mirror of
https://github.com/Monadical-SAS/reflector.git
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81 lines
2.2 KiB
Markdown
81 lines
2.2 KiB
Markdown
## API Key Management
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### Finding Your User ID
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```bash
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# Get your OAuth sub (user ID) - requires authentication
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curl -H "Authorization: Bearer <your_jwt>" http://localhost:1250/v1/me
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# Returns: {"sub": "your-oauth-sub-here", "email": "...", ...}
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```
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### Creating API Keys
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```bash
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curl -X POST http://localhost:1250/v1/user/api-keys \
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-H "Authorization: Bearer <your_jwt>" \
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-H "Content-Type: application/json" \
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-d '{"name": "My API Key"}'
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```
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### Using API Keys
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```bash
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# Use X-API-Key header instead of Authorization
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curl -H "X-API-Key: <your_api_key>" http://localhost:1250/v1/transcripts
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```
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## AWS S3/SQS usage clarification
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Whereby.com uploads recordings directly to our S3 bucket when meetings end.
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SQS Queue (AWS_PROCESS_RECORDING_QUEUE_URL)
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Filled by: AWS S3 Event Notifications
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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.
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AWS S3 → SQS Event Configuration:
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- Event Type: s3:ObjectCreated:*
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- Filter: *.mp4 files
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- Destination: Our SQS queue
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Our System's Role
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Polls SQS every 60 seconds via /server/reflector/worker/process.py:24-62:
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# Every 60 seconds, check for new recordings
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sqs = boto3.client("sqs", ...)
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response = sqs.receive_message(QueueUrl=queue_url, ...)
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# Requeue
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```bash
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uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
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```
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## Hatchet Setup (Fresh DB)
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After resetting the Hatchet database:
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1. Create API token at http://localhost:8889 → Settings → API Tokens
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2. Update `server/.env`: `HATCHET_CLIENT_TOKEN=<new-token>`
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3. Restart: `docker compose restart server hatchet-worker`
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Workflows register automatically when hatchet-worker starts.
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## Pipeline Management
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### Continue stuck pipeline from final summaries (identify_participants) step:
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```bash
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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}')"
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```
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### Run full post-processing pipeline (continues to completion):
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```bash
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uv run python -c "from reflector.pipelines.main_live_pipeline import pipeline_post; pipeline_post(transcript_id='TRANSCRIPT_ID')"
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```
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