cp

deployment/cloud-function/main.py

@@ -4,7 +4,8 @@ import base64
 from datetime import datetime
 from typing import Dict, Any, Optional
 import functions_framework
-from google.cloud import vision
+import vertexai
+from vertexai.vision_models import MultiModalEmbeddingModel, Image as VertexImage
 from google.cloud import firestore
 from google.cloud import storage
 from qdrant_client import QdrantClient
@@ -20,8 +21,15 @@ import uuid
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize clients
+# Initialize Vertex AI
-vision_client = vision.ImageAnnotatorClient()
+PROJECT_ID = os.environ.get('GOOGLE_CLOUD_PROJECT') or os.environ.get('GCP_PROJECT')
+LOCATION = os.environ.get('VERTEX_AI_LOCATION', 'us-central1')
+
+if PROJECT_ID:
+    vertexai.init(project=PROJECT_ID, location=LOCATION)
+    logger.info(f"Initialized Vertex AI with project {PROJECT_ID} in location {LOCATION}")
+else:
+    logger.error("PROJECT_ID not found in environment variables")
 
 # Get Firestore configuration from environment variables
 FIRESTORE_PROJECT_ID = os.environ.get('FIRESTORE_PROJECT_ID')
@@ -63,7 +71,7 @@ try:
             qdrant_client.create_collection(
                 collection_name=QDRANT_COLLECTION,
                 vectors_config=VectorParams(
-                    size=512,  # Fixed size for image embeddings
+                    size=1408,  # Vertex AI multimodal embedding size
                     distance=Distance.COSINE
                 )
             )
@@ -161,7 +169,7 @@ def process_image(image_id: str, storage_path: str, team_id: str, retry_count: i
         # Download image data
         image_data = blob.download_as_bytes()
         
-        # Generate embeddings using Google Cloud Vision
+        # Generate embeddings using Vertex AI
         embeddings = generate_image_embeddings(image_data)
         
         if embeddings is None:
@@ -178,7 +186,7 @@ def process_image(image_id: str, storage_path: str, team_id: str, retry_count: i
                 'team_id': team_id,
                 'storage_path': storage_path,
                 'created_at': datetime.utcnow().isoformat(),
-                'model': 'google-vision-v1'
+                'model': 'vertex-ai-multimodal'
             }
             
             # Create point for Qdrant
@@ -197,7 +205,7 @@ def process_image(image_id: str, storage_path: str, team_id: str, retry_count: i
             logger.info(f"Stored embeddings for image {image_id} in Qdrant with point ID {point_id}")
             
             # Update Firestore with embedding info
-            update_image_embedding_info(image_id, point_id, 'google-vision-v1')
+            update_image_embedding_info(image_id, point_id, 'vertex-ai-multimodal')
             
         return True
         
@@ -207,7 +215,7 @@ def process_image(image_id: str, storage_path: str, team_id: str, retry_count: i
 
 def generate_image_embeddings(image_data: bytes) -> Optional[np.ndarray]:
     """
-    Generate image embeddings using Google Cloud Vision API
+    Generate image embeddings using Vertex AI multimodal embedding model
     
     Args:
         image_data: Binary image data
@@ -216,88 +224,30 @@ def generate_image_embeddings(image_data: bytes) -> Optional[np.ndarray]:
         Numpy array of embeddings or None if failed
     """
     try:
-        # Create Vision API image object
+        # Create Vertex AI image object
-        image = vision.Image(content=image_data)
+        vertex_image = VertexImage(image_data)
         
-        # Use object localization to get feature vectors
+        # Use multimodal embedding model to get embeddings
-        # This provides rich semantic information about the image
+        model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")
-        response = vision_client.object_localization(image=image)
+        embeddings = model.get_embeddings(image=vertex_image)
         
-        if response.error.message:
+        if embeddings is None or embeddings.image_embedding is None:
-            logger.error(f"Vision API error: {response.error.message}")
+            logger.error("Failed to generate embeddings - no image embedding returned")
             return None
         
-        # Extract features from detected objects
+        # Get the image embedding vector
-        features = []
+        embedding_vector = embeddings.image_embedding
         
