# anonymize A RocketRide filter node that detects and masks sensitive entities in text flowing through a pipeline. ## What it does Scans text for personally identifiable information (names, emails, phone numbers, addresses, and more) using a locally-run GLiNER zero-shot NER model and replaces every character of each detected entity span with a configurable masking character (default `█`, U+2588). Text length and structure are preserved: only the entity character positions are replaced, and overlapping or adjacent spans are merged before masking. Example: ``` Input: John Smith is a patient at St. Mary's Hospital. Output: ████ █████ is a patient at ██ █████████████████. ``` Models are loaded via `ai.common.models.GLiNER`, which runs inference locally and automatically routes to a model server when the engine is started with the `--modelserver` flag. Models are downloaded from Hugging Face on first use; no API key is required. The node declares the `gpu` capability, so GPU acceleration is used when available. Large documents are split into 1024-character chunks with a 128-character overlap so entities at chunk boundaries are not missed. Chunks are processed in parallel using up to 4 threads, entity labels are batched in groups of 32, and entities found in the overlap regions are de-duplicated before the final masking pass. Note: AI-based detection cannot guarantee 100% accuracy. Review results before using in production. --- ## Configuration ### Lanes | Lane | Direction | Behaviour | |--------|-----------|-----------| | `text` | in -> out | Incoming text chunks are buffered for the whole object (downstream delivery is suspended via `preventDefault`). At object close, the buffered text is anonymized once and forwarded downstream as a single write. | When an upstream classifier node is present, the node also receives classifications and adjusts its behaviour (see "Entity labels" below). ### Fields The node is configured by choosing a model profile. Each profile exposes the masking character field; the `custom` profile additionally exposes a free-form model name field. | Field | Type | Description | |---|---|---| | `anonymizeChar` | string | Character | | `model` | string | Gliner model to use for anonymization | | `profile` | string | Default "glinerMergedLarge". Anonymize model | --- ## Model profiles | Profile key | Model | Best for | |-------------|-------|----------| | `glinerSmall` | `urchade/gliner_small-v2.1` | General English PII, fastest | | `glinerMedium` | `urchade/gliner_medium-v2.1` | General English PII, balanced | | `glinerLarge` | `urchade/gliner_large-v2.1` | General English PII, highest accuracy | | `glinerPIILarge` | `knowledgator/gliner-pii-large-v1.0` | High-accuracy English PII | | `glinerMergedLarge` (default) | `xomad/gliner-model-merge-large-v1.0` | Combined from multiple datasets, broad coverage | | `glinerMulti` | `urchade/gliner_multi` | Multilingual text | | `glinerMultiPII` | `urchade/gliner_multi_pii-v1` | Multilingual PII | | `gretelSmall` | `gretelai/gretel-gliner-bi-small-v1.0` | Business-oriented NER, compact | | `gretelLarge` | `gretelai/gretel-gliner-bi-large-v1.0` | Business-oriented NER, large scale | | `glinerKo` | `taeminlee/gliner_ko` | Korean | | `glinerIt` | `DeepMount00/GLiNER_PII_ITA` | Italian | | `glinerAr` | `NAMAA-Space/gliner_arabic-v2.1` | Arabic | | `glinerCommunitySmall` | `gliner-community/gliner_small-v2.5` | Community general, compact | | `glinerCommunityMedium` | `gliner-community/gliner_medium-v2.5` | Community general, balanced | | `glinerCommunityLarge` | `gliner-community/gliner_large-v2.5` | Community general, largest | | `glinerBiomedSmall` | `Ihor/gliner-biomed-small-v1.0` | Biomedical and clinical text, compact | | `glinerBiomedLarge` | `Ihor/gliner-biomed-large-v1.0` | Biomedical and clinical text, high accuracy | | `custom` | user-supplied | Any Hugging Face GLiNER model name | The default profile when adding the node is `glinerSmall` (as set in `preconfig.default`); the `anonymize.profile` field UI default is `glinerMergedLarge`. --- ## Entity labels What gets detected and masked depends on whether an upstream classifier node feeds this node. ### Standalone (no upstream classifier) The node runs GLiNER with a built-in set of 15 PII labels: `person`, `name`, `email`, `phone number`, `address`, `social security number`, `credit card number`, `date of birth`, `organization`, `company`, `location`, `ip address`, `bank account`, `passport number`, `driver license` ### With an upstream classifier When classification data arrives before the object closes, the node: 1. Masks the exact character spans (`offset`, `length`) reported in the classification `textMatches`. 2. Resolves classification rule `idRef` values to English names via the Nucleuz rule pack (`nucleuz/rulePack.dat` under the engine path). 3. Extracts keyword `` entries from the classification rules as additional GLiNER labels. 4. Runs GLiNER with the combined label set and merges the results with the spans from step 1. If `nucleuz/rulePack.dat` is not present, rule-name resolution silently produces no results and the node falls back to GLiNER-only mode using the labels extracted from the classification rules. --- ## Running the tests The node ships automated test cases in `services.json`. The standard test runs against the `glinerSmall` profile; the full test exercises every model profile. ```bash # Standard test (glinerSmall profile) builder nodes:test # Full test across all model profiles builder nodes:test-full ``` --- ## Schema | Field | Type | Description | Default | |---|---|---|---| | `anonymize.model` | `string` | **Model name**
Gliner model to use for anonymization | | | `anonymize.profile` | `string` | **Model**
Anonymize model | `"glinerMergedLarge"` | | `anonymizeChar` | `string` | **Character to use for anonymization**
Character | | ## Dependencies - `gliner` ## Source [ View source](https://github.com/rocketride-org/rocketride-server/tree/develop/nodes/src/nodes/anonymize)