![]() Enterprises implement internal chargeback and costs aggregated at the resource group scope helps the platform to charge back the cost based on consumption to the respective teams.ĪI capabilities are better appreciated when they are delivered directly to end users. RBAC helps to ensure that only relevant teams / individuals have the requisite access control to their respective content and Azure resources in the resource group for e.g., “LoB1 RG”. Content : Resource groups enable grouping of related Azure resources based on the logical segregation of content required in the enterprise while providing ability to define RBAC and achieve cost transparency.The pipeline must cater for a one-time bulk load of content and cater for auto ingesting incremental content which is either new or revised. The extracted text is vectorized using Azure OpenAI embeddings model and is stored in the index. Content Ingestion: A reusable pipeline to onboard enterprise knowledge sources into storage which are further indexed into Cognitive Search using built-in skillsets to translate the documents or using custom skillsets where Document Intelligence is used to extract text and tables from PDFs & images.Below are the key functions of this component: The logical segregation is achieved using resource groups which is represented for e.g., as “LoB1 RG” in the architecture view. The content landing zone is where enterprise knowledge sources are collated but logically segregated based on organizational boundaries i.e., line of business (LoB) / product offerings / internal org content etc. The components listed below inherently aim to address the platform principles defined in the “ Platform Tenets” section. ![]() The logical architecture view represents the solution components required to build a smart enterprise knowledge search platform which is powered by services such as Azure Open AI, Azure Cognitive Search, AI Content Safety and API management. Upcoming sections of the platform architecture will address on how the above principles are achieved as part of the implementation of platform components. Access Control: RBAC and Information access control.Below are few key principles that Platform must deliver to achieve broad adoption, usage and intended value. Platform Tenets are the key guiding principles and considerations for defining the technical architecture of the smart enterprise knowledge search platform. Instead, enterprises must aim to build a platform which collates knowledge sources, provides conversational experience to access information & knowledge, standardizes the implementation that adheres to organizational AI Governance processes & practices. In enterprises we have observed implementation of the RAG pattern is repeated by various teams often siloed from each other. We will also discuss other essential components of the platform to build a holistic system which caters for enterprise guardrails.Įnterprise knowledge search / Semantic Search use cases leverage Retrieval Augmented Generation (RAG) pattern that augments and provides relevant content to the LLMs aiding to deliver the outcome defined in the user prompt. In this blog, we will share our perspective on how enterprises can leverage Azure Open AI to develop a platform for smart enterprise knowledge search. The most significant challenge for customers across industries is to quickly find the most relevant and accurate information from a vast ocean of knowledge available within their enterprise. "Chat with my data" and "Talk to your docs" are common themes and use cases of semantic search that we discuss with our customers. Enterprises are looking to leverage OpenAI models capabilities of content generation, summarization, code generation and semantic search to deliver next generation of user experiences and increase productivity of employees. Generative AI and large language models (LLMs) are at the heart of innovation and top of mind for all enterprises.
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