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35
.env.example
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@ -1,32 +1,25 @@
# Application Settings # Project Environment Variables
PROJECT_NAME="Image Management API" ENVIRONMENT=development
API_V1_STR="/api/v1" LOG_LEVEL=DEBUG
LOG_LEVEL=INFO
# CORS Settings # CORS settings - Must be a valid JSON list of strings or comma-separated values
CORS_ORIGINS=* CORS_ORIGINS=["*"]
# Database Settings
# Firestore settings # Firestore settings
FIRESTORE_PROJECT_ID=gen-lang-client-0424120530 FIRESTORE_PROJECT_ID=gen-lang-client-0424120530
FIRESTORE_DATABASE_NAME=sereact-imagedb FIRESTORE_DATABASE_NAME=sereact-imagedb
FIRESTORE_CREDENTIALS_FILE=firestore-credentials.json FIRESTORE_CREDENTIALS_FILE=firestore-credentials.json
# Google Cloud Firestore Settings (used when DATABASE_TYPE=firestore) # Google Cloud Storage settings
# Path to service account credentials file (optional, uses application default credentials if not set) GCS_BUCKET_NAME=sereact-images
GCS_CREDENTIALS_FILE=path/to/credentials.json GCS_CREDENTIALS_FILE=firestore-credentials.json
# Google Cloud Storage Settings # Security settings
GCS_BUCKET_NAME=your-bucket-name
# Security Settings
API_KEY_SECRET=super-secret-key-for-development-only API_KEY_SECRET=super-secret-key-for-development-only
API_KEY_EXPIRY_DAYS=365 API_KEY_EXPIRY_DAYS=365
# Vector Database Settings (for image embeddings) # Vector Database settings
VECTOR_DB_API_KEY=your-pinecone-api-key QDRANT_HOST=35.193.174.125
VECTOR_DB_ENVIRONMENT=your-pinecone-environment QDRANT_PORT=6333
VECTOR_DB_INDEX_NAME=image-embeddings QDRANT_HTTPS=false
QDRANT_PREFER_GRPC=false
# Rate Limiting
RATE_LIMIT_PER_MINUTE=100

60
README.md
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@ -137,23 +137,7 @@ The system includes a dedicated Google Compute Engine VM running Qdrant vector d
- **VM Specifications**: 2 vCPUs, 8GB RAM, 50GB disk (e2-standard-2) - **VM Specifications**: 2 vCPUs, 8GB RAM, 50GB disk (e2-standard-2)
- **Operating System**: Ubuntu 22.04 LTS - **Operating System**: Ubuntu 22.04 LTS
- **Vector Database**: Qdrant (latest version via Docker) - **Vector Database**: Qdrant (latest version via Docker)
- **Ports**: 6333 (HTTP API), 6334 (gRPC API)
- **Features**:
- Automatic installation and configuration via startup script
- Daily automated backups
- Health monitoring
- Firewall configuration
- Optional static IP assignment
- API key authentication support
### **Vector Database Features**
- **High Performance**: Optimized for image vector similarity search
- **Scalable**: Can handle millions of image vectors
- **Persistent Storage**: Data persisted on VM disk with automated backups
- **RESTful API**: Easy integration with Python client
- **Cosine Similarity**: Optimized for image embedding comparisons
- **Metadata Filtering**: Support for complex search filters
### **AI Embedding Model** ### **AI Embedding Model**
@ -163,11 +147,6 @@ Uses Google's Vertex AI multimodal embedding model for generating high-quality i
- **Provider**: Google Vertex AI - **Provider**: Google Vertex AI
- **Type**: Multimodal embedding model (supports both images and text) - **Type**: Multimodal embedding model (supports both images and text)
- **Output Dimensions**: **1408-dimensional vectors** - **Output Dimensions**: **1408-dimensional vectors**
- **Data Type**: Float32, normalized vectors (norm ≈ 1.0)
- **Similarity Metric**: Cosine similarity
- **Use Case**: Semantic image search and similarity matching
**⚠️ Important Configuration**: Ensure your Qdrant collection is configured with **1408 dimensions** to match the Vertex AI model output. Dimension mismatches will cause embedding storage failures.
