cp

deployment/scripts/setup_local_env.sh

@@ -0,0 +1,167 @@
+#!/bin/bash
+
+# Setup Local Environment Script
+# This script extracts configuration from terraform and sets up local development
+
+set -e
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)"
+TERRAFORM_DIR="$PROJECT_ROOT/deployment/terraform"
+
+echo "Setting up local development environment..."
+
+# Check if terraform directory exists
+if [ ! -d "$TERRAFORM_DIR" ]; then
+    echo "Error: Terraform directory not found at $TERRAFORM_DIR"
+    exit 1
+fi
+
+# Change to terraform directory
+cd "$TERRAFORM_DIR"
+
+# Check if terraform state exists
+if [ ! -f "terraform.tfstate" ]; then
+    echo "Error: Terraform state not found. Please run 'terraform apply' first."
+    exit 1
+fi
+
+echo "Extracting configuration from terraform..."
+
+# Get terraform outputs
+QDRANT_HOST=$(terraform output -raw vector_db_vm_external_ip 2>/dev/null || echo "")
+QDRANT_PORT="6333"
+FIRESTORE_PROJECT_ID=$(terraform output -raw firestore_database_id 2>/dev/null | cut -d'/' -f2 || echo "")
+GCS_BUCKET_NAME=$(terraform output -raw storage_bucket_name 2>/dev/null || echo "")
+
+if [ -z "$QDRANT_HOST" ]; then
+    echo "Error: Could not extract Qdrant host from terraform outputs"
+    exit 1
+fi
+
+echo "Configuration extracted:"
+echo "  Qdrant Host: $QDRANT_HOST"
+echo "  Qdrant Port: $QDRANT_PORT"
+echo "  Firestore Project: $FIRESTORE_PROJECT_ID"
+echo "  GCS Bucket: $GCS_BUCKET_NAME"
+
+# Go back to project root
+cd "$PROJECT_ROOT"
+
+# Update start_dev.sh with extracted values
+echo "Updating start_dev.sh..."
+
+cat > start_dev.sh << EOF
+#!/bin/bash
+
+# Development startup script for Sereact API
+# This script sets the environment variables and starts the application
+# Auto-generated by deployment/scripts/setup_local_env.sh
+
+# Activate virtual environment
+source venv/Scripts/activate
+
+# Set environment variables from deployed infrastructure
+export QDRANT_HOST=$QDRANT_HOST
+export QDRANT_PORT=$QDRANT_PORT
+export FIRESTORE_PROJECT_ID=$FIRESTORE_PROJECT_ID
+export GCS_BUCKET_NAME=$GCS_BUCKET_NAME
+export ENVIRONMENT=development
+
+# Start the application
+echo "Starting Sereact API with deployed infrastructure..."
+echo "Qdrant endpoint: http://\$QDRANT_HOST:\$QDRANT_PORT"
+echo "Firestore project: \$FIRESTORE_PROJECT_ID"
+echo "GCS bucket: \$GCS_BUCKET_NAME"
+echo "API will be available at: http://localhost:8000"
+echo "API documentation: http://localhost:8000/docs"
+
+uvicorn main:app --host 0.0.0.0 --port 8000 --reload
+EOF
+
+chmod +x start_dev.sh
+
+# Update docker-compose.yml with extracted values
+echo "Updating docker-compose.yml..."
+
+cat > docker-compose.yml << EOF
+version: '3.8'
+
+services:
+  api:
+    build: .
+    ports:
+      - "8000:8000"
+    volumes:
+      - .:/app
+      - \${GOOGLE_APPLICATION_CREDENTIALS:-./firestore-credentials.json}:/app/firestore-credentials.json:ro
+    environment:
+      - PYTHONUNBUFFERED=1
+      - ENVIRONMENT=development
+      - FIRESTORE_CREDENTIALS_FILE=/app/firestore-credentials.json
+      - GOOGLE_APPLICATION_CREDENTIALS=/app/firestore-credentials.json
+      - FIRESTORE_PROJECT_ID=\${FIRESTORE_PROJECT_ID:-$FIRESTORE_PROJECT_ID}
+      - QDRANT_HOST=$QDRANT_HOST
+      - QDRANT_PORT=$QDRANT_PORT
+      - GCS_BUCKET_NAME=$GCS_BUCKET_NAME
+    command: uvicorn main:app --host 0.0.0.0 --port 8000 --reload
+EOF
+
+# Update test script
+echo "Updating test_qdrant_connection.py..."
+
+cat > test_qdrant_connection.py << EOF
+#!/usr/bin/env python3
+"""
+Simple test script to verify Qdrant connection
+Auto-generated by deployment/scripts/setup_local_env.sh
+"""
+
+import os
+import sys
+from src.services.vector_db import VectorDatabaseService
+
+def test_qdrant_connection():
+    """Test the connection to Qdrant"""
+    
+    # Set environment variables from deployed infrastructure
+    os.environ['QDRANT_HOST'] = '$QDRANT_HOST'
+    os.environ['QDRANT_PORT'] = '$QDRANT_PORT'
+    
+    try:
+        print("Testing Qdrant connection...")
+        print(f"Host: {os.environ['QDRANT_HOST']}")
+        print(f"Port: {os.environ['QDRANT_PORT']}")
+        
+        # Initialize the service
+        vector_db = VectorDatabaseService()
+        
+        # Test health check
+        is_healthy = vector_db.health_check()
+        print(f"Health check: {'✓ PASSED' if is_healthy else '✗ FAILED'}")
+        
+        # Get collection info
+        collection_info = vector_db.get_collection_info()
+        print(f"Collection info: {collection_info}")
+        
+        print("\n✓ Qdrant connection test PASSED!")
+        return True
+        
+    except Exception as e:
+        print(f"\n✗ Qdrant connection test FAILED: {e}")
+        return False
+
+if __name__ == "__main__":
+    success = test_qdrant_connection()
+    sys.exit(0 if success else 1)
+EOF
+
+echo ""
+echo "✓ Local development environment setup complete!"
+echo ""
+echo "You can now:"
+echo "  1. Run './start_dev.sh' to start the API with deployed infrastructure"
+echo "  2. Run 'docker-compose up' to use Docker with deployed Qdrant"
+echo "  3. Run 'python test_qdrant_connection.py' to test Qdrant connection"
+echo ""
+echo "All configuration has been automatically extracted from your terraform deployment." 

