chore(main): release 0.45.0 (#953)

* feat: make video recording optional, deleting video tracks

CHANGELOG.md

@@ -1,5 +1,18 @@
 # Changelog
 
+## [0.45.0](https://github.com/GreyhavenHQ/reflector/compare/v0.44.0...v0.45.0) (2026-04-09)
+
+
+### Features
+
+* make video recording optional, deleting video tracks ([#954](https://github.com/GreyhavenHQ/reflector/issues/954)) ([ee8db36](https://github.com/GreyhavenHQ/reflector/commit/ee8db36f2cd93b8f1ff4f4318e331fe2bac219c5))
+
+
+### Bug Fixes
+
+* better topic chunking and subject extraction ([#952](https://github.com/GreyhavenHQ/reflector/issues/952)) ([5f0c563](https://github.com/GreyhavenHQ/reflector/commit/5f0c5635eb77955b70168242ad7c336a20c98dd0))
+* inline imports ([#955](https://github.com/GreyhavenHQ/reflector/issues/955)) ([739cd51](https://github.com/GreyhavenHQ/reflector/commit/739cd513751cd52d8e3d6d80b64568b1cf409414))
+
 ## [0.44.0](https://github.com/GreyhavenHQ/reflector/compare/v0.43.0...v0.44.0) (2026-04-07)
 
 

CLAUDE.md

@@ -201,4 +201,4 @@ If you need to do any worker/pipeline related work, search for "Pipeline" classe
 ## Code Style
 
 - Always put imports at the top of the file. Let ruff/pre-commit handle sorting and formatting of imports.
-- Exception: In Hatchet pipeline task functions, DB controller imports (e.g., `transcripts_controller`, `meetings_controller`) stay as deferred/inline imports inside `fresh_db_connection()` blocks — this is intentional to avoid sharing DB connections across forked processes. Non-DB imports (utilities, services) should still go at the top of the file.
+- The **only** imports allowed to remain inline are from `reflector.db.*` modules (e.g., `reflector.db.transcripts`, `reflector.db.meetings`, `reflector.db.recordings`, `reflector.db.rooms`). These stay as deferred/inline imports inside `fresh_db_connection()` blocks in Hatchet pipeline task functions — this is intentional to avoid sharing DB connections across forked processes. All other imports (utilities, services, processors, storage, third-party libs) **must** go at the top of the file, even in Hatchet workflows.

server/migrations/versions/c1d2e3f4a5b6_add_store_video_to_room_and_meeting.py

@@ -0,0 +1,43 @@
+"""add store_video to room and meeting
+
+Revision ID: c1d2e3f4a5b6
+Revises: b4c7e8f9a012
+Create Date: 2026-04-08 00:00:00.000000
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "c1d2e3f4a5b6"
+down_revision: Union[str, None] = "b4c7e8f9a012"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "room",
+        sa.Column(
+            "store_video",
+            sa.Boolean(),
+            nullable=False,
+            server_default=sa.false(),
+        ),
+    )
+    op.add_column(
+        "meeting",
+        sa.Column(
+            "store_video",
+            sa.Boolean(),
+            nullable=False,
+            server_default=sa.false(),
+        ),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("meeting", "store_video")
+    op.drop_column("room", "store_video")

server/reflector/db/meetings.py

@@ -69,6 +69,7 @@ meetings = sa.Table(
     sa.Column("daily_composed_video_duration", sa.Integer, nullable=True),
     # Email recipients for transcript notification
     sa.Column("email_recipients", JSONB, nullable=True),
+    sa.Column("store_video", sa.Boolean, nullable=False, server_default=sa.false()),
     sa.Index("idx_meeting_room_id", "room_id"),
     sa.Index("idx_meeting_calendar_event", "calendar_event_id"),
 )
@@ -122,6 +123,7 @@ class Meeting(BaseModel):
     # Email recipients for transcript notification
     # Each entry is {"email": str, "include_link": bool} or a legacy plain str
     email_recipients: list[dict | str] | None = None
+    store_video: bool = False
 
 
 class MeetingController:
@@ -152,6 +154,7 @@ class MeetingController:
             calendar_event_id=calendar_event_id,
             calendar_metadata=calendar_metadata,
             platform=room.platform,
+            store_video=room.store_video,
         )
         query = meetings.insert().values(**meeting.model_dump())
         await get_database().execute(query)

server/reflector/db/rooms.py

@@ -64,6 +64,9 @@ rooms = sqlalchemy.Table(
         server_default=sqlalchemy.sql.false(),
     ),
     sqlalchemy.Column("email_transcript_to", sqlalchemy.String, nullable=True),
+    sqlalchemy.Column(
+        "store_video", sqlalchemy.Boolean, nullable=False, server_default=false()
+    ),
     sqlalchemy.Index("idx_room_is_shared", "is_shared"),
     sqlalchemy.Index("idx_room_ics_enabled", "ics_enabled"),
 )
@@ -94,6 +97,7 @@ class Room(BaseModel):
     platform: Platform = Field(default_factory=lambda: settings.DEFAULT_VIDEO_PLATFORM)
     skip_consent: bool = False
     email_transcript_to: str | None = None
+    store_video: bool = False
 
 
 class RoomController:
@@ -150,6 +154,7 @@ class RoomController:
         platform: Platform = settings.DEFAULT_VIDEO_PLATFORM,
         skip_consent: bool = False,
         email_transcript_to: str | None = None,
+        store_video: bool = False,
     ):
         """
         Add a new room
@@ -176,6 +181,7 @@ class RoomController:
             "platform": platform,
             "skip_consent": skip_consent,
             "email_transcript_to": email_transcript_to,
+            "store_video": store_video,
         }
 
         room = Room(**room_data)

server/reflector/hatchet/run_workers_llm.py

@@ -10,6 +10,7 @@ from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,
 )
+from reflector.hatchet.workflows.failed_runs_monitor import failed_runs_monitor
 from reflector.hatchet.workflows.file_pipeline import file_pipeline
 from reflector.hatchet.workflows.live_post_pipeline import live_post_pipeline
 from reflector.hatchet.workflows.subject_processing import subject_workflow
@@ -54,10 +55,6 @@ def main():
         ]
     )
     if _zulip_dag_enabled:
-        from reflector.hatchet.workflows.failed_runs_monitor import (  # noqa: PLC0415
-            failed_runs_monitor,
-        )
-
         workflows.append(failed_runs_monitor)
         logger.info(
             "FailedRunsMonitor cron enabled",

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -18,10 +18,11 @@ import json
 import tempfile
 import time
 from contextlib import asynccontextmanager
-from datetime import timedelta
+from datetime import datetime, timedelta
 from pathlib import Path
 from typing import Any, Callable, Coroutine, Protocol, TypeVar
 
+import databases
 import httpx
 from hatchet_sdk import (
     ConcurrencyExpression,
@@ -83,6 +84,7 @@ from reflector.hatchet.workflows.topic_chunk_processing import (
     topic_chunk_workflow,
 )
 from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+from reflector.llm import LLM
 from reflector.logger import logger
 from reflector.pipelines import topic_processing
 from reflector.processors.audio_mixdown_auto import AudioMixdownAutoProcessor
@@ -95,7 +97,9 @@ from reflector.processors.summary.prompts import (
 from reflector.processors.summary.summary_builder import SummaryBuilder
 from reflector.processors.types import TitleSummary, Word
 from reflector.processors.types import Transcript as TranscriptType
+from reflector.redis_cache import get_async_redis_client
 from reflector.settings import settings
+from reflector.storage import get_source_storage, get_transcripts_storage
 from reflector.utils.audio_constants import (
     PRESIGNED_URL_EXPIRATION_SECONDS,
     WAVEFORM_SEGMENTS,
@@ -105,8 +109,16 @@ from reflector.utils.daily import (
     filter_cam_audio_tracks,
     parse_daily_recording_filename,
 )
+from reflector.utils.livekit import parse_livekit_track_filepath
 from reflector.utils.string import NonEmptyString, assert_non_none_and_non_empty
-from reflector.utils.transcript_constants import TOPIC_CHUNK_WORD_COUNT
+from reflector.utils.transcript_constants import (
+    compute_max_subjects,
+    compute_topic_chunk_size,
+)
+from reflector.utils.webhook import (
+    fetch_transcript_webhook_payload,
+    send_webhook_request,
+)
 from reflector.zulip import post_transcript_notification
 
