Image Embeddings in Vector Databases (Multi Modal Embedded data) : From Novice to Master

Let’s unlock a powerful capability: using **image embedding models** to store and find data in Vector DBs. This allows for truly groundbreaking applications like reverse image search, visual similarity recommendations, and multimodal search (searching images with text queries). This guide will detail the concepts, use cases, and provide sample code to make you an expert in this exciting domain.

The Core Idea: Representing Visuals as Numbers

Just as text can be converted into numerical vectors that capture its meaning, images can be transformed into numerical vectors (image embeddings) that represent their visual features, content, and style. This is the foundation for visual similarity search.

What are Image Embedding Models?

An **image embedding model** (often a deep convolutional neural network, like ResNet, VGG, or more recently, multi-modal models like CLIP or DINO) is a specialized AI model trained to convert an image into a fixed-size numerical vector. The key property is that images that are visually similar or contain semantically related content will have embeddings that are “close” to each other in the high-dimensional vector space.

The Workflow: Image to Vector to Search

1. Image Acquisition: Get the image you want to store or query.
2. Embedding Generation: Pass the image through a pre-trained **image embedding model**. The model outputs a high-dimensional vector.
3. Vector Storage (Ingestion): Store this image vector in a **Vector Database**, associating it with a unique ID and relevant metadata (e.g., original image URL, product ID, description).
4. Query Image Embedding: When a user wants to find similar images, take their query image and generate its embedding using the *same* image embedding model.
5. Similarity Search: Use the query image’s vector to perform a similarity search in the Vector DB. The DB finds the vectors that are numerically closest to the query vector.
6. Result Retrieval: The Vector DB returns the IDs and metadata of the most similar image vectors. These IDs can then be used to retrieve the original images (e.g., from a cloud storage like S3 or a main database like MongoDB) and display them to the user.

Storing Image Data in a Vector DB

Unlike MongoDB where you might store the image itself (as Base64 or a binary field, though usually you store URLs), in a Vector DB, you store the *numerical representation* of the image.

Concepts for Storing Image Embeddings:

• Vector Index: A collection in the Vector DB specifically designed to hold image embeddings.
• Vector ID: A unique identifier for each image’s embedding. This should ideally map back to your primary data store (e.g., a `product_id` from MongoDB or an `image_id` from your image storage).
• Metadata: Additional descriptive information associated with each vector (e.g., image URL, product name, categories, date uploaded, object tags). This metadata allows for filtering results after the similarity search.
• Pre-processing: Images often need resizing, cropping, or normalization before being fed into an embedding model.

Sample Code: Storing an Image Embedding (Python with Hugging Face & Pinecone/Weaviate)

Let’s assume you have an image file (`my_product_image.jpg`) and want to store its embedding along with a reference to a product ID from MongoDB.

# --- Step 1: Install necessary libraries ---
# pip install transformers pillow torch
# pip install pinecone-client # or weaviate-client, milvus, etc.

from PIL import Image
from transformers import AutoImageProcessor, AutoModel
import torch
import numpy as np

# --- Step 2: Load a pre-trained Image Embedding Model ---
# Using a common vision model like ResNet-50 (part of a broader vision transformer)
# For better semantic search, consider multi-modal models like CLIP (requires additional setup)
model_name = "google/vit-base-patch16-224-in21k" # A Vision Transformer model
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# --- Step 3: Load and Pre-process your image ---
image_path = "path/to/your/image/my_product_image.jpg"
image = Image.open(image_path).convert("RGB") # Ensure RGB format

# --- Step 4: Generate the Image Embedding ---
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    # Typically, the last_hidden_state or pooler_output is used as the embedding
    # For ViT, often the [CLS] token's embedding (first token) or mean pooling
    image_embedding = outputs.last_hidden_state.mean(dim=1).squeeze().numpy() # Example: mean pooling
    # If using CLIP-like models, it might be model.get_image_features()

print(f"Generated embedding with shape: {image_embedding.shape}") # e.g., (768,) or (512,)

# --- Step 5: Store the Embedding in a Vector Database (e.g., Pinecone) ---
# Replace with your actual Pinecone API key and environment
# from pinecone import Pinecone, Index

