Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and generative models as foundational components. While pre-trained embeddings may exhibit proximity or disparity based on their original training objectives, they might not fully align with the unique characteristics of enterprise-specific data, leading to suboptimal alignment with the retrieval goals of enterprise environments. In this paper, we propose a comprehensive methodology for contextualizing pre-trained embedding models to enterprise environments, covering the entire process from data preparation to model fine-tuning and evaluation. By adapting the embeddings to better suit the retrieval tasks prevalent in enterprises, we aim to enhance the performance of information retrieval solutions. We discuss the process of fine-tuning, its effect on retrieval accuracy, and the potential benefits for enterprise information management. Our findings demonstrate the efficacy of fine-tuned embedding models in improving the precision and relevance of search results in enterprise settings.

翻译：企业面临着管理专有非结构化数据的重大挑战，这阻碍了高效的信息检索。这导致了人工智能驱动的信息检索解决方案的出现，这些解决方案旨在熟练地提取相关见解以解决员工查询。这些解决方案通常利用预训练的嵌入模型和生成模型作为基础组件。虽然预训练的嵌入可能根据其原始训练目标表现出接近性或差异性，但它们可能无法完全适应企业特定数据的独特特征，从而导致与企业环境的检索目标匹配不佳。在本文中，我们提出了一种将预训练嵌入模型情境化到企业环境的综合方法，涵盖了从数据准备到模型微调和评估的整个过程。通过调整嵌入以更好地适应企业中普遍存在的检索任务，我们旨在提升信息检索解决方案的性能。我们讨论了微调的过程、其对检索准确性的影响以及对企业信息管理的潜在益处。我们的研究结果证明了微调嵌入模型在提高企业环境中搜索结果的精确性和相关性方面的有效性。