This article is part three of a four-part series exploring how to extend Coveo’s powerful search platform with vector embeddings from Amazon Bedrock to enable advanced visual search experiences for ecommerce.

This blog series describes customer-managed implementation patterns for teams that need to experiment now. These are not the default or recommended Coveo architectures. Where Coveo offers or is introducing native capabilities, those should be evaluated first. The examples here are intended to explain design tradeoffs, orchestration patterns, and governance considerations, not to imply product commitments or turnkey support.

Why Coveo Headless?

Traditional Coveo implementations use pre-built UI components—fast to implement but limited in customization. For a luxury brand experience where every pixel matters, we need complete control.

According to Coveo documentation, the Headless library “provides an extendable set of reducers and controllers that allow you to connect your own search UI components to the Coveo APIs.”

Key benefits:

  • Framework agnostic: Works with React, Vue, Angular, or vanilla JS
  • Complete UI control: Build exactly what your brand requires
  • Automatic analytics: Search and click events tracked automatically
  • State management: Engine handles complex search state

The Headless library acts as a middle layer, opening communication between UI elements and the Coveo Platform while you maintain full control over presentation.

Architecture

React Application
├── CoveoProvider (Context + Engine)
│   ├── SearchBox Controller
│   ├── ResultList Controller
│   └── Facet Controllers
│
├── HeadlessSearchBox.jsx
├── HeadlessResultList.jsx
├── HeadlessFacets.jsx
└── ImageUpload.jsx (Visual Search)

The engine manages state and communicates with Coveo. Controllers dispatch actions based on user interactions.

Implementation

Complete source code is in ui/src/ in our GitHub repository.

Engine Configuration

JavaScript

The searchHub and pipeline settings connect to your Coveo query pipeline, where ART and Query Suggestions models are configured.

CoveoProvider

React Context provides the engine to all child components:

JavaScript

SearchBox with Query Suggestions

Coveo ML Query Suggestions recommends relevant queries as users type. The Headless SearchBox controller handles this automatically:

JavaScript

Interactive Results with Click Tracking

The buildInteractiveResult controller automatically logs click events for ART:

JavaScript

These click events feed into Automatic Relevance Tuning, which dynamically adjusts future search rankings.

Faceted Navigation

Facets enable filtering by AI-extracted metadata:

JavaScript

Combining Text and Image Search

The strategic long-term direction for Coveo is an image-aware conversational/search-box experience, where an image upload automatically triggers a guided discovery flow. This standalone “Image Search” mode (OpenSearch vector similarity) is presented here as a customer-managed interim pattern for teams that need to prototype image-led discovery now, while the core product capabilities mature.

Our UI offers three search modes:

  1. Text Search: Coveo Headless with ML-powered relevance
  2. Image Search: OpenSearch vector similarity via API Gateway
  3. AI Metadata: Extract attributes from uploaded images

JavaScript

Image Search Integration

Image search calls our Lambda backend:

JavaScript

Analytics Integration

Coveo Headless automatically tracks:

  • Search events (queries submitted)
  • Click events (results clicked)
  • Facet interactions

These events feed Coveo ML models. ART uses click-through data to boost relevant content. Query Suggestions learns which queries lead to successful outcomes.

For custom events (image search, metadata extraction), we log to console with plans for Coveo Usage Analytics integration:

JavaScript

Deployment

Build and deploy to Netlify:

Bash

Set environment variables in Netlify Dashboard:

  • VITE_COVEO_ORG_ID
  • VITE_COVEO_API_KEY
  • VITE_API_URL

Next Steps

With our UI complete, we have a fully functional visual search experience. In the final article, we’ll explore AI-powered metadata extraction using Amazon Bedrock Nova.

Even as native product capabilities evolve, the design principles in this article remain relevant: understand how to integrate external systems, ensure proper governance around new data signals, and separate exploratory prototypes from production-grade search architecture.

Resources: