Vector Databases

LanceDB for Multimodal RAG: Storing and Querying Image-Text Pairs

Sep 29, 2026 · 5 views
pythonvector databaseAI EngineeringLanceDBEmbeddingsMultimodal RAG

Retrieval-Augmented Generation (RAG) systems have largely been text-centric until recently. However, modern AI applications increasingly require the ability to retrieve and reason over both visual and textual data simultaneously. This is where multimodal RAG comes into play. While traditional vector databases struggle with the complexity of managing multiple data types and large binary payloads, LanceDB offers a robust, columnar storage solution designed specifically for AI workloads.

In this guide, we will explore how to leverage LanceDB to store and query image-text pairs efficiently. We will demonstrate how to handle the embedding generation, data ingestion, and the retrieval logic required for a multimodal system.

Why LanceDB for Multimodal Data?

LanceDB is an open-source, developer-friendly vector database that uses the Lance format. Unlike row-based databases, Lance is optimized for random access and large binary files, making it ideal for storing images alongside their vector embeddings. Key advantages include:

  • Zero-Op Storage: It runs embedded within your application, reducing infrastructure complexity.
  • Efficient Filtering: It supports metadata filtering alongside vector similarity, crucial for narrowing down search results by category, date, or source.
  • Large Object Support: It handles large binary data (like images) natively without external blob storage dependencies in many scenarios.

Setup and Installation

To get started, you need to install LanceDB and a multimodal embedding model. For this example, we will use transformers with a CLIP model for generating embeddings.


# Install dependencies
pip install lancedb transformers torch pillow

Step 1: Generating Multimodal Embeddings

The foundation of multimodal RAG is aligning images and text in the same vector space. We use a CLIP model to generate embeddings for both images and text queries. When searching, you can query with text to find relevant images, or vice versa.


import torch
from transformers import CLIPProcessor, CLIPModel
from PIL import Image
import base64
import numpy as np

# Load CLIP model
model_name = "openai/clip-vit-base-patch32"
processor = CLIPProcessor.from_pretrained(model_name)
model = CLIPModel.from_pretrained(model_name)

def get_image_embedding(image_path):
    """Generate embedding for an image."""
    image = Image.open(image_path)
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model.get_image_features(**inputs)
    return outputs.cpu().numpy().flatten()

def get_text_embedding(text):
    """Generate embedding for a text query."""
    inputs = processor(text=text, return_tensors="pt", padding=True)
    with torch.no_grad():
        outputs = model.get_text_features(**inputs)
    return outputs.cpu().numpy().flatten()

Step 2: Ingesting Image-Text Pairs

We will create a LanceDB table that stores the image data (encoded as Base64 for portability in this example, though raw bytes are preferred for performance), the caption, and the vector embedding.


import lancedb
import uuid

# Initialize database
db = lancedb.connect("./multimodal_db")

# Create a table if it doesn't exist
# Note: In production, you might store image bytes directly or use URIs
table = db.open_table("images") if db.table_names() else db.create_table("images", mode="create")

# Sample data
sample_data = [
    {
        "id": str(uuid.uuid4()),
        "caption": "A golden retriever playing in a park",
        "image_data": open("dog.jpg", "rb").read(), # Read image bytes
        "vector": get_image_embedding("dog.jpg"),
    },
    {
        "id": str(uuid.uuid4()),
        "caption": "A cozy library with bookshelves",
        "image_data": open("library.jpg", "rb").read(),
        "vector": get_image_embedding("library.jpg"),
    }
]

# Add data to the table
table.add(sample_data)

Step 3: Performing Multimodal Search

The power of multimodal RAG lies in cross-modal retrieval. A user might type "Find me photos of dogs," and the system should return the most visually similar images based on the text query.


def search_images(text_query, top_k=5):
    """Search for images based on a text query."""
    # Generate embedding for the text query
    query_vector = get_text_embedding(text_query)
    
    # Perform vector search with metadata filter
    # Here we simply search by vector similarity
    results = table.search(query_vector).limit(top_k).to_pandas()
    
    # Decode image data for display (optional)
    for idx, row in results.iterrows():
        print(f"Result {idx}: {row['caption']}")
        # In a real app, you'd decode row['image_data'] and display it
        # img = Image.open(io.BytesIO(row['image_data']))
        
    return results

# Execute search
results = search_images("cute dog playing outside")
print(results[['id', 'caption', '_distance']])

Conclusion

LanceDB provides a streamlined path to building multimodal RAG applications by simplifying the storage and retrieval of complex data types. By aligning text and image embeddings in a shared vector space, developers can create intuitive search experiences that understand the relationship between what something looks like and what it is described as. As multimodal models become more prevalent, tools like LanceDB will be essential for managing the underlying data infrastructure.

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