## Setup and Installation ## Setup and Installation
@ -199,30 +178,31 @@ Uses Google's Vertex AI multimodal embedding model for generating high-quality i
4. Create a `.env` file with the following environment variables: 4. Create a `.env` file with the following environment variables:
``` ```
# Firestore # Project Environment Variables
FIRESTORE_PROJECT_ID=your-gcp-project-id ENVIRONMENT=development
FIRESTORE_CREDENTIALS_FILE=path/to/firestore-credentials.json LOG_LEVEL=DEBUG
# Google Cloud Storage # CORS settings - Must be a valid JSON list of strings or comma-separated values
GCS_BUCKET_NAME=your-bucket-name CORS_ORIGINS=["*"]
GCS_CREDENTIALS_FILE=path/to/credentials.json
# Google Pub/Sub # Firestore settings
PUBSUB_TOPIC=image-processing-topic FIRESTORE_PROJECT_ID=gen-lang-client-0424120530
PUBSUB_SUBSCRIPTION=image-processing-subscription FIRESTORE_DATABASE_NAME=sereact-imagedb
FIRESTORE_CREDENTIALS_FILE=firestore-credentials.json
# Google Vertex AI (for image embeddings) # Google Cloud Storage settings
VERTEX_AI_LOCATION=us-central1 GCS_BUCKET_NAME=sereact-images
GOOGLE_CLOUD_PROJECT=your-gcp-project-id GCS_CREDENTIALS_FILE=firestore-credentials.json
# Security # Security settings
API_KEY_SECRET=your-secret-key API_KEY_SECRET=super-secret-key
API_KEY_EXPIRY_DAYS=365
# Vector database (Qdrant) # Vector Database settings
QDRANT_HOST=your-vm-external-ip QDRANT_HOST=35.193.174.125
QDRANT_API_KEY=your-qdrant-api-key # Optional QDRANT_PORT=6333
QDRANT_COLLECTION=image_vectors QDRANT_HTTPS=false
QDRANT_HTTPS=false QDRANT_PREFER_GRPC=false
``` ```
5. **Deploy Infrastructure** (Required for vector database): 5. **Deploy Infrastructure** (Required for vector database):

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deployment/README.md
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# Deployment Options for Sereact
This directory contains multiple options for deploying the Sereact application:
## Terraform Infrastructure (`/terraform`)
The Terraform configuration automates the provisioning of all required Google Cloud resources:
- Google Cloud Run service
- Google Container Registry (GCR)
- Cloud Firestore
- Cloud Storage buckets
See [terraform/README.md](terraform/README.md) for detailed instructions.
## Cloud Run Deployment (`/cloud-run`)
The `service.yaml` file defines the Cloud Run service configuration which can be deployed using:
```bash
gcloud run services replace deployment/cloud-run/service.yaml --region=us-central1
```
## Deployment Script (`deploy.sh`)
For convenience, a deployment script is provided to handle the entire deployment workflow:
```bash
# Provision infrastructure with Terraform
./deployment/deploy.sh --provision
# Build and push Docker image
./deployment/deploy.sh --build
# Deploy to Cloud Run
./deployment/deploy.sh --deploy
# Do everything (provision, build, deploy)
./deployment/deploy.sh --all
```
## CI/CD Pipelines
For CI/CD integration, consider using:
1. **GitHub Actions**: Sample workflow included in terraform/README.md
2. **Cloud Build**: Configure a `cloudbuild.yaml` in your repository
3. **Jenkins**: Use the `deploy.sh` script in your pipeline
## Managing Secrets
Sensitive data should be managed using Google Secret Manager:
```bash
# Create a secret
gcloud secrets create sereact-api-key-secret --replication-policy="automatic"
gcloud secrets create sereact-vector-db-key --replication-policy="automatic"
# Add a secret version
echo -n "your-api-key-secret" | gcloud secrets versions add sereact-api-key-secret --data-file=-
echo -n "your-vector-db-key" | gcloud secrets versions add sereact-vector-db-key --data-file=-
# Update Cloud Run service to use the secrets
gcloud run services update sereact \
--update-secrets=API_KEY_SECRET=sereact-api-key-secret:latest,VECTOR_DB_API_KEY=sereact-vector-db-key:latest
```

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deployment/terraform/README.md
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# Terraform Configuration for Sereact
This directory contains Terraform configurations to provision the required Google Cloud resources for Sereact:
- Google Cloud Run service
- Google Cloud Function for image processing
- Google Container Registry (GCR)
- Firestore database
- Cloud Storage bucket
- Pub/Sub topics and subscriptions
- Qdrant vector database VM
## Prerequisites
1. Install [Terraform](https://www.terraform.io/downloads) (v1.0.0+)
2. Install [Google Cloud SDK](https://cloud.google.com/sdk/docs/install)
3. Authenticate with Google Cloud:
```bash