deployment/terraform/main.tf

@@ -52,6 +52,12 @@ resource "google_cloud_run_service" "sereact" {
   name     = "sereact"
   location = var.region
 
+  metadata {
+    annotations = {
+      "run.googleapis.com/ingress" = "all"
+    }
+  }
+
   template {
     spec {
       containers {
@@ -75,13 +81,8 @@ resource "google_cloud_run_service" "sereact" {
         }
         
         env {
-          name  = "FIRESTORE_CREDENTIALS_FILE"
-          value = "/var/secrets/google/key.json"
-        }
-        
-        env {
-          name  = "GOOGLE_APPLICATION_CREDENTIALS"
-          value = "/var/secrets/google/key.json"
+          name  = "FIRESTORE_DATABASE_NAME"
+          value = var.firestore_db_name
         }
         
         env {
@@ -99,6 +100,21 @@ resource "google_cloud_run_service" "sereact" {
           value = var.vector_db_index_name
         }
         
+        env {
+          name  = "QDRANT_HOST"
+          value = var.use_static_ip ? google_compute_address.vector_db_static_ip.address : google_compute_instance.vector_db_vm.network_interface[0].access_config[0].nat_ip
+        }
+        
+        env {
+          name  = "QDRANT_PORT"
+          value = "6333"
+        }
+        
+        env {
+          name  = "QDRANT_API_KEY"
+          value = var.qdrant_api_key
+        }
+        
         env {
           name  = "LOG_LEVEL"
           value = "INFO"
@@ -109,7 +125,6 @@ resource "google_cloud_run_service" "sereact" {
     metadata {
       annotations = {
         "autoscaling.knative.dev/maxScale" = "10"
-        "run.googleapis.com/ingress" = "all"
       }
     }
   }
@@ -119,7 +134,7 @@ resource "google_cloud_run_service" "sereact" {
     latest_revision = true
   }
   
-  depends_on = [google_project_service.services]
+  depends_on = [google_project_service.services, google_compute_instance.vector_db_vm]
 }
 
 # Make the Cloud Run service publicly accessible

deployment/terraform/outputs.tf

@@ -48,3 +48,19 @@ output "qdrant_grpc_endpoint" {
   value       = "http://${google_compute_instance.vector_db_vm.network_interface[0].access_config[0].nat_ip}:6334"
   description = "The gRPC endpoint for Qdrant vector database"
 }
+
+# Cloud Run environment configuration
+output "cloud_run_qdrant_host" {
+  value       = var.use_static_ip ? google_compute_address.vector_db_static_ip.address : google_compute_instance.vector_db_vm.network_interface[0].access_config[0].nat_ip
+  description = "The Qdrant host configured for Cloud Run"
+}
+
+output "deployment_summary" {
+  value = {
+    cloud_run_url = google_cloud_run_service.sereact.status[0].url
+    qdrant_endpoint = "http://${google_compute_instance.vector_db_vm.network_interface[0].access_config[0].nat_ip}:6333"
+    firestore_database = var.firestore_db_name
+    storage_bucket = var.storage_bucket_name
+  }
+  description = "Summary of deployed resources"
+} 

deployment/terraform/pubsub.tf

@@ -4,11 +4,6 @@ locals {
   cloud_function_service_account = var.cloud_function_service_account != "" ? var.cloud_function_service_account : "${data.google_project.current.number}-compute@developer.gserviceaccount.com"
 }
 
-# Get current project information
-data "google_project" "current" {
-  project_id = var.project_id
-}
-
 # Pub/Sub topic for image processing tasks
 resource "google_pubsub_topic" "image_processing" {
   name = var.pubsub_topic_name
@@ -31,10 +26,10 @@ resource "google_pubsub_subscription" "image_processing" {
     maximum_backoff = "600s"
   }
 
-  # Dead letter policy after 3 failed attempts
+  # Dead letter policy after 5 failed attempts
   dead_letter_policy {
     dead_letter_topic     = google_pubsub_topic.image_processing_dlq.id
-    max_delivery_attempts = 3
+    max_delivery_attempts = 5
   }
 