 
@@ -135,8 +147,6 @@ async def fresh_db_connection():
     The real fix would be making the db module fork-aware instead of bypassing it.
     Current pattern is acceptable given Hatchet's process model.
     """
-    import databases  # noqa: PLC0415
-
     from reflector.db import _database_context  # noqa: PLC0415
 
     _database_context.set(None)
@@ -173,8 +183,6 @@ async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:
 
 def _spawn_storage():
     """Create fresh storage instance for writing to our transcript bucket."""
-    from reflector.storage import get_transcripts_storage  # noqa: PLC0415
-
     return get_transcripts_storage()
 
 
@@ -388,10 +396,6 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
 
         if input.source_platform == "livekit":
             # LiveKit: participant identity is in the track dict or can be parsed from filepath
-            from reflector.utils.livekit import (
-                parse_livekit_track_filepath,  # noqa: PLC0415
-            )
-
             # Look up identity → Reflector user_id mapping from Redis
             # (stored at join time in rooms.py)
             identity_to_user_id: dict[str, str] = {}
@@ -399,9 +403,6 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
                 from reflector.db.meetings import (
                     meetings_controller as mc,  # noqa: PLC0415
                 )
-                from reflector.redis_cache import (
-                    get_async_redis_client,  # noqa: PLC0415
-                )
 
                 meeting = (
                     await mc.get_by_id(transcript.meeting_id)
@@ -543,12 +544,6 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
     # OGG files don't have embedded start_time metadata, so we pre-calculate.
     track_padding: dict[int, float] = {}
     if input.source_platform == "livekit":
-        from datetime import datetime  # noqa: PLC0415
-
-        from reflector.utils.livekit import (
-            parse_livekit_track_filepath,  # noqa: PLC0415
-        )
-
         timestamps = []
         for i, track in enumerate(input.tracks):
             ts_str = track.get("timestamp")
@@ -885,7 +880,8 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
         transcripts_controller,
     )
 
-    chunk_size = TOPIC_CHUNK_WORD_COUNT
+    duration_seconds = words[-1].end - words[0].start if words else 0
+    chunk_size = compute_topic_chunk_size(duration_seconds, len(words))
     chunks = []
     for i in range(0, len(words), chunk_size):
         chunk_words = words[i : i + chunk_size]
@@ -975,7 +971,7 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
 
     ctx.log(f"detect_topics complete: found {len(topics_list)} topics")
 
-    return TopicsResult(topics=topics_list)
+    return TopicsResult(topics=topics_list, duration_seconds=duration_seconds)
 
 
 @daily_multitrack_pipeline.task(
@@ -1069,10 +1065,9 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
             participant_name_to_id={},
         )
 
-    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
-    # sharing DB connections and LLM HTTP pools across forks
+    # Deferred DB import: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections across forks
     from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-    from reflector.llm import LLM  # noqa: PLC0415
 
     async with fresh_db_connection():
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
@@ -1112,8 +1107,14 @@ async def extract_subjects(input: PipelineInput, ctx: Context) -> SubjectsResult
                 participant_names, participant_name_to_id=participant_name_to_id
             )
 
+        max_subjects = compute_max_subjects(topics_result.duration_seconds)
+        ctx.log(
+            f"extract_subjects: duration={topics_result.duration_seconds:.0f}s, "
+            f"max_subjects={max_subjects}"
+        )
+
         ctx.log("extract_subjects: calling LLM to extract subjects")
-        await builder.extract_subjects()
+        await builder.extract_subjects(max_subjects=max_subjects)
 
         ctx.log(f"extract_subjects complete: {len(builder.subjects)} subjects")
 
@@ -1196,14 +1197,13 @@ async def generate_recap(input: PipelineInput, ctx: Context) -> RecapResult:
     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
+    # Deferred DB import: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections across forks
     from reflector.db.transcripts import (  # noqa: PLC0415
         TranscriptFinalLongSummary,
         TranscriptFinalShortSummary,
         transcripts_controller,
     )
-    from reflector.llm import LLM  # noqa: PLC0415
 
     subject_summaries = process_result.subject_summaries
 
@@ -1292,13 +1292,12 @@ async def identify_action_items(
         ctx.log("identify_action_items: no transcript text, returning empty")
         return ActionItemsResult(action_items=ActionItemsResponse())
 
-    # Deferred imports: Hatchet workers fork processes, fresh imports avoid
-    # sharing DB connections and LLM HTTP pools across forks
+    # Deferred DB import: Hatchet workers fork processes, fresh imports avoid
+    # sharing DB connections across forks
     from reflector.db.transcripts import (  # noqa: PLC0415
         TranscriptActionItems,
         transcripts_controller,
     )
-    from reflector.llm import LLM  # noqa: PLC0415
 
     # TODO: refactor SummaryBuilder methods into standalone functions
     llm = LLM(settings=settings)
@@ -1435,10 +1434,6 @@ async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
         )
         from reflector.db.recordings import recordings_controller  # noqa: PLC0415
         from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-        from reflector.storage import (  # noqa: PLC0415
-            get_source_storage,
-            get_transcripts_storage,
-        )
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if not transcript:
@@ -1587,10 +1582,6 @@ async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
 
     async with fresh_db_connection():
         from reflector.db.rooms import rooms_controller  # noqa: PLC0415
-        from reflector.utils.webhook import (  # noqa: PLC0415
-            fetch_transcript_webhook_payload,
-            send_webhook_request,
-        )
 
         room = await rooms_controller.get_by_id(input.room_id)
         if not room or not room.webhook_url:

server/reflector/hatchet/workflows/file_pipeline.py

@@ -15,6 +15,8 @@ import json
 from datetime import timedelta
 from pathlib import Path
 
+import av
+import httpx
 from hatchet_sdk import Context
 from pydantic import BaseModel
 
@@ -47,9 +49,30 @@ from reflector.hatchet.workflows.models import (
 )
 from reflector.logger import logger
 from reflector.pipelines import topic_processing
+from reflector.pipelines.transcription_helpers import transcribe_file_with_processor
+from reflector.processors import AudioFileWriterProcessor
+from reflector.processors.file_diarization import FileDiarizationInput
+from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
+from reflector.processors.transcript_diarization_assembler import (
+    TranscriptDiarizationAssemblerInput,
+    TranscriptDiarizationAssemblerProcessor,
+)
+from reflector.processors.types import (
+    DiarizationSegment,
+    Word,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
 from reflector.settings import settings
+from reflector.storage import get_source_storage, get_transcripts_storage
 from reflector.utils.audio_constants import WAVEFORM_SEGMENTS
 from reflector.utils.audio_waveform import get_audio_waveform
+from reflector.utils.webhook import (
+    fetch_transcript_webhook_payload,
+    send_webhook_request,
+)
+from reflector.zulip import post_transcript_notification
 
 
 class FilePipelineInput(BaseModel):
@@ -135,10 +158,6 @@ async def extract_audio(input: FilePipelineInput, ctx: Context) -> ExtractAudioR
         ctx.log(f"extract_audio: processing {audio_file}")
 
         # Extract audio and write as MP3
-        import av  # noqa: PLC0415
-
-        from reflector.processors import AudioFileWriterProcessor  # noqa: PLC0415
-
         duration_ms_container = [0.0]
 
         async def capture_duration(d):
@@ -189,8 +208,6 @@ async def upload_audio(input: FilePipelineInput, ctx: Context) -> UploadAudioRes
     extract_result = ctx.task_output(extract_audio)
     audio_path = extract_result.audio_path
 
-    from reflector.storage import get_transcripts_storage  # noqa: PLC0415
-
     storage = get_transcripts_storage()
     if not storage:
         raise ValueError(
@@ -232,10 +249,6 @@ async def transcribe(input: FilePipelineInput, ctx: Context) -> TranscribeResult
             raise ValueError(f"Transcript {input.transcript_id} not found")
         source_language = transcript.source_language
 
-    from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
-        transcribe_file_with_processor,
-    )
-
     result = await transcribe_file_with_processor(audio_url, source_language)
 
     ctx.log(f"transcribe complete: {len(result.words)} words")
@@ -264,13 +277,6 @@ async def diarize(input: FilePipelineInput, ctx: Context) -> DiarizeResult:
     upload_result = ctx.task_output(upload_audio)
     audio_url = upload_result.audio_url
 