gcloud auth login
gcloud auth application-default login
```
4. Create or select a Google Cloud project:
```bash
gcloud projects create PROJECT_ID --name="Sereact Project" # optional
gcloud config set project PROJECT_ID
```
## Architecture Overview
The infrastructure includes:
- **Cloud Run Service**: Main application service
- **Cloud Function**: Image processing and embedding generation
- **Qdrant VM**: Vector database running on Compute Engine
- **Firestore**: Document database
- **Cloud Storage**: File storage
- **Pub/Sub**: Message queue for asynchronous image processing
- **Automatic IP Configuration**: Qdrant VM IP is automatically passed to Cloud Run and Cloud Function
## Cloud Function Integration
The Cloud Function is now fully integrated into Terraform and handles:
1. **Image Processing**: Triggered by Pub/Sub messages
2. **Embedding Generation**: Uses Google Cloud Vision API
3. **Vector Storage**: Stores embeddings in Qdrant
4. **Status Updates**: Updates Firestore with processing results
### Automatic Configuration
The Cloud Function is automatically configured with:
- **Qdrant Connection**: VM IP address passed as environment variable
- **IAM Permissions**: Firestore, Storage, and Vision API access
- **Retry Policy**: Automatic retries on failure
- **Scaling**: 0-10 instances based on demand
## Setup and Usage
### Option 1: Automated Deployment (Recommended)
Use the provided script that handles API enablement and proper deployment order:
```bash
cd deployment/terraform
./deploy-with-apis.sh
```
This script will:
1. Enable all required APIs
2. Wait for API propagation
3. Deploy infrastructure in the correct order
4. Handle Cloud Functions v2 dependencies
### Option 2: Manual Deployment
1. Copy the example variables file and edit it with your values:
```bash
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your project-specific values
```
2. Initialize Terraform:
```bash
terraform init
```
3. Enable APIs first (required for Cloud Functions v2):
```bash
terraform apply -target=google_project_service.services
```
4. Wait for APIs to propagate (2-3 minutes), then deploy everything:
```bash
terraform apply
```
## Required APIs
The following APIs are automatically enabled by Terraform:
- `cloudresourcemanager.googleapis.com` - Resource management
- `containerregistry.googleapis.com` - Container Registry
- `run.googleapis.com` - Cloud Run
- `firestore.googleapis.com` - Firestore database
- `storage.googleapis.com` - Cloud Storage
- `compute.googleapis.com` - Compute Engine (for Qdrant VM)
- `cloudfunctions.googleapis.com` - Cloud Functions
- `cloudbuild.googleapis.com` - Cloud Build (for function deployment)
- `eventarc.googleapis.com` - Eventarc (required for Cloud Functions v2)
- `pubsub.googleapis.com` - Pub/Sub messaging
**Note**: If you get an Eventarc API error, use the automated deployment script or wait 2-3 minutes after enabling APIs before deploying the Cloud Function.
5. After provisioning, you'll see outputs including:
- Cloud Run service URL
- Storage bucket name
- Firestore database ID
- Container Registry URL
## Managing Secrets
Secrets for environment variables (API_KEY_SECRET, VECTOR_DB_API_KEY, etc.) should be managed separately using Google Secret Manager:
```bash
# Create secrets
gcloud secrets create sereact-api-key-secret --replication-policy="automatic"
gcloud secrets create sereact-vector-db-key --replication-policy="automatic"
# Add secret versions
echo -n "your-secret-value" | gcloud secrets versions add sereact-api-key-secret --data-file=-
echo -n "your-secret-value" | gcloud secrets versions add sereact-vector-db-key --data-file=-
# Update Cloud Run service to use secrets
gcloud run services update sereact \
--update-secrets=API_KEY_SECRET=sereact-api-key-secret:latest,VECTOR_DB_API_KEY=sereact-vector-db-key:latest
```
## CI/CD Integration
To integrate this with CI/CD, store the `terraform.tfvars` securely in your CI/CD system and run Terraform as part of your deployment pipeline:
```yaml
# Example GitHub Actions step
- name: Terraform Apply
run: |
cd deployment/terraform
terraform init
terraform apply -auto-approve
env:
GOOGLE_APPLICATION_CREDENTIALS: ${{ secrets.GCP_SA_KEY }}
```
## Destroying Resources
To destroy all provisioned resources:
```bash
terraform destroy
```
# Terraform Infrastructure for Sereact
This directory contains Terraform configurations to deploy the complete Sereact infrastructure on Google Cloud Platform, including automatic configuration of Qdrant VM IP addresses for Cloud Run.