   # Message retention

deployment/terraform/variables.tf

@@ -23,7 +23,7 @@ variable "storage_bucket_name" {
 variable "firestore_db_name" {
   description = "The name of the Firestore database"
   type        = string
-  default     = "imagedb"
+  default     = "sereact-imagedb"
 }
 
 variable "environment" {

docker-compose.yml

@@ -13,5 +13,8 @@ services:
       - ENVIRONMENT=development
       - FIRESTORE_CREDENTIALS_FILE=/app/firestore-credentials.json
       - GOOGLE_APPLICATION_CREDENTIALS=/app/firestore-credentials.json
-      - FIRESTORE_PROJECT_ID=${FIRESTORE_PROJECT_ID:-}
+      - FIRESTORE_PROJECT_ID=${FIRESTORE_PROJECT_ID:-gen-lang-client-0424120530}
+      - QDRANT_HOST=34.171.134.17
+      - QDRANT_PORT=6333
+      - GCS_BUCKET_NAME=sereact-images
     command: uvicorn main:app --host 0.0.0.0 --port 8000 --reload

src/api/v1/__init__.py

@@ -1,2 +1 @@
-from src.api.v1 import teams, auth
-from src.api.v1 import users, images, search
+# API v1 package

src/api/v1/search.py

@@ -1,11 +1,12 @@
 import logging
-from typing import Optional, List
+from typing import Optional, List, Dict, Any
 from fastapi import APIRouter, Depends, Query, Request, HTTPException
 
 from src.api.v1.auth import get_current_user
-from src.db.repositories.image_repository import image_repository
-from src.services.vector_store import VectorStoreService
+from src.services.vector_db import VectorDatabaseService
 from src.services.embedding_service import EmbeddingService
+from src.db.repositories.image_repository import image_repository
+from src.db.repositories.team_repository import team_repository
 from src.models.user import UserModel
 from src.schemas.image import ImageResponse
 from src.schemas.search import SearchResponse, SearchRequest
@@ -16,7 +17,7 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=["Search"], prefix="/search")
 
 # Initialize services
-vector_store_service = VectorStoreService()
+vector_db_service = VectorDatabaseService()
 embedding_service = EmbeddingService()
 
 @router.get("", response_model=SearchResponse)
@@ -51,11 +52,11 @@ async def search_images(
             raise HTTPException(status_code=400, detail="Failed to generate search embedding")
         
         # Search in vector database
-        search_results = await vector_store_service.search_similar(
-            query_embedding,
+        search_results = vector_db_service.search_similar_images(
+            query_vector=query_embedding,
             limit=limit,
-            threshold=threshold,
-            team_id=str(current_user.team_id)
+            score_threshold=threshold,
+            filter_conditions={"team_id": str(current_user.team_id)} if current_user.team_id else None
         )
         
         if not search_results:
@@ -68,8 +69,8 @@ async def search_images(
             )
         
         # Get image IDs and scores from search results
-        image_ids = [result['id'] for result in search_results]
-        scores = {result['id']: result['score'] for result in search_results}
+        image_ids = [result['image_id'] for result in search_results if result['image_id']]
+        scores = {result['image_id']: result['score'] for result in search_results if result['image_id']}
         
         # Get image metadata from database
         images = await image_repository.get_by_ids(image_ids)
@@ -155,11 +156,11 @@ async def search_images_advanced(
             raise HTTPException(status_code=400, detail="Failed to generate search embedding")
         
         # Search in vector database
-        search_results = await vector_store_service.search_similar(
-            query_embedding,
+        search_results = vector_db_service.search_similar_images(
+            query_vector=query_embedding,
             limit=search_request.limit,
-            threshold=search_request.threshold,
-            team_id=str(current_user.team_id)
+            score_threshold=search_request.threshold,
+            filter_conditions={"team_id": str(current_user.team_id)} if current_user.team_id else None
         )
         
         if not search_results:
@@ -172,8 +173,8 @@ async def search_images_advanced(
             )
         
         # Get image IDs and scores from search results
-        image_ids = [result['id'] for result in search_results]
-        scores = {result['id']: result['score'] for result in search_results}
+        image_ids = [result['image_id'] for result in search_results if result['image_id']]
+        scores = {result['image_id']: result['score'] for result in search_results if result['image_id']}
         
         # Get image metadata from database
         images = await image_repository.get_by_ids(image_ids)
@@ -288,20 +289,22 @@ async def find_similar_images(
             raise HTTPException(status_code=400, detail="Reference image does not have embeddings")
         
         # Get the embedding for the reference image
-        reference_embedding = await vector_store_service.get_embedding(reference_image.embedding_id)
-        if not reference_embedding:
+        reference_data = vector_db_service.get_image_vector(image_id)
+        if not reference_data or not reference_data.get('vector'):
             raise HTTPException(status_code=400, detail="Failed to get reference image embedding")
         
+        reference_embedding = reference_data['vector']
+        
         # Search for similar images
-        search_results = await vector_store_service.search_similar(
-            reference_embedding,
+        search_results = vector_db_service.search_similar_images(
+            query_vector=reference_embedding,
             limit=limit + 1,  # +1 to account for the reference image itself
-            threshold=threshold,
-            team_id=str(current_user.team_id)
+            score_threshold=threshold,
+            filter_conditions={"team_id": str(current_user.team_id)} if current_user.team_id else None
         )
         
