AI and machine learning models deal with vast amounts of unstructured data. It’s important to have reliable methods for storing, searching, and accessing this data efficiently. Data scientists, AI researchers and developers rely on vector databases to store and analyse these complex datasets.
Traditional databases store data in rows, columns, and tables to manage the stored data. They're good at handling structured data, with well-defined schemas that facilitate data organisation and querying but don't capture the nuanced, multidimensional nature of unstructured data. That is where the vector database comes in, which represents data as mathematical vector embeddings.
Unlike traditional databases, vector databases work with vectors and handle unstructured data like embeddings. They compare a query vector with stored vectors to identify similarities rather than exact matches.
Vector databases use efficient similarity searches rather than exact matching, allowing them to find semantically similar items. These are especially useful for data engineering, machine learning, natural language processing, and computer vision, where data is complex and less structured.
Vector databases are used for similarity search, semantic search, multi-modal search, recommendations engines, large language models (LLMs) and object detection to efficiently process and search high dimensional data. For instance:
There are many vector databases available, including Qdrant, Pinecone, Milvus, Chroma, Weaviate, and others. Each has its own advantages, limitations, and best use cases. Below is a comparison of popular vector databases to help you decide which one fits your needs.
Vector database | When to use | Key features |
---|---|---|
Pinecone | When you want a fully managed service that’s easy to set up for text or semantic search | Managed service, very user-friendly, but can get costly |
Milvus | For large-scale projects with images, videos, or massive data, especially if you want to self-host | Open source vector database, and scalable, but requires more setup and maintenance |
Chroma | Ideal for small projects or quick local testing | Lightweight and simple to use, but not designed for huge datasets |
Weaviate | When you need hybrid search combining vectors and metadata filtering, with cloud or open-source solutions | Supports complex queries but requires schema planning |
Faiss | Best for research or custom use cases needing fast similarity search libraries | It’s a library, not a full database; you must handle storage and management |
Elasticsearch | When you want combined traditional keyword and vector search in one system | Powerful but complex and resource-intensive |
Qdrant | For fast, real-time search with filtering needs and easy self-hosting | High performance, strong filtering features, open-source |
The growth of artificial intelligence and large language models has increased the demand for vector databases. Several key trends are expected to shape their future development:
Vector databases play a critical role in AI-driven applications, enabling efficient storage and querying of high-dimensional vector data.
Security and reliability will be major focus areas as well. Future vector databases will come with stronger cybersecurity protections to keep data safe and systems running smoothly, even if there are hardware failures or cyber threats. Overall, vector databases are set to play a major role in building more secure systems across many industries.
Choosing the right vector database for your work involves carefully assessing your specific needs, the features each database provides, and how your data may grow over time. It’s important to consider factors such as scalability and community support.
This thoughtful evaluation will help you select a database that not only meets your requirements but also enhances your application development overall efficiency. Remember, your choice of database is a critical factor in your project’s success, so use these guidelines to make an informed decision.
Examples of vector databases that provide vector search capabilities include Pinecone, Weaviate, and Milvus. These databases are designed to efficiently handle high dimensional vectors generated by embedding models for tasks like similarity search.
The main difference is that SQL databases are optimised for structured data and relational queries, whereas vector databases offer vector search support to efficiently store and query high-dimensional vectors often produced by neural networks. Vector databases use specialised approximate nearest neighbour algorithms to speed up similarity searches.
MongoDB is a NoSQL document store and does not natively provide vector search capabilities. While it supports flexible document storage, it lacks built-in features for managing high-dimensional vectors or performing approximate nearest neighbour searches.
No, SQL databases do not specialise in storing or querying high-dimensional vectors. They are not designed for vector search support or handling embeddings generated by neural network models, which require efficient storage and indexing methods unique to vector databases.
Vector data refers to numerical representations of information as points in multidimensional space, often generated by embedding models. These vectors capture semantic meaning and are used in vector search capabilities for tasks like image or text similarity.
A vector index is a data structure that enables fast and efficient storage of high-dimensional vectors to support approximate nearest neighbour searches. It plays a crucial role in vector search support by organising data for rapid retrieval based on similarity.
Vector search is a method of retrieving data based on the similarity of vector embeddings, which represent unstructured content like text, images, or audio.
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