15,233
Search optimization
2024-03-11
Trieve
Infrastructure for building AI search into your applications
Overview
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Trieve is an AI search infrastructure platform tailored to deliver more relevant and faster search results. It combines highly efficient language models with flexible tools for fine-tuning ranking and relevance.
Key features include the ability to conduct semantic and full-text searches that utilize both dense and sparse vectors. A cross-encoder re-ranker model helps in flame-tuning the search results.
To keep the results relevant to the user, there is a ranking function that allows for biasing based on the recency of the data. Besides these, Trieve also encompasses features such as document expansion and sub-sentence highlighting.Users have the freedom to bring in their own embedding model or choose from various open-source models that Trieve hosts.
To further accentuate efficiency, Trieve supports hybrid searches that integrate full-text and semantic vector searches with cross-encoder re-ranker models.
Additionally, it offers private managed embedding models and features like semantic vector search and recency biasing to boost search results. Trieve also provides solutions for more specific needs like duplicate detection and merging, recommendation based on user's history and content similarity, message history management, and more.The service is designed to be hosted by the user, offering more control over data privacy and vendor agreements and is made for easy integration with API usage while also offering thorough documentation.
This makes Tieve a powerful, customizable and comprehensive tool for incorporating AI-based search into applications.
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Trieve was manually vetted by our editorial team and was first featured on April 8th 2024.
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2 alternatives to Trieve for Search optimization
Pros and Cons
Pros
Semantic and full-text searches
Utilizes dense and sparse vectors
Cross-encoder re-ranker model
Recency bias ranking
Document expansion
Sub-sentence highlighting
Customer embedded model choice
Hybrid searches: full-text and semantic
Private managed embedding models
Duplicate detection and merging
Recommendation based on user history
Content similarity recommendation
Message history management
Customer hosted service
Easy API integration
Thorough documentation
User control over data privacy
Merchandising relevance tuning
SPLADE full-text neural search
Open-source model availability
Supports date recency biasing
Sub-sentence search result highlighting
Built-in terraform templates
Large API surface
Build RAG experiences
Result citation management
Flexible ranking and relevance tuning
Open-source hosted default availability
Can host service independently
Recommendation and generation APIs
Boosting based on sales/popularity
Search customization based on recency
Fine-tuning tools for results
Customer freedom in embedding models
Supports user data update
Efficient language modeling
Hybrid search support
Facilitates hosting with no external dependencies
Multifunctional: search and RAG
User history based recommendations
Content similarity-based recommendations
Cons
User-hosted
May require fine-tuning
Dependency on user's model
Over-reliance on recency biasing
Potential misinterpretation in hybrid searches
Hard to manage duplicate content
Complexity of embedding models
Sub-sentence highlighting might be confusing
Integration can be tedious
Q&A
What is Trieve?
Trieve is an AI-based search infrastructure platform. It uses highly efficient language models alongside a set of diverse tools for fine-tuning search rankings and relevance. Trieve offers services such as semantic search, full-text search, relevance ranking and many more that are designed to deliver the most relevant and quicker search results.
What is the main purpose of Trieve?
The primary aim of Trieve is to provide a comprehensive and efficient tool for AI-based search integration in applications. The platform is built to deliver faster and more relevant search results by using a mix of powerful language models and highly flexible tools for fine-tuning the ranking and relevance of search results.
How does Trieve's semantic search work?
Trieve's semantic search works by using both dense and sparse vectors. The user has the option to bring in their own embedding model or select from various open-source models that Trieve hosts. This, coupled with the semantic vector search feature, ensures that users can conduct searches based on the meaning of the query, not just keyword matching.
How does the full-text search in Trieve function?
Trieve's full-text search function works with the assistance of a highly efficient feature, SPLADE. SPLADE, being a state-of-the-art retrieval model for full-text search, makes full-text neural search possible allowing a detailed exploration of the available text documents. Moreover, Trieve allows for both dense vector semantic searches and sparse vector full-text searches.
What is the function of the cross-encoder re-ranker model in Trieve?
The cross-encoder re-ranker model in Trieve has a crucial role in fine-tuning the search results. By integrating full-text and semantic vector searches with a cross-encoder re-ranker, Trieve accentuates search efficiency. This integration aids in the re-ranking of the search results ensuring that users receive a refined subset of search outcomes.
