feat: reduce Hatchet payload size by removing words from topic chunk workflows

Remove ~6.5MB of redundant Word data from Hatchet task boundaries:
- Remove words from TopicChunkInput/TopicChunkResult (child workflow I/O)
- detect_topics maps words from local chunks by chunk_index instead
- TopicsResult carries empty transcript words (persisted to DB already)
- extract_subjects refetches topics from DB instead of task output
- Clear topics at detect_topics start for retry idempotency

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -720,7 +720,6 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
                 chunk_text=chunk["text"],
                 timestamp=chunk["timestamp"],
                 duration=chunk["duration"],
-                words=chunk["words"],
             )
         )
         for chunk in chunks
@@ -732,31 +731,41 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
         TopicChunkResult(**result[TaskName.DETECT_CHUNK_TOPIC]) for result in results
     ]
 
+    # Build index-to-words map from local chunks (words not in child workflow results)
+    chunks_by_index = {chunk["index"]: chunk["words"] for chunk in chunks}
+
     async with fresh_db_connection():
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if not transcript:
             raise ValueError(f"Transcript {input.transcript_id} not found")
 
+        # Clear topics for idempotency on retry (each topic gets a fresh UUID,
+        # so upsert_topic would append duplicates without this)
+        await transcripts_controller.update(transcript, {"topics": []})
+
         for chunk in topic_chunks:
+            chunk_words = chunks_by_index[chunk.chunk_index]
             topic = TranscriptTopic(
                 title=chunk.title,
                 summary=chunk.summary,
                 timestamp=chunk.timestamp,
-                transcript=" ".join(w.text for w in chunk.words),
+                transcript=" ".join(w.text for w in chunk_words),
-                words=chunk.words,
+                words=chunk_words,
             )
             await transcripts_controller.upsert_topic(transcript, topic)
             await append_event_and_broadcast(
                 input.transcript_id, transcript, "TOPIC", topic, logger=logger
             )
 
+    # Words omitted from TopicsResult — already persisted to DB above.
+    # Downstream tasks that need words refetch from DB.
     topics_list = [
         TitleSummary(
             title=chunk.title,
             summary=chunk.summary,
             timestamp=chunk.timestamp,
             duration=chunk.duration,
-            transcript=TranscriptType(words=chunk.words),
+            transcript=TranscriptType(words=[]),
         )
         for chunk in topic_chunks
     ]
@@ -842,9 +851,8 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
     ctx.log(f"extract_subjects: starting for transcript_id={input.transcript_id}")
 
     topics_result = ctx.task_output(detect_topics)
-    topics = topics_result.topics
 
-    if not topics:
+    if not topics_result.topics:
         ctx.log("extract_subjects: no topics, returning empty subjects")
         return SubjectsResult(
             subjects=[],
@@ -857,11 +865,13 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
     # 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.types import words_to_segments  # noqa: PLC0415
 
     async with fresh_db_connection():
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
 
-        # Build transcript text from topics (same logic as TranscriptFinalSummaryProcessor)
+        # Build transcript text from DB topics (words omitted from task output
+        # to reduce Hatchet payload size — refetch from DB where they were persisted)
         speakermap = {}
         if transcript and transcript.participants:
             speakermap = {
@@ -871,8 +881,8 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
             }
 
         text_lines = []
-        for topic in topics:
+        for db_topic in transcript.topics:
-            for segment in topic.transcript.as_segments():
+            for segment in words_to_segments(db_topic.words):
                 name = speakermap.get(segment.speaker, f"Speaker {segment.speaker}")
                 text_lines.append(f"{name}: {segment.text}")
 

server/reflector/hatchet/workflows/models.py

@@ -95,7 +95,6 @@ class TopicChunkResult(BaseModel):
     summary: str
     timestamp: float
     duration: float
-    words: list[Word]
 
 
 class TopicsResult(BaseModel):

server/reflector/hatchet/workflows/topic_chunk_processing.py

@@ -20,7 +20,6 @@ from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_MEDIUM
 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):
@@ -30,7 +29,6 @@ class TopicChunkInput(BaseModel):
     chunk_text: str
     timestamp: float
     duration: float
-    words: list[Word]
 
 
 hatchet = HatchetClientManager.get_client()
@@ -99,5 +97,4 @@ async def detect_chunk_topic(input: TopicChunkInput, ctx: Context) -> TopicChunk
         summary=response.summary,
         timestamp=input.timestamp,
         duration=input.duration,
-        words=input.words,
     )

