Vector Search

Unstructured Data Search

As the most rapidly expanding category of information, unstructured data is becoming increasingly important. It represents an estimated 80% to 90% of all new enterprise data according to Gartner, and its volume is growing three times faster than structured data. Vector search provides the ability to efficiently and effectively query it.

Contextual Interpretation

Vector search goes beyond simple keyword matching to interpret the underlying intent behind a query. Since words, phrases, and sentences can have substantially different meanings in different contexts, this enables delivery of more accurate and relevant results.

Semantic Search

Vectors capture the relationships and similarities between data items based on their features. This adds deeper meaning to the data and facilitates semantic search — the retrieval of similar or related information.

AI/ML Integration

Machine learning algorithms such as K-Nearest Neighbors (KNN) (precise but computationally expensive) and Approximate Nearest Neighbors (ANN) (balances efficiency and accuracy and is particularly useful for large datasets) power vector search, and vector search in turn powers modern AI/ML. It streamlines high-dimensional data storage and retrieval and makes it possible for businesses to build large language models (LLMs) and AI-enabled applications that successfully leverage massive volumes of unstructured data.

Vector Embedding

Generating and storing vector embeddings in a vector database is a much more time-consuming and resource-intensive process than is required to support keyword-based search in a relational database.

Vector Database Indexing

Vector indexes are organized and optimized for efficient search and retrieval. Given this, incrementally updating them with new vectors and removing obsolete information rapidly deteriorates their fast lookup capability. They must instead be periodically rebuilt from scratch, which can increase query latency.

Speed and Scalability

As the number of vectors in a vector database grows, the computational overhead required for similarity scoring increases and can create performance bottlenecks. Likewise, search accuracy may be negatively affected.

Metadata Filtering

Pure vector search is rarely able to fully meet the demands of modern applications in the real world, which often need to simultaneously retrieve related structured data as well. This requirement can be met by augmenting queries with metadata filtering, but there may be performance trade-offs.

Total Cost of Ownership (TCO)

The considerable compute demands of vector search — especially as a vector database expands — can make its overall cost higher than traditional keyword search technology. Sizable amounts of processing (both central processing units [CPUs] and graphics processing units [GPUs]) and memory resources are often required.

Anomaly Detection

Businesses can generate baseline vector embeddings that represent normal data patterns and leverage vector search to find and flag outliers. This capability is useful for recognizing anomalous behavior during activities such as fraud detection, security, and quality assurance in multiple domains.

Customer Segmentation

The ability to group customers based on shared characteristics such as behavior, demographics, and geography is key to optimizing user experiences and driving satisfaction and loyalty. Vector search enables companies to target their marketing, detect emotion and intent (sentiment analysis), and enhance predictive capabilities.

Know Your Customer (KYC)/Anti-Money Laundering (AML)

Organizations can incorporate similarity search into their existing KYC processes to facilitate biometric and facial recognition and automate verification of documents used for identification and proof of address, such as driver’s licenses, passports, and utility bills. This streamlines their workflows and supports efforts to comply with AML laws and regulations.

Loan Processing

Lenders can use vector search to identify and analyze patterns in unstructured data while processing loan applications online. This helps their systems make automated, data-informed credit decisions regarding affordability and risk and offer more tailored terms to customers.

Natural Language Processing (NLP)

Applications like chatbots, digital assistants, and translators originally had to rely on keyword matching to retrieve information, which greatly limited their capabilities and reliability. But by leveraging similarity search, modern NLP is able to effectively interpret user intent and deliver an appropriate response.

Payment Processing Fraud Detection

Vector search allows companies to augment existing fraud detection mechanisms and identify transactions that significantly deviate from expected behavior (e.g., originate from an unusual location or are for a large amount). They can then flag these anomalies for further investigation as potential fraudulent activity.

Real-Time Personalization

Presenting the right recommendation at the right time during an online experience boosts engagement and conversion. By representing profiles and search criteria along with data item features as vectors, businesses can use vector search to identify tailored offers as users shop, bank, or engage with content.

Handle Large Amounts of Data With Ease

Vector search equips you with the means to store, manage, query, and retrieve vectors and their associated metadata at speed and scale, no matter the size of the dataset. It also provides more sophisticated search capabilities, including the ability to retrieve related structured data via metadata filtering.

Enable New Classes of Search

Because vector search goes beyond traditional search techniques that rely on keyword matching and makes unstructured data accessible — including audio, images, and video — you can enable multimodal search options in your applications. This allows you to meet users where they are and deliver information that more fully meets their needs.

Drive Modernization and Engagement

By making it possible to efficiently handle large, high-dimensional datasets on the back end and enhance search functionality on the front end, vector search allows you to modernize business-critical applications while simplifying and optimizing the user experience.

Power Advanced AI/ML

Vector search is a key requirement for enterprise AI. Its high-dimensional data storage and retrieval capabilities empower you to build LLMs and AI-enabled applications that can leverage ever-growing volumes of unstructured data. And it is the foundation for future-forward AI techniques like retrieval-augmented generation (RAG), which enhances the accuracy and reliability of generative AI models with facts fetched from external sources.

Vector search’s integral role as an AI/ML enabler guarantees that solution providers will continue to invest heavily in its evolution and enhancement, expanding its capabilities to handle ever more complex and diverse datasets with ever-greater efficiency and accuracy. And since dynamic scalability is paramount to ensuring applications can handle increases in data volume and concurrency without impacting the user experience, the best solutions will scale linearly as more system resources are added, allowing businesses to accurately predict throughput and latency. This will lead to accelerated adoption for an increasing number of use cases, including medical diagnosis and research and autonomous systems like self-driving cars.

Vector search in Hazelcast Platform enables you to deploy a scalable, high-performance pipeline to process and query structured and unstructured data on a unified real-time data platform that combines distributed computing capabilities with an enterprise-grade fast data store.

The data pipeline ingests updates immediately from any source, using cluster compute to generate vector embeddings and an auto-update embedding model to ensure a continuously up-to-date vector database index for the most accurate similarity search.