If the term “vector database” is new to you, don’t worry you’re not alone. While these databases have been around for a few years, it’s only recently that they’ve garnered the attention of the broader developer community, thanks in part to the release of advanced language models like ChatGPT. Since late 2022, the public has been gaining insights into the capabilities of Large Language Models (LLMs). And, developers have come to realize that vector databases can significantly enhance these models.
The excitement surrounding vector databases is closely linked to the surge in interest in generative AI. Organizations across various sectors, from healthcare to finance, retail to government agencies, are keen on leveraging generative AI, with the potential to inject trillions into the global economy, according to a McKinsey report. At the heart of this transformative technology lies the mathematical concept of a vector. Through vectorization and the power of large language models, generative AI achieves groundbreaking potential.
Keep reading as this article aims to discuss what a vector database is, how it works, its advantages, and many more things along with it.
What is a Vector?
To understand the importance of vector databases, it’s quite necessary to grasp the concept of a vector first.
In mathematics and physics, a vector is a quantity that has both magnitude (or size) and direction. Breaking down a vector involves understanding its components. For example, in a two-dimensional space, a vector possesses both an X (horizontal) and Y (vertical) component.
In the field of data science and machine learning, a vector represents an ordered list or sequence of numbers representing data. This data could span various forms, including unstructured data, which lacks a predefined data model or schema — spanning from textual content to images, audio files to videos. Typically, a vector is depicted as an array or list of numbers, where each number signifies a distinct feature or attribute of the data.
What is a Vector Database?
A vector database stands out as a particular kind of database designed to store data as high-dimensional vectors, which are mathematical representations of features or attributes. Each vector has a specific number of dimensions, ranging from tens to thousands. It depends on the complexity and detail of the data at hand. Typically, these vectors are generated by applying transformation or embedding functions to raw data, such as text, images, audio, video, and more. The embedding function could be rooted in various methodologies, including machine learning models, word embeddings, or feature extraction algorithms.
The primary advantage of a vector database lies in its ability to simplify quick and accurate similarity searches and data retrievals based on vector distance or similarity. Instead of relying on traditional methods involving exact matches or predefined criteria, a vector database enables users to pinpoint the most similar or relevant data based on semantic or contextual meaning.
Consider this: you can utilize a vector database to:
1. Identify images similar to a given image based on visual content and style.
2. Locate documents akin to a given document based on topic and sentiment.
3. Find products resembling a given product based on features and ratings.
To execute similarity searches and retrievals in a vector database, one must use a query vector representing the desired information or criteria. This vector can either stem from the same type of data as the stored vectors (e.g., using an image as a query for an image database) or differ (e.g., using text as a query for an image database). Subsequently, a similarity measure is used to calculate the closeness or distance between two vectors in the vector space. This measure can be based on various metrics like cosine similarity, Euclidean distance, Hamming distance, or the Jaccard index.
The outcome of such searches and retrievals typically yields a ranked list of vectors with the highest similarity scores to the query vector. Subsequently, users can access the corresponding raw data linked with each vector from the original source or index.
How Vector Databases Work
Vector databases operate through algorithms that index and query vector embeddings. Approximate nearest neighbour (ANN) search techniques, such as hashing, quantization, or graph-based search, enable efficient searching through high-dimensional vectors.
The vector database pipeline involves:
- Indexing: Mapping vectors to a data structure using techniques like hashing, quantization, or graph-based methods for faster searching.
- Hashing: Utilizing algorithms like locality-sensitive hashing (LSH) for speedy and approximate nearest neighbour search.
- Quantization: Breaking vectors into smaller parts, representing them with codes, and matching them against a codebook for similarity.
- Graph-based: Employing algorithms like Hierarchical Navigable Small World (HNSW) to represent vectors as nodes in hierarchical graphs.
Querying involves comparing indexed vectors to a query vector using similarity measures like cosine similarity, Euclidean distance, or dot product similarity. Post-processing may occur, re-ranking nearest neighbours based on a different similarity measure or filtering results based on metadata.
What Are the Advantages of Vector Databases
The growing interest in vector databases is attributed to their tangible benefits for enterprise AI applications:
1.) Speed and Performance: Vector databases, employing various indexing techniques, ensure faster searches. The combination of vector indexing and distance-calculating algorithms, such as nearest neighbour search, proves particularly valuable when dealing with large datasets.
2.) Scalability: Vector databases can efficiently store and manage massive amounts of unstructured data by scaling horizontally. This ensures optimal performance as both query demands and data volumes increase.
3.) Cost of Ownership: Vector databases offer a cost-effective alternative to training models from scratch or fine-tuning them. This reduces costs and speeds up inferencing processes.
4.) Flexibility: Tailored to handle multi-dimensional data, vector databases accommodate various data types, making them versatile for applications ranging from semantic search to conversational AI.
5.) Long-Term Memory of LLMs: Vector databases support the enhancement of general-purpose models like IBM Watsonx.ai’s Granite series, Meta’s Llama-2, or Google’s Flan models. By incorporating user-specific data, organizations can optimize the output of models critical to retrieval augmented generation.
6.) Data Management Components: Vector databases offer built-in features for easy updating and insertion of new unstructured data.
Conclusion
At last, vector databases are specifically designed to handle the difficulties caused by managing vector embeddings in real-world production settings. Their improved scalability, effective data management, real-time updates, and smooth interaction with larger data processing ecosystems set them apart from conventional scalar-based databases and standalone vector indexes. Vector databases are becoming more and more important as the AI landscape gets more and more dominated by them, especially for businesses looking to use generative AI.
