1. Description
With the rapid adoption of artificial intelligence and the innovation happening around large language models , we need, at the center of it all, the ability to take large amounts of data, contextualize it, process it, and make it meaningfully searchable.
Generative AI processes and applications built to natively incorporate generative AI capabilities rely on the ability to access vector embeddings, a data type that provides the semantics required for AI to have long-term memory processing similar to ours , allowing it to exploit and invoke information for complex task execution.
Vector embeddings are data representations used and generated by AI models such as LLM to make complex decisions. Just like memory in the human brain, there are complexities, dimensions, patterns, and relationships, all of which need to be stored and represented as part of the underlying structure, making all of them difficult to manage.
This is why for AI workloads we need a purpose-built database (or brain), designed for highly scalable access, and built specifically to store and access these vector embeddings. Vector databases like Datastax Astra DB (built on Apache Cassandra) are designed to provide optimized storage and data access capabilities for embedding.
A vector database is a database specifically designed for storing and querying high-dimensional vectors. A vector is a mathematical representation of an object or data point in a multidimensional space, where each dimension corresponds to a specific characteristic or attribute.
This is ultimately the advantage and power of vector databases. It is the ability to store and retrieve large amounts of data as vectors in a multi-dimensional space, culminating in vector search, which is used by artificial intelligence processes to provide correlations of data by comparing the mathematical embedding or encoding of the data with search parameters and Returns the same results as the query track. This allows for a wider range of results than traditional keyword searches, and more data can be considered when adding or learning new data.
In this two-minute video, Dr. Charna Parkey explains three reasons for using a vector database.
Perhaps the most well-known example is a recommendation engine that takes a user's query and recommends to them other content that may be of interest to them. Let's say I'm watching my favorite streaming service and I'm watching a sci-fi western themed show. With vector search, I can easily and quickly recommend other nearest neighbor matching shows or movies using vector search of the entire media library without having to tag each media with a topic. Additionally, I may get other nearest neighbor results for other topics for me. Might not be a specific query but is relevant to the viewing patterns of shows I'm interested in.
Unlike vector indexes, which only improve the search and retrieval of vector embeddings, vector databases provide a well-known way to manage large amounts of data at scale while being specifically designed to handle the complexities of vector embeddings . Vector databases bring all the features of traditional databases with specific optimizations for storing vector embeddings, while providing the specialization required for high-performance access to embeddings that traditional scalar and relational databases lack. Ultimately, vector databases natively implement storage and the ability to retrieve large amounts of data to enable vector search capabilities .
2. How does the vector database work?
For generative AI to work, it requires a brain to efficiently access all embeddings in real time to form insights, perform complex data analysis, and make generative predictions about what is being asked. Think about how you process information and memory. One of the main ways we process memories is by comparing them to other events that have already happened. For example, we know not to put our hands in boiling water because we have been burned by boiling water in the past, or we know not to eat a specific food because we have memories of how that food affected us. This is how vector databases work, they align data (memory) for fast mathematical comparison so that a general AI model can find the most likely outcome. For example, something like ChatGPT needs to be able to compare what logically completes a thought or sentence by quickly and efficiently comparing all the different options for a given query and presenting highly accurate and responsive results.
The challenge is that generative AI cannot do this with traditional scalar and relational approaches, which are slow, rigid and narrowly focused. Generative AI requires a database to store mathematical representations, its brain is designed to process and provide extremely high performance, scalability, and adaptability to make the most of all the available data it has, and it requires something designed to be more human-like Something in the brain that is capable of storing memory imprints and quickly accessing, correlating, and processing those imprints as needed.
With vector databases, we are able to quickly load and store events as embeddings and use our vector databases as the brains that power our AI models, providing contextual information, long-term memory retrieval, semantic data association, and more.
