Files
reflector/server
Mathieu Virbel 2d2c23f7cc Create video_platforms/whereby structure and WherebyClient
- Create video_platforms/whereby/ directory with __init__.py, client.py, tasks.py
- Implement WherebyClient inheriting from VideoPlatformClient interface
- Move all functions from whereby.py into WherebyClient methods
- Use VideoPlatform.WHEREBY enum for PLATFORM_NAME
- Register WherebyClient in platform registry
- Update factory.py to include S3 bucket config for whereby
- Update worker process to use platform abstraction for get_room_sessions
- Preserve exact API behavior for meeting activity detection
- Maintain AWS S3 configuration handling in WherebyClient
- Fix linting and formatting issues

Addresses PR feedback point 7: implement video_platforms/whereby structure
Note: whereby.py kept for legacy fallback until task 7 cleanup
2025-09-02 17:40:32 -06:00
..
2025-02-03 16:11:01 +01:00
2025-08-20 20:56:45 -04:00
2025-07-16 18:10:11 -06:00
2023-08-29 10:58:27 +02:00
2025-07-16 18:10:11 -06:00

AWS S3/SQS usage clarification

Whereby.com uploads recordings directly to our S3 bucket when meetings end.

SQS Queue (AWS_PROCESS_RECORDING_QUEUE_URL)

Filled by: AWS S3 Event Notifications

The S3 bucket is configured to send notifications to our SQS queue when new objects are created. This is standard AWS infrastructure - not in our codebase.

AWS S3 → SQS Event Configuration:

  • Event Type: s3:ObjectCreated:*
  • Filter: *.mp4 files
  • Destination: Our SQS queue

Our System's Role

Polls SQS every 60 seconds via /server/reflector/worker/process.py:24-62:

Every 60 seconds, check for new recordings

sqs = boto3.client("sqs", ...) response = sqs.receive_message(QueueUrl=queue_url, ...)

Requeue

uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID

Pipeline Management

Continue stuck pipeline from final summaries (identify_participants) step:

uv run python -c "from reflector.pipelines.main_live_pipeline import task_pipeline_final_summaries; result = task_pipeline_final_summaries.delay(transcript_id='TRANSCRIPT_ID'); print(f'Task queued: {result.id}')"

Run full post-processing pipeline (continues to completion):

uv run python -c "from reflector.pipelines.main_live_pipeline import pipeline_post; pipeline_post(transcript_id='TRANSCRIPT_ID')"

.