-        # Get object detection features
+        # Convert to numpy array
-        for obj in response.localized_object_annotations:
+        embeddings_array = np.array(embedding_vector, dtype=np.float32)
-            # Use object name and confidence as features
-            features.extend([
-                hash(obj.name) % 1000 / 1000.0,  # Normalized hash of object name
-                obj.score,  # Confidence score
-                obj.bounding_poly.normalized_vertices[0].x,  # Bounding box features
-                obj.bounding_poly.normalized_vertices[0].y,
-                obj.bounding_poly.normalized_vertices[2].x - obj.bounding_poly.normalized_vertices[0].x,  # Width
-                obj.bounding_poly.normalized_vertices[2].y - obj.bounding_poly.normalized_vertices[0].y,  # Height
-            ])
-        
-        # Also get label detection for additional semantic information
-        label_response = vision_client.label_detection(image=image)
-        
-        for label in label_response.label_annotations[:10]:  # Top 10 labels
-            features.extend([
-                hash(label.description) % 1000 / 1000.0,  # Normalized hash of label
-                label.score  # Confidence score
-            ])
-        
-        # Get text detection for additional context
-        text_response = vision_client.text_detection(image=image)
-        
-        if text_response.text_annotations:
-            # Add text features
-            text_content = text_response.text_annotations[0].description if text_response.text_annotations else ""
-            text_hash = hash(text_content.lower()) % 1000 / 1000.0
-            features.extend([text_hash, len(text_content) / 1000.0])  # Normalized text length
-        
-        # Get face detection for additional features
-        face_response = vision_client.face_detection(image=image)
-        
-        face_count = len(face_response.face_annotations)
-        features.append(min(face_count / 10.0, 1.0))  # Normalized face count
-        
-        # Add image properties
-        try:
-            # Get image properties
-            properties_response = vision_client.image_properties(image=image)
-            
-            if properties_response.image_properties_annotation:
-                # Add dominant colors as features
-                colors = properties_response.image_properties_annotation.dominant_colors.colors
-                for i, color in enumerate(colors[:5]):  # Top 5 colors
-                    features.extend([
-                        color.color.red / 255.0,
-                        color.color.green / 255.0,
-                        color.color.blue / 255.0,
-                        color.score
-                    ])
-        except Exception as e:
-            logger.warning(f"Could not extract image properties: {e}")
-        
-        # Pad or truncate to fixed size (512 dimensions)
-        target_size = 512
-        if len(features) < target_size:
-            features.extend([0.0] * (target_size - len(features)))
-        else:
-            features = features[:target_size]
         
         # Normalize the feature vector
-        features_array = np.array(features, dtype=np.float32)
+        norm = np.linalg.norm(embeddings_array)
-        norm = np.linalg.norm(features_array)
         if norm > 0:
-            features_array = features_array / norm
+            embeddings_array = embeddings_array / norm
         
-        return features_array
+        logger.info(f"Generated embeddings with shape: {embeddings_array.shape}")
+        return embeddings_array
         
     except Exception as e:
         logger.error(f"Error generating embeddings: {e}")

deployment/cloud-function/requirements.txt

@@ -1,5 +1,5 @@
 functions-framework==3.4.0
-google-cloud-vision==3.4.5
+google-cloud-aiplatform==1.38.0
 google-cloud-firestore==2.11.1
 google-cloud-storage==2.12.0
 qdrant-client==1.7.0

