This session dives deep into Drupal AI Search, an emerging capability that transforms traditional keyword-based search into semantic, intelligent discovery. Learn how to implement Retrieval Augmented Generation (RAG), vector databases, and LLM-powered assistants to deliver smarter, faster, and more relevant search experiences on your Drupal site.

1. How AI Search Works in Drupal
Overview of the AI Search module and its integration with Search API

How content is chunked, embedded, and indexed using vector representations

What makes semantic search more accurate than keyword scoring

2. Key Technologies & Modules
AI (Artificial Intelligence) module: The unified framework for AI in Drupal

Vector Database Providers: Milvus, Zilliz, Pinecone support for embedding-based retrieval

AI Assistants: Use RAG actions to pull relevant content during chat interactions

3. How to Implement It

Enable AI Search and a Vector Database Provider

Create a Search API Server & Index with AI Search backend

Index content with contextual metadata (title, URL, etc.)

Configure score thresholds and boost processors

Combine AI Search with SOLR or database search for hybrid relevance

4. Use Cases & Demos
Knowledge base: Ask questions and get answers from your own content

E-commerce: Match user queries to product descriptions semantically

Intranet search: Retrieve documents and pages based on meaning, not keywords