Embeddings and Multimodal Models: The Future of Search in 2025

Search is undergoing its most significant transformation since Google's PageRank algorithm rewrote the rules two decades ago. The shift is being driven by two converging technologies: high-dimensional embeddings and multimodal models that can understand text, images, and audio within a single mathematical space.

For developers and product teams building search experiences in 2025, understanding these technologies is no longer optional. Industry data suggests that 63% of enterprise search deployments now use some form of vector search, and that number is climbing rapidly. Companies that adopted embedding-based retrieval early are reporting retrieval accuracy improvements of 30-50% over traditional keyword search.

Let's break down exactly what's happening, why it matters, and how you can implement it.

What Are Embeddings, Really?

At its core, an embedding is a list of numbers that represents the meaning of some content. A word, a sentence, an image, a song, or a user's behavior can all be converted into a dense vector of typically 384 to 4,096 floating-point numbers.

The magic is this: similar things land near each other in this vector space. If the embedding for "dog" is close to the embedding for "puppy," then mathematically we can capture semantic relationships. Modern embedding models like OpenAI's text-embedding-3-large, Cohere's embed-english-v3.0, and open-source alternatives like BGE-M3 and E5-mistral-7b produce vectors with remarkable fidelity to human notions of similarity.

Why Vectors Beat Keywords

Traditional keyword search treats documents as bags of words. It fails when:

• A user searches "how to fix a leaking faucet" and the article is titled "Plumbing repair for dripping taps"
• Synonyms, paraphrases, and context don't get matched
• Multilingual content needs to be searched across languages

Vector search solves these problems by mapping meaning into geometry. Two vectors pointing in roughly the same direction represent semantically similar content, regardless of the surface-level words used.

Enter Multimodal Models

Multimodal models go one step further. They produce embeddings that represent multiple modalities — text, images, audio, video — in the same vector space. This means:

• A photo of a golden retriever has an embedding near the text "happy dog playing in a park"
• A spoken phrase and its written transcript land near each other
• A product image and its description can be matched by similarity

Models like CLIP (Contrastive Language-Image Pretraining) from OpenAI, Google's SigLIP, Meta's ImageBind, and OpenCLIP have made cross-modal retrieval production-ready. The latest variants handle six or more modalities simultaneously.

How Cross-Modal Alignment Works

During training, a multimodal model sees pairs of data — for CLIP, that's (image, caption) pairs scraped from the web. It learns to map both the image and its caption to nearby points in vector space by optimizing a contrastive loss:

• Pull paired examples closer together
• Push unpaired examples further apart

This simple but powerful objective teaches the model a joint representation that captures abstract concepts rather than surface features.

Why Search Needs Both

The combination of embeddings and multimodal capabilities addresses the biggest pain points in modern search:

1. Intent Understanding — Users don't always know the right words. Someone searching for "something to keep my coffee warm" should find travel mugs.

2. Cross-Modal Queries — Shoppers want to upload a photo of a chair and find similar ones. Photographers want to search their library by describing what they remember.

3. Multilingual Retrieval — A company with global customers can index English product docs and serve Spanish-speaking users without maintaining parallel indexes.

4. Long-Tail Performance — Vector search handles rare queries better because it generalizes from concept space rather than matching exact tokens.

Building a Multimodal Search System: A Practical Example

Let's walk through a real-world implementation. We'll build a search system that handles both text and image queries against a corpus of products.

Step 1: Choose Your Models

For text embeddings, we'll use BAAI/bge-large-en-v1.5 (a strong open-source model). For images, we'll use CLIP-ViT-B-32 from OpenCLIP.

from sentence_transformers import SentenceTransformer
import open_clip
import torch

# Text model
text_model = SentenceTransformer('BAAI/bge-large-en-v1.5')

# Image model - CLIP handles both text and images
clip_model, _, preprocess = open_clip.create_model_and_transforms(
    'ViT-B-32', pretrained='laion2b_s34b_b79k'
)

Step 2: Index Your Data

For each product in your catalog, generate two embeddings — one from the text description and one from the product image. Normalize them so they all live on the unit hypersphere.

import numpy as np
from PIL import Image

def normalize(v):
    return v / np.linalg.norm(v, axis=-1, keepdims=True)

def encode_text(text):
    return normalize(text_model.encode(text))

def encode_image(path):
    image = preprocess(Image.open(path)).unsqueeze(0)
    with torch.no_grad():
        features = clip_model.encode_image(image)
    return normalize(features.numpy())

# Indexing loop
for product in catalog:
    text_emb = encode_text(product.description)
    image_emb = encode_image(product.image_path)
    # Concatenate or store separately based on use case
    product['vector'] = np.concatenate([text_emb, image_emb])

Step 3: Store and Search at Scale

Production systems need a vector database. Popular choices include:

• Pinecone — managed, easy to scale, generous free tier
• Weaviate — open source, built-in hybrid search, modular
• Qdrant — Rust-based, very fast, written in a memory-efficient way
• pgvector — Postgres extension, great for existing PG shops
• Milvus — battle-tested at massive scale

Here's how to query Weaviate with hybrid (vector + keyword) search:

import weaviate

client = weaviate.connect_to_local()
collection = client.collections.get("Products")

response = collection.query.hybrid(
    query="comfortable office chair",
    alpha=0.75,  # Weight: 0 = pure keyword, 1 = pure vector
    limit=10
)

for obj in response.objects:
    print(f"{obj.properties['name']} - score: {obj.metadata.score}")

Step 4: Add Re-ranking

Vector search is fast but approximate. For highest accuracy, add a re-ranker that scores the top candidates more carefully. Cohere's rerank-english-v3.0 or a cross-encoder like BAAI/bge-reranker-large typically improves top-10 precision by 10-20%.

