  This paper presents an analysis of open-source large language models (LLMs)
and their application in Retrieval-Augmented Generation (RAG) tasks, specific
for enterprise-specific data sets scraped from their websites. With the
increasing reliance on LLMs in natural language processing, it is crucial to
evaluate their performance, accessibility, and integration within specific
organizational contexts. This study examines various open-source LLMs, explores
their integration into RAG frameworks using enterprise-specific data, and
assesses the performance of different open-source embeddings in enhancing the
retrieval and generation process. Our findings indicate that open-source LLMs,
combined with effective embedding techniques, can significantly improve the
accuracy and efficiency of RAG systems, offering a viable alternative to
proprietary solutions for enterprises.