deployment/cloud-function/test_vertex_ai_embeddings.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+"""
+Simple test script to verify Vertex AI multimodal embeddings work correctly.
+Run this script to test the embedding generation before deploying.
+"""
+
+import os
+import sys
+import logging
+from PIL import Image
+import io
+import numpy as np
+
+# Add the current directory to the path so we can import main
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def create_test_image():
+    """Create a simple test image"""
+    # Create a simple 100x100 RGB image with a red square
+    img = Image.new('RGB', (100, 100), color='white')
+    pixels = img.load()
+    
+    # Draw a red square in the center
+    for i in range(25, 75):
+        for j in range(25, 75):
+            pixels[i, j] = (255, 0, 0)  # Red
+    
+    # Convert to bytes
+    img_bytes = io.BytesIO()
+    img.save(img_bytes, format='PNG')
+    return img_bytes.getvalue()
+
+def test_vertex_ai_embeddings():
+    """Test Vertex AI embedding generation"""
+    try:
+        # Set required environment variables for testing
+        os.environ['GOOGLE_CLOUD_PROJECT'] = os.environ.get('GOOGLE_CLOUD_PROJECT', 'your-project-id')
+        os.environ['VERTEX_AI_LOCATION'] = os.environ.get('VERTEX_AI_LOCATION', 'us-central1')
+        
+        # Import the function we want to test
+        from main import generate_image_embeddings
+        
+        # Create test image
+        logger.info("Creating test image...")
+        test_image_data = create_test_image()
+        logger.info(f"Created test image with {len(test_image_data)} bytes")
+        
+        # Generate embeddings
+        logger.info("Generating embeddings using Vertex AI...")
+        embeddings = generate_image_embeddings(test_image_data)
+        
+        if embeddings is None:
+            logger.error("Failed to generate embeddings!")
+            return False
+        
+        # Verify embeddings
+        logger.info(f"Generated embeddings with shape: {embeddings.shape}")
+        logger.info(f"Embeddings dtype: {embeddings.dtype}")
+        logger.info(f"Embeddings range: [{embeddings.min():.4f}, {embeddings.max():.4f}]")
+        logger.info(f"Embeddings norm: {np.linalg.norm(embeddings):.4f}")
+        
+        # Basic validation
+        assert isinstance(embeddings, np.ndarray), "Embeddings should be numpy array"
+        assert embeddings.dtype == np.float32, "Embeddings should be float32"
+        assert len(embeddings.shape) == 1, "Embeddings should be 1D array"
+        assert embeddings.shape[0] == 1408, f"Expected 1408 dimensions, got {embeddings.shape[0]}"
+        
+        # Check if normalized (should be close to 1.0)
+        norm = np.linalg.norm(embeddings)
+        assert 0.9 <= norm <= 1.1, f"Embeddings should be normalized, norm is {norm}"
+        
+        logger.info("✅ All tests passed! Vertex AI embeddings are working correctly.")
+        return True
+        
+    except ImportError as e:
+        logger.error(f"Import error: {e}")
+        logger.error("Make sure you have installed the required dependencies:")
+        logger.error("pip install google-cloud-aiplatform")
+        return False
+    except Exception as e:
+        logger.error(f"Test failed with error: {e}")
+        return False
+
+if __name__ == "__main__":
+    logger.info("Testing Vertex AI multimodal embeddings...")
+    
+    # Check if required environment variables are set
+    project_id = os.environ.get('GOOGLE_CLOUD_PROJECT')
+    if not project_id:
+        logger.error("Please set GOOGLE_CLOUD_PROJECT environment variable")
+        logger.error("Example: export GOOGLE_CLOUD_PROJECT=your-project-id")
+        sys.exit(1)
+    
+    logger.info(f"Using project: {project_id}")
+    
+    success = test_vertex_ai_embeddings()
+    
+    if success:
+        logger.info("🎉 Test completed successfully!")
+        sys.exit(0)
+    else:
+        logger.error("❌ Test failed!")
+        sys.exit(1) 

