Bit of the Basics of Vector Databases

As the world of data changes rapidly, many companies struggle to keep pace. Experts forecast that by 2025, unstructured data will constitute more than 80% of all data. However, a Deloitte survey reveals that only 18% of organizations can analyse such data. This preparation gap implies that most companies lack the suitable tools to effectively utilize a significant portion of their data.

Traditional databases have proven effective in managing simple data types like keywords, metrics, strings, and structured objects like JSON. These databases and basic search engines help answer straightforward questions, such as which documents contain a particular set of words or which items meet specific criteria.

Understanding Vector Databases

Vector databases are specialized tools that index and store vector embeddings for fast retrieval and similarity search. These databases offer functionalities like traditional relational databases, such as CRUD operations (create, read, update, and delete), data persistence, and metadata filtering. This combination of vector search and database operations makes vector databases a powerful tool.

Several leading tech platforms already leverage vector databases. For example, Spotify uses them for personalized music recommendations, Amazon for product suggestions, and YouTube for relevant content recommendations based on viewing history.

Vector databases can be built and maintained in-house using open-source projects or outsourced to managed services. This flexibility allows organizations to choose the approach that best suits their needs.

The Power of Vectors in Machine Learning

Machine learning models offer a solution by creating numerical representations of complex data types. These numerical representations, known as vector embeddings, are designed to correspond semantically similar items to nearby points in a high-dimensional space. The proximity of these representations is determined by the angle or distance between them.

Vector embeddings allow us to communicate with machines in a more human-like manner. For instance, when applied to text, users can ask questions in natural language. The query is then converted into a vector using the same model that transformed the search items into vectors. This query vector is compared to all object vectors to find similar matches. Similarly, image or audio files can be converted into vectors for matching based on mathematical similarity.

Several vector transformer models are available today, making it easier to convert your data into vectors. BERT and Word2Vec are models that focus on embedding text. Image embedding has models such as VGG and Inception. Even audio recordings can be transformed into vectors using image embedding transformations over the audio frequency's visual representation.

Applications of Vector Databases

Vector databases are primarily used for similarity search or "vector search", which involves comparing the proximity of multiple vectors in the index to a search query or subject item. Some typical applications of vector databases include:

• Semantic search: Natural language processing (NLP) models convert text and documents into vector embeddings, allowing users to query using natural language without knowing specific keywords.
• Similarity search for audio, video, images, and other unstructured data: Users can query the database using similar objects and the same machine-learning model to find similar matches.
• Deduplication and record matching: Vector databases can use a machine learning model to determine similarity and remove duplicate items from a catalogue.
• Recommendation and ranking engines: Vector databases can recommend similar products, content, or services.
• Anomaly detection: Vector databases can identify outliers significantly different from all other objects, which is helpful for IT operations, security threat assessments, and fraud detection.

Key Features of Vector Databases

Vector databases offer several vital functionalities:

• Vector Indexing and Similarity Search: Vector databases use algorithms designed to index and retrieve vectors efficiently.
• Single-stage filtering: By merging vector and metadata indexes into a single index, vector databases balance pre-filtering accuracy and post-filtering speed.
• API: An Application Programming Interface (API) makes it easy for developers and applications to interact with the vector database.
• Hybrid storage: Vector databases use a combination of in-memory and on-disk storage to optimize performance and storage costs.

As unstructured data proliferates, more than traditional databases is needed. Vector databases offer a promising solution for organizing, storing, and analysing this complex data, unlocking valuable insights to solve complex problems.