How did you handle large context when working with documents?

When working with large contexts in documents (for example, when processing long texts in NLP or generative models), the following approaches are used:

• Chunking: dividing the document into logical or fixed-size segments for separate processing.
• Sliding window: processing consecutive overlapping fragments to preserve context.
• Hierarchical models: first extracting key ideas from parts, then combining the results.
• Vector representation and embedding search: for quick retrieval of relevant parts of the document.

For example, when working with LLMs that have input length limitations, the document is divided into paragraphs, each of which is encoded separately, and then the results are aggregated.