Vector search powers the next generation of search experiences
Vector search provides the foundation for implementing semantic search for text or similarity search for images, videos, or audio. Retrieve relevant context of your data by relying on machine learning to encode your data, and apply generative AI to create more human-like experiences.
Find what you mean
Search based on meaning, not just matching keywords. Semantic search applies to both internal and external use cases - internally empowering your employees to find relevant information faster from your knowledge bases, externally increasing the relevance of search results.
Perform similarity search
Find visually similar images, video clips, and audio that match specific styles or samples. Similarity search enables applications such as reverse image search, image recommendation, and video and audio matching.
Model user behaviors and profiles, and find items similar to the ones a user has shown interest in. This lets you personalize recommendations for consumer products, movies, music, and more, and dynamically adapt any user experience to individual or cohort of users.
Natural language processing
Use NLP effortlessly
Modern natural language processing (NLP) lets you enrich search experiences. Use vector search to retrieve a configurable subset of relevant documents. In a second step, identify the paragraph answering a specific question using a question-answer transformer, extract named entities (NER), or determine emotional content by applying sentiment analysis.
Transform search experiences
Leverage large language models (LLMs) on business-specific information from your organization's private data (not just publicly trained data). Use Elasticsearch for high relevance context windows that draw on your proprietary data to improve LLM output and deliver the information in a secure, concise, actionable and conversational experience.