts."""
+    if not participant_names:
+        return ""
+
+    participants_list = ", ".join(participant_names)
+    return dedent(
+        f"""
+        # IMPORTANT: Participant Names
+        The following participants are identified in this conversation: {participants_list}
+
+        You MUST use these specific participant names when referring to people in your response.
+        Do NOT use generic terms like "a participant", "someone", "attendee", "Speaker 1", "Speaker 2", etc.
+        Always refer to people by their actual names (e.g., "John suggested..." not "A participant suggested...").
+        """
+    ).strip()
+
+
+DETAILED_SUBJECT_PROMPT_TEMPLATE = dedent(
+    """
+    Get me information about the topic "{subject}"
+
+    # RESPONSE GUIDELINES
+    Follow this structured approach to create the topic summary:
+    - Highlight important arguments, insights, or data presented.
+    - Outline decisions made.
+    - Indicate any decisions reached, including any rationale or key factors
+      that influenced these decisions.
+    - Detail action items and responsibilities.
+    - For each decision or unresolved issue, list specific action items agreed
+      upon, along with assigned individuals or teams responsible for each task.
+    - Specify deadlines or timelines if mentioned. For each action item,
+      include any deadlines or timeframes discussed for completion or follow-up.
+    - Mention unresolved issues or topics needing further discussion, aiding in
+      planning future meetings or follow-up actions.
+    - Do not include topic unrelated to {subject}.
+
+    # OUTPUT
+    Your summary should be clear, concise, and structured, covering all major
+    points, decisions, and action items from the meeting. It should be easy to
+    understand for someone not present, providing a comprehensive understanding
+    of what transpired and what needs to be done next. The summary should not
+    exceed one page to ensure brevity and focus.
+    """
+).strip()
+
+PARAGRAPH_SUMMARY_PROMPT = dedent(
+    """
+    Summarize the mentioned topic in 1 paragraph.
+    It will be integrated into the final summary, so just for this topic.
+    """
+).strip()
+
+RECAP_PROMPT = dedent(
+    """
+    Provide a high-level quick recap of the following meeting, fitting in one paragraph.
+    Do not include decisions, action items or unresolved issue, just highlight the high moments.
+    Just dive into the meeting, be concise and do not include unnecessary details.
+    As we already know it is a meeting, do not start with 'During the meeting' or equivalent.
+    """
+).strip()
+
+
+def build_summary_markdown(recap: str, summaries: list[dict[str, str]]) -> str:
+    """Build markdown summary from recap and subject summaries."""
+    lines: list[str] = []
+    if recap:
+        lines.append("# Quick recap")
+        lines.append("")
+        lines.append(recap)
+        lines.append("")
+
+    if summaries:
+        lines.append("# Summary")
+        lines.append("")
+        for summary in summaries:
+            lines.append(f"**{summary['subject']}**")
+            lines.append(summary["summary"])
+            lines.append("")
+
+    return "\n".join(lines)

server/reflector/processors/summary/summary_builder.py

@@ -15,6 +15,13 @@ import structlog
 from pydantic import BaseModel, Field
 
 from reflector.llm import LLM
+from reflector.processors.summary.prompts import (
+    DETAILED_SUBJECT_PROMPT_TEMPLATE,
+    PARAGRAPH_SUMMARY_PROMPT,
+    RECAP_PROMPT,
+    build_participant_instructions,
+    build_summary_markdown,
+)
 from reflector.settings import settings
 
 T = TypeVar("T", bound=BaseModel)
@@ -52,50 +59,6 @@ SUBJECTS_PROMPT = dedent(
     """
 ).strip()
 
-DETAILED_SUBJECT_PROMPT_TEMPLATE = dedent(
-    """
-    Get me information about the topic "{subject}"
-
-    # RESPONSE GUIDELINES
-    Follow this structured approach to create the topic summary:
-    - Highlight important arguments, insights, or data presented.
-    - Outline decisions made.
-    - Indicate any decisions reached, including any rationale or key factors
-      that influenced these decisions.
-    - Detail action items and responsibilities.
-    - For each decision or unresolved issue, list specific action items agreed
-      upon, along with assigned individuals or teams responsible for each task.
-    - Specify deadlines or timelines if mentioned. For each action item,
-      include any deadlines or timeframes discussed for completion or follow-up.
-    - Mention unresolved issues or topics needing further discussion, aiding in
-      planning future meetings or follow-up actions.
-    - Do not include topic unrelated to {subject}.
-
-    # OUTPUT
-    Your summary should be clear, concise, and structured, covering all major
-    points, decisions, and action items from the meeting. It should be easy to
-    understand for someone not present, providing a comprehensive understanding
-    of what transpired and what needs to be done next. The summary should not
-    exceed one page to ensure brevity and focus.
-    """
-).strip()
-
-PARAGRAPH_SUMMARY_PROMPT = dedent(
-    """
-    Summarize the mentioned topic in 1 paragraph.
-    It will be integrated into the final summary, so just for this topic.
-    """
-).strip()
-
-RECAP_PROMPT = dedent(
-    """
-    Provide a high-level quick recap of the following meeting, fitting in one paragraph.
-    Do not include decisions, action items or unresolved issue, just highlight the high moments.
-    Just dive into the meeting, be concise and do not include unnecessary details.
-    As we already know it is a meeting, do not start with 'During the meeting' or equivalent.
-    """
-).strip()
-
 ACTION_ITEMS_PROMPT = dedent(
     """
     Identify action items from this meeting transcript. Your goal is to identify what was decided and what needs to happen next.
@@ -331,17 +294,7 @@ class SummaryBuilder:
         participants_md = self.format_list_md(participants)
         self.transcript += f"\n\n# Participants\n\n{participants_md}"
 
