mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2025-12-20 20:29:06 +00:00
Mathieu Virbel
af16178f86
* ci: use github-token to get around potential api throttling * build: put pyannote-audio separate to the project * fix: now that we have a readme, use it * build: add UV_NO_CACHE
45 lines
1.3 KiB
Markdown
45 lines
1.3 KiB
Markdown
## 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
|
|
|
|
```bash
|
|
uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
|
|
```
|
|
|
|
## Pipeline Management
|
|
|
|
### Continue stuck pipeline from final summaries (identify_participants) step:
|
|
|
|
```bash
|
|
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):
|
|
|
|
```bash
|
|
uv run python -c "from reflector.pipelines.main_live_pipeline import pipeline_post; pipeline_post(transcript_id='TRANSCRIPT_ID')"
|
|
```
|
|
|
|
.
|