  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.
