Getting Started
To learn more about OpenSearch search tools and start building innovative ML and AI solutions, visit the vector search documentation.
Machine Learning and AI
Build flexible, scalable, and future-proof machine learning and artificial intelligence applications
The artificial intelligence (AI) revolution 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, this framework unlocks powerful ML and AI tooling and capabilities.
OpenSearch brings traditional search, analytics, and vector search together in one complete 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 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 a greater pool of data using OpenSearch.
Power AI applications with a mature search and analytics engine that tens of thousands of users trust in production.
Build stable applications with a data platform proven to scale to up to tens of billions of vectors with low latency and high availability.
Take advantage of open-source integrations into popular open frameworks and use managed services from major cloud providers.
Future-proof your AI applications with vector, lexical, and hybrid search, 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
Create semantic search applications by running human-language instead of vector-based queries.
Extensible ML framework
Power neural search through integrated models that share a unified API, whether they run on-cluster or externally.
Anomaly detection
Detect anomalies in your OpenSearch data automatically, in near real time, using the Random Cut Forest (RCF) algorithm.
Efficient filtering
Intelligently evaluate strategies to optimize between recall and latency.
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 visual, semantic, and multimodal search using models that work best for your key scenarios. |
| Generative AI agents | Use generative AI to build intelligent agents that deliver better results for chatbots or automated conversation entities. |
| Recommendation engine | Generate product and user embeddings using 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 | Enhance your data quality processes by using similarity search to automate pattern matching and duplication discovery in data. |
| Data and ML platforms | Operationalize embeddings and power vector search by building your platform on an integrated, Apache 2.0-licensed database. |