Amazon Nova Multimodal Embeddings + S3 Vectors: Crossmodal Visual and Text Search for Retail

Crossmodal search with Amazon Nova Multimodal Embeddings

TL;DR

One unified embedding model can map text, images, audio and video into the same vector space so you can compare them directly—no more stitching separate image and text pipelines.
Amazon Nova Multimodal Embeddings (via Amazon Bedrock) + S3 Vectors lets you build crossmodal visual search and mixed text+image queries with a straightforward pipeline: embed → store → search → rerank.
Matryoshka embeddings let you truncate vectors (3072, 1024, 384, 256) to trade storage cost for accuracy; 1024 is a common midpoint (~4 KB per vector, ~4 GB per million vectors when using float32).
Production work centers on ANN indexing, reranking with business signals, governance for user data, and continuous evaluation (precision@k, MRR, CTR).

Why unify embeddings? The problem with split pipelines

Traditional visual search projects run two parallel tracks: a vision embedding pipeline and a language embedding pipeline. They rarely align perfectly, which creates brittle engineering, duplicate infrastructure and bad user experiences when a photo and a text query should match the same product.

A unified multimodal embedding maps every supported modality into a single vector space so similarity is meaningful across inputs. For retail that means a customer can snap a photo, paste a product description, or do both—and the system can return relevant SKUs with a single similarity score.

One model, one space: a single model converts text, images, video and audio into embeddings in the same vector space so similarity can be computed directly across modalities.

How Amazon Nova + S3 Vectors fits together

At a system level the pattern is simple and repeatable:

Embedding generation — use Amazon Nova Multimodal Embeddings via Amazon Bedrock (example model id: amazon.nova-2-multimodal-embeddings-v1:0) to turn text, images, audio and video into vectors.
Vector storage — persist and index vectors in a vector store; the walkthrough uses S3 Vectors (native vector storage in S3) but you can use FAISS, Milvus, Pinecone or a vector extension in your DB.
Similarity search — perform nearest-neighbor search with cosine distance (convert to 1 − distance for an intuitive similarity score) and combine with a reranker for business signals.

Why S3 Vectors? It reduces operational burden by keeping storage and index infrastructure managed and colocated with your product data in S3. Alternatives may be faster or offer different features (real-time updates, hybrid indexes), so match the choice to your SLA and scale needs.

Matryoshka embeddings — nested dolls for vectors

Matryoshka representation learning arranges information hierarchically across vector dimensions so the most important information sits in the first slots. Think of it like stacked nested dolls: you can peel off the outer ones (truncate dimensions) to save space while retaining the core signal.

Available sizes: 3072, 1024, 384, 256.
Storage math (float32): 4 bytes × dims. Example: 1024 dims ≈ 4 KB per vector → ~4 GB per 1M vectors.
Tradeoff: larger dims usually give better recall and fine-grained ranking; smaller dims cut storage and network cost and are often good enough for many catalog tasks.

Typical flow — real customer example: snap-to-buy for a TV

Follow a single user journey to see how components hook together:

User snaps a photo of a TV on a storefront.
Preprocess image (resize, normalize) and call Bedrock to get an image embedding from amazon.nova-2-multimodal-embeddings-v1:0.
Query the vector index (S3 Vectors) with cosine-based ANN search to retrieve top-k candidate SKUs.
Rerank candidates by a small ML model or business rules (price, availability, sponsored inventory, CTR prediction).
Return results and log click/conversion signals for online learning and evaluation.

For mixed queries (photo + short text like “55in OLED under $1500”), compute both embeddings and combine them into one query vector—simple averaging (mean fusion) is a practical starting point:

combined_query = (image_vector + text_vector) / 2

This “mean fusion” often works well; later you can replace it with weighted averaging, concatenation + projection, or a small trained fusion network if needed.

Implementation checklist and practical knobs

Model & API: Get Bedrock access and test amazon.nova-2-multimodal-embeddings-v1:0 on representative queries (user photos, product descriptions).
Choose dimension: Benchmark 3072 vs 1024 vs 384 using offline labels. Start with 1024 as a balanced default.
Vector store & ANN: Pick S3 Vectors if you want managed storage; otherwise FAISS, HNSWlib or Milvus. Select ANN strategy (HNSW for recall + latency, IVF+PQ for extreme scale and compression).
Batching & upload: Use batching (example demo used batch_size = 10). For production, tune batch size to maximize throughput without exhausting memory or API limits.
Fusion: Start with mean fusion for mixed queries. Log per-modality scores so you can diagnose which modality drove the result.
Rerank layer: Combine vector scores with business signals—CTR, conversion prediction, stock, margin, promotions—before final sorting.
Monitoring: Track precision@k, recall@k, MRR/NDCG, P95 latency, P99 cost per query, and CTR by query type (image, text, mixed).
Governance: Enforce consent, retention, encryption-at-rest, and data minimization for user-uploaded images and embeddings.

