Embeddings and multimodal models: the future of search

Introduction

Search technology has evolved from keyword matching to semantic understanding, thanks to embeddings and multimodal models. These advancements allow search engines to grasp the meaning behind queries, not just literal terms, and to handle diverse data types like text, images, and audio. In this post, we explore how embeddings and multimodal models are shaping the future of search.

What are Embeddings?

Embeddings are dense vector representations of data (e.g., words, sentences, images) in a high-dimensional space. They capture semantic relationships: similar items are placed close together. For example, the embedding of "king" minus "man" plus "woman" approximates "queen".

How Embeddings Improve Search

Traditional search relies on exact keyword matches. Embeddings enable semantic search where the intent matters. For instance, a query "best way to learn piano" can match documents about "piano tutorials" even if they don't contain the exact phrase.

# Example: Using sentence-transformers to embed text
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
query = "best way to learn piano"
doc = "Piano tutorials for beginners"
query_emb = model.encode(query)
doc_emb = model.encode(doc)
similarity = query_emb @ doc_emb.T

Multimodal Models: Beyond Text

Multimodal models, like CLIP (Contrastive Language–Image Pre-training), learn joint embeddings for text and images. This allows searching images with text queries and vice versa. For example, searching "a dog playing in snow" retrieves relevant images without any text labels.

Cross-Modal Retrieval

Embeddings from different modalities are mapped into a shared space. A query, regardless of its modality, can retrieve items from any modality. This is powerful for databases containing mixed content.

# Example: Using CLIP to compute text-image similarity
import clip
import torch

model, preprocess = clip.load("ViT-B/32")
image = preprocess(Image.open("dog.jpg")).unsqueeze(0)
text = clip.tokenize(["a dog playing in snow"])

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    similarity = (image_features @ text_features.T).item()

Real-World Applications

E-commerce

Multimodal search lets users find products by combining text and image queries. For instance, a user can upload a photo of a dress and type "similar but in blue". The system retrieves matching items using joint embeddings.

Healthcare

Radiology reports can be searched with image embeddings. A query like "lung nodule CT scan" returns relevant images and reports, aiding diagnosis.

Content Management

Media libraries benefit from visual search. Searching for "sunset over mountains" retrieves relevant videos, photos, and text descriptions.

Building a Multimodal Search System

A basic pipeline involves:

1. Embed every item in the database (text, images, etc.) using a multimodal model.
2. Embed the query using the same model.
3. Compute similarity (e.g., cosine similarity) between query and database embeddings.
4. Return top-k results.

Scalability

For large datasets, use approximate nearest neighbor libraries like FAISS or Annoy.

import faiss

# Build index
index = faiss.IndexFlatIP(embedding_dim)  # Inner product
index.add(all_database_embeddings)

# Search
D, I = index.search(query_embedding.reshape(1, -1), k=10)

Challenges and Solutions

• Data alignment: Ensuring different modalities are aligned in one space requires large paired datasets. Techniques like contrastive learning help.
• Computational cost: Multimodal models are heavy. Use smaller models (e.g., DistilBERT, TinyCLIP) and caching.
• Ambiguity: Queries may be vague. Incorporate user feedback or context.

The Future

Multimodal search will become more interactive, with voice, gesture, and even brain-computer interfaces. Models like GPT-4V already combine vision and language. As hardware improves, real-time multimodal search on devices will be common.

Conclusion

Embeddings and multimodal models are transforming search from a text-based look-up to an intelligent, cross-modal experience. By understanding semantics and relationships across data types, these technologies pave the way for more intuitive and powerful search systems.

For further reading, check out OpenAI's CLIP and FAISS documentation.