Provide GenAI responses grounded in your real-world data and relationships. GraphRAG combines knowledge graphs with Retrieval-Augmented Generation (RAG), enabling you to build GenAI applications that deliver mor
Deployment is very easy and is ideal for information workers who would normally not have admin rights or the knowledge of deploying web applications on an IIS server. These Access web apps support any browser that SharePoint supports including Windows, Mac, and mobile devices. Though running in ...
Unlike traditional databases, vector databases store data in a mathematical representation called vectors, enabling approximate nearest neighbor (ANN) search for similar results. This feature enhances search and matching operations, making them more suited for AI applications. What are the key advantages ...
A hotfix is available to resolve these issues. To learn more about this issue, as well as the hotfix install location, seeKnowledge Base article 962866. How do I get Database and Development Editions together? An excerpt from the press release dated Sept. 29, 2008: ...
Classification systems are based on an organized or classified body of knowledge. Classification systems have been used most often with physical materials such as books and periodicals in print or microform formats. At first glance, in a virtual collection, classification seems less important. However...
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Data Mining and Knowledge Discovery II.D.7 Parallel Processing High-performance parallel database systems are displacing traditional systems in very large databases that have complex and time-consuming querying and processing requirements. Relational queries are ideally suited to parallel execution since the...
for saving the results as JSON. Let’s do a query that gets Customer data, see what those results look like, then save them to JSON. With the list of tables in front of me, I can start typing my query and, asFigure 4shows, IntelliSense kicks in with knowledge of the database ...
RAG enables the model to apply a broader knowledge base by incorporating relevant external sources into the generation process, resulting in more comprehensive and informed responses. For more information on "grounding" LLMs, see grounding LLMs. [Go back]...
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