Denser Retriever: Revolutionizing AI Integration
Description
Denser Retriever is an innovative enterprise-level AI retriever designed to seamlessly integrate artificial intelligence into various applications, ensuring users experience the highest level of accuracy. By combining multiple search technologies, Denser Retriever offers a unique platform that enhances search precision and relevance.
Denser Retriever uses a machine learning technique called gradient boosting (xgboost), which efficiently merges three distinct search methods:
- Keyword-based searches: These searches focus on fetching results explicitly mentioned in the query, ensuring precise targeting.
- Vector databases: Ideal for identifying a broad spectrum of potentially relevant answers, vector databases expand the search's reach.
- Machine Learning rerankers: By reordering the results, these rerankers make sure that the most pertinent answers are at the top of the list.
Experiments with the MTEB datasets demonstrate that combining keyword search, vector search, and reranking (denoted as ES+VS+RR_n) can significantly enhance the performance compared to the baseline vector search alone.
Features
Denser Retriever comes packed with features that make it stand out:
- Support for various retrievers: It integrates keyword searches, vector searches, and machine learning model reranking, making it versatile.
- Advanced ML techniques: The use of xgboost ensures that the combination of different search methods is effective.
- Cutting-edge accuracy: It achieves state-of-the-art performance on benchmarks such as the MTEB Retrieval benchmarking.
- End-to-end application support: Denser Retriever is not just a search tool. It can power entire applications, including chatbots and semantic search systems.
Installation
Denser encourages using Python via Anaconda for installation, due to some reported issues with Numpy when using the standard Python installer. To install Denser Retriever, users can choose from:
Using Pip
pip install git+https://github.com/denser-org/denser-retriever.git#main
Using Poetry
poetry add git+https://github.com/denser-org/denser-retriever.git#main
Documentation
The official documentation for Denser Retriever is available at retriever.denser.ai, where users can find detailed guides, including a quick start section to facilitate learning and implementation.
Development
Developers interested in contributing or customizing Denser Retriever can begin their work on a local machine. Detailed instructions for getting started with development are provided in the DEVELOPMENT.md file available in the repository.
License
Denser Retriever is released under the MIT license, making it open for modification and redistribution under specified terms. For more information, users can refer to the LICENSE in the repository.
Citation
Should users wish to cite Denser Retriever in their work, a standard BibTeX entry is provided:
@misc{denser-retriever,
author = {denser-org},
title = {An enterprise-grade AI retriever designed to streamline AI integration into your applications, ensuring cutting-edge accuracy.},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/denser-org/denser-retriever}}
}
Denser Retriever stands as a significant leap in AI retriever technology, solving complex integration challenges and propelling applications towards improved accuracy and efficiency.