-    from reflector.processors.file_diarization import (  # noqa: PLC0415
-        FileDiarizationInput,
-    )
-    from reflector.processors.file_diarization_auto import (  # noqa: PLC0415
-        FileDiarizationAutoProcessor,
-    )
-
     processor = FileDiarizationAutoProcessor()
     input_data = FileDiarizationInput(audio_url=audio_url)
 
@@ -353,18 +359,6 @@ async def assemble_transcript(
     transcribe_result = ctx.task_output(transcribe)
     diarize_result = ctx.task_output(diarize)
 
-    from reflector.processors.transcript_diarization_assembler import (  # noqa: PLC0415
-        TranscriptDiarizationAssemblerInput,
-        TranscriptDiarizationAssemblerProcessor,
-    )
-    from reflector.processors.types import (  # noqa: PLC0415
-        DiarizationSegment,
-        Word,
-    )
-    from reflector.processors.types import (  # noqa: PLC0415
-        Transcript as TranscriptType,
-    )
-
     words = [Word(**w) for w in transcribe_result.words]
     transcript_data = TranscriptType(
         words=words, translation=transcribe_result.translation
@@ -437,17 +431,6 @@ async def detect_topics(input: FilePipelineInput, ctx: Context) -> TopicsResult:
         TranscriptTopic,
         transcripts_controller,
     )
-    from reflector.processors.transcript_diarization_assembler import (  # noqa: PLC0415
-        TranscriptDiarizationAssemblerInput,
-        TranscriptDiarizationAssemblerProcessor,
-    )
-    from reflector.processors.types import (  # noqa: PLC0415
-        DiarizationSegment,
-        Word,
-    )
-    from reflector.processors.types import (  # noqa: PLC0415
-        Transcript as TranscriptType,
-    )
 
     words = [Word(**w) for w in transcribe_result.words]
     transcript_data = TranscriptType(
@@ -688,10 +671,6 @@ async def cleanup_consent(input: FilePipelineInput, ctx: Context) -> ConsentResu
         )
         from reflector.db.recordings import recordings_controller  # noqa: PLC0415
         from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-        from reflector.storage import (  # noqa: PLC0415
-            get_source_storage,
-            get_transcripts_storage,
-        )
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if not transcript:
@@ -807,7 +786,6 @@ async def post_zulip(input: FilePipelineInput, ctx: Context) -> ZulipResult:
 
     async with fresh_db_connection():
         from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-        from reflector.zulip import post_transcript_notification  # noqa: PLC0415
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if transcript:
@@ -837,10 +815,6 @@ async def send_webhook(input: FilePipelineInput, ctx: Context) -> WebhookResult:
 
     async with fresh_db_connection():
         from reflector.db.rooms import rooms_controller  # noqa: PLC0415
-        from reflector.utils.webhook import (  # noqa: PLC0415
-            fetch_transcript_webhook_payload,
-            send_webhook_request,
-        )
 
         room = await rooms_controller.get_by_id(input.room_id)
         if not room or not room.webhook_url:
@@ -856,8 +830,6 @@ async def send_webhook(input: FilePipelineInput, ctx: Context) -> WebhookResult:
             ctx.log(f"send_webhook skipped (could not build payload): {payload}")
             return WebhookResult(webhook_sent=False, skipped=True)
 
-        import httpx  # noqa: PLC0415
-
         try:
             response = await send_webhook_request(
                 url=room.webhook_url,

server/reflector/hatchet/workflows/live_post_pipeline.py

@@ -14,6 +14,7 @@ are not shared across forks, avoiding connection pooling issues.
 
 from datetime import timedelta
 
+import httpx
 from hatchet_sdk import Context
 from pydantic import BaseModel
 
@@ -40,7 +41,24 @@ from reflector.hatchet.workflows.models import (
     ZulipResult,
 )
 from reflector.logger import logger
+from reflector.pipelines.main_live_pipeline import (
+    PipelineMainTitle,
+    PipelineMainWaveform,
+    pipeline_convert_to_mp3,
+    pipeline_diarization,
+    pipeline_remove_upload,
+    pipeline_summaries,
+    pipeline_upload_mp3,
+)
+from reflector.pipelines.main_live_pipeline import (
+    cleanup_consent as _cleanup_consent,
+)
 from reflector.settings import settings
+from reflector.utils.webhook import (
+    fetch_transcript_webhook_payload,
+    send_webhook_request,
+)
+from reflector.zulip import post_transcript_notification
 
 
 class LivePostPipelineInput(BaseModel):
@@ -91,9 +109,6 @@ async def waveform(input: LivePostPipelineInput, ctx: Context) -> WaveformResult
 
     async with fresh_db_connection():
         from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            PipelineMainWaveform,
-        )
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if not transcript:
@@ -118,10 +133,6 @@ async def generate_title(input: LivePostPipelineInput, ctx: Context) -> TitleRes
     ctx.log(f"generate_title: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            PipelineMainTitle,
-        )
-
         runner = PipelineMainTitle(transcript_id=input.transcript_id)
         await runner.run()
 
@@ -142,10 +153,6 @@ async def convert_mp3(input: LivePostPipelineInput, ctx: Context) -> ConvertMp3R
     ctx.log(f"convert_mp3: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            pipeline_convert_to_mp3,
-        )
-
         await pipeline_convert_to_mp3(transcript_id=input.transcript_id)
 
     ctx.log("convert_mp3 complete")
@@ -165,10 +172,6 @@ async def upload_mp3(input: LivePostPipelineInput, ctx: Context) -> UploadMp3Res
     ctx.log(f"upload_mp3: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            pipeline_upload_mp3,
-        )
-
         await pipeline_upload_mp3(transcript_id=input.transcript_id)
 
     ctx.log("upload_mp3 complete")
@@ -190,10 +193,6 @@ async def remove_upload(
     ctx.log(f"remove_upload: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            pipeline_remove_upload,
-        )
-
         await pipeline_remove_upload(transcript_id=input.transcript_id)
 
     ctx.log("remove_upload complete")
@@ -213,10 +212,6 @@ async def diarize(input: LivePostPipelineInput, ctx: Context) -> DiarizeResult:
     ctx.log(f"diarize: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            pipeline_diarization,
-        )
-
         await pipeline_diarization(transcript_id=input.transcript_id)
 
     ctx.log("diarize complete")
@@ -236,10 +231,6 @@ async def cleanup_consent(input: LivePostPipelineInput, ctx: Context) -> Consent
     ctx.log(f"cleanup_consent: transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            cleanup_consent as _cleanup_consent,
-        )
-
         await _cleanup_consent(transcript_id=input.transcript_id)
 
     ctx.log("cleanup_consent complete")
@@ -261,10 +252,6 @@ async def final_summaries(
     ctx.log(f"final_summaries: starting for transcript_id={input.transcript_id}")
 
     async with fresh_db_connection():
-        from reflector.pipelines.main_live_pipeline import (  # noqa: PLC0415
-            pipeline_summaries,
-        )
-
         await pipeline_summaries(transcript_id=input.transcript_id)
 
     ctx.log("final_summaries complete")
@@ -289,7 +276,6 @@ async def post_zulip(input: LivePostPipelineInput, ctx: Context) -> ZulipResult:
 
     async with fresh_db_connection():
         from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
-        from reflector.zulip import post_transcript_notification  # noqa: PLC0415
 
         transcript = await transcripts_controller.get_by_id(input.transcript_id)
         if transcript:
@@ -319,10 +305,6 @@ async def send_webhook(input: LivePostPipelineInput, ctx: Context) -> WebhookRes
 
     async with fresh_db_connection():
         from reflector.db.rooms import rooms_controller  # noqa: PLC0415
-        from reflector.utils.webhook import (  # noqa: PLC0415
-            fetch_transcript_webhook_payload,
-            send_webhook_request,
-        )
 
         room = await rooms_controller.get_by_id(input.room_id)
         if not room or not room.webhook_url:
@@ -338,8 +320,6 @@ async def send_webhook(input: LivePostPipelineInput, ctx: Context) -> WebhookRes
             ctx.log(f"send_webhook skipped (could not build payload): {payload}")
             return WebhookResult(webhook_sent=False, skipped=True)
 
-        import httpx  # noqa: PLC0415
-
         try:
             response = await send_webhook_request(
                 url=room.webhook_url,

server/reflector/hatchet/workflows/models.py

@@ -102,6 +102,7 @@ class TopicsResult(BaseModel):
     """Result from detect_topics task."""
 