         # Remove the reference image from results
-        search_results = [result for result in search_results if result['id'] != image_id][:limit]
+        search_results = [result for result in search_results if result['image_id'] != image_id][:limit]
         
         if not search_results:
             return SearchResponse(
@@ -313,8 +316,8 @@ async def find_similar_images(
             )
         
         # Get image IDs and scores from search results
-        image_ids = [result['id'] for result in search_results]
-        scores = {result['id']: result['score'] for result in search_results}
+        image_ids = [result['image_id'] for result in search_results if result['image_id']]
+        scores = {result['image_id']: result['score'] for result in search_results if result['image_id']}
         
         # Get image metadata from database
         images = await image_repository.get_by_ids(image_ids)

src/services/vector_db.py

@@ -20,7 +20,7 @@ class VectorDatabaseService:
     def __init__(
         self,
         host: str = None,
-        port: int = 6333,
+        port: int = None,
         api_key: str = None,
         collection_name: str = "image_vectors"
     ):
@@ -34,7 +34,7 @@ class VectorDatabaseService:
             collection_name: Name of the collection to use
         """
         self.host = host or os.getenv("QDRANT_HOST", "localhost")
-        self.port = port
+        self.port = port or int(os.getenv("QDRANT_PORT", "6333"))
         self.api_key = api_key or os.getenv("QDRANT_API_KEY")
         self.collection_name = collection_name
         