## Architecture Overview
The infrastructure includes:
- **Cloud Run Service**: Main application service
- **Qdrant VM**: Vector database running on Compute Engine
- **Firestore**: Document database
- **Cloud Storage**: File storage
- **Automatic IP Configuration**: Qdrant VM IP is automatically passed to Cloud Run
## Qdrant VM IP Address Integration
The Terraform configuration automatically handles passing the Qdrant VM IP address to your Cloud Run service:
### How It Works
1. **VM Creation**: Terraform creates a Compute Engine VM running Qdrant
2. **IP Address Extraction**: Terraform extracts the VM's external IP address
3. **Environment Variable Injection**: The IP is automatically set as `QDRANT_HOST` in Cloud Run
4. **Dependency Management**: Cloud Run waits for the VM to be created before deploying
### Configuration Options
#### Static vs Dynamic IP
You can choose between static and dynamic IP addresses:
```hcl
# In terraform.tfvars
use_static_ip = true # Use static IP (recommended for production)
use_static_ip = false # Use ephemeral IP (cheaper for development)
```
#### Environment Variables Set Automatically
The following environment variables are automatically configured in Cloud Run:
```bash
QDRANT_HOST=<vm-external-ip> # Automatically set from VM
QDRANT_PORT=6333 # HTTP port
QDRANT_API_KEY=<your-api-key> # From terraform.tfvars
```
## Quick Start
### 1. Prerequisites
```bash
# Install required tools
gcloud auth login
gcloud config set project YOUR_PROJECT_ID
terraform --version
```
### 2. Configuration
```bash
# Copy and edit configuration
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your values
```
### 3. Deploy Everything
```bash
# From the root directory
./deployment/deploy.sh --deploy --build
```
### 4. Get Qdrant Information
```bash
# Get Qdrant VM IP and endpoints
./deployment/terraform/scripts/get_qdrant_ip.sh
# Get internal IP (for VPC connections)
./deployment/terraform/scripts/get_qdrant_ip.sh --internal
```
## Terraform Outputs
The configuration provides several outputs for integration:
```bash
# Get specific outputs
terraform output cloud_run_url # Cloud Run service URL
terraform output vector_db_vm_external_ip # Qdrant VM external IP
terraform output vector_db_vm_internal_ip # Qdrant VM internal IP
terraform output qdrant_http_endpoint # Full HTTP endpoint
terraform output cloud_run_qdrant_host # IP configured for Cloud Run
# Get deployment summary
terraform output deployment_summary
```
## File Structure
```
terraform/
├── main.tf # Cloud Run and main resources
├── vm.tf # Qdrant VM configuration
├── outputs.tf # Output definitions
├── variables.tf # Variable definitions
├── terraform.tfvars # Your configuration values
├── scripts/
│ ├── get_qdrant_ip.sh # Helper script to get VM IP
│ └── install_qdrant.sh # VM startup script
└── README.md # This file
```
## Environment Variables Reference
### Automatically Configured
These are set automatically by Terraform:
| Variable | Source | Description |
|----------|--------|-------------|
| `QDRANT_HOST` | VM external IP | Qdrant server IP address |
| `QDRANT_PORT` | Static value | Qdrant HTTP port (6333) |
| `QDRANT_API_KEY` | terraform.tfvars | Qdrant authentication key |
| `FIRESTORE_PROJECT_ID` | terraform.tfvars | GCP project ID |
| `GCS_BUCKET_NAME` | terraform.tfvars | Storage bucket name |
### Manual Configuration Required
Set these in `terraform.tfvars`:
```hcl
project_id = "your-gcp-project-id"
storage_bucket_name = "your-bucket-name"
qdrant_api_key = "your-qdrant-api-key"
use_static_ip = true # or false
```
## Networking Configuration
### Firewall Rules
The configuration automatically creates firewall rules to allow:
- Qdrant HTTP traffic (port 6333)
- Qdrant gRPC traffic (port 6334)
- Access from Cloud Run to Qdrant VM
### IP Address Types
#### External IP (Default)
- Used for Cloud Run → Qdrant communication
- Accessible from internet (with firewall rules)
- Can be static or ephemeral
#### Internal IP (Alternative)
- Available for VPC-native connections
- More secure but requires VPC configuration
- Use `cloud_run_qdrant_host_internal` output
## Troubleshooting
### Check Qdrant Connectivity
```bash
# Get IP and test connection
./deployment/terraform/scripts/get_qdrant_ip.sh
# Manual test
QDRANT_IP=$(terraform output -raw vector_db_vm_external_ip)
curl http://$QDRANT_IP:6333/health
```
### Verify Cloud Run Environment
```bash
# Check Cloud Run service
gcloud run services describe sereact --region=us-central1
# Check environment variables
gcloud run services describe sereact --region=us-central1 \
--format="value(spec.template.spec.containers[0].env[].name,spec.template.spec.containers[0].env[].value)"
```
### Common Issues
1. **VM not ready**: Qdrant installation takes 2-3 minutes after VM creation
2. **Firewall blocking**: Check that your IP is in `allowed_cidr_blocks`
3. **Wrong IP type**: Ensure you're using external IP for Cloud Run connections
## Security Considerations
### Production Recommendations
1. **Use Static IP**: Set `use_static_ip = true`
2. **Restrict Access**: Set specific CIDR blocks in `allowed_cidr_blocks`
3. **Use Internal IP**: Consider VPC-native networking for internal communication
4. **Secure API Key**: Store `qdrant_api_key` in Secret Manager
### Development Setup
```hcl
# terraform.tfvars for development
use_static_ip = false
allowed_cidr_blocks = "0.0.0.0/0" # Open access (not for production!)
