Artificial intelligence (AI) has transformed process optimization, analytics, and customer experiences. Now, machine learning (ML) models are powering the next leap forward through vector search. By embedding models that can encode the meaning and context of documents, images, and audio into vectors for similarity-driven searches, vector search unlocks powerful ML and AI tooling and capabilities.
OpenSearch brings traditional search, analytics, and vector search together in one solution. By reducing the effort you need to operationalize, manage, and integrate AI-generated assets, OpenSearch’s vector database capabilities accelerate ML and AI application development. Built-in performance and scalability allow you to power vector, lexical, and hybrid search and analytics across all your models, vectors, and metadata. Enhance information retrieval and analytics, improve efficiency and stability, and give your generative AI models the resources to deliver more accurate and intelligent responses.
Proven in production
Power AI applications with a mature search and analytics engine that tens of thousands of users trust in production.
Stable at scale
Build stable applications with a data platform proven to scale to up to tens of billions of vectors with low latency and high availability.
Open and flexible
Take advantage of open-source integrations into popular open frameworks and use managed services from major cloud providers.
Built for the future
Prepare your AI applications for future innovations with vector, lexical, and hybrid search, as well as analytics and observability capabilities, all in one software suite.
Key Features
Vector database
Use low-latency queries to discover assets by degree of similarity through k-nearest neighbors (k-NN) functionality.
Neural search
Improve accuracy and relevancy for human language queries through searches that consider context and relationships.
Extensible ML framework
Power neural search through OpenSearch’s pre-trained models, upload your own, or connect to externally hosted models.
Anomaly detection
Automatically detect unusual behavior in your data in near real time using the Random Cut Forest (RCF) algorithm.
Efficient filtering
Apply intelligent strategies to optimize recall and latency for vector search.
Vector quantization support
Improve performance and cost by reducing your index size and query latency with minimal impact on recall.
Use Cases
| Machine Learning and AI | |
|---|---|
| Search | Power multimodal search and accommodate different data types using the models that work best for your key scenarios. |
| Generative AI agents | Use large language models (LLMs) and generative AI to build intelligent agents that deliver better results for chatbots or automated conversation entities. |
| Recommendation engine | Generate product and user embeddings through collaborative filtering techniques. |
| User-level content targeting | Personalize web pages by retrieving content ranked by user propensities through embeddings trained on user interactions. |
| Automated pattern matching and de-duplication | Personalize web pages by retrieving content ranked by user propensities with embeddings trained on human interactions. |
| Data and ML platforms | Operationalize embeddings and power vector search by building your platform on an integrated, Apache 2.0-licensed database. |
Getting Started
To learn more about OpenSearch search tools and start building innovative ML and AI solutions, visit the vector search documentation.