src/services/vector_store.py

@@ -1,267 +0,0 @@
-import logging
-from typing import List, Dict, Any, Optional, Tuple
-import pinecone
-from bson import ObjectId
-
-from src.config.config import settings
-
-logger = logging.getLogger(__name__)
-
-class VectorStoreService:
-    """Service for managing vector embeddings in Pinecone"""
-    
-    def __init__(self):
-        self.api_key = settings.VECTOR_DB_API_KEY
-        self.environment = settings.VECTOR_DB_ENVIRONMENT
-        self.index_name = settings.VECTOR_DB_INDEX_NAME
-        self.dimension = 512  # CLIP model embedding dimension
-        self.initialized = False
-        self.index = None
-    
-    def initialize(self):
-        """
-        Initialize Pinecone connection and create index if needed
-        """
-        if self.initialized:
-            return
-        
-        if not self.api_key or not self.environment:
-            logger.warning("Pinecone API key or environment not provided, vector search disabled")
-            return
-        
-        try:
-            logger.info(f"Initializing Pinecone with environment {self.environment}")
-            
-            # Initialize Pinecone
-            pinecone.init(
-                api_key=self.api_key,
-                environment=self.environment
-            )
-            
-            # Check if index exists
-            if self.index_name not in pinecone.list_indexes():
-                logger.info(f"Creating Pinecone index: {self.index_name}")
-                
-                # Create index
-                pinecone.create_index(
-                    name=self.index_name,
-                    dimension=self.dimension,
-                    metric="cosine"
-                )
-            
-            # Connect to index
-            self.index = pinecone.Index(self.index_name)
-            self.initialized = True
-            logger.info("Pinecone initialized successfully")
-        except Exception as e:
-            logger.error(f"Error initializing Pinecone: {e}")
-            # Don't raise - we want to gracefully handle this and fall back to non-vector search
-    
-    def store_embedding(self, image_id: str, team_id: str, embedding: List[float], metadata: Dict[str, Any] = None) -> Optional[str]:
-        """
-        Store an embedding in Pinecone
-        
-        Args:
-            image_id: Image ID
-            team_id: Team ID
-            embedding: Image embedding
-            metadata: Additional metadata
-            
-        Returns:
-            Vector ID if successful, None otherwise
-        """
-        self.initialize()
-        
-        if not self.initialized:
-            logger.warning("Pinecone not initialized, cannot store embedding")
-            return None
-        
-        try:
-            # Create metadata dict
-            meta = {
-                "image_id": image_id,
-                "team_id": team_id
-            }
-            
-            if metadata:
-                meta.update(metadata)
-            
-            # Create a unique vector ID
-            vector_id = f"{team_id}_{image_id}"
-            
-            # Upsert the vector
-            self.index.upsert(
-                vectors=[
-                    (vector_id, embedding, meta)
-                ]
-            )
-            
-            logger.info(f"Stored embedding for image {image_id}")
-            return vector_id
-        except Exception as e:
-            logger.error(f"Error storing embedding: {e}")
-            return None
-    
-    async def search_similar(
-        self, 
-        query_embedding: List[float], 
-        limit: int = 10, 
-        threshold: float = 0.7,
-        team_id: str = None
-    ) -> List[Dict[str, Any]]:
-        """
-        Search for similar embeddings
-        
-        Args:
-            query_embedding: Query embedding vector
-            limit: Maximum number of results
-            threshold: Similarity threshold (0-1)
-            team_id: Optional team filter
-            
-        Returns:
-            List of results with id and score
-        """
-        self.initialize()
-        
-        if not self.initialized:
-            logger.warning("Pinecone not initialized, cannot search")
-            return []
-        
-        try:
-            # Create filter for team_id if provided
-            filter_dict = None
-            if team_id:
-                filter_dict = {
-                    "team_id": {"$eq": team_id}
-                }
-            
-            # Query the index
-            results = self.index.query(
-                vector=query_embedding,
-                filter=filter_dict,
-                top_k=limit,
-                include_metadata=True
-            )
-            
-            # Format the results and apply threshold
-            formatted_results = []
-            for match in results.matches:
-                if match.score >= threshold:
-                    formatted_results.append({
-                        "id": match.metadata.get("image_id", match.id),
-                        "score": match.score,
-                        "metadata": match.metadata
-                    })
-            
-            return formatted_results
-        except Exception as e:
-            logger.error(f"Error searching similar embeddings: {e}")
-            return []
-    
-    async def get_embedding(self, embedding_id: str) -> Optional[List[float]]:
-        """
-        Get embedding by ID
-        
-        Args:
-            embedding_id: Embedding ID
-            
-        Returns:
-            Embedding vector or None if not found
-        """
-        self.initialize()
-        
-        if not self.initialized:
-            logger.warning("Pinecone not initialized, cannot get embedding")
-            return None
-        
-        try:
-            # Fetch the vector
-            result = self.index.fetch(ids=[embedding_id])
-            
-            if embedding_id in result.vectors:
-                return result.vectors[embedding_id].values
-            else:
-                logger.warning(f"Embedding not found: {embedding_id}")
-                return None
-        except Exception as e:
-            logger.error(f"Error getting embedding: {e}")
-            return None
-    
-    def search_by_embedding(self, team_id: str, query_embedding: List[float], limit: int = 10) -> List[Dict[str, Any]]:
-        """
-        Search for similar images by embedding
-        
-        Args:
-            team_id: Team ID
-            query_embedding: Query embedding
-            limit: Maximum number of results
-            
-        Returns:
-            List of results with image ID and similarity score
-        """
-        self.initialize()
-        
-        if not self.initialized:
-            logger.warning("Pinecone not initialized, cannot search by embedding")
-            return []
-        
-        try:
-            # Create filter for team_id
-            filter_dict = {
-                "team_id": {"$eq": team_id}
-            }
-            
-            # Query the index
-            results = self.index.query(
-                vector=query_embedding,
-                filter=filter_dict,
-                top_k=limit,
-                include_metadata=True
-            )
-            
-            # Format the results
-            formatted_results = []
-            for match in results.matches:
-                formatted_results.append({
-                    "image_id": match.metadata["image_id"],
-                    "score": match.score,
-                    "metadata": match.metadata
-                })
-            
-            return formatted_results
-        except Exception as e:
-            logger.error(f"Error searching by embedding: {e}")
-            return []
-    
-    def delete_embedding(self, image_id: str, team_id: str) -> bool:
-        """
-        Delete an embedding from Pinecone
-        
-        Args:
-            image_id: Image ID
-            team_id: Team ID
-            
-        Returns:
-            True if successful, False otherwise
-        """
-        self.initialize()
-        
-        if not self.initialized:
-            logger.warning("Pinecone not initialized, cannot delete embedding")
-            return False
-        
-        try:
-            # Create the vector ID
-            vector_id = f"{team_id}_{image_id}"
-            
-            # Delete the vector
-            self.index.delete(ids=[vector_id])
-            
-            logger.info(f"Deleted embedding for image {image_id}")
-            return True
-        except Exception as e:
-            logger.error(f"Error deleting embedding: {e}")
-            return False
-
-# Create a singleton service
-vector_store_service = VectorStoreService() 

start_dev.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+# Development startup script for Sereact API
+# This script sets the environment variables and starts the application
+# Auto-generated by deployment/scripts/setup_local_env.sh
+
+# Activate virtual environment
+source venv/Scripts/activate
+
+# Set environment variables from deployed infrastructure
+export QDRANT_HOST=34.171.134.17
+export QDRANT_PORT=6333
+export FIRESTORE_PROJECT_ID=gen-lang-client-0424120530
+export GCS_BUCKET_NAME=sereact-images
+export ENVIRONMENT=development
+
+# Start the application
+echo "Starting Sereact API with deployed infrastructure..."
+echo "Qdrant endpoint: http://$QDRANT_HOST:$QDRANT_PORT"
+echo "Firestore project: $FIRESTORE_PROJECT_ID"
+echo "GCS bucket: $GCS_BUCKET_NAME"
+echo "API will be available at: http://localhost:8000"
+echo "API documentation: http://localhost:8000/docs"
+
+uvicorn main:app --host 0.0.0.0 --port 8000 --reload