To achieve efficient similarity searches, vector databases employ specialized indexing structures and algorithms, such as tree-based structures (e.g., kd-tree), graph-based structures (e.g., k-nearest neighbor graphs), or hashing techniques (e.g., locality-sensitive hashing). These indexing methods help organize and partition vectors to facilitate quick retrieval of similar vectors.
In vector databases, vectors are typically stored together with their associated metadata such as tags, identifiers, or any other relevant information. The database is optimized to efficiently store, retrieve, and query vectors based on their similarity or distance to other vectors.
3. What are the advantages of vector database?
Unlike traditional databases that store multiple standard data types in rows and columns, such as strings, numbers, and other scalar data types, vector databases introduce a new data type, vector, and build optimizations around this data type , specifically designed to enable fast storage, retrieval, and nearest neighbor search semantics. In a traditional database, rows in the database are queried using indexes or key-value pairs that find exact matches and return rows related to those queries.
Traditional relational databases are optimized to provide vertical scalability around structured data, while traditional NOSQL databases provide horizontal scalability for unstructured data. Solutions like Apache Cassandra are designed to provide optimizations around structured and unstructured data with the added ability to store vector embeddings, and solutions like Datastax Astra DB are ideal for both traditional and AI-based storage models .
One of the biggest differences with vector databases is that traditional models are designed to provide precise results, but with vector databases, the data is stored as a series of floating point numbers, and searching and matching the data does not necessarily mean an exact match, but can be a lookup consistent with our The operation of querying the most similar results.
Vector databases use a number of different algorithms that all engage in approximate nearest neighbor (ANN) search and allow large amounts of relevant information to be retrieved quickly and efficiently. This is where purpose-built vector databases like DataStax Astra DB offer significant advantages for generative AI applications. Traditional databases simply cannot scale to the amount of high-dimensional data that needs to be searched. AI applications require the ability to store, retrieve, and query closely related data in highly distributed, highly flexible solutions.
4. How vector databases help improve artificial intelligence
One of the biggest benefits that vector databases bring to AI is the ability to leverage existing models across large data sets by efficiently accessing and retrieving data for real-time operations. Vector databases provide the basis for memory recall, the same memory recall we use in organic brains. Through vector databases, artificial intelligence is divided into cognitive functions ( LLM ), memory recall (vector databases), specialized memory engrams and encodings (vector embeddings), and neural pathways (data pipelines).
These processes work together to enable AI to learn, grow, and access information seamlessly. The vector database holds all memory engrams and provides cognitive functions with the ability to recall information that triggered similar experiences. Just like our human memories, when one event occurs, our brains recall other events that evoke the same feelings of joy, sadness, fear, or hope.
With vector databases, generative AI processes are able to access large amounts of data, correlate that data in efficient ways, and use that data to make contextual decisions about what happens next. When fed into the nervous system, data pipelines allow for the storage and creation of new memories as they are made. With access, AI models are able to adaptively learn and grow by leveraging workflows that provide historical, analytical, or real-time information.
Whether you are building recommendation systems, image processing systems, or anomaly detection, at the core of all these AI capabilities, you need an efficient, optimized vector database such as Astra DB . Astra DB is designed and built to power the cognitive processes of artificial intelligence, which can stream data as data pipelines from multiple sources, such as Astra Streams , and use these to evolve and learn to deliver faster , more efficient results.
5. Use DataStax to start using vector databases
As generative AI rapidly grows and accelerates across all industries, we need a purpose-built way to store the vast amounts of data used to drive contextual decisions. Vector databases are purpose-built for this task and provide specialized solutions to the challenges of managing vector embeddings for AI. This is where the real power of vector databases lies, the ability to enable static and dynamic contextual data to provide core memory recall for AI processing.
While this sounds complicated, Vector Search on DataStax Astra DB solves all of these problems for you with a fully integrated solution that provides all the pieces you need for contextual data. From neural systems built on data pipelines to embeddings, to core in-memory storage and retrieval, access and processing, to easy-to-use cloud platforms. Try it now for free .