# Cloud Function for Image Processing

This directory contains the source code for the Cloud Function that processes image embeddings.

## Deployment

**Note: This Cloud Function is now deployed via Terraform, not the previous bash script.**

The Cloud Function is automatically deployed when you run:

```bash
cd deployment/terraform
terraform apply
```

## What the Cloud Function Does

1. **Triggered by Pub/Sub**: Listens to the `image-processing-topic` for new image processing tasks
2. **Downloads Images**: Retrieves images from Google Cloud Storage
3. **Generates Embeddings**: Uses Google Cloud Vision API to create image embeddings
4. **Stores Vectors**: Saves embeddings to the Qdrant vector database
5. **Updates Status**: Updates Firestore with processing status and embedding metadata

## Environment Variables

The following environment variables are automatically configured by Terraform:

- `QDRANT_HOST`: IP address of the vector database VM
- `QDRANT_PORT`: Port for Qdrant (default: 6333)
- `QDRANT_API_KEY`: API key for Qdrant authentication
- `QDRANT_COLLECTION`: Collection name for storing vectors (default: image_vectors)

## Function Configuration

- **Runtime**: Python 3.11
- **Memory**: 512MB
- **Timeout**: 540 seconds (9 minutes)
- **Max Instances**: 10
- **Min Instances**: 0 (scales to zero when not in use)
- **Retry Policy**: Automatic retries on failure

## Dependencies

See `requirements.txt` for the complete list of Python dependencies.

## Monitoring

The function automatically logs to Google Cloud Logging. You can monitor:

- Function executions
- Error rates
- Processing times
- Pub/Sub message handling

## Manual Testing

To manually trigger the function, publish a message to the Pub/Sub topic:

```json
{
  "image_id": "test-image-123",
  "storage_path": "your-bucket/path/to/image.jpg",
  "team_id": "team-456",
  "retry_count": 0
}
```