How does Trieve manage recency biasing?
Trieve manages recency biasing with a particular ranking function. This biasing feature allows for the emphasis of the currency of data. It ensures that the most recent results are showcased first, keeping the results relevant to the user by considering the recency of the data.
What functionalities does Trieve provide for document expansion and sub-sentence highlighting?
Trieve offers advanced functionalities for document expansion and sub-sentence highlighting. Document expansion helps in widening the scope of the search by including more terms related to the original query. Meanwhile, sub-sentence highlighting helps users pinpoint the specific portions of the search results that match their query, lending more accuracy to search results.
Can I use my own embedding model in Trieve?
Yes, you can use your own embedding model in Trieve. It offers this flexibility to its users, along with the option to choose from various open-source models that Trieve hosts. This is part of the private managed embedding models feature.
How does Trieve's hybrid search feature work?
Trieve's hybrid search feature combines full-text and semantic vector searches with cross-encoder re-ranker models. This mechanism, thus, takes advantage of both search types to maximize the scope and relevance of search results. It ensures both detail and contextual coverage, delivering one-off and signifies an integrated, comprehensive search.
What does Trieve offer for duplicate detection and merging?
Trieve provides a novel approach for duplicate detection and merging. This feature helps in the optimal management of data, ensuring that recurring content doesn't hamper the quality of search results. This approach is particularly useful in knowledge management where duplicate content is a common issue.
How can Trieve's features be integrated with other applications?
Trieve is designed for easy integration with API usage. Its features can easily be integrated with other applications through its highly efficient and user-friendly API. Moreover, Trieve provides thorough documentation to help users navigate through the integration process.
What specific solutions does Trieve provide for recommendation based on user's history and content similarity?
Trieve provides targeted solutions for recommendation based on user's history and content similarity. It supports recommendations that are rooted in user history and content similarity, ensuring personalized and relevant suggestions. This feature plays a critical role in user engagement, helping users discover new content that matches their preferences.
Can I host Trieve's service on my own platform?
Yes, you can host the Trieve service on your own platform. This feature gives users more control over their data privacy and vendor agreements. By hosting the service themselves, users have the autonomy to regulate their data, thereby providing additional security and control.
Is there a way to control data privacy in Trieve?
Trieve offers its users control over data privacy. As the service can be hosted by the user, it keeps the data protection in the hands of the user, thus minimising the risk of vendor dependency or data leakage. This significantly enhances the privacy and security associated with data handling.
How efficient is Trieve in delivering relevant search results?
Trieve is extremely efficient in delivering relevant search results. It employs language models, vector searches, cross-encoder re-ranker, and recency biasing among other features to enhance the relevance and speed of search results. User can even leverage their own embedding model to further customize the accuracy and relevance of the search results.
What models are available to use in Trieve's private managed embedding models?
Trieve offers private managed embedding models, where users can bring in their own embedding model. Additionally, users can choose from various available open-source hosted default models. This not only provides a variety of options to users but also ensures the relevance and accuracy of search results.
How can Trieve boost search results?
Trieve boosts search results by combining several advanced features. These include semantic and full-text searches utilising both dense and sparse vectors, cross-encoder re-ranker model for fine-tuning results, biasing based on recency, and document expansion. The platform also allows users to use their own or choose from open-source embedding models to enhance the effectiveness of searches.
Does Trieve offer solutions for message history management?
Trieve offers solutions for message history management. This is integral for creating relevant response generation, typically used in customer support or chatbot applications. Although the specifics are not provided, this feature would typically involve keeping track of a user's message history and using that information for generating contextually appropriate responses.
How easy is it to integrate Trieve using API?
Integration with Trieve's API is made user-friendly and efficient. Their API surface is large, providing thorough documentation that guides users through the integration process. Furthermore, Trieve is designed to be hosted by the user, offering more control and easing the integration process.
What support is provided for understanding and using Trieve’s functions?
Trieve provides comprehensive support for understanding and utilizing its functions. It offers thorough documentation to help users understand how to use the platform and its features. In addition, support is available via various communication channels, including direct contact information and active communities on platforms such as Discord and Matrix.
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