What is Vector Index?

What Is a Vector Index?

A Vector Index is a data structure specifically designed to perform fast similarity searches through large collections of vectors. In AI, a vector is a list of numbers that represents the meaning or characteristics of a piece of data, whether that is a sentence of text, an image, a product description, or a customer profile. These numerical representations, often called embeddings, capture the essence of the data in a way that machines can compare and search.

To understand this concept, imagine a massive library where instead of organising books by title or author, you organise them by their meaning and content. When you search for a book about "growing tropical fruits in humid climates," the system finds not just exact keyword matches but books that are conceptually similar, even if they use completely different words. A Vector Index is the technology that makes this kind of meaning-based search possible at scale.

How Vector Indexes Work

The challenge that Vector Indexes solve is one of speed. Comparing a search query against every single item in a database one by one, known as a brute-force search, is accurate but impractical when you have millions or billions of items. A single comparison might take microseconds, but performing billions of them makes the search unacceptably slow.

Vector Indexes solve this by organising vectors into structures that allow the search algorithm to quickly narrow down the most likely matches without checking every single entry. The main approaches include:

• Tree-based indexes: Divide the vector space into regions using hierarchical tree structures. The search navigates the tree to find the relevant region rather than scanning everything.
• Graph-based indexes (HNSW): Build a network of connections between similar vectors. The search hops along connections from node to node, quickly converging on the most similar vectors. This approach, known as Hierarchical Navigable Small World, is one of the most popular due to its excellent balance of speed and accuracy.
• Quantisation-based indexes (IVF): Compress and cluster vectors into groups. The search first identifies the most relevant clusters, then performs detailed comparisons only within those clusters.

Each approach involves a trade-off between search speed, accuracy, and memory usage. Approximate nearest neighbour search, which returns results that are very close to the best match rather than guaranteed perfect, is the standard practice because the small accuracy trade-off enables dramatically faster performance.

Why Vector Indexes Matter for Business

For businesses in Southeast Asia, Vector Indexes power many of the AI applications that drive customer engagement and operational efficiency:

• Semantic search: Instead of relying on exact keyword matching, vector-powered search understands what users mean. A customer searching for "affordable family holiday in Bali" on a travel platform finds relevant results even if listings use terms like "budget getaway" or "family-friendly resort."
• Recommendation engines: E-commerce platforms use Vector Indexes to find products similar to what a customer is viewing or has purchased, driving cross-selling and increased basket sizes.
• Retrieval-Augmented Generation (RAG): One of the most important current applications. When combined with large language models, Vector Indexes enable AI chatbots and assistants to search through your company's documents, policies, and knowledge base to provide accurate, grounded answers.
• Fraud detection: Financial services companies use Vector Indexes to find transactions that are similar to known fraudulent patterns, enabling real-time fraud screening.

Vector Databases and Tools

Several purpose-built vector databases have emerged to provide production-grade Vector Index capabilities:

• Pinecone: A fully managed vector database popular for its simplicity and scalability
• Weaviate: An open-source vector database with built-in machine learning capabilities
• Milvus: An open-source vector database designed for billion-scale similarity search
• pgvector: An extension for PostgreSQL that adds vector search capabilities to your existing database

For businesses already running PostgreSQL, pgvector offers a low-friction entry point. For larger-scale deployments or applications where vector search is the primary workload, dedicated vector databases offer better performance and more advanced features.

Getting Started with Vector Indexes

For organisations building AI-powered search or recommendation systems:

1. Define your use case clearly. Semantic search, recommendations, and RAG each have different requirements for accuracy, speed, and scale.
2. Choose an embedding model that converts your data into vectors. Popular options include OpenAI embeddings for text and CLIP for images.
3. Start with a managed service like Pinecone or a cloud-hosted vector database to avoid operational complexity.
4. Benchmark with your actual data to determine the right index type and configuration for your specific performance requirements.
5. Plan for scale by understanding how your vector collection will grow and testing performance at projected future volumes.

Vector Indexes are a foundational technology for the current generation of AI applications. As businesses across ASEAN adopt retrieval-augmented generation, semantic search, and personalisation, the ability to search through millions of data points by meaning rather than keywords becomes a critical competitive capability.