Ways to find data in Hyper-Converged Database (HCD)
Finding data is one of the most common and important interactions between applications and databases.
In addition to reading data, you also find data in order to modify it, delete it, or otherwise use in subsequent operations. For example, dynamic few-shot prompting amends user-submitted questions with predefined question-response pairs to improve the LLM’s response. Vector search is used to find the most relevant predefined pairs before generating a response for the user.
There are several ways to find data in Hyper-Converged Database (HCD) databases:
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Filters: Find data matching a specific value or a range of values, with or without ascending/descending sorting.
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Vector search: Compare vector embeddings in your database against a query vector, and then rank the results by similarity. This is the quintessential use case for vector databases in machine learning.
