Vector databases store and manage high-dimensional data representations (vectors) that power AI applications like recommendation systems, semantic search, and anomaly detection.
Vector databases store and manage high-dimensional data representations (vectors) that power AI applications like recommendation systems, semantic search, and anomaly detection. Unlike traditional databases optimized for structured queries, vector databases enable efficient similarity searches using algorithms like k-nearest neighbours (k-NN).
AI and machine learning models rely on embeddings—vector representations of data—to process natural language, images, and other complex data types. Vector databases make it possible to store, retrieve, and compare these embeddings at scale, unlocking AI-driven functionalities such as personalised recommendations (e.g., Netflix, Spotify); image and video recognition, and semantic search for text and documents.
Dedicated vector databases such as FAISS, Pinecone, and Weaviate offer highly optimised vector storage and retrieval tailored for large-scale AI applications.
These solutions are engineered for high-speed retrieval, employing approximate nearest neighbour (ANN) search techniques, and provide built-in optimisations for handling massive datasets. Additionally, they feature scalable infrastructures specifically designed to support AI workloads. However, adopting these specialised solutions often involves additional infrastructure and maintenance overhead, and they may require learning new database systems.
For teams already using Postgres, integrating pgvector allows vector search capabilities within existing relational databases. This approach avoids the complexity of maintaining a separate vector database while leveraging Postgres’s robust query capabilities.
pgvector is a Postgres extension that adds support for vector data types and similarity search. It enables AI-powered applications to use vector search while keeping everything within the familiar Postgres ecosystem.
You can compile and install the extension on Postgres 13+ from its GitHub repository and it comes preinstalled with Postgres.app and many hosted providers.
Assuming you're up and running with Postgres, in a terminal:
CREATE EXTENSION IF NOT EXISTS vector;
Creating a SQL table with vector embeddings:
CREATE TABLE items (
id SERIAL PRIMARY KEY,
embedding vector(1536) -- Example: OpenAI embeddings dimension );
Inserting data (again in SQL):
INSERT INTO items (embedding) VALUES ('[0.1, 0.2, 0.3, ..., 0.9]');
Running similarity queries in SQL:
SELECT id, embedding <-> '[0.1, 0.2, 0.3, ..., 0.9]' AS distance FROM items ORDER BY distance ASC LIMIT 5;
Vector databases open up a world of possibilities for real-world applications. For instance, they enable AI-powered search capabilities in e-commerce, knowledge bases, and support chatbots.
Recommendation systems can be enhanced to match users with products, music, or films based on similarity scores.
In financial services, vector databases can aid in fraud detection by identifying anomalies through vector-based comparisons. Additionally, they enable image and video recognition by storing embeddings of visual data for similarity-based retrieval.
By pairing vector search with AI models, developers can create smarter applications that go beyond keyword-based queries to truly understand intent and context.
Hybrid search combines the strengths of traditional keyword-based search with vector search. This approach allows applications to deliver more comprehensive and relevant results by leveraging both exact matches and semantic understanding. In practice, hybrid search can enhance user experiences in applications like document retrieval, where users benefit from results that consider both the context and the specific terms of their queries.
Why Learn Vector Databases?
Mastering vector databases equips developers with essential AI skills that are in high demand. Understanding how to store and query vector embeddings allows you to build smarter applications with minimal infrastructure overhead.
Begin your journey by installing pgvector and experimenting with running similarity queries. Dive into embedding models like OpenAI's CLIP or BERT to gain hands-on experience. Consider building a semantic search engine powered by Postgres and pgvector to solidify your understanding.
If you’re looking for more structured guidance, our two-day workshop offers comprehensive support. By the end of this immersive experience, you'll be well-equipped to apply these skills to real-world challenges, enhancing your capability to develop cutting-edge AI applications. Whether you're looking to advance your career or bring innovative solutions to your organisation, mastering vector databases is a significant step forward.
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