Vector Databases: The Infrastructure Behind Semantic Search

Traditional databases store data in tables and search for exact matches. Vector databases work differently:

Step 1 — Embedding: An AI model converts your content (text, images, products) into high-dimensional vectors. Each vector is a numerical representation of the meaning of a piece of content.

Step 2 — Storage: These vectors are indexed in the vector database, optimized for fast similarity search.

Step 3 — Query: A search query is also converted into a vector. The database finds the vectors closest to the query vector — i.e., the content that is most similar in meaning.

The result: search by meaning instead of keywords. A user who enters "How do I protect my website from attacks?" finds your page about security updates — even if that phrase appears nowhere on it.

Retrieval-Augmented Generation (RAG) is the architecture that connects vector databases with Large Language Models. Here is how it works:

1. A user asks your chatbot a question 2. The question is searched as a vector in the vector database 3. The most relevant content from your website is found 4. This content is provided to an LLM along with the question 5. The LLM generates an answer based on your own content

The advantage over an LLM without RAG: the model answers with your information instead of its general training. This reduces hallucinations and ensures the chatbot accurately reflects your products, services, and positions.

RAG is the architecture arocom uses for AI-powered features in Drupal platforms.

Pinecone: Managed service, easy to get started, scales well. Data is processed in the cloud — data privacy check needed.

Weaviate: Open source, self-hostable, strong community. Good choice for businesses with their own infrastructure.

Milvus: Open source, high performance with large datasets. Established in the enterprise space.

pgvector (PostgreSQL): Vector search capabilities directly in PostgreSQL — the database many Drupal installations already use. For smaller datasets, the most pragmatic solution since no additional infrastructure is needed.

For Drupal projects, arocom often recommends starting with pgvector: no new database, no new infrastructure, fast proof of concept. As requirements grow, migration to a dedicated vector database is possible.

Semantic website search: Users find content by meaning instead of keywords. Search quality improves measurably — especially for websites with many technical terms or multilingual content.

AI chatbot with RAG: A chatbot that answers questions based on your own content. No generic answers, but information from your website, your documents, your FAQs.

Recommendation systems: "Similar articles," "matching products," or "related services" — based on content proximity instead of manual assignment.

Since 2012, arocom has developed Drupal platforms. Vector databases are a building block that makes existing installations intelligent — without rebuilding the architecture from scratch.