Files
reflector/server
Igor Monadical 5bf64b5a41 feat: docker-compose for production frontend (#664)
* docker-compose for production frontend

* fix: Remove external Redis port mapping for Coolify compatibility

Redis should only be accessible within the internal Docker network in Coolify deployments to avoid port conflicts with other applications.

* fix: Remove external port mapping for web service in Coolify

Coolify handles port exposure through its proxy (Traefik), so services should not expose ports directly in the docker-compose file.

* server side client envs

* missing vars

* nextjs experimental

* fix claude 'fix'

* remove build env vars compose

* docker

* remove ports for coolify

* review

* cleanup

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Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
2025-09-24 11:15:27 -04:00
..
2025-09-17 18:52:03 +02:00
2025-02-03 16:11:01 +01:00
2025-09-17 16:43:20 -06:00
2025-08-20 20:56:45 -04:00
2025-07-16 18:10:11 -06:00
2023-08-29 10:58:27 +02:00
2025-09-17 16:43:20 -06:00
2025-09-17 16:43:20 -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')"

.