Introduction

What is an AI Retriever?

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.

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 Retrieval benchmarking
  • Demonstrating how to use Denser retriever to power an end-to-end applications such as chatbots and semantic search

Why Denser Retriever?

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