# pinecone_api_key = "YOUR_PINECONE_API_KEY"
# pinecone_env = "YOUR_PINECONE_ENVIRONMENT"
# index_name = "product-image-embeddings"

# pc = Pinecone(api_key=pinecone_api_key, environment=pinecone_env)

# # Create index if it doesn't exist
# if index_name not in pc.list_indexes().names():
#     pc.create_index(
#         name=index_name,
#         dimension=image_embedding.shape[0], # Dimension of your vectors
#         metric="cosine", # Or 'euclidean', 'dotproduct'
#         spec={"serverless": {"cloud": "aws", "region": "us-west-2"}} # Example spec
#     )
# index = pc.Index(index_name)

# # Define your product_id from MongoDB or your main data store
# product_id = "mongodb_prod_id_XYZ"
# product_name = "Designer Leather Bag"
# image_url = "https://yourcdn.com/images/my_product_image.jpg"

# # Upsert the vector
# index.upsert(
#     vectors=[
#         {
#             "id": f"image-{product_id}", # Unique ID for this vector entry
#             "values": image_embedding.tolist(),
#             "metadata": {
#                 "product_id": product_id,
#                 "product_name": product_name,
#                 "image_url": image_url,
#                 "visual_tags": ["leather", "bag", "designer", "fashion"] # Optional descriptive tags
#             }
#         }
#     ]
# )
# print(f"Image embedding for product {product_id} stored successfully.")

# --- Alternative for Weaviate ---
# import weaviate
# client = weaviate.Client("http://localhost:8080") # Replace with your Weaviate instance URL

# class_name = "ProductImage"
# client.schema.create({"classes": [{
#     "class": class_name,
#     "properties": [
#         {"name": "productId", "dataType": ["text"]},
#         {"name": "productName", "dataType": ["text"]},
#         {"name": "imageUrl", "dataType": ["text"]}
#     ],
#     "vectorizer": "none" # We are providing our own vectors
# }]})

# data_object = {
#     "productId": product_id,
#     "productName": product_name,
#     "imageUrl": image_url
# }

# client.data_object.create(
#     data_object=data_object,
#     class_name=class_name,
#     vector=image_embedding # Pass the numpy array directly
# )
# print(f"Image embedding for product {product_id} stored successfully in Weaviate.")

Finding Image Data in a Vector DB (Similarity Search)

Finding data in a Vector DB with image embeddings means performing a **visual similarity search**. You provide a query image (or even a text query if using a multi-modal model like CLIP), convert it to a vector, and then ask the Vector DB for the closest vectors.

Concepts for Finding Image Embeddings:

• Query Image/Text: The input from the user (e.g., a photo they uploaded, or a natural language description).
• Query Embedding: The input (image or text) is converted into a vector using the *same* embedding model used for ingestion.
• Distance Metric: How “closeness” between vectors is calculated (e.g., Cosine Similarity for semantic similarity, Euclidean Distance for magnitude difference).
• k-Nearest Neighbors (k-NN) / Approximate Nearest Neighbors (ANN): The process of finding the `k` closest vectors to the query vector. ANN algorithms are used for efficiency with large datasets.
• Post-filtering: After the similarity search, you can often apply filters on the metadata to narrow down results (e.g., “find similar images, but only those from category ‘shoes’”).

Sample Code: Finding Similar Images (Python with Pinecone/Weaviate)

Let’s find images similar to a user-provided query image.

# --- Step 1: Load the same image embedding model as used for ingestion ---
# (Assumed 'processor' and 'model' are already loaded from previous section)

# --- Step 2: Load and Pre-process the query image ---
query_image_path = "path/to/user/query_image.jpg" # e.g., a photo of a red dress
query_image = Image.open(query_image_path).convert("RGB")

# --- Step 3: Generate the Query Image Embedding ---
query_inputs = processor(images=query_image, return_tensors="pt")
with torch.no_grad():
    query_embedding = model(**query_inputs).last_hidden_state.mean(dim=1).squeeze().numpy()

print(f"Generated query embedding with shape: {query_embedding.shape}")