     topics: list[TitleSummary]
+    duration_seconds: float = 0
 
 
 class TitleResult(BaseModel):

server/reflector/hatchet/workflows/padding_workflow.py

@@ -13,6 +13,8 @@ from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.constants import TIMEOUT_AUDIO
 from reflector.hatchet.workflows.models import PadTrackResult
 from reflector.logger import logger
+from reflector.processors.audio_padding_auto import AudioPaddingAutoProcessor
+from reflector.storage import get_source_storage, get_transcripts_storage
 from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
 from reflector.utils.audio_padding import extract_stream_start_time_from_container
 
@@ -51,11 +53,6 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
     )
 
     try:
-        from reflector.storage import (  # noqa: PLC0415
-            get_source_storage,
-            get_transcripts_storage,
-        )
-
         # Source reads: use platform-specific credentials
         source_storage = get_source_storage(input.source_platform)
         source_url = await source_storage.get_file_url(
@@ -104,10 +101,6 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
         )
 
-        from reflector.processors.audio_padding_auto import (  # noqa: PLC0415
-            AudioPaddingAutoProcessor,
-        )
-
         processor = AudioPaddingAutoProcessor()
         result = await processor.pad_track(
             track_url=source_url,

server/reflector/hatchet/workflows/subject_processing.py

@@ -15,12 +15,14 @@ from pydantic import BaseModel
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_HEAVY
 from reflector.hatchet.workflows.models import SubjectSummaryResult
+from reflector.llm import LLM
 from reflector.logger import logger
 from reflector.processors.summary.prompts import (
     DETAILED_SUBJECT_PROMPT_TEMPLATE,
     PARAGRAPH_SUMMARY_PROMPT,
     build_participant_instructions,
 )
+from reflector.settings import settings
 
 
 class SubjectInput(BaseModel):
@@ -60,11 +62,6 @@ async def generate_detailed_summary(
         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)

server/reflector/hatchet/workflows/topic_chunk_processing.py

@@ -18,9 +18,13 @@ from pydantic import BaseModel
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_MEDIUM
 from reflector.hatchet.workflows.models import TopicChunkResult
+from reflector.llm import LLM
 from reflector.logger import logger
 from reflector.processors.prompts import TOPIC_PROMPT
+from reflector.processors.transcript_topic_detector import TopicResponse
 from reflector.processors.types import Word
+from reflector.settings import settings
+from reflector.utils.text import clean_title
 
 
 class TopicChunkInput(BaseModel):
@@ -64,15 +68,6 @@ async def detect_chunk_topic(input: TopicChunkInput, ctx: Context) -> TopicChunk
         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)
 
     prompt = TOPIC_PROMPT.format(text=input.chunk_text)

server/reflector/hatchet/workflows/track_processing.py

@@ -9,9 +9,9 @@ because Hatchet workflow DAGs are defined statically, but the number of tracks v
 at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
 standard pattern for dynamic fan-out. See `process_tracks` in daily_multitrack_pipeline.py.
 
-Note: This file uses deferred imports (inside tasks) intentionally.
+Note: DB imports (reflector.db.*) are kept inline (deferred) intentionally.
 Hatchet workers run in forked processes; fresh imports per task ensure
-storage/DB connections are not shared across forks.
+DB connections are not shared across forks.
 """
 
 from datetime import timedelta
@@ -24,6 +24,9 @@ from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
 from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
 from reflector.logger import logger
+from reflector.pipelines.transcription_helpers import transcribe_file_with_processor
+from reflector.processors.audio_padding_auto import AudioPaddingAutoProcessor
+from reflector.storage import get_source_storage, get_transcripts_storage
 from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
 from reflector.utils.audio_padding import extract_stream_start_time_from_container
 
@@ -72,11 +75,6 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
     )
 
     try:
-        from reflector.storage import (  # noqa: PLC0415
-            get_source_storage,
-            get_transcripts_storage,
-        )
-
         # Source reads: use platform-specific credentials
         source_storage = get_source_storage(input.source_platform)
         source_url = await source_storage.get_file_url(
@@ -120,10 +118,6 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
             expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
         )
 
-        from reflector.processors.audio_padding_auto import (  # noqa: PLC0415
-            AudioPaddingAutoProcessor,
-        )
-
         processor = AudioPaddingAutoProcessor()
         result = await processor.pad_track(
             track_url=source_url,
@@ -179,11 +173,6 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
             raise ValueError("Missing padded_key from pad_track")
 
         # Presign URL on demand (avoids stale URLs on workflow replay)
-        from reflector.storage import (  # noqa: PLC0415
-            get_source_storage,
-            get_transcripts_storage,
-        )
-
         # If bucket_name is set, file is still in the platform's source bucket (no padding applied).
         # If bucket_name is None, padded file was written to our transcript storage.
         if bucket_name:
@@ -198,10 +187,6 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
             bucket=bucket_name,
         )
 
-        from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
-            transcribe_file_with_processor,
-        )
-
         transcript = await transcribe_file_with_processor(audio_url, input.language)
 
         # Tag all words with speaker index

server/reflector/pipelines/main_live_pipeline.py

@@ -38,6 +38,7 @@ from reflector.db.transcripts import (
     TranscriptWaveform,
     transcripts_controller,
 )
+from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.runner import PipelineMessage, PipelineRunner
 from reflector.processors import (
@@ -814,8 +815,6 @@ async def pipeline_post(*, transcript_id: str, room_id: str | None = None):
     """
     Run the post pipeline via Hatchet.
     """
-    from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
-
     await HatchetClientManager.start_workflow(
         "LivePostProcessingPipeline",
         {

server/reflector/pipelines/topic_processing.py

@@ -18,7 +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
+from reflector.utils.transcript_constants import compute_topic_chunk_size
 
 
 class EmptyPipeline:
@@ -39,7 +39,10 @@ async def detect_topics(
     on_topic_callback: Callable,
     empty_pipeline: EmptyPipeline,
 ) -> list[TitleSummary]:
-    chunk_size = TOPIC_CHUNK_WORD_COUNT
+    duration_seconds = (
+        transcript.words[-1].end - transcript.words[0].start if transcript.words else 0
+    )
+    chunk_size = compute_topic_chunk_size(duration_seconds, len(transcript.words))
     topics: list[TitleSummary] = []
 
     async def on_topic(topic: TitleSummary):

server/reflector/processors/audio_padding_pyav.py

@@ -10,6 +10,7 @@ import os
 import tempfile
 
 import av
+import requests
 
 from reflector.logger import logger
 from reflector.processors.audio_padding import AudioPaddingProcessor, PaddingResponse
@@ -65,8 +66,6 @@ class AudioPaddingPyavProcessor(AudioPaddingProcessor):
         track_index: int,
     ) -> PaddingResponse:
         """Blocking padding work: download, pad with PyAV, upload."""
-        import requests
-
         log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
         temp_dir = tempfile.mkdtemp()
         input_path = None

server/reflector/processors/summary/prompts.py

@@ -43,7 +43,8 @@ DETAILED_SUBJECT_PROMPT_TEMPLATE = dedent(
       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}.
+    - Be specific and cite participant names when attributing statements or actions.
+    - Do not include topics unrelated to {subject}.
 
