---
title: Introduction
icon: Album
description: DenserRetriever Introduction
---

import { MessageCircleIcon, PawPrintIcon, ScaleIcon, SearchCodeIcon, ShoppingBagIcon } from "lucide-react"

## What is an AI Retriever?

![](/images/retriever_dog.png)

In the world of AI, a "retriever" is a tool used to sift through vast amounts of data to find information that is relevant to a user's query. Think of it as a highly intelligent search engine that helps AI systems understand and gather the exact information needed to answer questions effectively. The Retriever is the cornerstone of the Retriever Augmented Generation (RAG) framework, playing a crucial role in delivering an accurate and seamless experience in AI applications.

## How Does Denser Retriever Work?

Denser Retriever integrates 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.

## Use Cases of Denser Retriever

Denser Retriever can significantly enhance a variety of AI applications across different industries and domains. Here are a few examples.

<Cards>
  <Card
    icon={<MessageCircleIcon />}
    title="Chatbots"
    description="In customer service or interactive applications, Denser Retriever can retrieve contextually relevant information from vast datasets to help LLMs provide more accurate and helpful responses."
  />
  <Card
    icon={<SearchCodeIcon />}
    title="Semantic Search Engines"
    description="Denser Retriever can act as search engines to grasp the intent behind user queries, delivering more precise and contextually appropriate search results."
  />
  <Card
    icon={<ShoppingBagIcon />}
    title="Recommendation Systems"
    description="For platforms like e-commerce or streaming services, Denser Retriever can pull more relevant items or content, thereby increasing user engagement and satisfaction."
  />
  <Card
    icon={<PawPrintIcon />}
    title="Knowledge Management and Retrieval"
    description="In corporate environments, Denser Retriever can transform how organizations manage and retrieve knowledge. It can efficiently search through vast repositories of documents, emails, and other data to find relevant information, aiding decision-making and increasing workplace productivity."
  />
  <Card
    icon={<ScaleIcon />}
    title="Legal Document Analysis"
    description="Denser Retriever can assist in legal document analysis by quickly finding relevant cases and statutes from vast databases. This can aid legal professionals in preparing for cases more efficiently, ensuring they have access to all pertinent information."
  />
</Cards>

## Benefits for AI Developers and Users

- **Rapid Prototyping**: Developers can quickly build and test AI applications, like chatbots or search systems, making it easier to refine these applications based on real-world data and interactions.
- **Scalable Solutions**: As the needs grow, Denser Retriever helps scale applications efficiently, ensuring they remain robust and responsive under increased loads.
- **State-of-the-art Accuracy**: The integrated approach of Denser Retriever ensures that AI applications achieve the cutting edge accuracy.

## 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 chatbots and semantic search

## Why Denser Retriever?

<Accordions>
<Accordion title="Open Source Commitment">

Denser Retriever is open source, providing transparency and the opportunity for continuous community-driven enhancements.

</Accordion>
<Accordion title="Production-Ready">

It is designed to be ready for deployment in production environments, ensuring reliability and stability in real-world applications.

</Accordion>
<Accordion title="State-of-the-art Accuracy">

Denser Retriever delivers state-of-the-art accuracy, enhancing the effectiveness of your AI applications.

</Accordion>

<Accordion title="Scalability">

Whether you are handling growing data needs or expanding user demands, Denser Retriever scales seamlessly to meet your requirements.

</Accordion>

<Accordion title="Flexibility">

The tool is adaptable to a wide range of applications and can be tailored to specific needs, making it a versatile choice for diverse industries.

</Accordion>
</Accordions>

## Get in Touch

We are excited to hear your feedback and suggestions. Please feel free to reach out to us at

- [Github Repository](https://github.com/denser-org/denser-retriever)
  {/* - [Discord Channel](https://discord.gg/) */}
- [Email](mailto:support@denser.ai)