deployment/fix-cloud-run-deployment.sh

@@ -0,0 +1,135 @@
+#!/bin/bash
+set -e
+
+# Configuration
+PROJECT_ID="gen-lang-client-0424120530"
+REGION="us-central1"
+SERVICE_NAME="sereact"
+
+echo "=== Cloud Run Deployment Fix Script ==="
+echo "Project: $PROJECT_ID"
+echo "Region: $REGION"
+echo "Service: $SERVICE_NAME"
+echo ""
+
+# Function to check if gcloud is authenticated
+check_auth() {
+    if ! gcloud auth list --filter=status:ACTIVE --format="value(account)" | grep -q .; then
+        echo "ERROR: No active gcloud authentication found."
+        echo "Please run: gcloud auth login"
+        exit 1
+    fi
+}
+
+# Function to clean up problematic revisions
+cleanup_revisions() {
+    echo "Step 1: Cleaning up problematic revisions..."
+    
+    # Get all revisions for the service
+    REVISIONS=$(gcloud run revisions list --service=$SERVICE_NAME --region=$REGION --project=$PROJECT_ID --format="value(metadata.name)" 2>/dev/null || echo "")
+    
+    if [ -z "$REVISIONS" ]; then
+        echo "No revisions found or service doesn't exist."
+        return 0
+    fi
+    
+    echo "Found revisions:"
+    echo "$REVISIONS"
+    echo ""
+    
+    # Delete old revisions (keep the latest one for now)
+    REVISION_COUNT=$(echo "$REVISIONS" | wc -l)
+    if [ $REVISION_COUNT -gt 1 ]; then
+        echo "Deleting old revisions to prevent conflicts..."
+        echo "$REVISIONS" | head -n -1 | while read revision; do
+            if [ ! -z "$revision" ]; then
+                echo "Deleting revision: $revision"
+                gcloud run revisions delete "$revision" --region=$REGION --project=$PROJECT_ID --quiet || echo "Failed to delete $revision (may be in use)"
+            fi
+        done
+    fi
+}
+
+# Function to force a new deployment
+force_redeploy() {
+    echo "Step 2: Forcing a new deployment with Terraform..."
+    
+    cd "$(dirname "$0")/terraform"
+    
+    # Taint the Cloud Run service to force recreation
+    echo "Tainting Cloud Run service to force recreation..."
+    terraform taint google_cloud_run_service.sereact || echo "Service not in state or already tainted"
+    
+    # Apply the configuration
+    echo "Applying Terraform configuration..."
+    terraform apply -auto-approve
+    
+    cd - > /dev/null
+}
+
+# Function to verify deployment
+verify_deployment() {
+    echo "Step 3: Verifying deployment..."
+    
+    # Wait a moment for the service to be ready
+    sleep 10
+    
+    # Check service status
+    SERVICE_URL=$(gcloud run services describe $SERVICE_NAME --region=$REGION --project=$PROJECT_ID --format="value(status.url)" 2>/dev/null || echo "")
+    
+    if [ ! -z "$SERVICE_URL" ]; then
+        echo "✅ Service deployed successfully!"
+        echo "Service URL: $SERVICE_URL"
+        
+        # Test the service
+        echo "Testing service health..."
+        if curl -s -o /dev/null -w "%{http_code}" "$SERVICE_URL/health" | grep -q "200"; then
+            echo "✅ Service is responding correctly!"
+        else
+            echo "⚠️  Service deployed but health check failed. Check logs:"
+            echo "gcloud logging read \"resource.type=cloud_run_revision AND resource.labels.service_name=$SERVICE_NAME\" --limit=10 --project=$PROJECT_ID"
+        fi
+    else
+        echo "❌ Service deployment failed."
+        exit 1
+    fi
+}
+
+# Main execution
+main() {
+    echo "Starting Cloud Run deployment fix..."
+    echo ""
+    
+    check_auth
+    cleanup_revisions
+    force_redeploy
+    verify_deployment
+    
+    echo ""
+    echo "=== Deployment Fix Complete ==="
+    echo "If you continue to have issues, try:"
+    echo "1. Building a new image: ./deployment/deploy.sh --deploy --build"
+    echo "2. Checking logs: gcloud logging read \"resource.type=cloud_run_revision AND resource.labels.service_name=$SERVICE_NAME\" --limit=10 --project=$PROJECT_ID"
+}
+
+# Help function
+show_help() {
+    echo "Usage: $0 [--help]"
+    echo ""
+    echo "This script fixes Cloud Run deployment issues by:"
+    echo "1. Cleaning up problematic revisions"
+    echo "2. Forcing a new deployment with Terraform"
+    echo "3. Verifying the deployment"
+    echo ""
+    echo "Options:"
+    echo "  --help    Show this help message"
+}
+
+# Parse arguments
+if [ "$1" = "--help" ]; then
+    show_help
+    exit 0
+fi
+
+# Run main function
+main 

deployment/terraform/cloud-function.tf

@@ -60,8 +60,9 @@ resource "google_cloudfunctions2_function" "image_processor" {
       # Google Cloud Storage configuration
       GCS_BUCKET_NAME = var.storage_bucket_name
       
-      # Google Cloud Vision API
+      # Vertex AI configuration
-      VISION_API_ENABLED = "true"
+      GOOGLE_CLOUD_PROJECT = var.project_id
+      VERTEX_AI_LOCATION   = var.region
       
       # Logging
       LOG_LEVEL = "INFO"
@@ -97,9 +98,9 @@ resource "google_project_iam_member" "function_storage" {
   member  = "serviceAccount:${local.cloud_function_service_account}"
 }
 