test_qdrant_connection.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""
+Simple test script to verify Qdrant connection
+Auto-generated by deployment/scripts/setup_local_env.sh
+"""
+
+import os
+import sys
+from src.services.vector_db import VectorDatabaseService
+
+def test_qdrant_connection():
+    """Test the connection to Qdrant"""
+    
+    # Set environment variables from deployed infrastructure
+    os.environ['QDRANT_HOST'] = '34.171.134.17'
+    os.environ['QDRANT_PORT'] = '6333'
+    
+    try:
+        print("Testing Qdrant connection...")
+        print(f"Host: {os.environ['QDRANT_HOST']}")
+        print(f"Port: {os.environ['QDRANT_PORT']}")
+        
+        # Initialize the service
+        vector_db = VectorDatabaseService()
+        
+        # Test health check
+        is_healthy = vector_db.health_check()
+        print(f"Health check: {'✓ PASSED' if is_healthy else '✗ FAILED'}")
+        
+        # Get collection info
+        collection_info = vector_db.get_collection_info()
+        print(f"Collection info: {collection_info}")
+        
+        print("\n✓ Qdrant connection test PASSED!")
+        return True
+        
+    except Exception as e:
+        print(f"\n✗ Qdrant connection test FAILED: {e}")
+        return False
+
+if __name__ == "__main__":
+    success = test_qdrant_connection()
+    sys.exit(0 if success else 1)

tests/services/test_embedding_service.py

@@ -288,30 +288,25 @@ class TestEmbeddingService:
 class TestEmbeddingServiceIntegration:
     """Integration tests for embedding service with other components"""
     
-    def test_embedding_to_vector_store_integration(self, embedding_service, sample_image_data, sample_image_model):
-        """Test integration with vector store service"""
-        mock_embedding = np.random.rand(512).tolist()
+    def test_embedding_to_vector_db_integration(self, embedding_service, sample_image_data, sample_image_model):
+        """Test integration between embedding service and vector database"""
+        # Mock the vector database service
+        with patch('src.services.vector_db.VectorDatabaseService') as mock_vector_db:
+            # Setup mock
+            mock_store = mock_vector_db.return_value
+            mock_store.add_image_vector.return_value = "test_point_id"
             
-        with patch.object(embedding_service, 'generate_embedding_with_metadata') as mock_generate, \
-             patch('src.services.vector_store.VectorStoreService') as mock_vector_store:
-            
-            mock_generate.return_value = {
-                'embedding': mock_embedding,
-                'metadata': {'labels': [{'description': 'cat', 'score': 0.95}]},
-                'model': 'clip'
-            }
-            
-            mock_store = mock_vector_store.return_value
-            mock_store.store_embedding.return_value = 'embedding_id_123'
-            
-            # Process image and store embedding
-            result = embedding_service.process_and_store_image(
-                sample_image_data, sample_image_model
+            # Test storing embedding
+            embedding = [0.1] * 512  # Mock embedding
+            point_id = mock_store.add_image_vector(
+                image_id=str(sample_image_model.id),
+                vector=embedding,
+                metadata={"filename": sample_image_model.filename}
             )
             
-            # Verify integration
-            assert result['embedding_id'] == 'embedding_id_123'
-            mock_store.store_embedding.assert_called_once()
+            # Verify the call
+            mock_store.add_image_vector.assert_called_once()
+            assert point_id == "test_point_id"
     
     def test_pubsub_trigger_integration(self, embedding_service):
         """Test integration with Pub/Sub message processing"""

