> ## Documentation Index > Fetch the complete documentation index at: https://doc.lucidworks.com/llms.txt > Use this file to discover all available pages before exploring further. # NLP Annotator Index Stage export const schema = { "type": "object", "title": "NLP Annotator", "description": "Annotate text using NLP", "required": ["annotatorType", "source"], "properties": { "skip": { "type": "boolean", "title": "Skip This Stage", "description": "Set to true to skip this stage.", "default": false, "hints": ["advanced"] }, "label": { "type": "string", "title": "Label", "description": "A unique label for this stage.", "hints": ["advanced"], "maxLength": 255 }, "condition": { "type": "string", "title": "Condition", "description": "Define a conditional script that must result in true or false. This can be used to determine if the stage should process or not.", "hints": ["code", "code/javascript", "advanced"] }, "annotatorType": { "type": "string", "title": "Annotator Type", "enum": ["sparknlp", "opennlp"], "default": "sparknlp" }, "sparknlpNERModel": { "type": "string", "title": "Spark NLP NER Model", "description": "If Spark NLP annotator is used, specify the blobstore location of the NER model", "minLength": 1, "reference": "blob", "blobType": "model:spark-nlp" }, "sparknlpPOSModel": { "type": "string", "title": "Spark NLP POS Model", "description": "If Spark NLP annotator is used, specify the blobstore location of the POS model", "minLength": 1, "reference": "blob", "blobType": "model:spark-nlp" }, "failOnError": { "type": "boolean", "title": "Fail on Error", "description": "Flag to indicate if this stage should throw an exception if an error occurs while generating a prediction for a document.", "default": false }, "config": { "type": "string", "title": "Model Configuration", "description": "Advanced configuration for NLP implementations", "default": "{\n \"opennlp\": {\n \"posModelLocation\": \"nlp/models/en-pos-maxent.bin\",\n \"chunkerModelLocation\": \"nlp/models/en-chunker.bin\",\n \"tokenizerModelLocation\": \"nlp/models/en-token.bin\",\n \"sentenceModelLocation\": \"nlp/models/en-sent.bin\",\n \"tokenNameFinderModelLocations\": {\n \"MONEY\": \"nlp/models/en-ner-money.bin\",\n \"PERCENT\": \"nlp/models/en-ner-percentage.bin\",\n \"PERSON\": \"nlp/models/en-ner-person.bin\",\n \"LOCATION\": \"nlp/models/en-ner-location.bin\",\n \"TIME\": \"nlp/models/en-ner-time.bin\",\n \"DATE\": \"nlp/models/en-ner-date.bin\",\n \"ORG\": \"nlp/models/en-ner-organization.bin\"\n }\n }\n}\n", "hints": ["code", "hidden"] }, "source": { "type": "array", "title": "Source Fields", "description": "Input fields to annotate", "minItems": 1, "items": { "type": "string" } }, "extractorRules": { "type": "array", "title": "Extractor Rules", "description": "Define rules to extract annotated text into separate fields", "items": { "type": "object", "required": ["sourceFieldName", "extractedAnnotationType", "labelPattern", "targetFieldName"], "properties": { "sourceFieldName": { "type": "string", "title": "Source Field Name" }, "extractedAnnotationType": { "type": "string", "title": "Annotation Type to Extract", "enum": ["sentence", "named_entity", "part_of_speech", "noun_chunk"], "default": "named_entity" }, "labelPattern": { "type": "string", "title": "Label Pattern", "description": "Extract all annotations with labels that match this regular expression", "default": ".*", "format": "regex" }, "targetFieldName": { "type": "string", "title": "Target Field Name" } } } }, "indexAnnotations": { "type": "boolean", "title": "Index Annotations?", "description": "Index annotations as overlapping tokens using pre-analyzed fields", "default": false, "hints": ["advanced", "hidden"] } }, "category": "Natural Language Processing", "categoryPriority": 5, "unsafe": false }; export const SchemaParamFields = ({schema}) => { const sanitize = str => { if (typeof str !== "string") return str; return str.replace(/^"(.*)"$/s, "$1").replace(/\\/g, "").replace(/"/g, "'"); }; const formatDescription = str => { const s = sanitize(str); return (/[.!?]\)*$/).test(s) ? s : `${s}.`; }; const {description, properties = {}, required: requiredProps = []} = schema; const visibleProps = useMemo(() => Object.entries(properties).filter(([, prop]) => !prop.hints?.includes("hidden")), [properties]); return
{description &&