     # OUTPUT
     Your summary should be clear, concise, and structured, covering all major
@@ -58,6 +59,7 @@ PARAGRAPH_SUMMARY_PROMPT = dedent(
     """
     Summarize the mentioned topic in 1 paragraph.
     It will be integrated into the final summary, so just for this topic.
+    Preserve key decisions and action items. Do not introduce new information.
     """
 ).strip()
 

server/reflector/processors/summary/summary_builder.py

@@ -48,17 +48,24 @@ TRANSCRIPTION_TYPE_PROMPT = dedent(
     """
 ).strip()
 
-SUBJECTS_PROMPT = dedent(
-    """
-    What are the main / high level topic of the meeting.
-    Do not include direct quotes or unnecessary details.
-    Be concise and focused on the main ideas.
-    A subject briefly mentioned should not be included.
-    There should be maximum 6 subjects.
-    Do not write complete narrative sentences for the subject,
-    you must write a concise subject using noun phrases.
-    """
-).strip()
+_DEFAULT_MAX_SUBJECTS = 6
+
+
+def build_subjects_prompt(max_subjects: int = _DEFAULT_MAX_SUBJECTS) -> str:
+    """Build subjects extraction prompt with a dynamic subject cap."""
+    subject_word = "subject" if max_subjects == 1 else "subjects"
+    return dedent(
+        f"""
+        What are the main / high level topics of the meeting.
+        Do not include direct quotes or unnecessary details.
+        Be concise and focused on the main ideas.
+        A subject briefly mentioned should not be included.
+        There should be maximum {max_subjects} {subject_word}.
+        Do not write complete narrative sentences for the subject,
+        you must write a concise subject using noun phrases.
+        """
+    ).strip()
+
 
 ACTION_ITEMS_PROMPT = dedent(
     """
@@ -145,7 +152,7 @@ class SubjectsResponse(BaseModel):
     """Pydantic model for extracted subjects/topics"""
 
     subjects: list[str] = Field(
-        description="List of main subjects/topics discussed, maximum 6 items",
+        description="List of main subjects/topics discussed",
     )
 
 
@@ -345,11 +352,14 @@ class SummaryBuilder:
     # Summary
     # ----------------------------------------------------------------------------
 
-    async def extract_subjects(self) -> None:
+    async def extract_subjects(self, max_subjects: int = _DEFAULT_MAX_SUBJECTS) -> None:
         """Extract main subjects/topics from the transcript."""
-        self.logger.info("--- extract main subjects using TreeSummarize")
+        self.logger.info(
+            "--- extract main subjects using TreeSummarize",
+            max_subjects=max_subjects,
+        )
 
-        subjects_prompt = SUBJECTS_PROMPT
+        subjects_prompt = build_subjects_prompt(max_subjects)
 
         try:
             response = await self._get_structured_response(
@@ -358,7 +368,7 @@ class SummaryBuilder:
                 tone_name="Meeting assistant that talk only as list item",
             )
 
-            self.subjects = response.subjects
+            self.subjects = response.subjects[:max_subjects]
             self.logger.info(f"Extracted subjects: {self.subjects}")
 
         except Exception as e:

server/reflector/tools/process.py

@@ -333,7 +333,9 @@ if __name__ == "__main__":
         if not s3_urls:
             parser.error("At least one S3 URL required for multitrack processing")
 
-        from reflector.tools.cli_multitrack import process_multitrack_cli
+        from reflector.tools.cli_multitrack import (
+            process_multitrack_cli,  # circular import
+        )
 
         asyncio.run(
             process_multitrack_cli(

server/reflector/tools/runpipeline.py

@@ -5,6 +5,7 @@ This tools help to either create a pipeline from command line,
 or read a yaml description of a pipeline and run it.
 """
 
+import importlib
 import json
 
 from reflector.logger import logger
@@ -37,8 +38,6 @@ def get_jsonl(filename, filter_processor_name=None):
 
 
 def get_processor(name):
-    import importlib
-
     module_name = f"reflector.processors.{name}"
     class_name = snake_to_camel(name) + "Processor"
     module = importlib.import_module(module_name)

server/reflector/utils/transcript_constants.py

@@ -4,5 +4,67 @@ 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
+import math
+
+# Topic detection: legacy static chunk size, used as fallback
 TOPIC_CHUNK_WORD_COUNT = 300
+
+# Dynamic chunking curve parameters
+# Formula: target_topics = _COEFFICIENT * duration_minutes ^ _EXPONENT
+# Derived from anchors: 5 min -> 3 topics, 180 min -> 40 topics
+_TOPIC_CURVE_COEFFICIENT = 0.833
+_TOPIC_CURVE_EXPONENT = 0.723
+_MIN_TOPICS = 2
+_MAX_TOPICS = 50
+_MIN_CHUNK_WORDS = 375
+_MAX_CHUNK_WORDS = 1500
+
+
+def compute_topic_chunk_size(duration_seconds: float, total_words: int) -> int:
+    """Calculate optimal chunk size for topic detection based on recording duration.
+
+    Uses a power-curve function to scale topic count sublinearly with duration,
+    producing fewer LLM calls for longer recordings while maintaining topic quality.
+
+    Returns the number of words per chunk.
+    """
+    if total_words <= 0 or duration_seconds <= 0:
+        return _MIN_CHUNK_WORDS
+
+    duration_minutes = duration_seconds / 60.0
+    target_topics = _TOPIC_CURVE_COEFFICIENT * math.pow(
+        duration_minutes, _TOPIC_CURVE_EXPONENT
+    )
+    target_topics = int(round(max(_MIN_TOPICS, min(_MAX_TOPICS, target_topics))))
+
+    chunk_size = total_words // target_topics
+    chunk_size = max(_MIN_CHUNK_WORDS, min(_MAX_CHUNK_WORDS, chunk_size))
+    return chunk_size
+
+
+# Subject extraction: scale max subjects with recording duration
+# Short calls get fewer subjects to avoid over-analyzing trivial content
+_SUBJECT_DURATION_THRESHOLDS = [
+    (5 * 60, 1),  # ≤ 5 min  → 1 subject
+    (15 * 60, 2),  # ≤ 15 min → 2 subjects
+    (30 * 60, 3),  # ≤ 30 min → 3 subjects
+    (45 * 60, 4),  # ≤ 45 min → 4 subjects
+    (60 * 60, 5),  # ≤ 60 min → 5 subjects
+]
+_MAX_SUBJECTS = 6
+
+
+def compute_max_subjects(duration_seconds: float) -> int:
+    """Calculate maximum number of subjects to extract based on recording duration.
+
+    Uses a step function: short recordings get fewer subjects to avoid
+    generating excessive detail for trivial content.
+    """
+    if duration_seconds <= 0:
+        return 1
+
+    for threshold, max_subjects in _SUBJECT_DURATION_THRESHOLDS:
+        if duration_seconds <= threshold:
+            return max_subjects
+
+    return _MAX_SUBJECTS

server/reflector/views/daily.py

@@ -15,6 +15,7 @@ from reflector.dailyco_api import (
 from reflector.db.meetings import meetings_controller
 from reflector.logger import logger as _logger
 from reflector.settings import settings
+from reflector.storage import get_source_storage
 from reflector.video_platforms.factory import create_platform_client
 from reflector.worker.process import (
     poll_daily_room_presence_task,
@@ -219,6 +220,30 @@ async def _handle_recording_ready(event: RecordingReadyEvent):
 
         track_keys = [t.s3Key for t in tracks if t.type == "audio"]
 
+        # Delete video tracks when store_video is disabled (same pattern as LiveKit).
+        # Only delete if we have a meeting AND store_video is explicitly false.
+        # If no meeting found, leave files alone (can't confirm user intent).
+        video_track_keys = [t.s3Key for t in tracks if t.type == "video"]
+        if video_track_keys:
+            meeting = await meetings_controller.get_by_room_name(room_name)
+            if meeting is not None and not meeting.store_video:
+                storage = get_source_storage("daily")
+                for video_key in video_track_keys:
+                    try:
+                        await storage.delete_file(video_key)
+                        logger.info(
+                            "Deleted video track from raw-tracks recording",
+                            s3_key=video_key,
+                            room_name=room_name,
+                        )
+                    except Exception as e:
+                        # Non-critical — pipeline filters these out anyway
+                        logger.warning(
+                            "Failed to delete video track from raw-tracks recording",
+                            s3_key=video_key,
+                            error=str(e),
+                        )
+
         logger.info(
             "Raw-tracks recording queuing processing",
             recording_id=recording_id,

server/reflector/views/livekit.py

@@ -17,6 +17,7 @@ from reflector.db.meetings import meetings_controller
 from reflector.livekit_api.webhooks import create_webhook_receiver, verify_webhook
 from reflector.logger import logger as _logger
 from reflector.settings import settings
+from reflector.storage import get_source_storage
 
 router = APIRouter()
 
