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Extracting keywords, language, and embeddings

Use this for the enrichment surface around extraction: statistical keyword extraction, language detection, and vector embeddings. Keywords and language detection ride along with extraction and land on the result; embeddings are produced by a dedicated embed command.

Keywords (YAKE / RAKE)

Keyword extraction is configured via the [keywords] config block (or inline JSON), there is no single --keywords CLI flag. When enabled, extracted keywords appear on result.extracted_keywords (extractedKeywords in Node.js; the CLI JSON field is extracted_keywords). Two algorithms are available:

• YAKE ("yake"), statistical, unsupervised single-document extraction. Good general default.
• RAKE ("rake"), co-occurrence / phrase-based. Favors multi-word key phrases.

Feature-gated: keyword extraction requires the CLI to be built with the keywords-yake and/or keywords-rake Cargo features (both are in the default/full build). If the CLI was built without them, the [keywords] config block is silently ignored, result.extracted_keywords simply stays empty rather than erroring. The "yake" algorithm needs keywords-yake; "rake" needs keywords-rake.

Enable via inline JSON on the CLI:

xberg extract paper.pdf --format json \
  --config-json '{"keywords":{"algorithm":"yake","max_keywords":15,"language":"en"}}' \
  | jq '.result.extracted_keywords'

Or in a config file: