cleanup

.env.example

@@ -19,7 +19,6 @@ API_KEY_SECRET=super-secret-key-for-development-only
 API_KEY_EXPIRY_DAYS=365
 
 # Vector Database settings
-QDRANT_HOST=35.193.174.125
 QDRANT_PORT=6333
 QDRANT_HTTPS=false
 QDRANT_PREFER_GRPC=false

README.md

@@ -199,7 +199,6 @@ Uses Google's Vertex AI multimodal embedding model for generating high-quality i
     API_KEY_EXPIRY_DAYS=365
 
     # Vector Database settings
-    QDRANT_HOST=35.193.174.125
     QDRANT_PORT=6333
     QDRANT_HTTPS=false
     QDRANT_PREFER_GRPC=false
@@ -208,8 +207,13 @@ Uses Google's Vertex AI multimodal embedding model for generating high-quality i
 5. **Deploy Infrastructure**
    ```bash
    ./deployment/deploy.sh --build --deploy
+   python ./scripts/seed_firestore.py
    ```
 
+6. **Destroy Infrastructure**
+   ```bash
+   ./deployment/deploy.sh --destroy
+   ```
 
 ## API Endpoints
 

deployment/cloud-function/README.md

@@ -1,120 +0,0 @@
-# Cloud Function for Image Embedding Processing
-
-This Cloud Function processes images to generate embeddings using Google's Vertex AI multimodal embedding model and stores them in a Qdrant vector database.
-
-## Overview
-
-The function is triggered by Pub/Sub messages containing image processing tasks. It:
-
-1. Downloads images from Google Cloud Storage
-2. Generates embeddings using Vertex AI's `multimodalembedding@001` model
-3. Stores embeddings in Qdrant vector database
-4. Updates image metadata in Firestore
-
-## Key Features
-
-- **Vertex AI Multimodal Embeddings**: Uses Google's state-of-the-art multimodal embedding model
-- **1408-dimensional vectors**: High-quality embeddings for semantic image search
-- **Automatic retry**: Built-in retry logic for failed processing
-- **Status tracking**: Real-time status updates in Firestore
-- **Scalable**: Auto-scaling Cloud Function with configurable limits
-
-## Dependencies
-
-- `google-cloud-aiplatform`: Vertex AI SDK for multimodal embeddings
-- `google-cloud-firestore`: Firestore database client
-- `google-cloud-storage`: Cloud Storage client
-- `qdrant-client`: Vector database client
-- `numpy`: Numerical operations
-- `Pillow`: Image processing
-
-## Environment Variables
-
-The function requires these environment variables:
-
-```bash
-# Google Cloud Configuration
-GOOGLE_CLOUD_PROJECT=your-project-id
-VERTEX_AI_LOCATION=us-central1
-
-# Firestore Configuration
-FIRESTORE_PROJECT_ID=your-project-id
-FIRESTORE_DATABASE_NAME=(default)
-
-# Cloud Storage Configuration
-GCS_BUCKET_NAME=your-bucket-name
-
-# Qdrant Configuration
-QDRANT_HOST=your-qdrant-host
-QDRANT_PORT=6333
-QDRANT_API_KEY=your-api-key
-QDRANT_COLLECTION=image_vectors
-QDRANT_HTTPS=false
-
-# Logging
-LOG_LEVEL=INFO
-```
-
-## Testing
-
-### Local Testing
-
-1. Set up your environment:
-```bash
-export GOOGLE_CLOUD_PROJECT=your-project-id
-export VERTEX_AI_LOCATION=us-central1
-```
-
-2. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-3. Run the test script:
-```bash
-python test_vertex_ai_embeddings.py
-```
-
-This will create a test image and verify that embeddings are generated correctly.
-
-### Expected Output
-
-The test should output something like:
-```
-INFO:__main__:Testing Vertex AI multimodal embeddings...
-INFO:__main__:Using project: your-project-id
-INFO:__main__:Creating test image...
-INFO:__main__:Created test image with 1234 bytes
-INFO:__main__:Generating embeddings using Vertex AI...
-INFO:__main__:Generated embeddings with shape: (1408,)
-INFO:__main__:Embeddings dtype: float32
-INFO:__main__:Embeddings range: [-0.1234, 0.5678]
-INFO:__main__:Embeddings norm: 1.0000
-INFO:__main__:✅ All tests passed! Vertex AI embeddings are working correctly.
-INFO:__main__:🎉 Test completed successfully!
-```
-
-## Deployment
-
-The function is deployed using Terraform. See the main deployment documentation for details.
-
-## Monitoring
-
-- Check Cloud Function logs in Google Cloud Console
-- Monitor Firestore for image status updates
-- Check Qdrant for stored embeddings
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Authentication errors**: Ensure the service account has `roles/aiplatform.user` permission
-2. **API not enabled**: Ensure `aiplatform.googleapis.com` is enabled
-3. **Quota limits**: Check Vertex AI quotas in your project
-4. **Network issues**: Ensure the function can reach Qdrant and other services
-
-### Error Messages
-
-- `"Failed to generate embeddings - no image embedding returned"`: Check image format and size
-- `"PROJECT_ID not found in environment variables"`: Set `GOOGLE_CLOUD_PROJECT`
-- `"Error generating embeddings"`: Check Vertex AI API access and quotas