--- name: xberg-io/extracting-keywords source: https://app.decimal.ai/s/xberg-io-extracting-keywords@3/SKILL.md source_sha256: 54e50f0c2cb7 --- # 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: ```bash xberg extract paper.pdf --format json \ --config-json '{"keywords":{"algorithm":"yake","max_keywords":15,"language":"en"}}' \ | jq '.extracted_keywords' ``` Or in a config file: ```toml [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 ``` ```bash 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`: ```bash xberg extract multilingual.pdf --detect-language true --format json \ | jq '.detected_languages' ``` In a config file it lives under `[language_detection]`: ```toml [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. ```bash # 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 ` | **Default.** ONNX model, no API key. | | `llm` | `--model ` `--api-key ` | liter-llm routing, e.g. `openai/text-embedding-3-small`. | | `plugin` | `--plugin ` | A backend pre-registered in-process via the plugin API. | ```bash # 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: ```bash 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: ```python 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.