OpenSense Labs Outlines Drupal AI Search with PostgreSQL Vector Database in Ecosystem Series Part 4

Staff Reporter

OpenSense Labs has published Part 4 of its Drupal AI Ecosystem series, detailing how to implement AI-powered semantic search using PostgreSQL’s vector capabilities. The article, authored by Harivansh Sharma, explains how Drupal AI Search replaces traditional keyword matching by using embeddings to measure semantic similarity between user queries and indexed content. These embeddings are stored and queried using PostgreSQL’s pgvector extension, removing the need for third-party vector search services.

The tutorial guides readers through the full module stack required: AI Core, AI Provider, Postgres VDB Provider, and Drupal’s Search API. It includes PostgreSQL configuration, pgvector activation, and integration with Drupal indexing tools. Embeddings are generated using external AI models and compared using cosine similarity to retrieve content based on contextual relevance, not just keywords.

Beyond search, Sharma explores how to extend Drupal’s AI interface with AI Assistants for conversational query handling and AI Agents for multi-step task execution. Both use Retrieval-Augmented Generation (RAG) to access vectorized content and provide accurate, site-specific answers or actions. The article also covers testing tools and admin settings to verify setup success.

The tutorial ends with a demonstration of Deep Chat integration and how to enhance traditional keyword search by enabling vector-based boosting. This developer-focused guide offers a self-hosted, cost-efficient pathway to semantic discovery inside Drupal using infrastructure teams may already rely on.

Reference: Drupal AI Ecosystem Part 4: Drupal AI Search with PostgreSQL Vector Database by harivansh sharma, OpenSense Labs (19 November 2025)

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