@@ -189,8 +190,6 @@ async def _handle_egress_ended(event):
         filename = file_result.filename
         if filename and filename.endswith(".webm"):
             try:
-                from reflector.storage import get_source_storage  # noqa: PLC0415
-
                 storage = get_source_storage("livekit")
                 await storage.delete_file(filename)
                 logger.info(

server/reflector/views/rooms.py

@@ -1,4 +1,6 @@
 import logging
+import re
+import uuid
 from datetime import datetime, timedelta, timezone
 from enum import Enum
 from typing import Annotated, Any, Literal, Optional
@@ -14,7 +16,7 @@ from reflector.db import get_database
 from reflector.db.calendar_events import calendar_events_controller
 from reflector.db.meetings import meetings_controller
 from reflector.db.rooms import rooms_controller
-from reflector.redis_cache import RedisAsyncLock
+from reflector.redis_cache import RedisAsyncLock, get_async_redis_client
 from reflector.schemas.platform import Platform
 from reflector.services.ics_sync import ics_sync_service
 from reflector.utils.url import add_query_param
@@ -45,6 +47,7 @@ class Room(BaseModel):
     platform: Platform
     skip_consent: bool = False
     email_transcript_to: str | None = None
+    store_video: bool = False
 
 
 class RoomDetails(Room):
@@ -75,6 +78,7 @@ class Meeting(BaseModel):
     platform: Platform
     daily_composed_video_s3_key: str | None = None
     daily_composed_video_duration: int | None = None
+    store_video: bool = False
 
 
 class CreateRoom(BaseModel):
@@ -95,6 +99,7 @@ class CreateRoom(BaseModel):
     platform: Platform
     skip_consent: bool = False
     email_transcript_to: str | None = None
+    store_video: bool = False
 
 
 class UpdateRoom(BaseModel):
@@ -115,6 +120,7 @@ class UpdateRoom(BaseModel):
     platform: Optional[Platform] = None
     skip_consent: Optional[bool] = None
     email_transcript_to: Optional[str] = None
+    store_video: Optional[bool] = None
 
 
 class CreateRoomMeeting(BaseModel):
@@ -257,6 +263,7 @@ async def rooms_create(
         platform=room.platform,
         skip_consent=room.skip_consent,
         email_transcript_to=room.email_transcript_to,
+        store_video=room.store_video,
     )
 
 
@@ -325,6 +332,7 @@ async def rooms_create_meeting(
                         and meeting.recording_type == room.recording_type
                         and meeting.recording_trigger == room.recording_trigger
                         and meeting.platform == room.platform
+                        and meeting.store_video == room.store_video
                     )
                     if not settings_match:
                         logger.info(
@@ -600,9 +608,6 @@ async def rooms_join_meeting(
         meeting.room_url = add_query_param(meeting.room_url, "t", token)
 
     elif meeting.platform == "livekit":
-        import re
-        import uuid
-
         client = create_platform_client(meeting.platform)
         # Identity must be unique per participant to avoid S3 key collisions.
         # Format: {readable_name}-{short_uuid} ensures uniqueness even for same names.
@@ -625,8 +630,6 @@ async def rooms_join_meeting(
         # Store identity → Reflector user_id mapping for the pipeline
         # (so TranscriptParticipant.user_id can be set correctly)
         if user_id:
-            from reflector.redis_cache import get_async_redis_client  # noqa: PLC0415
-
             redis_client = await get_async_redis_client()
             mapping_key = f"livekit:participant_map:{meeting.room_name}"
             await redis_client.hset(mapping_key, participant_identity, user_id)

server/reflector/views/rtc_offer.py

@@ -11,6 +11,7 @@ from reflector.events import subscribers_shutdown
 from reflector.logger import logger
 from reflector.pipelines.runner import PipelineRunner
 from reflector.settings import settings
+from reflector.webrtc_ports import resolve_webrtc_host, rewrite_sdp_host
 
 sessions = []
 router = APIRouter()
@@ -128,8 +129,6 @@ async def rtc_offer_base(
 
     # Rewrite ICE candidate IPs when running behind Docker bridge networking
     if settings.WEBRTC_HOST:
-        from reflector.webrtc_ports import resolve_webrtc_host, rewrite_sdp_host
-
         host_ip = resolve_webrtc_host(settings.WEBRTC_HOST)
         sdp = rewrite_sdp_host(sdp, host_ip)
 

server/reflector/views/transcripts_webrtc.py

@@ -4,6 +4,7 @@ from fastapi import APIRouter, Depends, HTTPException, Request
 
 import reflector.auth as auth
 from reflector.db.transcripts import transcripts_controller
+from reflector.pipelines.main_live_pipeline import PipelineMainLive
 
 from .rtc_offer import RtcOffer, rtc_offer_base
 
@@ -28,8 +29,6 @@ async def transcript_record_webrtc(
         raise HTTPException(status_code=400, detail="Transcript is locked")
 
     # create a pipeline runner
-    from reflector.pipelines.main_live_pipeline import PipelineMainLive  # noqa: PLC0415
-
     pipeline_runner = PipelineMainLive(transcript_id=transcript_id)
 
     # FIXME do not allow multiple recording at the same time

server/reflector/webrtc_ports.py

@@ -11,6 +11,8 @@ This allows running the server in Docker with bridge networking
 import asyncio
 import socket
 
+import aioice.ice
+
 from reflector.logger import logger
 
 
@@ -36,9 +38,7 @@ def patch_aioice_port_range(min_port: int, max_port: int) -> None:
     Works by temporarily wrapping loop.create_datagram_endpoint() during
     aioice's get_component_candidates() to intercept bind(addr, 0) calls.
     """
-    import aioice.ice as _ice
-
-    _original = _ice.Connection.get_component_candidates
+    _original = aioice.ice.Connection.get_component_candidates
     _state = {"next_port": min_port}
 
     async def _patched_get_component_candidates(self, component, addresses, timeout=5):
@@ -78,7 +78,7 @@ def patch_aioice_port_range(min_port: int, max_port: int) -> None:
         finally:
             loop.create_datagram_endpoint = _orig_create
 
-    _ice.Connection.get_component_candidates = _patched_get_component_candidates
+    aioice.ice.Connection.get_component_candidates = _patched_get_component_candidates
     logger.info(
         "aioice patched for WebRTC port range",
         min_port=min_port,
@@ -102,8 +102,6 @@ def rewrite_sdp_host(sdp: str, target_ip: str) -> str:
     Replace container-internal IPs in SDP with target_ip so that
     ICE candidates advertise a routable address.
     """
-    import aioice.ice
-
     container_ips = aioice.ice.get_host_addresses(use_ipv4=True, use_ipv6=False)
     for ip in container_ips:
         if ip != "127.0.0.1" and ip != target_ip:

server/reflector/worker/app.py

@@ -30,6 +30,8 @@ def build_beat_schedule(
     whereby_api_key=None,
     aws_process_recording_queue_url=None,
     daily_api_key=None,
+    livekit_api_key=None,
+    livekit_url=None,
     public_mode=False,
     public_data_retention_days=None,
     healthcheck_url=None,
@@ -83,7 +85,7 @@ def build_beat_schedule(
     else:
         logger.info("Daily.co beat tasks disabled (no DAILY_API_KEY)")
 
-    _livekit_enabled = bool(settings.LIVEKIT_API_KEY and settings.LIVEKIT_URL)
+    _livekit_enabled = bool(livekit_api_key and livekit_url)
     if _livekit_enabled:
         beat_schedule["process_livekit_ended_meetings"] = {
             "task": "reflector.worker.process.process_livekit_ended_meetings",
@@ -175,6 +177,8 @@ else:
         whereby_api_key=settings.WHEREBY_API_KEY,
         aws_process_recording_queue_url=settings.AWS_PROCESS_RECORDING_QUEUE_URL,
         daily_api_key=settings.DAILY_API_KEY,
+        livekit_api_key=settings.LIVEKIT_API_KEY,
+        livekit_url=settings.LIVEKIT_URL,
         public_mode=settings.PUBLIC_MODE,
         public_data_retention_days=settings.PUBLIC_DATA_RETENTION_DAYS,
         healthcheck_url=settings.HEALTHCHECK_URL,

server/reflector/worker/process.py

@@ -1,3 +1,4 @@
+import asyncio
 import json
 import os
 import re
@@ -26,16 +27,26 @@ from reflector.db.transcripts import (
     transcripts_controller,
 )
 from reflector.hatchet.client import HatchetClientManager
+from reflector.pipelines.topic_processing import EmptyPipeline
+from reflector.processors.audio_file_writer import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.redis_cache import RedisAsyncLock
 from reflector.settings import settings
-from reflector.storage import get_transcripts_storage
+from reflector.storage import get_source_storage, get_transcripts_storage
 from reflector.utils.daily import (
     DailyRoomName,
     extract_base_room_name,
     filter_cam_audio_tracks,
     recording_lock_key,
 )
+from reflector.utils.livekit import (
+    extract_livekit_base_room_name,
+    filter_audio_tracks,
+    parse_livekit_track_filepath,
+)
+from reflector.utils.livekit import (
+    recording_lock_key as livekit_recording_lock_key,
+)
 from reflector.utils.string import NonEmptyString
 from reflector.video_platforms.factory import create_platform_client
 from reflector.video_platforms.whereby_utils import (
@@ -562,6 +573,15 @@ async def store_cloud_recording(
         )
         return False
 
