index-agent

Automating tag and topic index generation requires parsing YAML frontmatter from content files to extract metadata, which is then grouped into navigable index files. This process reduces manual effort and structures information for efficient knowledge base search.

Co-occurrence analysis works by scanning extracted tags and topics across content files to identify frequently paired concepts. This generates related groupings within index files, enhancing navigation by linking interconnected sources and topics together.

Yes, you need Python with pypdf and pdfplumber for PDF parsing, and the re module for regular expressions. These dependencies are required to extract text and parse YAML frontmatter from source documents during index generation.

Cleaning up orphaned index files involves scanning generated indexes and comparing them against currently used tags and topics. The system automatically identifies and removes index files for tags and topics no longer present in the content directory.

Yes, index generation specifically processes content files stored within the 'wiki/sources/' and 'wiki/concepts/' directories. It parses YAML frontmatter in these locations to automatically build tag and topic indexes for knowledge organization.

Index generation fails when regular expressions cannot properly parse YAML frontmatter or when PDF parsing libraries encounter unreadable file structures. Ensure PDF files use standard formatting compatible with pypdf and pdfplumber for successful extraction.