Multi-Modal Semantic Search

Beyond Text Search

Enterprise data isn’t just text. Product catalogs have images. Training materials include videos. Technical documentation contains diagrams. Marketing assets span all formats. Yet most search systems treat these modalities separately, if they handle them at all.

Multi-modal semantic search unifies these experiences. Users can search with text and find relevant images. They can upload an image and find similar products. They can describe a scene and find the right video clip. The modality of the query doesn’t constrain the modality of the results.

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Multi-Modal Search Examples

Query Type	Example
Text → Image	”Red dress with floral pattern” returns matching product images
Image → Image	Upload a competitor product, find similar items in your catalog
Text → Video	”Safety procedure for chemical spill” returns relevant training clips
Image → Text	Upload a diagram, find documentation that references it

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The Technical Foundation: CLIP and Beyond

The breakthrough enabling multi-modal search is contrastive learning on paired data. OpenAI’s CLIP, trained on 400 million image-text pairs, learns to map images and text into a shared embedding space where semantically similar content clusters together regardless of modality.

How CLIP Works

CLIP consists of two encoders-one for images, one for text-trained to maximize the similarity between matching pairs while minimizing similarity between non-matching pairs. After training, you can:

• Embed any image and any text into the same vector space
• Compare image-to-image, text-to-text, or image-to-text using cosine similarity
• Use standard vector search infrastructure for retrieval

from transformers import CLIPModel, CLIPProcessor

model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

# Embed an image
image_inputs = processor(images=image, return_tensors="pt")
image_embedding = model.get_image_features(**image_inputs)

# Embed text
text_inputs = processor(text=["red floral dress"], return_tensors="pt")
text_embedding = model.get_text_features(**text_inputs)

# These embeddings live in the same space!
similarity = cosine_similarity(image_embedding, text_embedding)

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Beyond CLIP: Production Considerations

Domain Adaptation

CLIP was trained on web data, which skews toward natural images and common objects. For specialized domains-medical imaging, technical diagrams, satellite imagery-fine-tuning on domain-specific pairs significantly improves performance.

Handling Video

Video adds temporal complexity. Our approach:

1. Frame sampling: Extract keyframes using scene detection, not fixed intervals
2. Frame embedding: Embed each keyframe with the image encoder
3. Temporal pooling: Aggregate frame embeddings using attention over time
4. Audio integration: Transcribe audio and embed text, then fuse with visual embeddings

Scaling to Millions of Assets

Vector search at scale requires approximate nearest neighbor (ANN) algorithms. We use a combination of:

• HNSW indexes for low-latency retrieval (under 50ms at 10M vectors)
• Product quantization to reduce memory footprint by 8-16x
• Hybrid filtering to combine vector similarity with metadata constraints

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Architecture for Multi-Modal RAG

Multi-modal search becomes even more powerful when combined with generative AI. Instead of just returning results, the system can answer questions about visual content.

# Multi-modal RAG pipeline
1. User query: "What safety equipment is shown in our training videos?"
2. Embed query → search video index → retrieve relevant clips
3. Extract frames from top clips
4. Send frames + query to vision LLM (GPT-4V, Gemini)
5. Generate answer with visual grounding

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Real-World Applications

E-commerce Visual Search

Customers upload a photo of something they like-from a magazine, social media, or a competitor-and find similar products in your catalog. Conversion rates for visual search are typically 2-3x higher than text search.

Enterprise Asset Management

Marketing teams search across millions of images, videos, and documents using natural language. “Find all photos of our Chicago office” returns results even if the images weren’t explicitly tagged with location.

Technical Documentation

Engineers upload a screenshot of an error or a photo of a hardware component and find relevant documentation, even when the docs don’t contain the exact error text.

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Getting Started

Multi-modal search is built into Elastiq Discover. If you’re sitting on a large corpus of images, videos, or mixed-media content, we can help you unlock its value with semantic search and multi-modal RAG.