In a previous blog post, we built a simple retrieval pipeline where we chunked the documentation, embedded the text, and queried PostgreSQL with vector similarity to find relevant passages. Then in the most recent post, we shifted gears and focused on the ingestion side and looked at extraction of facets such as version, operating system, document type, and component, and stored them alongside...
In my previous blog post, we built the simplest possible Retrieval Augmented Generation (RAG) pipeline inside PostgreSQL. We embedded our manuals, stored those vectors in a table, ran a similarity search, and handed the top 5 results straight to a Large Language Model. The result was encouraging, we could already see the model drawing on our content rather than inventing information. But as with...
How do you turn a jumble of vector embeddings into clear, human-readable answers? In this post, we walk through the journey from raw embeddings to precise, context-aware responses—bridging the gap between data and understanding.
When working with embeddings in PostgreSQL, particularly for use cases like semantic search, recommendation systems, or retrieval-augmented generation (RAG), how you prepare and ingest data matters just as much as how you query it.
At first glance, storing and querying embeddings in PostgreSQL may seem impractical—but with the right setup, it’s both efficient and effective. This post covers how to design your schema, store vectors properly, and perform fast similarity searches without the usual headaches.
Embeddings are the foundation of vector search, allowing us to represent meaning-rich content like documents or queries as numerical vectors. But to use them effectively, it’s essential to understand what’s actually being embedded—whether that’s individual words, full sentences, or larger chunks of text.
As vector search becomes a foundational feature in modern applications—from semantic search and recommendation engines to AI-driven insights—developers are increasingly adopting PostgreSQL with the pgvector extension. However, one concept often creates confusion: the difference between similarity and distance.