-resource "google_project_iam_member" "function_vision" {
+resource "google_project_iam_member" "function_vertex_ai" {
   project = var.project_id
-  role    = "roles/ml.developer"
+  role    = "roles/aiplatform.user"
   member  = "serviceAccount:${local.cloud_function_service_account}"
 }
 

deployment/terraform/main.tf

@@ -21,7 +21,8 @@ resource "google_project_service" "services" {
     "cloudfunctions.googleapis.com",
     "cloudbuild.googleapis.com",
     "eventarc.googleapis.com",
-    "pubsub.googleapis.com"
+    "pubsub.googleapis.com",
+    "aiplatform.googleapis.com"
   ])
   
   project = var.project_id
@@ -63,10 +64,20 @@ resource "google_cloud_run_service" "sereact" {
   }
 
   template {
+    metadata {
+      annotations = {
+        "autoscaling.knative.dev/maxScale" = "10"
+        # Force Cloud Run to always pull the latest image
+        "run.googleapis.com/execution-environment" = "gen2"
+        # Disable CPU throttling for better performance
+        "run.googleapis.com/cpu-throttling" = "false"
+      }
+    }
+    
     spec {
       containers {
         # Use our optimized image
-        image = "gcr.io/${var.project_id}/sereact-api:latest"
+        image = "gcr.io/${var.project_id}/sereact-api:${var.image_tag}"
         
         ports {
           container_port = 8000
@@ -133,38 +144,6 @@ resource "google_cloud_run_service" "sereact" {
           name  = "LOG_LEVEL"
           value = "INFO"
         }
-        
-        # # CORS Configuration - These were missing!
-        # env {
-        #   name  = "CORS_ORIGINS"
-        #   value = "[\"*\"]"
-        # }
-        
-        # env {
-        #   name  = "CORS_METHODS"
-        #   value = "GET,POST,PUT,DELETE,OPTIONS"
-        # }
-        
-        # env {
-        #   name  = "CORS_HEADERS"
-        #   value = "Content-Type,Authorization,X-Requested-With"
-        # }
-        
-        # env {
-        #   name  = "CORS_EXPOSE_HEADERS"
-        #   value = "Content-Length,Content-Range"
-        # }
-        
-        # env {
-        #   name  = "CORS_MAX_AGE"
-        #   value = "3600"
-        # }
-      }
-    }
-    
-    metadata {
-      annotations = {
-        "autoscaling.knative.dev/maxScale" = "10"
       }
     }
   }