# --- Step 4: Perform Similarity Search in the Vector Database (e.g., Pinecone) ---
# Assuming 'index' is already initialized from the ingestion step
# For Weaviate, 'client' and 'class_name' are assumed

# # Pinecone Query
# query_results = index.query(
#     vector=query_embedding.tolist(),
#     top_k=5, # Get top 5 most similar images
#     include_metadata=True # Ensure metadata (product_id, image_url) is returned
# )

# print("\nPinecone: Semantically similar images found:")
# for match in query_results.matches:
#     print(f"  Score: {match.score:.4f}, Product Name: {match.metadata.get('product_name')}, URL: {match.metadata.get('image_url')}")
#     # You can then use match.metadata['product_id'] to fetch full details from MongoDB if needed

# --- Alternative for Weaviate Query ---
# query_results = client.query.get(
#     class_name=class_name,
#     properties=["productId", "productName", "imageUrl"] # What metadata to return
# ).with_near_vector({
#     "vector": query_embedding.tolist()
# }).with_limit(5).do()

# print("\nWeaviate: Semantically similar images found:")
# for result in query_results["data"]["Get"][class_name]:
#     print(f"  Product Name: {result['productName']}, URL: {result['imageUrl']}")

# Example: Text to Image Search using CLIP (Conceptual)
# from transformers import CLIPProcessor, CLIPModel
# processor_clip = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# model_clip = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")

# # Generate image embedding using CLIP
# image_inputs = processor_clip(images=Image.open("your_image.jpg"), return_tensors="pt")
# image_features = model_clip.get_image_features(**image_inputs)
# image_embedding_clip = image_features.squeeze().detach().numpy()

# # Generate text embedding using CLIP
# text_inputs = processor_clip(text="a red dress", return_tensors="pt", padding=True)
# text_features = model_clip.get_text_features(**text_inputs)
# text_embedding_clip = text_features.squeeze().detach().numpy()

# # Now, store image_embedding_clip in your Vector DB.
# # When a user searches "a red dress", you generate text_embedding_clip
# # and query the Vector DB for similar vectors (which will be your image embeddings).
    

Key Use Cases & Architectural Patterns

Image embeddings in Vector DBs enable powerful new applications:

1. Reverse Image Search (Image-to-Image)

• Concept: Users upload an image, and the system finds visually similar images in a vast dataset.
• Use Case: E-commerce “Shop the Look”: A customer sees a shirt they like in a photo and uploads it to find similar shirts available for purchase in your catalog.
• Use Case: Stock Photography: Finding images with similar composition, color schemes, or subjects to a given reference image.
• Use Case: Plagiarism/Copyright Detection: Identifying images that are visually similar to copyrighted material.

2. Visual Recommendation Systems

• Concept: Recommend items that are visually similar to what a user has previously engaged with or expressed interest in.
• Use Case: Fashion Retail: A user browses a particular style of shoes; the system recommends visually similar shoes from other brands or seasons.
• Use Case: Interior Design: Recommending furniture or decor items that match the visual style of a room a user has designed or liked.

3. Multi-modal Search (Text-to-Image / Image-to-Text)

• Concept: Search for images using natural language descriptions, or generate text descriptions for images. This requires a multi-modal embedding model like CLIP.
• Use Case: Photo Album Search: Searching your personal photo collection with queries like “photos of my dog playing in the snow” without needing manual tags.
• Use Case: Content Creation: Finding images that perfectly match a written article’s context, or generating captions for images.
• Use Case: Real Estate Search: “Show me houses with a modern kitchen and a large backyard.”

4. Visual Anomaly Detection

• Concept: Identify images that are visually unusual or out of place compared to a baseline.
• Use Case: Manufacturing Quality Control: Automatically flagging products on an assembly line that have visual defects (e.g., scratches, misalignments) because their image embedding is distant from normal product embeddings.
• Use Case: Security & Surveillance: Detecting unusual objects or activities in video streams.