-        participants_list = ", ".join(participants)
+        self.participant_instructions = build_participant_instructions(participants)
-        self.participant_instructions = dedent(
-            f"""
-            # IMPORTANT: Participant Names
-            The following participants are identified in this conversation: {participants_list}
-
-            You MUST use these specific participant names when referring to people in your response.
-            Do NOT use generic terms like "a participant", "someone", "attendee", "Speaker 1", "Speaker 2", etc.
-            Always refer to people by their actual names (e.g., "John suggested..." not "A participant suggested...").
-            """
-        ).strip()
 
     async def identify_participants(self) -> None:
         """
@@ -377,18 +330,9 @@ class SummaryBuilder:
                 participants_md = self.format_list_md(unique_participants)
                 self.transcript += f"\n\n# Participants\n\n{participants_md}"
 
-                # Set instructions that will be automatically added to all prompts
+                self.participant_instructions = build_participant_instructions(
-                participants_list = ", ".join(unique_participants)
+                    unique_participants
-                self.participant_instructions = dedent(
+                )
-                    f"""
-                    # IMPORTANT: Participant Names
-                    The following participants are identified in this conversation: {participants_list}
-
-                    You MUST use these specific participant names when referring to people in your response.
-                    Do NOT use generic terms like "a participant", "someone", "attendee", "Speaker 1", "Speaker 2", etc.
-                    Always refer to people by their actual names (e.g., "John suggested..." not "A participant suggested...").
-                    """
-                ).strip()
             else:
                 self.logger.warning("No participants identified in the transcript")
 
@@ -613,22 +557,7 @@ class SummaryBuilder:
     # ----------------------------------------------------------------------------
 
     def as_markdown(self) -> str:
-        lines: list[str] = []
+        return build_summary_markdown(self.recap, self.summaries)
-        if self.recap:
-            lines.append("# Quick recap")
-            lines.append("")
-            lines.append(self.recap)
-            lines.append("")
-
-        if self.summaries:
-            lines.append("# Summary")
-            lines.append("")
-            for summary in self.summaries:
-                lines.append(f"**{summary['subject']}**")
-                lines.append(summary["summary"])
-                lines.append("")
-
-        return "\n".join(lines)
 
     def format_list_md(self, data: list[str]) -> str:
         return "\n".join([f"- {item}" for item in data])

server/reflector/processors/transcript_topic_detector.py

@@ -1,35 +1,12 @@
-from textwrap import dedent
-
 from pydantic import AliasChoices, BaseModel, Field
 
 from reflector.llm import LLM
 from reflector.processors.base import Processor
+from reflector.processors.prompts import TOPIC_PROMPT
 from reflector.processors.types import TitleSummary, Transcript
 from reflector.settings import settings
 from reflector.utils.text import clean_title
 
-TOPIC_PROMPT = dedent(
-    """
-    Analyze the following transcript segment and extract the main topic being discussed.
-    Focus on the substantive content and ignore small talk or administrative chatter.
-
-    Create a title that:
-    - Captures the specific subject matter being discussed
-    - Is descriptive and self-explanatory
-    - Uses professional language
-    - Is specific rather than generic
-
-    For the summary:
-    - Summarize the key points in maximum two sentences
-    - Focus on what was discussed, decided, or accomplished
-    - Be concise but informative
-
-    <transcript>
-    {text}
-    </transcript>
-    """
-).strip()
-
 
 class TopicResponse(BaseModel):
     """Structured response for topic detection"""

server/reflector/services/transcript_process.py

@@ -102,7 +102,8 @@ async def validate_transcript_for_processing(
     if transcript.locked:
         return ValidationLocked(detail="Recording is locked")
 
-    if transcript.status == "idle":
+    # hatchet is idempotent anyways + if it wasn't dispatched successfully
+    if transcript.status == "idle" and not settings.HATCHET_ENABLED:
         return ValidationNotReady(detail="Recording is not ready for processing")
 
     # Check Celery tasks
@@ -115,13 +116,11 @@ async def validate_transcript_for_processing(
     ):
         return ValidationAlreadyScheduled(detail="already running")
 
-    # Check Hatchet workflows (if enabled)
     if settings.HATCHET_ENABLED and transcript.workflow_run_id:
         try:
             status = await HatchetClientManager.get_workflow_run_status(
                 transcript.workflow_run_id
             )
-            # If workflow is running or queued, don't allow new processing
             if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
                 return ValidationAlreadyScheduled(
                     detail="Hatchet workflow already running"

server/reflector/utils/transcript_constants.py

@@ -0,0 +1,8 @@
+"""
+Shared transcript processing constants.
+
+Used by both Hatchet workflows and Celery pipelines for consistent processing.
+"""
+
+# Topic detection: number of words per chunk for topic extraction
+TOPIC_CHUNK_WORD_COUNT = 300