Real-World Performance Numbers

Internal benchmarks from several companies that have published their results show:

• Shopify reported a 2x improvement in product discovery conversions after deploying multimodal search
• Pinterest's visual search, powered by embeddings, processes over 600 million visual searches per month
• Notion's AI search uses embeddings to find content across documents in 30+ languages, with semantic accuracy improvements of 40%

These aren't marginal gains. They translate directly into revenue, engagement, and customer satisfaction.

The Hybrid Approach: Don't Throw Away Keywords

The best production systems in 2025 don't pick between vector and keyword search — they combine them. This is called hybrid search.

The intuition: keyword search excels at exact matches (product codes, names, technical terms), while vector search excels at semantic meaning. By combining scores, you get the best of both.

Reciprocal Rank Fusion (RRF) is a popular method for merging results:

def reciprocal_rank_fusion(rankings, k=60):
    """Combine multiple ranked lists into a single ranking."""
    scores = {}
    for ranking in rankings:
        for rank, doc_id in enumerate(ranking):
            scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

Most vector databases now support hybrid search natively, with alpha parameters to tune the balance. A good starting point is alpha=0.7 (mostly vector, some keyword).

Challenges You Should Know About

Multimodal embedding search isn't without trade-offs:

Compute costs — Generating embeddings for millions of items requires GPU time. CLIP encoding runs about 50-100 images per second on a modern GPU.

Storage — A 1536-dimensional float32 vector takes 6KB. That's manageable, but at 100M items, you're looking at 600GB of vector data plus overhead.

Bias — Embedding models inherit biases from their training data. CLIP famously associates certain professions with gender, and these biases show up in retrieval results.

Drift — As you fine-tune or use different model versions, vectors stop being comparable. You'll need re-indexing strategies.

Evaluation — Measuring semantic search quality requires labeled query-document pairs, which are expensive to create. Tools like BEIR, TREC benchmarks, and synthetic query generation help, but real evaluation is ongoing work.

What's Coming Next

The frontier is moving fast. Here are the trends to watch:

Multimodal Large Language Models — GPT-4V, Gemini, and Claude 3 can natively reason about images and text. They're starting to be used as re-rankers and even direct answer generators on top of vector search, replacing the traditional "ten blue links" with conversational answers.

Late Interaction Models — ColBERT-style models that tokenize each query and document separately, then compare them with maximum similarity. They are slower but more accurate than single-vector embeddings, especially for long documents.

Sparse-Dense Hybrids — Models like SPLADE and BM25+learned sparse vectors combine the interpretability of keyword search with the semantic power of dense embeddings.

On-Device Embeddings — Quantized models and efficient architectures are making it feasible to run embedding search entirely on mobile devices and edge hardware, opening new privacy-preserving use cases.

Multimodal Agents — Search is becoming one tool in an agent's toolbox. Agents can now plan, search, retrieve, and synthesize answers across modalities, fundamentally changing how users interact with information.

Getting Started: A 30-Day Plan

If you're convinced and want to act, here's a practical roadmap:

Week 1 — Pick a clear use case with measurable KPIs. Inventory your data. Choose an embedding model and a vector database.

Week 2 — Build a small prototype with 1,000-10,000 items. Measure relevance with a held-out test set. Compare against your current keyword baseline.

Week 3 — Tune hyperparameters: embedding dimensions, distance metric (cosine vs. dot product vs. Euclidean), hybrid alpha, chunk size for long documents.

Week 4 — Add re-ranking, build an evaluation harness, set up monitoring for query latency and user satisfaction. Plan the production rollout.

Conclusion

The combination of embeddings and multimodal models represents the biggest leap in search technology in 20 years. It's not a silver bullet — keyword search still matters, evaluation is hard, and costs add up — but it's rapidly becoming table stakes for any product that takes discovery seriously.

The question isn't whether to adopt vector search, but how quickly you can do it well. Start small, measure rigorously, and iterate based on real user behavior. The teams that win in the next five years will be those who figure out how to combine these new techniques with their existing systems.

Ready to dive deeper? At Tanok Tech, we've helped clients from ecommerce to enterprise SaaS deploy production-grade multimodal search systems. [Get in touch](#) to discuss how embedding-powered search can transform your product — or check out our other posts on vector databases, RAG architectures, and the practical realities of building AI features that ship.

The future of search isn't ten blue links. It's understanding what your users actually mean — in whatever form they express it.