Build an Enterprise Knowledge Base Using Vector Databases and Large Language Models

Large Language Models (LLMs) have revolutionized natural language processing, enabling new applications in enterprise knowledge management. This whitepaper explores the implementation of Retrieval-Augmented Generation (RAG) systems, which combine existing knowledge bases with LLMs to enable natural language querying of internal data.

We address key challenges in developing and deploying production-grade RAG systems discussing:

• RAGs and Vector Databases: An overview of these technologies and their role in transforming data querying and knowledge retrieval processes within organizations.
• Large Language Model Integration: Strategies for effectively incorporating LLMs into existing knowledge bases, including best practices for data processing and embedding.
• Computational Requirements: An analysis of GPU compute needs for various scales of implementation, enabling informed decision-making on infrastructure investments.