What type of projection should be defined to create a knowledge store with JSON representations of indexed documents?

To create a knowledge store with JSON representations of indexed documents, defining an Object projection is essential. This type of projection allows the structured representation of complex data in a way that is compatible with JSON formats. When working with a knowledge store, which often utilizes Azure Cognitive Search or similar services, the goal is to encapsulate data as JSON objects to store information neatly and to enable efficient retrieval and manipulation.

Using Object projections, you can effectively map the various fields and properties of indexed documents to corresponding JSON structures. This approach not only preserves the hierarchical structure often present in documents but also supports nested data types, providing flexibility in how the information is organized and accessed. In contrast, other types of projections, such as File, Table, or Array, may not adequately represent the complexities and relationships inherent within indexed documents in a manner conducive to JSON formatting. Hence, selecting Object projection is key for effectively creating a knowledge store designed to leverage the capabilities of JSON.