deployment/terraform/terraform.tfstate

@@ -1,7 +1,7 @@
 {
   "version": 4,
   "terraform_version": "1.10.1",
-  "serial": 399,
+  "serial": 410,
   "lineage": "a183cd95-f987-8698-c6dd-84e933c394a5",
   "outputs": {
     "cloud_function_name": {
@@ -98,16 +98,16 @@
           "attributes": {
             "exclude_symlink_directories": null,
             "excludes": null,
-            "id": "045029ac803155784c12f8d587fee56b85b1fbe9",
+            "id": "7a7a706b5bba3a12744f2dd109eb18de7112f351",
-            "output_base64sha256": "b/FgNMMT30JSXfrLRXNkWeNc6i22YAmT3YwQRTw1+A4=",
+            "output_base64sha256": "0wAfDV7tH41jEspQ3LBLvIEnrQ6XU2aEtK2GWsMdyCA=",
-            "output_base64sha512": "7GDDTkHwwQVAlwSxe7yzgtGccMNIRCQ7t72ZRk7bcfDI1tzpruhJ5G/0AbrUMXWQO6LffnWtwumQ7XdFHAIzBA==",
+            "output_base64sha512": "glsNAiHzSTOy9mGDckkSyDJhBVFDtLh8Xr6+hSxtCT8nok9qNGO+61iTRLU42OPxaPS/BrbDAXJeT86F3riefA==",
             "output_file_mode": null,
-            "output_md5": "34d81725abbd4f423de71ecd4215d116",
+            "output_md5": "a5d3a7fe131c972bf8d0edf309545042",
             "output_path": "./function-source.zip",
-            "output_sha": "045029ac803155784c12f8d587fee56b85b1fbe9",
+            "output_sha": "7a7a706b5bba3a12744f2dd109eb18de7112f351",
-            "output_sha256": "6ff16034c313df42525dfacb45736459e35cea2db6600993dd8c10453c35f80e",
+            "output_sha256": "d3001f0d5eed1f8d6312ca50dcb04bbc8127ad0e97536684b4ad865ac31dc820",
-            "output_sha512": "ec60c34e41f0c105409704b17bbcb382d19c70c34844243bb7bd99464edb71f0c8d6dce9aee849e46ff401bad43175903ba2df7e75adc2e990ed77451c023304",
+            "output_sha512": "825b0d0221f34933b2f66183724912c83261055143b4b87c5ebebe852c6d093f27a24f6a3463beeb589344b538d8e3f168f4bf06b6c301725e4fce85deb89e7c",
-            "output_size": 5014,
+            "output_size": 4487,
             "source": [],
             "source_content": null,
             "source_content_filename": null,
@@ -170,11 +170,9 @@
                   "run.googleapis.com/ingress": "all"
                 },
                 "effective_annotations": {
-                  "run.googleapis.com/client-name": "gcloud",
-                  "run.googleapis.com/client-version": "431.0.0",
                   "run.googleapis.com/ingress": "all",
                   "run.googleapis.com/ingress-status": "all",
-                  "run.googleapis.com/operation-id": "e4d7484f-39e4-4dde-8105-28d285eb927b",
+                  "run.googleapis.com/operation-id": "7869f742-fe94-42d0-8d82-a1462681980d",
                   "run.googleapis.com/urls": "[\"https://sereact-761163285547.us-central1.run.app\",\"https://sereact-p64zpdtkta-uc.a.run.app\"]",
                   "serving.knative.dev/creator": "johnpccd3@gmail.com",
                   "serving.knative.dev/lastModifier": "johnpccd3@gmail.com"
@@ -183,15 +181,15 @@
                   "cloud.googleapis.com/location": "us-central1",
                   "goog-terraform-provisioned": "true"
                 },
-                "generation": 2,
+                "generation": 1,
-                "labels": {},
+                "labels": null,
                 "namespace": "gen-lang-client-0424120530",
-                "resource_version": "AAY16Gy+yWQ",
+                "resource_version": "AAY16UbSm4k",
                 "self_link": "/apis/serving.knative.dev/v1/namespaces/761163285547/services/sereact",
                 "terraform_labels": {
                   "goog-terraform-provisioned": "true"
                 },
-                "uid": "c67276c9-0c25-4a6c-8f39-4ea942599769"
+                "uid": "d5532269-ab10-4b77-b90f-698306bf0919"
               }
             ],
             "name": "sereact",
@@ -218,14 +216,14 @@
                     "type": "RoutesReady"
                   }
                 ],
-                "latest_created_revision_name": "sereact-00002-cew",
+                "latest_created_revision_name": "sereact-00001-9rv",
-                "latest_ready_revision_name": "sereact-00002-cew",
+                "latest_ready_revision_name": "sereact-00001-9rv",
-                "observed_generation": 2,
+                "observed_generation": 1,
                 "traffic": [
                   {
                     "latest_revision": true,
                     "percent": 100,
-                    "revision_name": "sereact-00002-cew",
+                    "revision_name": "sereact-00001-9rv",
                     "tag": "",
                     "url": ""
                   }
@@ -239,14 +237,14 @@
                   {
                     "annotations": {
                       "autoscaling.knative.dev/maxScale": "10",
-                      "run.googleapis.com/client-name": "gcloud",
+                      "run.googleapis.com/cpu-throttling": "false",
-                      "run.googleapis.com/client-version": "431.0.0"
+                      "run.googleapis.com/execution-environment": "gen2"
                     },
                     "generation": 0,
                     "labels": {
                       "run.googleapis.com/startupProbeType": "Default"
                     },
-                    "name": "sereact-00002-cew",
+                    "name": "",
                     "namespace": "",
                     "resource_version": "",
                     "self_link": "",
@@ -258,8 +256,8 @@
                     "container_concurrency": 80,
                     "containers": [
                       {
-                        "args": [],
+                        "args": null,
-                        "command": [],
