Comparative Analysis of Retrieval Systems in the Real World

TL;DR

This paper compares various state-of-the-art retrieval systems integrating language models, evaluating their accuracy and efficiency to guide deployment in AI-driven question-answering applications.

Contribution

It provides a comprehensive comparison of multiple retrieval methods, including novel approaches like KG-FID Retrieval, based on performance metrics like RobustQA.

Findings

Pinecone's Canopy framework shows high efficiency.

Google's RAG on Cloud VertexAI-Search achieves top accuracy.

KG-FID Retrieval offers a promising new hybrid approach.

Abstract

This research paper presents a comprehensive analysis of integrating advanced language models with search and retrieval systems in the fields of information retrieval and natural language processing. The objective is to evaluate and compare various state-of-the-art methods based on their performance in terms of accuracy and efficiency. The analysis explores different combinations of technologies, including Azure Cognitive Search Retriever with GPT-4, Pinecone's Canopy framework, Langchain with Pinecone and different language models (OpenAI, Cohere), LlamaIndex with Weaviate Vector Store's hybrid search, Google's RAG implementation on Cloud VertexAI-Search, Amazon SageMaker's RAG, and a novel approach called KG-FID Retrieval. The motivation for this analysis arises from the increasing demand for robust and responsive question-answering systems in various domains. The RobustQA metric is used to evaluate the performance of these systems under diverse paraphrasing of questions. The report aims to provide insights into the strengths and weaknesses of each method, facilitating informed decisions in the deployment and development of AI-driven search and retrieval systems.