tests/services/test_vector_store.py

@@ -1,391 +0,0 @@
-import pytest
-import numpy as np
-from unittest.mock import patch, MagicMock, AsyncMock
-from bson import ObjectId
-
-from src.services.vector_store import VectorStoreService
-from src.models.image import ImageModel
-
-
-class TestVectorStoreService:
-    """Test vector store operations for semantic search"""
-    
-    @pytest.fixture
-    def mock_pinecone_index(self):
-        """Mock Pinecone index for testing"""
-        mock_index = MagicMock()
-        mock_index.upsert = MagicMock()
-        mock_index.query = MagicMock()
-        mock_index.delete = MagicMock()
-        mock_index.describe_index_stats = MagicMock()
-        return mock_index
-    
-    @pytest.fixture
-    def vector_store_service(self, mock_pinecone_index):
-        """Create vector store service with mocked dependencies"""
-        with patch('src.services.vector_store.pinecone') as mock_pinecone:
-            mock_pinecone.Index.return_value = mock_pinecone_index
-            service = VectorStoreService()
-            service.index = mock_pinecone_index
-            return service
-    
-    @pytest.fixture
-    def sample_embedding(self):
-        """Generate a sample embedding vector"""
-        return np.random.rand(512).tolist()  # 512-dimensional vector
-    
-    @pytest.fixture
-    def sample_image(self):
-        """Create a sample image model"""
-        return ImageModel(
-            filename="test-image.jpg",
-            original_filename="test_image.jpg",
-            file_size=1024,
-            content_type="image/jpeg",
-            storage_path="images/test-image.jpg",
-            team_id=ObjectId(),
-            uploader_id=ObjectId(),
-            description="A test image",
-            tags=["test", "image"]
-        )
-    
-    def test_store_embedding(self, vector_store_service, sample_embedding, sample_image):
-        """Test storing an embedding in the vector database"""
-        # Store the embedding
-        embedding_id = vector_store_service.store_embedding(
-            image_id=str(sample_image.id),
-            embedding=sample_embedding,
-            metadata={
-                "filename": sample_image.filename,
-                "team_id": str(sample_image.team_id),
-                "tags": sample_image.tags,
-                "description": sample_image.description
-            }
-        )
-        
-        # Verify the embedding was stored
-        assert embedding_id is not None
-        vector_store_service.index.upsert.assert_called_once()
-        
-        # Check the upsert call arguments
-        call_args = vector_store_service.index.upsert.call_args
-        vectors = call_args[1]['vectors']
-        assert len(vectors) == 1
-        assert vectors[0]['id'] == embedding_id
-        assert len(vectors[0]['values']) == len(sample_embedding)
-        assert 'metadata' in vectors[0]
-    
-    def test_search_similar_images(self, vector_store_service, sample_embedding):
-        """Test searching for similar images using vector similarity"""
-        # Mock search results
-        mock_results = {
-            'matches': [
-                {
-                    'id': 'embedding1',
-                    'score': 0.95,
-                    'metadata': {
-                        'image_id': str(ObjectId()),
-                        'filename': 'similar1.jpg',
-                        'team_id': str(ObjectId()),
-                        'tags': ['cat', 'animal']
-                    }
-                },
-                {
-                    'id': 'embedding2', 
-                    'score': 0.87,
-                    'metadata': {
-                        'image_id': str(ObjectId()),
-                        'filename': 'similar2.jpg',
-                        'team_id': str(ObjectId()),
-                        'tags': ['dog', 'animal']
-                    }
-                }
-            ]
-        }
-        vector_store_service.index.query.return_value = mock_results
-        
-        # Perform search
-        results = vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=str(ObjectId()),
-            top_k=10,
-            score_threshold=0.8
-        )
-        
-        # Verify search was performed
-        vector_store_service.index.query.assert_called_once()
-        
-        # Check results
-        assert len(results) == 2
-        assert results[0]['score'] == 0.95
-        assert results[1]['score'] == 0.87
-        assert all('image_id' in result for result in results)
-    
-    def test_search_with_filters(self, vector_store_service, sample_embedding):
-        """Test searching with metadata filters"""
-        team_id = str(ObjectId())
-        
-        # Perform search with team filter
-        vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=team_id,
-            top_k=5,
-            filters={"tags": {"$in": ["cat", "dog"]}}
-        )
-        
-        # Verify filter was applied
-        call_args = vector_store_service.index.query.call_args
-        assert 'filter' in call_args[1]
-        assert call_args[1]['filter']['team_id'] == team_id
-    
-    def test_delete_embedding(self, vector_store_service):
-        """Test deleting an embedding from the vector database"""
-        embedding_id = "test-embedding-123"
-        
-        # Delete the embedding
-        success = vector_store_service.delete_embedding(embedding_id)
-        
-        # Verify deletion was attempted
-        vector_store_service.index.delete.assert_called_once_with(ids=[embedding_id])
-        assert success is True
-    
-    def test_batch_store_embeddings(self, vector_store_service, sample_embedding):
-        """Test storing multiple embeddings in batch"""
-        # Create batch data
-        batch_data = []
-        for i in range(5):
-            batch_data.append({
-                'image_id': str(ObjectId()),
-                'embedding': sample_embedding,
-                'metadata': {
-                    'filename': f'image{i}.jpg',
-                    'team_id': str(ObjectId()),
-                    'tags': [f'tag{i}']
-                }
-            })
-        
-        # Store batch
-        embedding_ids = vector_store_service.batch_store_embeddings(batch_data)
-        
-        # Verify batch storage
-        assert len(embedding_ids) == 5
-        vector_store_service.index.upsert.assert_called_once()
-        
-        # Check batch upsert call
-        call_args = vector_store_service.index.upsert.call_args
-        vectors = call_args[1]['vectors']
-        assert len(vectors) == 5
-    
-    def test_get_index_stats(self, vector_store_service):
-        """Test getting vector database statistics"""
-        # Mock stats response
-        mock_stats = {
-            'total_vector_count': 1000,
-            'dimension': 512,
-            'index_fullness': 0.1
-        }
-        vector_store_service.index.describe_index_stats.return_value = mock_stats
-        
-        # Get stats
-        stats = vector_store_service.get_index_stats()
-        
-        # Verify stats retrieval
-        vector_store_service.index.describe_index_stats.assert_called_once()
-        assert stats['total_vector_count'] == 1000
-        assert stats['dimension'] == 512
