Description
Trieve is an AI search infrastructure platform designed to provide faster, more relevant search results. It combines powerful language models with customizable tools to fine-tune relevance and ranking.
Key capabilities include semantic and full-text searches utilizing both dense and sparse vectors. A cross-encoder re-ranker model assists in fine-tuning the search results.
To ensure results are relevant to the user, a ranking function allows for prioritizing based on the data's freshness. In addition to these, Trieve includes features like document expansion and sub-sentence highlighting. Users can use their own embedding model or select from a variety of open-source models hosted by Trieve.
Trieve enhances efficiency by supporting hybrid searches that combine full-text and semantic vector searches with cross-encoder re-ranker models.
It also provides private managed embedding models and features such as semantic vector search and recency biasing to improve search results. Furthermore, Trieve offers solutions for specific needs like duplicate detection and merging, content similarity and history-based recommendations, message history management, and more. The service is designed for user hosting, giving greater control over vendor agreements and data privacy. It's designed for easy integration with API usage and provides comprehensive documentation.
This positions Trieve as a comprehensive, customizable, and robust tool for integrating AI-based search into applications.
