hiivmind-corpus-build

Automating documentation index builds from multiple sources requires orchestrating source preparation, scanning, segmentation, and index generation to produce a structured index.yaml and rendered index.md with consistent metadata across all sources.

Creating a searchable documentation corpus with graph relationships and embeddings involves configuring corpus initialization, applying optional embeddings, and generating graph data to produce a production-ready index with semantic search capabilities.

Configurable segmentation for multi-source documentation corpora is supported, allowing tiered or single-file indexing with user preferences applied during the segmentation phase to ensure consistent metadata across sources.

Generating index.yaml and index.md files for API references and learning resources requires running an end-to-end corpus indexing process that handles configuration, scanning, segmentation, and finalization to output both structured and rendered index formats.

Corpus indexing supports repeatable, auditable builds for documentation by orchestrating the full pipeline from initial config through finalization, ensuring consistent metadata and producing production-ready index files across multiple sources.

Building a documentation corpus index from zero involves phases for config, preparation, scanning, segmentation, user preferences, index generation, graph data, embeddings, and finalization to produce a structured and rendered index.