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Using OpenSearch as a Vector Database

Traditional lexical search, based on term frequency models like blend term frequency models (BM25), is widely used and effective for many search applications. However, lexical search techniques require significant investment in time and expertise to tune them to account for the meaning or relevance of the terms searched.  Today, more and more developers want to embed semantic understanding into their search applications.

Enter machine learning (ML) embedding models that can encode the meaning and context of documents, images, and audio into vectors for similarity search. These embedded meanings can, in turn, be searched using the k-nearest neighbors (k-NN) functionality provided by OpenSearch.

Embeddings for powering text search functionality.

Embeddings for powering text search functionality.

Using OpenSearch as a vector database brings together the power of traditional search, analytics, and vector search in one complete package. OpenSearch’s vector database capabilities can accelerate artificial intelligence (AI) application development by reducing the effort for builders to operationalize, manage, and integrate AI-generated assets. Using the OpenSearch vector database, you can integrate your models, vectors, and metadata to power vector, lexical, and hybrid search and analytics, with performance and scalability built in.

What is a vector database?

Information comes in many forms: unstructured data, like text documents, rich media, and audio, and structured data, like geospatial coordinates, tables, and graphs. Innovations in AI have enabled the use of models, or embeddings, to encode all types of data into vectors. These vectors are data points in a high-dimensional space that capture the meaning and context of an asset, allowing search tools to find similar assets by searching for neighboring data points.

Vector databases allow you to store and index vectors and metadata, unlocking the ability to use low-latency queries to discover assets by degree of similarity. Typically powered by k-NN indexes built using algorithms like Hierarchical Navigable Small Worlds (HNSW) and Inverted File (IVF) System, vector databases augment k-NN functionality by providing a foundation for applications like data management, fault tolerance, resource access controls, and a query engine.

HNSW

HNSW is a graph-based algorithm that enables fast searches for similar vectors in large datasets. HNSW is a popular and effective index type for vector similarity search.

HNSW creates multiple layers of graphs, with each layer containing a subset of the vectors in the layer below. As you move up the layers, you can quickly find the general area of the graph. Once you’ve found the general area, you can move down the layers to perform a more local search.

IVF System

IVF is an indexing technique that speeds up similarity searches, particularly in large datasets. Instead of comparing a query vector against every vector in the database, IVF first groups vectors into clusters based on their similarity.

When a search is performed, the system quickly narrows down the search space by looking at only the most relevant clusters rather than the entire dataset. This significantly improves search efficiency while maintaining high accuracy.

OpenSearch provides an integrated  vector database that can support AI systems by serving as a knowledge base. This benefits AI applications like generative AI and natural language search by providing a long-term memory of AI-generated outputs. These outputs can be used to enhance information retrieval and analytics, improve efficiency and stability, and give generative AI models a broader and deeper pool of data from which to draw more accurate and truthful responses to queries.

Vector database benefits

Vector databases enable efficient storage, indexing, and retrieval of high-dimensional data, making them ideal for applications like semantic search, recommendation systems, and AI-powered analytics. By leveraging advanced similarity search algorithms, they deliver faster and more accurate results compared to traditional databases, enhancing performance for ML and natural language processing tasks.

Trusted in production

Vector databases provide developers with a mature search and analytics engine trusted in production by tens of thousands of users to power AI applications.

Proven at scale

Developers use vector databases to build stable applications with a data platform that can scale up to tens of billions of vectors, with low latency and high availability.

Open and flexible

Vector databases are supported by a myriad of open-source tools and integrations with popular open frameworks, with the option of using managed services from major cloud providers.

Build for the future

With vector databases, organizations can future-proof their AI applications with vector, lexical, and hybrid search, analytics, and observability capabilities, all in one software suite.

Vector Database Use Cases

The OpenSearch vector database can power a variety of applications—here are some examples of solutions you can build:

Search

Vector databases enhance search by enabling similarity-based retrieval, allowing users to find relevant results based on meaning, context, or patterns rather than exact keyword matches. The following are a few of the search algorithms and methods used by vector databases.

k-NN

k-NN is a widely-used algorithm for finding the most similar items to a given input by comparing their features. It works by measuring the “distance” between the query and other data points, where the distance is typically calculated using metrics like Euclidean distance or cosine similarity. The algorithm then identifies the ‘k’ closest data points (neighbors) to the query and makes a prediction based on their attributes, such as classification or regression.

Visual Search

Vector databases are ideal for handling complex, high-dimensional data like images. In image recognition, each image can be represented as a high-dimensional vector (based on features such as color, texture, and shape). Using vector databases, developers can create applications that allow users to take a photograph and search for similar images without having to manually tag images.

Semantic Search

Enhance search relevancy by powering vector search with text embedding models that capture semantic meaning and use hybrid scoring to BM25 for improved results. Vector databases are ideal for semantic search because they can represent words, phrases, or entire documents as high-dimensional vectors, capturing their meanings and contextual relationships rather than just exact word matches.

Multimodal search

Vector databases can be used to create state-of-the-art models that fuse and encode text, image, and audio inputs to generate more accurate digital fingerprints of rich media and enable more relevant search and insights.

Generative AI agents

Build intelligent agents with the power of generative AI while minimizing hallucinations by using OpenSearch to power retrieval augmented generation (RAG) workflows with large language models (LLMs). (Whether you refer to them as chatbots, automated conversation entities, question answering bots, or something else, OpenSearch’s vector database functionality can help them deliver better results).

Personalization

Vector databases enable personalization by analyzing user preferences and behavior to deliver tailored recommendations, content, and experiences based on contextual similarity.

Recommendation engine

Generate product and user embeddings using collaborative filtering techniques and use OpenSearch to power your recommendation engine.

User-level content targeting

Personalize web pages by using OpenSearch to retrieve content ranked by user propensities using embeddings trained on user interactions.

Predicting customer lifetime value (LTV)

Predict customer lifetime value (LTV) by identifying customers with similar purchase patterns and demographics in a dataset, essentially predicting a new customer’s LTV based on the average LTV of their closest “neighbors” in the data.

Churn Prediction

Churn prediction is the process of using data analysis and ML to identify customers who are likely to stop using a product or service. You can then target these customers to re-engage or entice them to purchase.

Data Quality

Vector databases enhance data quality by identifying duplicates, detecting anomalies, and clustering similar data points, ensuring more accurate and consistent information.

Automate pattern matching and deduplication

Vector databases enable similarity search to automate pattern recognition and identify duplicates, streamlining data quality processes.

Vector database engine

Vector databases optimize AI and ML workflows by efficiently storing and retrieving high-dimensional feature embeddings, enabling faster and more accurate model inference. They also enhance applications like natural language processing and computer vision by facilitating similarity search, anomaly detection, and real-time decision-making.

Data and ML platforms

Build your platform with an integrated, Apache 2.0-licensed vector database that provides a reliable and scalable solution to operationalize embeddings and power vector search.

A world of integrations

OpenSearch seamlessly integrates with leading LLMs and ML technologies used by organizations today, enhancing search, discovery, and predictive analytics applications. Here are some key integrations:

Getting started with OpenSearch Vector Database

The following resources from users, application developers, and other members of the OpenSearch community showcase how OpenSearch can be deployed as a powerful vector database solution.

Get started with OpenSearch’s vector database capabilities by:

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