+                        "command": null,
                         "env": [
                           {
                             "name": "FIRESTORE_DATABASE_NAME",
@@ -334,7 +332,7 @@
                               "cpu": "1",
                               "memory": "1Gi"
                             },
-                            "requests": {}
+                            "requests": null
                           }
                         ],
                         "startup_probe": [
@@ -356,7 +354,7 @@
                         "working_dir": ""
                       }
                     ],
-                    "node_selector": {},
+                    "node_selector": null,
                     "service_account_name": "761163285547-compute@developer.gserviceaccount.com",
                     "serving_state": "",
                     "timeout_seconds": 300,
@@ -437,7 +435,7 @@
           "schema_version": 0,
           "attributes": {
             "condition": [],
-            "etag": "BwY16Etxb+g=",
+            "etag": "BwY16UdHJ00=",
             "id": "v1/projects/gen-lang-client-0424120530/locations/us-central1/services/sereact/roles/run.invoker/allUsers",
             "location": "us-central1",
             "member": "allUsers",
@@ -471,7 +469,7 @@
                 "automatic_update_policy": [
                   {}
                 ],
-                "build": "projects/761163285547/locations/us-central1/builds/1b8e28d1-ee4d-4d2f-acf2-47e2b03aa421",
+                "build": "projects/761163285547/locations/us-central1/builds/b2b7e513-e00e-462a-8ac8-94abdfb4a0b9",
                 "docker_repository": "projects/gen-lang-client-0424120530/locations/us-central1/repositories/gcf-artifacts",
                 "entry_point": "process_image_embedding",
                 "environment_variables": {},
@@ -485,7 +483,7 @@
                       {
                         "bucket": "gen-lang-client-0424120530-cloud-function-source",
                         "generation": 1748123369545880,
-                        "object": "function-source-34d81725abbd4f423de71ecd4215d116.zip"
+                        "object": "function-source-a5d3a7fe131c972bf8d0edf309545042.zip"
                       }
                     ]
                   }
@@ -554,7 +552,7 @@
               "goog-terraform-provisioned": "true"
             },
             "timeouts": null,
-            "update_time": "2025-05-24T22:08:16.899711009Z",
+            "update_time": "2025-05-24T22:31:52.525335119Z",
             "url": "https://us-central1-gen-lang-client-0424120530.cloudfunctions.net/process-image-embedding"
           },
           "sensitive_attributes": [
@@ -870,8 +868,8 @@
             "database_edition": "STANDARD",
             "delete_protection_state": "DELETE_PROTECTION_DISABLED",
             "deletion_policy": "ABANDON",
-            "earliest_version_time": "2025-05-24T21:16:19.051906Z",
+            "earliest_version_time": "2025-05-24T21:29:52.924798Z",
-            "etag": "INn0mIORvY0DMKrW4vCEvY0D",
+            "etag": "IPHgo4eUvY0DMKrW4vCEvY0D",
             "id": "projects/gen-lang-client-0424120530/databases/sereact-imagedb",
             "key_prefix": "",
             "location_id": "us-central1",
@@ -1495,21 +1493,21 @@
             "content_encoding": "",
             "content_language": "",
             "content_type": "application/zip",
-            "crc32c": "YXAlNA==",
+            "crc32c": "Y4Q5hw==",
             "customer_encryption": [],
-            "detect_md5hash": "NNgXJau9T0I95x7NQhXRFg==",
+            "detect_md5hash": "pdOn/hMclyv40O3zCVRQQg==",
             "event_based_hold": false,
-            "generation": 1748124439573408,
+            "generation": 1748125796837241,
-            "id": "gen-lang-client-0424120530-cloud-function-source-function-source-34d81725abbd4f423de71ecd4215d116.zip",
+            "id": "gen-lang-client-0424120530-cloud-function-source-function-source-a5d3a7fe131c972bf8d0edf309545042.zip",
             "kms_key_name": "",
-            "md5hash": "NNgXJau9T0I95x7NQhXRFg==",
+            "md5hash": "pdOn/hMclyv40O3zCVRQQg==",
-            "md5hexhash": "34d81725abbd4f423de71ecd4215d116",
+            "md5hexhash": "a5d3a7fe131c972bf8d0edf309545042",
-            "media_link": "https://storage.googleapis.com/download/storage/v1/b/gen-lang-client-0424120530-cloud-function-source/o/function-source-34d81725abbd4f423de71ecd4215d116.zip?generation=1748124439573408\u0026alt=media",
+            "media_link": "https://storage.googleapis.com/download/storage/v1/b/gen-lang-client-0424120530-cloud-function-source/o/function-source-a5d3a7fe131c972bf8d0edf309545042.zip?generation=1748125796837241\u0026alt=media",
-            "metadata": {},
+            "metadata": null,
-            "name": "function-source-34d81725abbd4f423de71ecd4215d116.zip",
+            "name": "function-source-a5d3a7fe131c972bf8d0edf309545042.zip",
-            "output_name": "function-source-34d81725abbd4f423de71ecd4215d116.zip",
+            "output_name": "function-source-a5d3a7fe131c972bf8d0edf309545042.zip",
             "retention": [],
-            "self_link": "https://www.googleapis.com/storage/v1/b/gen-lang-client-0424120530-cloud-function-source/o/function-source-34d81725abbd4f423de71ecd4215d116.zip",
+            "self_link": "https://www.googleapis.com/storage/v1/b/gen-lang-client-0424120530-cloud-function-source/o/function-source-a5d3a7fe131c972bf8d0edf309545042.zip",
             "source": "./function-source.zip",
             "storage_class": "STANDARD",
             "temporary_hold": false,

