Vespa Features
Vespa enhances real-time analytics and machine learning with its diverse features.
Search
Vespa is a fully-featured search engine and vector database, supporting vector search (ANN), lexical search, and structured data search in the same query. It integrates machine-learned model inference for real-time data analysis.
Recommendation and Personalization
Vespa enables online evaluation of recommender models over content items for personalized recommendations, combining fast vector search with machine-learned model evaluation.
Pluggable
Send out your own Java components to implement customized logic.
Elastic and fault tolerant
Add, remove, and replace machines without losing any data, even when live.
Conversational AI
Vespa supports large language models for conversational AI, allowing real-time storage and search of vector and text data, and orchestrating multiple operations for complex tasks.
Semi-structured Navigation
Vespa provides features for applications like e-commerce that require structured navigation in combination with search and recommendation, leveraging structured data on a unified architecture.
Machine Learning Support
Vespa is engineered for scalable and efficient support of machine-learned model inference and supports most machine-learned models from popular tools.
Data Management
Vespa automatically manages data distribution over nodes and redistributes data in the background as changes occur, simplifying data management.
Unbeatable End-to-End Performance
Built on a C++ core, Vespa scales to handle any amount of data and traffic, utilizing modern hardware stacks efficiently for optimal performance.
Advanced Machine Learning Techniques
Vespa supports neural networks for deep learning, natural language processing (NLP) for improved search and recommendation, reinforcement learning for continuous optimization, and strategies to handle cold-start problems.
Monitoring and Maintenance
Continuous monitoring, regular data updates, performance tracking, and A/B testing are essential practices for maintaining optimal performance of Vespa's machine learning implementations.