```
## Cost Optimization
### Development
- Use `use_static_ip = false` to avoid static IP charges
- Use smaller VM sizes: `machine_type = "e2-micro"`
### Production
- Use `use_static_ip = true` for consistent connectivity
- Use appropriate VM sizes based on load
## Advanced Configuration
### Custom VM Configuration
Edit `vm.tf` to customize:
- Machine type
- Disk size
- Startup script
- Network configuration
### VPC-Native Networking
For internal-only communication:
1. Create VPC connector
2. Configure Cloud Run to use VPC
3. Use internal IP output: `cloud_run_qdrant_host_internal`
## Support
For issues with:
- **Terraform**: Check `terraform plan` output
- **VM connectivity**: Use the helper script `get_qdrant_ip.sh`
- **Cloud Run**: Check logs with `gcloud run logs tail sereact`

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deployment/terraform/main.tf
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@ -105,15 +105,6 @@ resource "google_cloud_run_service" "sereact" {
value = var.storage_bucket_name value = var.storage_bucket_name
} }
env {
name = "VECTOR_DB_ENVIRONMENT"
value = var.vector_db_environment
}
env {
name = "VECTOR_DB_INDEX_NAME"
value = var.vector_db_index_name
}
env { env {
name = "QDRANT_HOST" name = "QDRANT_HOST"

1
deployment/terraform/terraform.tfvars.example
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@ -11,6 +11,5 @@ use_static_ip = false # Set to true if you want a static
# Secret Configuration (all optional - random values will be generated if not provided) # Secret Configuration (all optional - random values will be generated if not provided)
api_key_secret = "" # Optional: will generate random key if empty api_key_secret = "" # Optional: will generate random key if empty
vector_db_api_key = "" # Optional: for external vector DB like Pinecone
gcs_credentials_json = "" # Optional: JSON content of GCS service account key gcs_credentials_json = "" # Optional: JSON content of GCS service account key
firestore_credentials_json = "" # Optional: JSON content of Firestore service account key firestore_credentials_json = "" # Optional: JSON content of Firestore service account key

8
src/config/config.py
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@ -84,14 +84,6 @@ class Settings(BaseSettings):
API_KEY_SECRET: str = os.getenv("API_KEY_SECRET", "super-secret-key-for-development-only") API_KEY_SECRET: str = os.getenv("API_KEY_SECRET", "super-secret-key-for-development-only")
API_KEY_EXPIRY_DAYS: int = int(os.getenv("API_KEY_EXPIRY_DAYS", "365")) API_KEY_EXPIRY_DAYS: int = int(os.getenv("API_KEY_EXPIRY_DAYS", "365"))
# Vector Database settings (for image embeddings)
VECTOR_DB_API_KEY: str = os.getenv("VECTOR_DB_API_KEY", "")
VECTOR_DB_ENVIRONMENT: str = os.getenv("VECTOR_DB_ENVIRONMENT", "")
VECTOR_DB_INDEX_NAME: str = os.getenv("VECTOR_DB_INDEX_NAME", "image-embeddings")
# Rate limiting
RATE_LIMIT_PER_MINUTE: int = int(os.getenv("RATE_LIMIT_PER_MINUTE", "100"))
# Qdrant settings # Qdrant settings
QDRANT_HOST: str = os.getenv("QDRANT_HOST", "localhost") QDRANT_HOST: str = os.getenv("QDRANT_HOST", "localhost")
QDRANT_PORT: int = int(os.getenv("QDRANT_PORT", "6333")) QDRANT_PORT: int = int(os.getenv("QDRANT_PORT", "6333"))