Trieve is an AI search infrastructure platform designed to enhance search efficiency and relevance. It integrates advanced language models with customizable ranking and relevance tuning tools.
Key features include semantic and full-text searches using dense and sparse vectors, along with a cross-encoder re-ranker model for fine-tuning search results. Users can bias results based on data recency and utilize document expansion and sub-sentence highlighting for better context.
Trieve allows users to bring their own embedding model or choose from various open-source models. It supports hybrid searches that combine full-text and semantic vector searches with cross-encoder re-ranker models.
In addition to private managed embedding models, Trieve offers features like semantic vector search, recency biasing, duplicate detection and merging, and recommendation based on user history and content similarity. It also provides solutions for message history management.
The platform can be hosted by users for better data privacy and control over vendor agreements. It offers easy integration with API usage and comprehensive documentation, making it a versatile tool for incorporating AI-based search into applications.
More details about Trieve
What functionalities does Trieve provide for document expansion and sub-sentence highlighting?
Trieve provides advanced features like document expansion and sub-sentence highlighting to enhance search accuracy and scope. Document expansion broadens search results by incorporating additional terms related to the original query. Sub-sentence highlighting allows users to identify specific portions of search results that match their query, improving precision and relevance.
How does Trieve manage recency biasing?
Trieve employs recency biasing through a dedicated ranking function, emphasizing the freshness of data. This feature ensures that the most recent results are prioritized, maintaining relevance for users by taking into account the recency of the data.
What models are available to use in Trieve’s private managed embedding models?
Trieve provides users with the flexibility to either utilize private managed embedding models or select from a range of default models hosted by the platform. This diverse array of options ensures that users can tailor their search experience according to their specific needs while guaranteeing the relevance and precision of search results.
How does the full-text search in Trieve function?
Trieve’s full-text search prowess is empowered by SPLADE, a cutting-edge retrieval model. This technology enables comprehensive exploration of text documents, facilitating detailed searches. Trieve offers the flexibility of conducting both dense vector semantic searches and sparse vector full-text searches, enhancing the user’s search experience.