server/tests/test_hatchet_payload_thinning.py

@@ -0,0 +1,185 @@
+"""
+Tests for Hatchet payload thinning optimizations.
+
+Verifies that:
+1. TopicChunkInput no longer carries words
+2. TopicChunkResult no longer carries words
+3. words_to_segments() matches Transcript.as_segments(is_multitrack=False) — behavioral equivalence
+   for the extract_subjects refactoring
+4. TopicsResult can be constructed with empty transcript words
+"""
+
+from reflector.hatchet.workflows.models import TopicChunkResult
+from reflector.hatchet.workflows.topic_chunk_processing import TopicChunkInput
+from reflector.processors.types import Word
+
+
+def _make_words(speaker: int = 0, start: float = 0.0) -> list[Word]:
+    return [
+        Word(text="Hello", start=start, end=start + 0.5, speaker=speaker),
+        Word(text=" world.", start=start + 0.5, end=start + 1.0, speaker=speaker),
+    ]
+
+
+class TestTopicChunkInputNoWords:
+    """TopicChunkInput must not have a words field."""
+
+    def test_no_words_field(self):
+        assert "words" not in TopicChunkInput.model_fields
+
+    def test_construction_without_words(self):
+        inp = TopicChunkInput(
+            chunk_index=0, chunk_text="Hello world.", timestamp=0.0, duration=1.0
+        )
+        assert inp.chunk_index == 0
+        assert inp.chunk_text == "Hello world."
+
+    def test_rejects_words_kwarg(self):
+        """Passing words= should raise a validation error (field doesn't exist)."""
+        import pydantic
+
+        try:
+            TopicChunkInput(
+                chunk_index=0,
+                chunk_text="text",
+                timestamp=0.0,
+                duration=1.0,
+                words=_make_words(),
+            )
+            # If pydantic is configured to ignore extra, this won't raise.
+            # Verify the field is still absent from the model.
+            assert "words" not in TopicChunkInput.model_fields
+        except pydantic.ValidationError:
+            pass  # Expected
+
+
+class TestTopicChunkResultNoWords:
+    """TopicChunkResult must not have a words field."""
+
+    def test_no_words_field(self):
+        assert "words" not in TopicChunkResult.model_fields
+
+    def test_construction_without_words(self):
+        result = TopicChunkResult(
+            chunk_index=0,
+            title="Test",
+            summary="Summary",
+            timestamp=0.0,
+            duration=1.0,
+        )
+        assert result.title == "Test"
+        assert result.chunk_index == 0
+
+    def test_serialization_roundtrip(self):
+        """Serialized TopicChunkResult has no words key."""
+        result = TopicChunkResult(
+            chunk_index=0,
+            title="Test",
+            summary="Summary",
+            timestamp=0.0,
+            duration=1.0,
+        )
+        data = result.model_dump()
+        assert "words" not in data
+        reconstructed = TopicChunkResult(**data)
+        assert reconstructed == result
+
+
+class TestWordsToSegmentsBehavioralEquivalence:
+    """words_to_segments() must produce same output as Transcript.as_segments(is_multitrack=False).
+
+    This ensures the extract_subjects refactoring (from task output topic.transcript.as_segments()
+    to words_to_segments(db_topic.words)) preserves identical behavior.
+    """
+
+    def test_single_speaker(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        words = _make_words(speaker=0)
+        direct = words_to_segments(words)
+        via_transcript = TranscriptType(words=words).as_segments(is_multitrack=False)
+
+        assert len(direct) == len(via_transcript)
+        for d, v in zip(direct, via_transcript):
+            assert d.text == v.text
+            assert d.speaker == v.speaker
+            assert d.start == v.start
+            assert d.end == v.end
+
+    def test_multiple_speakers(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        words = [
+            Word(text="Hello", start=0.0, end=0.5, speaker=0),
+            Word(text=" world.", start=0.5, end=1.0, speaker=0),
+            Word(text=" How", start=1.0, end=1.5, speaker=1),
+            Word(text=" are", start=1.5, end=2.0, speaker=1),
+            Word(text=" you?", start=2.0, end=2.5, speaker=1),
+        ]
+
+        direct = words_to_segments(words)
+        via_transcript = TranscriptType(words=words).as_segments(is_multitrack=False)
+
+        assert len(direct) == len(via_transcript)
+        for d, v in zip(direct, via_transcript):
+            assert d.text == v.text
+            assert d.speaker == v.speaker
+
+    def test_empty_words(self):
+        from reflector.processors.types import Transcript as TranscriptType
+        from reflector.processors.types import words_to_segments
+
+        assert words_to_segments([]) == []
+        assert TranscriptType(words=[]).as_segments(is_multitrack=False) == []
+
+
+class TestTopicsResultEmptyWords:
+    """TopicsResult can carry topics with empty transcript words."""
+
+    def test_construction_with_empty_words(self):
+        from reflector.hatchet.workflows.models import TopicsResult
+        from reflector.processors.types import TitleSummary
+        from reflector.processors.types import Transcript as TranscriptType
+
+        topics = [
+            TitleSummary(
+                title="Topic A",
+                summary="Summary A",
+                timestamp=0.0,
+                duration=5.0,
+                transcript=TranscriptType(words=[]),
+            ),
+            TitleSummary(
+                title="Topic B",
+                summary="Summary B",
+                timestamp=5.0,
+                duration=5.0,
+                transcript=TranscriptType(words=[]),
+            ),
+        ]
+        result = TopicsResult(topics=topics)
+        assert len(result.topics) == 2
+        for t in result.topics:
+            assert t.transcript.words == []
+
+    def test_serialization_roundtrip(self):
+        from reflector.hatchet.workflows.models import TopicsResult
+        from reflector.processors.types import TitleSummary
+        from reflector.processors.types import Transcript as TranscriptType
+
+        topics = [
+            TitleSummary(
+                title="Topic",
+                summary="Summary",
+                timestamp=0.0,
+                duration=1.0,
+                transcript=TranscriptType(words=[]),
+            )
+        ]
+        result = TopicsResult(topics=topics)
+        data = result.model_dump()
+        reconstructed = TopicsResult(**data)
+        assert len(reconstructed.topics) == 1
+        assert reconstructed.topics[0].transcript.words == []