fix vector db dimensionality

README.md

@@ -8,7 +8,7 @@ SEREACT is a secure API for storing, organizing, and retrieving images with adva
 - Team-based organization and access control
 - **Hybrid authentication model**: Public management endpoints + API key protected data endpoints
 - **Asynchronous image processing with Pub/Sub and Cloud Functions**
-- **AI-powered image embeddings using Google Cloud Vision API**
+- **AI-powered image embeddings using Google Vertex AI Multimodal Embedding API**
 - **Semantic search using vector similarity with Qdrant Vector Database**
 - **Self-hosted vector database on Google Compute Engine VM**
 - **Automatic retry mechanism for failed processing (up to 3 attempts)**
@@ -102,7 +102,7 @@ sereact/
    - **Cloud Function is triggered by Pub/Sub message**
    - Function updates image status to `"processing"`
    - **Function downloads image from Cloud Storage**
-   - **Function calls Google Cloud Vision API to generate embeddings**
+   - **Function calls Google Vertex AI Multimodal Embedding API to generate 1408-dimensional embeddings**
    - **Embeddings are stored in Qdrant Vector Database on dedicated VM**
    - **Firestore is updated with embedding info and status: "success"**
 
@@ -123,7 +123,7 @@ sereact/
 - **Google Cloud Storage** - Image storage
 - **Google Pub/Sub** - Message queue for async processing
 - **Google Cloud Functions** - Serverless image processing
-- **Google Cloud Vision API** - AI-powered image analysis and embedding generation
+- **Google Vertex AI Multimodal Embedding API** - AI-powered image analysis and embedding generation
 - **Qdrant** - Self-hosted vector database for semantic search (on Google Compute Engine VM)
 - **Google Compute Engine** - VM hosting for vector database
 - **Pydantic** - Data validation
@@ -155,6 +155,20 @@ The system includes a dedicated Google Compute Engine VM running Qdrant vector d
 - **Cosine Similarity**: Optimized for image embedding comparisons
 - **Metadata Filtering**: Support for complex search filters
 
+### **AI Embedding Model**
+
+SEREACT uses Google's Vertex AI multimodal embedding model for generating high-quality image embeddings:
+
+- **Model**: `multimodalembedding@001`
+- **Provider**: Google Vertex AI
+- **Type**: Multimodal embedding model (supports both images and text)
+- **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
 
 ### Prerequisites
@@ -197,8 +211,9 @@ The system includes a dedicated Google Compute Engine VM running Qdrant vector d
    PUBSUB_TOPIC=image-processing-topic
    PUBSUB_SUBSCRIPTION=image-processing-subscription
    
-   # Google Cloud Vision
-   VISION_API_ENABLED=true
+   # Google Vertex AI (for image embeddings)
+   VERTEX_AI_LOCATION=us-central1
+   GOOGLE_CLOUD_PROJECT=your-gcp-project-id
    
    # Security
    API_KEY_SECRET=your-secret-key
@@ -206,6 +221,8 @@ The system includes a dedicated Google Compute Engine VM running Qdrant vector d
    # Vector database (Qdrant)
    QDRANT_HOST=your-vm-external-ip
    QDRANT_API_KEY=your-qdrant-api-key  # Optional
+   QDRANT_COLLECTION=image_vectors
+   QDRANT_HTTPS=false
    ```
 
 5. **Deploy Infrastructure** (Required for vector database):

src/services/vector_db.py

@@ -86,7 +86,7 @@ class VectorDatabaseService:
                 self.client.create_collection(
                     collection_name=self.collection_name,
                     vectors_config=VectorParams(
-                        size=512,  # Typical size for image embeddings
+                        size=1408,  # Typical size for image embeddings
                         distance=Distance.COSINE
                     )
                 )