I want to prepare a corpus for search and indexing, but my source files are not convenient to parse directly.

This feature helps you turn hard-to-parse source files into a clean, consistent corpus that is ready for search and indexing. It reduces the effort needed to normalize messy inputs and produces structured, index-friendly outputs.

This feature is designed for situations where your original source files are difficult to parse reliably, such as inconsistent formatting, mixed file types, or content embedded in complex layouts. It supports preparing a corpus by extracting usable text, normalizing it, and organizing it into a consistent structure suitable for indexing. You can use it to standardize content boundaries (for example, splitting large files into smaller documents or sections) to improve retrieval accuracy. It helps ensure the resulting corpus is predictable and easy to process downstream by search and indexing pipelines. The feature is useful when you need repeatable corpus generation across many sources while minimizing manual cleanup. It can be applied when building internal knowledge bases, documentation search, or enterprise content discovery where input quality varies. By producing a more uniform corpus, it improves indexing stability and reduces failures caused by malformed or irregular inputs. It also helps you maintain clearer traceability from the indexed content back to the originating files. Overall, it streamlines the path from inconvenient raw files to a search-ready dataset without requiring you to directly parse every original format in-place.