Enterprise-grade AI retriever solution that seamlessly integrates to enhance your AI applications.
Project description
Denser Retriever
An enterprise-grade AI retriever designed to streamline AI integration into your applications, ensuring cutting-edge accuracy.
📝 Description
Denser Retriever combines multiple search technologies into a single platform. It utilizes gradient boosting ( xgboost) machine learning technique to combine:
- Keyword-based searches that focus on fetching precisely what the query mentions.
- Vector databases that are great for finding a wide range of potentially relevant answers.
- Machine Learning rerankers that fine-tune the results to ensure the most relevant answers top the list.
- Our experiments on MTEB datasets show that the combination of keyword search, vector search and a reranker via a xgboost model (denoted as ES+VS+RR_n) can significantly improve the vector search (VS) baseline.
- Check out Denser Retriever experiments using the Anthropic Contextual Retrieval dataset at here.
🚀 Features
The initial release of Denser Retriever provides the following features.
- Supporting heterogeneous retrievers such as keyword search, vector search, and ML model reranking
- Leveraging xgboost ML technique to effectively combine heterogeneous retrievers
- State-of-the-art accuracy on MTEB Retrieval benchmarking
- Demonstrating how to use Denser retriever to power an end-to-end applications such as chatbot and semantic search
📦 Installation
We recommend installing Python via Anaconda, as we have received feedback about issues with Numpy installation when using the installer from https://www.python.org/downloads/. We are working on providing a solution to this problem. To install Denser Retriever, you can run:
Pip
pip install denser-retriever
Poetry
poetry add denser-retriever
📃 Documentation
The official documentation is hosted on retriever.denser.ai. Click here to get started.
👨🏼💻 Development
You can start developing Denser Retriever on your local machine.
See DEVELOPMENT.md for more details.
🛡 License
This project is licensed under the terms of the MIT
license.
See LICENSE for more details.
📃 Citation
@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}}
}
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