deployment/terraform/terraform.tfstate.backup

@@ -1,7 +1,7 @@
 {
   "version": 4,
   "terraform_version": "1.10.1",
-  "serial": 397,
+  "serial": 404,
   "lineage": "a183cd95-f987-8698-c6dd-84e933c394a5",
   "outputs": {
     "cloud_function_name": {
@@ -239,8 +239,8 @@
                   {
                     "annotations": {
                       "autoscaling.knative.dev/maxScale": "10",
-                      "run.googleapis.com/client-name": "gcloud",
+                      "run.googleapis.com/cpu-throttling": "false",
-                      "run.googleapis.com/client-version": "431.0.0"
+                      "run.googleapis.com/execution-environment": "gen2"
                     },
                     "generation": 0,
                     "labels": {
@@ -598,6 +598,13 @@
         }
       ]
     },
+    {
+      "mode": "managed",
+      "type": "google_compute_address",
+      "name": "vector_db_static_ip",
+      "provider": "provider[\"registry.terraform.io/hashicorp/google\"]",
+      "instances": []
+    },
     {
       "mode": "managed",
       "type": "google_compute_firewall",
@@ -804,6 +811,12 @@
             "zone": "us-central1-a"
           },
           "sensitive_attributes": [
+            [
+              {
+                "type": "get_attr",
+                "value": "metadata_startup_script"
+              }
+            ],
             [
               {
                 "type": "get_attr",
@@ -837,12 +850,6 @@
                 "type": "get_attr",
                 "value": "disk_encryption_key_raw"
               }
-            ],
-            [
-              {
-                "type": "get_attr",
-                "value": "metadata_startup_script"
-              }
             ]
           ],
           "private": "eyJlMmJmYjczMC1lY2FhLTExZTYtOGY4OC0zNDM2M2JjN2M0YzAiOnsiY3JlYXRlIjoxMjAwMDAwMDAwMDAwLCJkZWxldGUiOjEyMDAwMDAwMDAwMDAsInVwZGF0ZSI6MTIwMDAwMDAwMDAwMH0sInNjaGVtYV92ZXJzaW9uIjoiNiJ9",
@@ -870,8 +877,8 @@
             "database_edition": "STANDARD",
             "delete_protection_state": "DELETE_PROTECTION_DISABLED",
             "deletion_policy": "ABANDON",
-            "earliest_version_time": "2025-05-24T21:15:22.382696Z",
+            "earliest_version_time": "2025-05-24T21:26:23.088753Z",
-            "etag": "IPeLluiQvY0DMKrW4vCEvY0D",
+            "etag": "IOqynKOTvY0DMKrW4vCEvY0D",
             "id": "projects/gen-lang-client-0424120530/databases/sereact-imagedb",
             "key_prefix": "",
             "location_id": "us-central1",

deployment/terraform/variables.tf

@@ -80,4 +80,10 @@ variable "use_static_ip" {
   description = "Whether to use a static IP for the vector database VM"
   type        = bool
   default     = false
+}
+
+variable "image_tag" {
+  description = "The Docker image tag for the Cloud Run service"
+  type        = string
+  default     = "latest"
 }