5. Deduplication & Clustering of Images

• Concept: Find duplicate or near-duplicate images within a large dataset, or group visually similar images together.
• Use Case: Large Photo Archives: Removing redundant images from a collection to save storage or improve search results.
• Use Case: Data Cleaning: Identifying and cleaning up duplicate product images in an e-commerce catalog.

Typical Architectural Pattern:

A common setup involves a hybrid approach:

1. Primary Data Store (e.g., MongoDB, PostgreSQL, S3): Stores the original image files (or their URLs) and associated structured metadata (e.g., product name, SKU, price, textual description).
2. Image Embedding Service: A microservice that takes raw images as input, processes them, and uses an image embedding model to generate vectors.
3. Vector Database: Stores the generated image embeddings, along with unique IDs that link back to the original image/product in the primary data store, and any relevant metadata for filtering.
4. Application Layer:
   • Takes user’s image query.
   • Sends it to the Image Embedding Service to get a query vector.
   • Queries the Vector DB for similar vectors.
   • Uses the returned IDs to fetch full details (including the original image URL) from the Primary Data Store.
   • Presents results to the user.

Becoming a Master: Advanced Considerations

• Choosing the Right Embedding Model: Different models excel at different tasks. For general visual similarity, a large ResNet or EfficientNet might be good. For text-to-image search, CLIP is essential. For fine-grained similarity (e.g., specific dog breeds), you might need a specialized model or fine-tune an existing one.
• Embedding Dimensionality: Higher dimensions can capture more nuance but increase storage and query time. Balance is key.
• Distance Metrics: Cosine similarity is very common for semantic tasks, but Euclidean distance or dot product might be preferred depending on the embedding model and task.
• Indexing Algorithms (ANN): Understand HNSW, IVF, LSH. These are the backbone of fast similarity search in Vector DBs.
• Data Freshness: How often do you need to update embeddings for new images or changes to existing ones? This impacts pipeline design.
• Multi-modal Search Integration: If combining text and image search, ensure your embedding models generate vectors in a compatible space (e.g., using CLIP or a similar architecture).
• Cost: Running embedding models and managing Vector DBs can incur significant computational and storage costs, especially at scale.
• Metadata Filtering: Leverage the metadata stored alongside vectors in the Vector DB. Often you want “similar images *where* category is ‘shirts’ and price

Tutorials and Further Learning Resources

To put this knowledge into practice and truly master image embeddings in Vector DBs, explore these resources:

Image Embedding Models & Libraries:

• Hugging Face Transformers – Vision Models: Browse and experiment with many pre-trained image models.
  • https://huggingface.co/models?pipeline_tag=image-feature-extraction&sort=downloads
• CLIP GitHub Repository (OpenAI): Dive into the seminal multi-modal model.
  • https://github.com/openai/CLIP
• PyTorch Image Models (timm): A vast collection of image models for research and deployment.
  • https://github.com/huggingface/pytorch-image-models

Vector Database Specific (Image Examples):

• Pinecone – Image Search Tutorial: Hands-on guide for building an image search application.
  • https://www.pinecone.io/learn/image-search/
  • Pinecone Image Search Guide
• Weaviate – Image Search with CLIP: Demonstrates using CLIP for multimodal image search.
  • https://weaviate.io/blog/image-search-with-clip-embeddings
  • Weaviate Image Search Quickstart
• Milvus – Image Search Example: Guide for building an image search system with Milvus.
  • https://milvus.io/docs/v2.3.x/example_code.md#Milvus-for-image-search
• Qdrant – Image Search Example:
  • https://qdrant.tech/documentation/examples/image-search/

Conceptual & Design Best Practices:

• “The Vector Database Index: How Does it Work?” (Pinecone Blog): Explains ANN algorithms.
  • https://www.pinecone.io/learn/vector-database-index/
• Deep Learning for Computer Vision: Understand the basics of how image models work (e.g., through online courses on Coursera, Udacity, or books like “Deep Learning” by Goodfellow et al.).

By diligently working through these resources, experimenting with different image embedding models, and building small projects, you will gain the practical skills and theoretical understanding to architect sophisticated systems that leverage the power of visual search and similarity, truly becoming a master in the realm of image embeddings and vector databases.