+    if not meeting.store_video:
+        logger.info(
+            f"Cloud recording ({source}): skipped, store_video=false",
+            recording_id=recording_id,
+            room_name=room_name,
+            meeting_id=meeting.id,
+        )
+        return False
+
     success = await meetings_controller.set_cloud_recording_if_missing(
         meeting_id=meeting.id,
         s3_key=s3_key,
@@ -923,11 +943,6 @@ async def convert_audio_and_waveform(transcript) -> None:
             transcript_id=transcript.id,
         )
 
-        from reflector.pipelines.topic_processing import EmptyPipeline  # noqa: PLC0415
-        from reflector.processors.audio_file_writer import (
-            AudioFileWriterProcessor,  # noqa: PLC0415
-        )
-
         upload_path = transcript.data_path / "upload.webm"
         mp3_path = transcript.audio_mp3_filename
 
@@ -1206,17 +1221,13 @@ async def process_livekit_multitrack(
     Tracks are discovered via S3 listing (source of truth), not webhooks.
     Called from room_finished webhook (fast-path) or beat task (fallback).
     """
-    from reflector.utils.livekit import (  # noqa: PLC0415
-        recording_lock_key,
-    )
-
     logger.info(
         "Processing LiveKit multitrack recording",
         room_name=room_name,
         meeting_id=meeting_id,
     )
 
-    lock_key = recording_lock_key(room_name)
+    lock_key = livekit_recording_lock_key(room_name)
     async with RedisAsyncLock(
         key=lock_key,
         timeout=600,
@@ -1243,19 +1254,10 @@ async def _process_livekit_multitrack_inner(
     # 1. Discover tracks by listing S3 prefix.
     # Wait briefly for egress files to finish flushing to S3 — the room_finished
     # webhook fires after empty_timeout, but egress finalization may still be in progress.
-    import asyncio as _asyncio  # noqa: PLC0415
-
-    from reflector.storage import get_source_storage  # noqa: PLC0415
-    from reflector.utils.livekit import (  # noqa: PLC0415
-        extract_livekit_base_room_name,
-        filter_audio_tracks,
-        parse_livekit_track_filepath,
-    )
-
     EGRESS_FLUSH_DELAY = 10  # seconds — egress typically flushes within a few seconds
     EGRESS_RETRY_DELAY = 30  # seconds — retry if first listing finds nothing
 
-    await _asyncio.sleep(EGRESS_FLUSH_DELAY)
+    await asyncio.sleep(EGRESS_FLUSH_DELAY)
 
     storage = get_source_storage("livekit")
     s3_prefix = f"livekit/{room_name}/"
@@ -1271,7 +1273,7 @@ async def _process_livekit_multitrack_inner(
             room_name=room_name,
             retry_delay=EGRESS_RETRY_DELAY,
         )
-        await _asyncio.sleep(EGRESS_RETRY_DELAY)
+        await asyncio.sleep(EGRESS_RETRY_DELAY)
         all_keys = await storage.list_objects(prefix=s3_prefix)
         audio_keys = filter_audio_tracks(all_keys) if all_keys else []
 
@@ -1290,7 +1292,7 @@ async def _process_livekit_multitrack_inner(
                 expected=expected_audio,
                 found=len(audio_keys),
             )
-            await _asyncio.sleep(EGRESS_RETRY_DELAY)
+            await asyncio.sleep(EGRESS_RETRY_DELAY)
             all_keys = await storage.list_objects(prefix=s3_prefix)
             audio_keys = filter_audio_tracks(all_keys) if all_keys else []
 

server/tests/test_beat_schedule.py

@@ -32,6 +32,10 @@ DAILY_TASKS = {
     "trigger_daily_reconciliation",
     "reprocess_failed_daily_recordings",
 }
+LIVEKIT_TASKS = {
+    "process_livekit_ended_meetings",
+    "reprocess_failed_livekit_recordings",
+}
 PLATFORM_TASKS = {
     "process_meetings",
     "sync_all_ics_calendars",
@@ -47,6 +51,7 @@ class TestNoPlatformConfigured:
         task_names = set(schedule.keys())
         assert not task_names & WHEREBY_TASKS
         assert not task_names & DAILY_TASKS
+        assert not task_names & LIVEKIT_TASKS
         assert not task_names & PLATFORM_TASKS
 
     def test_only_healthcheck_disabled_warning(self):
@@ -72,6 +77,7 @@ class TestWherebyOnly:
         assert WHEREBY_TASKS <= task_names
         assert PLATFORM_TASKS <= task_names
         assert not task_names & DAILY_TASKS
+        assert not task_names & LIVEKIT_TASKS
 
     def test_whereby_sqs_url(self):
         schedule = build_beat_schedule(
@@ -81,6 +87,7 @@ class TestWherebyOnly:
         assert WHEREBY_TASKS <= task_names
         assert PLATFORM_TASKS <= task_names
         assert not task_names & DAILY_TASKS
+        assert not task_names & LIVEKIT_TASKS
 
     def test_whereby_task_count(self):
         schedule = build_beat_schedule(whereby_api_key="test-key")
@@ -97,6 +104,7 @@ class TestDailyOnly:
         assert DAILY_TASKS <= task_names
         assert PLATFORM_TASKS <= task_names
         assert not task_names & WHEREBY_TASKS
+        assert not task_names & LIVEKIT_TASKS
 
     def test_daily_task_count(self):
         schedule = build_beat_schedule(daily_api_key="test-daily-key")
@@ -104,6 +112,33 @@ class TestDailyOnly:
         assert len(schedule) == 6
 
 
+class TestLiveKitOnly:
+    """When only LiveKit is configured."""
+
+    def test_livekit_keys(self):
+        schedule = build_beat_schedule(
+            livekit_api_key="test-lk-key", livekit_url="ws://livekit:7880"
+        )
+        task_names = set(schedule.keys())
+        assert LIVEKIT_TASKS <= task_names
+        assert PLATFORM_TASKS <= task_names
+        assert not task_names & WHEREBY_TASKS
+        assert not task_names & DAILY_TASKS
+
+    def test_livekit_task_count(self):
+        schedule = build_beat_schedule(
+            livekit_api_key="test-lk-key", livekit_url="ws://livekit:7880"
+        )
+        # LiveKit (2) + Platform (3) = 5
+        assert len(schedule) == 5
+
+    def test_livekit_needs_both_key_and_url(self):
+        schedule_key_only = build_beat_schedule(livekit_api_key="test-lk-key")
+        schedule_url_only = build_beat_schedule(livekit_url="ws://livekit:7880")
+        assert not set(schedule_key_only.keys()) & LIVEKIT_TASKS
+        assert not set(schedule_url_only.keys()) & LIVEKIT_TASKS
+
+
 class TestBothPlatforms:
     """When both Whereby and Daily.co are configured."""
 

