data-ingest

To convert chat logs into Obsidian wiki pages, you ingest unstructured text sources to automatically parse formats, extract topics and entities, and generate distilled pages with frontmatter and wikilinks. This process organizes conversations and logs into a searchable knowledge vault.

Organizing messy text exports and CSVs into a structured knowledge base involves clustering related content across files, deduplicating information, and creating linked wiki pages. This approach enforces provenance and placement rules to keep your vault fully searchable without manual copying.

Yes, you can process images and HTML files when ingesting chat exports into a wiki. The system detects formats like HTML and images, transcribes visible text, describes diagrams, and marks inferred content while setting the source type in the manifest index.

Deduplication when extracting facts and decisions from multiple transcripts works by checking existing wiki pages and grouping related content across files. It then merges or creates new pages with proper frontmatter and tags to prevent repetitive knowledge entries.

No, you do not need to manually update the vault index and manifest after parsing mixed-file folders. The ingestion process automatically updates the manifest, index, and log files with metadata and lists of created or updated pages.

A limitation of processing huge JSON and JSONL files for knowledge extraction is memory load, which is mitigated by reading files incrementally. This avoids loading massive files entirely into memory during format detection and parsing.