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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 '.extracted_keywords'

Or in a config file:

[keywords]
algorithm = "rake"       # "yake" or "rake"
max_keywords = 10        # default 10
min_score = 0.0          # filter below this score (normalized 0.0-1.0 for both algorithms)
ngram_range = [1, 3]     # unigrams..trigrams (default); config-file only
language = "en"          # stopword language; omit to skip stopword filtering

xberg extract report.pdf --config xberg.toml --format json | jq '.extracted_keywords'

Field notes:

• max_keywords caps how many keywords are returned (default 10).
• min_score filters low-scoring keywords. Both YAKE and RAKE normalize their scores to the 0.0-1.0 range with higher-is-better, so min_score retains keywords with score >= min_score identically for either algorithm.
• ngram_range is [min, max]: [1,1] unigrams only, [1,2] adds bigrams, [1,3] (default) adds trigrams. Config-file only — it is not a field on the language bindings' KeywordConfig.
• language enables stopword filtering for that language; omit it to disable stopword filtering entirely.

Language detection

Language detection is a real CLI flag: --detect-language. Detected languages appear on result.detected_languages:

xberg extract multilingual.pdf --detect-language true --format json \
  | jq '.detected_languages'

In a config file it lives under [language_detection]:

[language_detection]
enabled = true
min_confidence = 0.8
detect_multiple = false

The CLI flag enables detection with min_confidence = 0.8 and single-language mode; use the config block to detect multiple languages or tune confidence.

Embeddings (embed command)

The standalone embed command produces vector embeddings for text from --text (repeatable) or stdin. It does not run extraction — pipe extracted content in if you want document embeddings.

# Local ONNX preset model (default provider)
xberg embed --text "first passage" --text "second passage" --preset balanced

# Embed extracted document text
xberg extract report.pdf | xberg embed --preset quality

Presets for the local provider: fast, balanced (default), quality, multilingual. Output defaults to JSON (--format json).

--provider selects the embedding source:

Provider	Flag	Notes
local	--preset <fast|balanced|quality|multilingual>	Default. ONNX model, no API key.
llm	--model <id> --api-key <key>	liter-llm routing, e.g. openai/text-embedding-3-small.
plugin	--plugin <name>	A backend pre-registered in-process via the plugin API.

# Provider-hosted embeddings via an LLM
xberg embed --text "query text" \
  --provider llm --model openai/text-embedding-3-small --api-key "$OPENAI_API_KEY"

Local embedding presets must be downloaded first if not cached. Pre-warm them with the cache command:

xberg cache warm --embedding-model balanced   # one preset
xberg cache warm --all-embeddings             # all available presets (currently 8)

Programmatic access

Keywords and detected languages live on the document in the result envelope:

from xberg import ExtractInput, extract, ExtractionConfig, KeywordConfig, KeywordAlgorithm

config = ExtractionConfig(
    keywords=KeywordConfig(algorithm=KeywordAlgorithm.YAKE, max_keywords=15, language="en"),
)
result = await extract(ExtractInput(uri="paper.pdf"), config)
doc = result.results[0]
print(doc.extracted_keywords)   # extracted keywords (when enabled)
print(doc.detected_languages)   # detected languages (when enabled)

See references/python-api.md and references/configuration.md in the sibling xberg skill for the keyword / language-detection config classes and the embedding presets.

Common pitfalls

• No --keywords flag — keyword extraction is config-only. Use --config-json '{"keywords":{...}}' or a [keywords] config block.
• min_score direction — scores are normalized to 0.0-1.0 with higher-is-better for both YAKE and RAKE, so the same threshold behaves identically for either algorithm.
• Embeddings ≠ extraction — embed only takes raw text. Pipe xberg extract output into it for document vectors.
• Cold embedding models — first local run downloads the preset; run xberg cache warm --all-embeddings to pre-populate.

See references/advanced-features.md for the embeddings pipeline and references/cli-reference.md for the embed and cache warm flag sets.