server/tests/test_topic_chunking.py

@@ -0,0 +1,99 @@
+import math
+
+import pytest
+
+from reflector.utils.transcript_constants import (
+    compute_max_subjects,
+    compute_topic_chunk_size,
+)
+
+
+@pytest.mark.parametrize(
+    "duration_min,total_words,expected_topics_range",
+    [
+        (5, 750, (1, 3)),
+        (10, 1500, (3, 6)),
+        (30, 4500, (8, 14)),
+        (60, 9000, (14, 22)),
+        (120, 18000, (24, 35)),
+        (180, 27000, (30, 42)),
+    ],
+)
+def test_topic_count_in_expected_range(
+    duration_min, total_words, expected_topics_range
+):
+    chunk_size = compute_topic_chunk_size(duration_min * 60, total_words)
+    num_topics = math.ceil(total_words / chunk_size)
+    assert expected_topics_range[0] <= num_topics <= expected_topics_range[1], (
+        f"For {duration_min}min/{total_words}words: got {num_topics} topics "
+        f"(chunk_size={chunk_size}), expected {expected_topics_range[0]}-{expected_topics_range[1]}"
+    )
+
+
+def test_chunk_size_within_bounds():
+    for duration_min in [5, 10, 30, 60, 120, 180]:
+        chunk_size = compute_topic_chunk_size(duration_min * 60, duration_min * 150)
+        assert (
+            375 <= chunk_size <= 1500
+        ), f"For {duration_min}min: chunk_size={chunk_size} out of bounds [375, 1500]"
+
+
+def test_zero_duration_falls_back():
+    assert compute_topic_chunk_size(0, 1000) == 375
+
+
+def test_zero_words_falls_back():
+    assert compute_topic_chunk_size(600, 0) == 375
+
+
+def test_negative_inputs_fall_back():
+    assert compute_topic_chunk_size(-10, 1000) == 375
+    assert compute_topic_chunk_size(600, -5) == 375
+
+
+def test_very_short_transcript():
+    """A 1-minute call with very few words should still produce at least 1 topic."""
+    chunk_size = compute_topic_chunk_size(60, 100)
+    # chunk_size is at least 375, so 100 words = 1 chunk
+    assert chunk_size >= 375
+
+
+def test_very_long_transcript():
+    """A 4-hour call should cap at max topics."""
+    chunk_size = compute_topic_chunk_size(4 * 3600, 36000)
+    num_topics = math.ceil(36000 / chunk_size)
+    assert num_topics <= 50
+
+
+# --- compute_max_subjects tests ---
+
+
+@pytest.mark.parametrize(
+    "duration_seconds,expected_max",
+    [
+        (0, 1),  # zero/invalid → 1
+        (-10, 1),  # negative → 1
+        (60, 1),  # 1 min → 1
+        (120, 1),  # 2 min → 1
+        (300, 1),  # 5 min (boundary) → 1
+        (301, 2),  # just over 5 min → 2
+        (900, 2),  # 15 min (boundary) → 2
+        (901, 3),  # just over 15 min → 3
+        (1800, 3),  # 30 min (boundary) → 3
+        (1801, 4),  # just over 30 min → 4
+        (2700, 4),  # 45 min (boundary) → 4
+        (2701, 5),  # just over 45 min → 5
+        (3600, 5),  # 60 min (boundary) → 5
+        (3601, 6),  # just over 60 min → 6
+        (7200, 6),  # 2 hours → 6
+        (14400, 6),  # 4 hours → 6
+    ],
+)
+def test_max_subjects_scales_with_duration(duration_seconds, expected_max):
+    assert compute_max_subjects(duration_seconds) == expected_max
+
+
+def test_max_subjects_never_exceeds_cap():
+    """Even very long recordings should cap at 6 subjects."""
+    for hours in range(1, 10):
+        assert compute_max_subjects(hours * 3600) <= 6

www/app/(app)/rooms/page.tsx

@@ -95,6 +95,7 @@ const roomInitialState = {
   platform: "whereby",
   skipConsent: false,
   emailTranscriptTo: "",
+  storeVideo: false,
 };
 
 export default function RoomsList() {
@@ -185,6 +186,7 @@ export default function RoomsList() {
             platform: detailedEditedRoom.platform,
             skipConsent: detailedEditedRoom.skip_consent || false,
             emailTranscriptTo: detailedEditedRoom.email_transcript_to || "",
+            storeVideo: detailedEditedRoom.store_video || false,
           }
         : null,
     [detailedEditedRoom],
@@ -335,6 +337,7 @@ export default function RoomsList() {
         platform,
         skip_consent: room.skipConsent,
         email_transcript_to: room.emailTranscriptTo || null,
+        store_video: room.storeVideo,
       };
 
       if (isEditing) {
@@ -400,6 +403,7 @@ export default function RoomsList() {
       platform: roomData.platform,
       skipConsent: roomData.skip_consent || false,
       emailTranscriptTo: roomData.email_transcript_to || "",
+      storeVideo: roomData.store_video || false,
     });
     setEditRoomId(roomId);
     setIsEditing(true);
@@ -842,6 +846,38 @@ export default function RoomsList() {
                         </Field.HelperText>
                       </Field.Root>
                     )}
+                    {room.platform === "daily" &&
+                      room.recordingType === "cloud" && (
+                        <Field.Root mt={4}>
+                          <Checkbox.Root
+                            name="storeVideo"
+                            checked={room.storeVideo}
+                            onCheckedChange={(e) => {
+                              const syntheticEvent = {
+                                target: {
+                                  name: "storeVideo",
+                                  type: "checkbox",
+                                  checked: e.checked,
+                                },
+                              };
+                              handleRoomChange(syntheticEvent);
+                            }}
+                          >
+                            <Checkbox.HiddenInput />
+                            <Checkbox.Control>
+                              <Checkbox.Indicator />
+                            </Checkbox.Control>
+                            <Checkbox.Label>
+                              Store video recording
+                            </Checkbox.Label>
+                          </Checkbox.Root>
+                          <Field.HelperText>
+                            When enabled, a composed video recording will be
+                            saved alongside audio. Disabling saves significant
+                            storage.
+                          </Field.HelperText>
+                        </Field.Root>
+                      )}
                   </Tabs.Content>
 
                   <Tabs.Content value="share" pt={6}>

www/app/[roomName]/components/DailyRoom.tsx

@@ -267,12 +267,13 @@ export default function DailyRoom({ meeting, room }: DailyRoomProps) {
 
   const handleFrameJoinMeeting = useCallback(() => {
     if (meeting.recording_type === "cloud") {
-      console.log("Starting dual recording via REST API", {
+      console.log("Starting recording via REST API", {
         cloudInstanceId,
         rawTracksInstanceId,
+        storeVideo: meeting.store_video,
       });
 
-      // Start both cloud and raw-tracks via backend REST API (with retry on 404)
+      // Start recordings via backend REST API (with retry on 404)
       // Daily.co needs time to register call as "hosting" for REST API
       const startRecordingWithRetry = (
         type: DailyRecordingType,
@@ -320,12 +321,17 @@ export default function DailyRoom({ meeting, room }: DailyRoomProps) {
         }, RECORDING_START_DELAY_MS);
       };
 
-      // Start both recordings
-      startRecordingWithRetry("cloud", cloudInstanceId);
+      // Always start raw-tracks (needed for transcription pipeline)
       startRecordingWithRetry("raw-tracks", rawTracksInstanceId);
+
+      // Only start cloud (composed video) if store_video is enabled
+      if (meeting.store_video) {
+        startRecordingWithRetry("cloud", cloudInstanceId);
+      }
     }
   }, [
     meeting.recording_type,
+    meeting.store_video,
     meeting.id,
     startRecordingMutation,
     cloudInstanceId,

www/app/reflector-api.d.ts

@@ -1134,6 +1134,11 @@ export interface components {
       skip_consent: boolean;
       /** Email Transcript To */
       email_transcript_to?: string | null;
+      /**
+       * Store Video
+       * @default false
+       */
+      store_video: boolean;
     };
     /** CreateRoomMeeting */
     CreateRoomMeeting: {
@@ -1852,6 +1857,11 @@ export interface components {
       daily_composed_video_s3_key?: string | null;
       /** Daily Composed Video Duration */
       daily_composed_video_duration?: number | null;
+      /**
+       * Store Video
+       * @default false
+       */
+      store_video: boolean;
     };
     /** MeetingConsentRequest */
     MeetingConsentRequest: {
@@ -1955,6 +1965,11 @@ export interface components {
       skip_consent: boolean;
       /** Email Transcript To */
       email_transcript_to?: string | null;
+      /**
+       * Store Video
+       * @default false
+       */
+      store_video: boolean;
     };
     /** RoomDetails */
     RoomDetails: {
@@ -2013,6 +2028,11 @@ export interface components {
       skip_consent: boolean;
       /** Email Transcript To */
       email_transcript_to?: string | null;
+      /**
+       * Store Video
+       * @default false
+       */
+      store_video: boolean;
       /** Webhook Url */
       webhook_url: string | null;
       /** Webhook Secret */
@@ -2389,6 +2409,8 @@ export interface components {
       skip_consent?: boolean | null;
       /** Email Transcript To */
       email_transcript_to?: string | null;
+      /** Store Video */
+      store_video?: boolean | null;
     };
     /** UpdateTranscript */
     UpdateTranscript: {