# 
Denser Retriever
[](https://pypi.org/project/denser-retriever/)
[](https://github.com/denser-org/denser-retriever/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)
[](https://github.com/astral-sh/ruff)
[](https://github.com/PyCQA/bandit)
[](https://github.com/denser-org/denser-retriever/blob/main/.pre-commit-config.yaml)
[](https://github.com/denser-org/denser-retriever/releases)
[](https://github.com/denser-org/denser-retriever/blob/main/LICENSE)
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](https://github.com/denser-org/denser-retriever/tree/main/experiments/data/contextual-embeddings)**.
## 🚀 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](https://github.com/embeddings-benchmark/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](https://www.anaconda.com/download), 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
```bash
pip install git+https://github.com/denser-org/denser-retriever.git#main
```
### Poetry
```bash
poetry add git+https://github.com/denser-org/denser-retriever.git#main
```
## 📃 Documentation
The official documentation is hosted on [retriever.denser.ai](https://retriever.denser.ai).
Click [here](https://retriever.denser.ai/docs/quick-start) to get started.
## 👨🏼💻 Development
You can start developing Denser Retriever on your local machine.
See [DEVELOPMENT.md](DEVELOPMENT.md) for more details.
## 🛡 License
[](https://github.com/denser-org/denser-retriever/blob/main/LICENSE)
This project is licensed under the terms of the `MIT` license.
See [LICENSE](https://github.com/denser-org/denser-retriever/blob/main/LICENSE) for more details.
## 📃 Citation
```bibtex
@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}}
}
```