Evaluation, A/B test design and KPIs

Offline metrics to run before any live experiment:

Precision@K and Recall@K on a labeled validation set.
Mean Reciprocal Rank (MRR) and NDCG for graded relevance.
Per-modality lift: measure image-only, text-only and combined queries separately.

Simple A/B experiment template for visual search impact:

Hypothesis: Adding unified multimodal search will increase product-detail-page CTR for image queries by X% and improve conversion rate.
Variants: Control = current search; Treatment = Nova + S3 Vectors pipeline + reranker.
Metrics: Primary: CTR on results page for image-origin queries, conversion rate; Secondary: AOV, add-to-cart rate, query-to-click latency.
Sample size & duration: Estimate needed sample size for the expected lift (statistical power). Practical approach: run until you have at least several thousand image-origin searches per variant or 2–4 weeks to capture variability.
Success criteria: Statistically significant lift in CTR and no degradation in overall relevance metrics (NDCG).
Post-test: Bucket analysis (by brand, image quality, product category) to find where gains are concentrated.

Tradeoffs, alternatives and failure modes

Unified embeddings simplify architecture, but they’re not a silver bullet. Alternatives include:

Specialized models + alignment layer: Keep best-in-class vision and language embeddings and train a lightweight projection/adapter to align spaces. This can sometimes preserve modality-specific fidelity.
Hybrid retrieval: Use vector search for semantic recall, then combine with keyword or inverted-index search for exact matches (SKUs, model numbers).

Common failure modes to watch for:

Poor performance on domain-shifted images (user photos with occlusion or phone glare).
Embeddings that over-emphasize brand logos or backgrounds, causing false positives.
Latency spikes from large ANN indexes or heavy reranking models.
Privacy and IP issues when indexing user-uploaded images without clear consent.

Governance and privacy guidance

Require consent before indexing user images; expose opt-out and deletion flows.
Encrypt embeddings and images at rest; apply strict IAM policies around vector access.
Audit retained embeddings for PII or sensitive content and purge according to retention policy.
Apply content-moderation filters to user uploads (NSFW, brand/IP claims) before indexing.

Costs and capacity thinking

Estimate storage cost quickly from dimensionality:

Float32 size = 4 bytes × dims. Example: 1024 dims ≈ 4 KB per vector → ≈ 4 GB per 1M vectors.
Truncating from 3072 → 1024 will cut storage roughly by two-thirds, but validate recall loss.
ANN indices add overhead (graph structures, compressed tables) and memory footprints vary by algorithm.

Key questions & quick answers

What problem does a unified multimodal embedding solve?

It removes the need to maintain separate visual and text embedding pipelines and incompatible vector spaces, reducing engineering overhead while enabling direct crossmodal retrieval like photo→product or mixed text+image queries.
Which model and services are used?

The walkthrough uses Amazon Nova Multimodal Embeddings via Amazon Bedrock (model id: amazon.nova-2-multimodal-embeddings-v1:0) and S3 Vectors for storing and searching vectors. You can also use alternative vector DBs depending on requirements.
How do you combine text and image queries?

Start with simple averaging (mean fusion) of the modality vectors to make a single multimodal query vector. You can evolve to weighted or learned fusion based on empirical results.
What embedding dimensions matter and why?

Matryoshka dimensions (3072, 1024, 384, 256) let you trade accuracy for storage. 1024 is a sensible mid-point; evaluate on your catalog to pick the right balance for recall and cost.
How should I scale and safeguard production?

Pick ANN indexes for latency/recall tradeoffs, build a lightweight reranker to fold in business signals, monitor retrievability and drift, and enforce strong data governance for user content and embeddings.

“Matryoshka representation learning packs the most important features into earlier vector slots so you can truncate dimensions to trade storage for accuracy.”

Want help operationalizing this pattern? I can prepare a concise architecture checklist for retail, or a ready-made A/B test template that maps expected sample size to lift targets and KPIs—pick one and I’ll draft it.