How can I ensure the retrieval tool fully processes all file contents, especially for structured data?

Below is some guidance based on API behavior and community observations:

• Use a sufficiently rich JSON file:
  The retrieval tool sometimes “skims” or doesn’t index very small files fully. If you’re uploading a JSON file with structured data (like an array of books), make sure the file is large enough so that the entire content is reliably indexed. In some experiments, adding extra context around the structured data appears to help.

• Consider a more “flattened” or text-based representation:
  Although JSON is the natural choice for structured data, converting the data into a textual format (for example, a clear list or table with headings) sometimes encourages the assistant to process all entries. This may be particularly useful when your questions depend on global properties of the dataset.

• Per-item files can help (but aren’t ideal):
  Multiple users have found that breaking up the data (for example, one file per record) leads to more consistent retrieval. However, this approach brings its own complexity with management and isn’t recommended as a standard solution if you can adjust your file’s content or format instead.

• Instructions to the model:
  Remember that when using JSON mode, you must include a system or user instruction asking the model to produce JSON output if that’s needed. Although this isn’t directly about file retrieval, it’s an important part of ensuring correct, consistent answers when interrogating the retrieved data.

In summary, the recommended approach is to upload structured data as a JSON file—but ensure it isn’t too small. Adjust its content (for example, add some descriptive text or formatting) to make it easier for the retrieval tool to index the complete file. If the inconsistency persists, consider reformatting the data into a more text-based format that preserves its structured meaning, or in a pinch, fallback to multiple files per record.