-    
-    def test_search_with_score_threshold(self, vector_store_service, sample_embedding):
-        """Test filtering search results by score threshold"""
-        # Mock results with varying scores
-        mock_results = {
-            'matches': [
-                {'id': 'emb1', 'score': 0.95, 'metadata': {'image_id': '1'}},
-                {'id': 'emb2', 'score': 0.75, 'metadata': {'image_id': '2'}},
-                {'id': 'emb3', 'score': 0.65, 'metadata': {'image_id': '3'}},
-                {'id': 'emb4', 'score': 0.45, 'metadata': {'image_id': '4'}}
-            ]
-        }
-        vector_store_service.index.query.return_value = mock_results
-        
-        # Search with score threshold
-        results = vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=str(ObjectId()),
-            top_k=10,
-            score_threshold=0.7
-        )
-        
-        # Only results above threshold should be returned
-        assert len(results) == 2
-        assert all(result['score'] >= 0.7 for result in results)
-    
-    def test_update_embedding_metadata(self, vector_store_service):
-        """Test updating metadata for an existing embedding"""
-        embedding_id = "test-embedding-123"
-        new_metadata = {
-            'tags': ['updated', 'tag'],
-            'description': 'Updated description'
-        }
-        
-        # Update metadata
-        success = vector_store_service.update_embedding_metadata(
-            embedding_id, new_metadata
-        )
-        
-        # Verify update was attempted
-        # This would depend on the actual implementation
-        assert success is True
-    
-    def test_search_by_image_id(self, vector_store_service):
-        """Test searching for a specific image's embedding"""
-        image_id = str(ObjectId())
-        
-        # Mock search by metadata
-        mock_results = {
-            'matches': [
-                {
-                    'id': 'embedding1',
-                    'score': 1.0,
-                    'metadata': {
-                        'image_id': image_id,
-                        'filename': 'target.jpg'
-                    }
-                }
-            ]
-        }
-        vector_store_service.index.query.return_value = mock_results
-        
-        # Search by image ID
-        result = vector_store_service.get_embedding_by_image_id(image_id)
-        
-        # Verify result
-        assert result is not None
-        assert result['metadata']['image_id'] == image_id
-    
-    def test_bulk_delete_embeddings(self, vector_store_service):
-        """Test deleting multiple embeddings"""
-        embedding_ids = ['emb1', 'emb2', 'emb3']
-        
-        # Delete multiple embeddings
-        success = vector_store_service.bulk_delete_embeddings(embedding_ids)
-        
-        # Verify bulk deletion
-        vector_store_service.index.delete.assert_called_once_with(ids=embedding_ids)
-        assert success is True
-    
-    def test_search_pagination(self, vector_store_service, sample_embedding):
-        """Test paginated search results"""
-        # This would test pagination if implemented
-        # Implementation depends on how pagination is handled in the vector store
-        pass
-    
-    def test_vector_dimension_validation(self, vector_store_service):
-        """Test validation of embedding dimensions"""
-        # Test with wrong dimension
-        wrong_dimension_embedding = np.random.rand(256).tolist()  # Wrong size
-        
-        with pytest.raises(ValueError):
-            vector_store_service.store_embedding(
-                image_id=str(ObjectId()),
-                embedding=wrong_dimension_embedding,
-                metadata={}
-            )
-    
-    def test_connection_error_handling(self, vector_store_service):
-        """Test handling of connection errors"""
-        # Mock connection error
-        vector_store_service.index.query.side_effect = Exception("Connection failed")
-        
-        # Search should handle the error gracefully
-        with pytest.raises(Exception):
-            vector_store_service.search_similar(
-                query_embedding=[0.1] * 512,
-                team_id=str(ObjectId()),
-                top_k=10
-            )
-    
-    def test_empty_search_results(self, vector_store_service, sample_embedding):
-        """Test handling of empty search results"""
-        # Mock empty results
-        vector_store_service.index.query.return_value = {'matches': []}
-        
-        # Search should return empty list
-        results = vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=str(ObjectId()),
-            top_k=10
-        )
-        
-        assert results == []
-
-
-class TestVectorStoreIntegration:
-    """Integration tests for vector store with other services"""
-    
-    def test_embedding_lifecycle(self, vector_store_service, sample_embedding, sample_image):
-        """Test complete embedding lifecycle: store, search, update, delete"""
-        # Store embedding
-        embedding_id = vector_store_service.store_embedding(
-            image_id=str(sample_image.id),
-            embedding=sample_embedding,
-            metadata={'filename': sample_image.filename}
-        )
-        
-        # Search for similar embeddings
-        mock_results = {
-            'matches': [
-                {
-                    'id': embedding_id,
-                    'score': 1.0,
-                    'metadata': {'image_id': str(sample_image.id)}
-                }
-            ]
-        }
-        vector_store_service.index.query.return_value = mock_results
-        
-        results = vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=str(sample_image.team_id),
-            top_k=1
-        )
-        
-        assert len(results) == 1
-        assert results[0]['id'] == embedding_id
-        
-        # Delete embedding
-        success = vector_store_service.delete_embedding(embedding_id)
-        assert success is True
-    
-    def test_team_isolation(self, vector_store_service, sample_embedding):
-        """Test that team data is properly isolated"""
-        team1_id = str(ObjectId())
-        team2_id = str(ObjectId())
-        
-        # Mock search results that should be filtered by team
-        mock_results = {
-            'matches': [
-                {
-                    'id': 'emb1',
-                    'score': 0.9,
-                    'metadata': {'image_id': '1', 'team_id': team1_id}
-                },
-                {
-                    'id': 'emb2',
-                    'score': 0.8,
-                    'metadata': {'image_id': '2', 'team_id': team2_id}
-                }
-            ]
-        }
-        vector_store_service.index.query.return_value = mock_results
-        
-        # Search for team1 should only return team1 results
-        results = vector_store_service.search_similar(
-            query_embedding=sample_embedding,
-            team_id=team1_id,
-            top_k=10
-        )
-        
-        # Verify team filter was applied in the query
-        call_args = vector_store_service.index.query.call_args
-        assert 'filter' in call_args[1]
-        assert call_args[1]['filter']['team_id'] == team1_id