# Get your OAuth sub (user ID) - requires authentication
curl -H "Authorization: Bearer <your_jwt>" http://localhost:1250/v1/me
# Returns: {"sub": "your-oauth-sub-here", "email": "...", ...}

Creating API Keys

curl -X POST http://localhost:1250/v1/user/api-keys \
  -H "Authorization: Bearer <your_jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "My API Key"}'

Using API Keys

# Use X-API-Key header instead of Authorization
curl -H "X-API-Key: <your_api_key>" http://localhost:1250/v1/transcripts

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

Hatchet Setup (Fresh DB)

After resetting the Hatchet database:

Option A: Automatic (CLI)

# Get default tenant ID and create token in one command
TENANT_ID=$(docker compose exec -T postgres psql -U reflector -d hatchet -t -c \
  "SELECT id FROM \"Tenant\" WHERE slug = 'default';" | tr -d ' \n') && \
TOKEN=$(docker compose exec -T hatchet /hatchet-admin token create \
  --config /config --tenant-id "$TENANT_ID" 2>/dev/null | tr -d '\n') && \
echo "HATCHET_CLIENT_TOKEN=$TOKEN"

Copy the output to server/.env.

Option B: Manual (UI)

Create API token at http://localhost:8889 → Settings → API Tokens
Update server/.env: HATCHET_CLIENT_TOKEN=<new-token>

Then restart workers

docker compose restart server hatchet-worker

Workflows register automatically when hatchet-worker starts.

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')"