{formatDescription(description)}

} {visibleProps.map(([name, prop]) => { const isRequired = requiredProps.includes(name); const hasDefault = prop.default !== undefined; const rawDefault = prop.default; const isComplexDefault = hasDefault && (typeof rawDefault === "object" || typeof rawDefault === "string" && (rawDefault.length > 20 || rawDefault.includes('"'))); const fieldProps = { key: name, body: prop.title || name, type: prop.type, ...prop.title && ({ post: [<>API property: {name}] }), ...isRequired && ({ required: true }), ...!isComplexDefault && hasDefault ? { default: sanitize(String(rawDefault)) } : {} }; const isObject = prop.type === "object" && prop.properties; const isArrayOfObjects = prop.type === "array" && prop.items?.type === "object" && prop.items.properties; return {prop.description &&

{formatDescription(prop.description)}

} {isComplexDefault &&

Default: 

                  
                    {JSON.stringify(rawDefault, null, 2)}
} {isArrayOfObjects &&

Object attributes: 

                
                  {'{\n'}
                  {Object.entries(prop.items.properties).map(([iname, iprop]) => <>
                      {`  ${iname}`}
                      {prop.items?.required?.includes(iname) &&  required}
                      {`: {\n    display name: ${sanitize(iprop.title || '')}\n    type: ${iprop.type}\n  }\n`}
                    )}
                  {'}'}
} {isObject && } ; })}
; }; export const LwTemplate = ({title = "Key questions to get you started", icon = "sparkles", cta = "Powered by Agent Studio", linkHref = "https://lucidworks.com/demo/?utm_source=docs&utm_medium=referral&utm_campaign=docs_cta_ai"}) => { const [isLoaded, setIsLoaded] = useState(false); useEffect(() => { const timer = setTimeout(() => { setIsLoaded(true); }, 500); return () => clearTimeout(timer); }, []); return
{isLoaded && ` }} />} Powered by Lucidworks Agent Studio
; }; [localhost link]: http://localhost:3000/docs/4/fusion-server/reference/pipeline-stages/indexing/nlp-annotator-index-stage [mintlify link]: https://doc.lucidworks.com/docs/4/fusion-server/reference/pipeline-stages/indexing/nlp-annotator-index-stage [old doc.lw link]: https://doc.lucidworks.com/fusion/5.9/204 The NLP Annotator index stage performs [Natural Language Processing](/docs/4/fusion-ai/concepts/nlp) tasks. You can choose from different NLP libraries, either [OpenNLP](https://opennlp.apache.org/) or the [JohnSnow Lab](https://nlp.johnsnowlabs.com/) library, which runs on Spark. Note: Only the pre-trained NER model is supported. If choosing an NER model, download **NerDLModel** instead of NerCRFModel. The NLP Annotator supports the following tasks: * If choosing JohnSnow Lab (recommended for large dataset processing): * NER (Name Entity Recognition) Fusion AI uses the deep learning pre-trained NER model that JohnSnowLab provides. Currently, the pre-trained extraction model covers the following name entities: * ORG (organization) * PER (person) * LOC (location) This means that there are the only three types of entities Fusion will recognize from the source field. * Sentence detection * POS(Part of Speech) Tagging * If choosing OpenNLP: * NER * Sentence detection * POS Tagging * Shallow Parsing (Chunking) **Example of how to use NLP Annotator Index stage:** 1. Add NLP Annotator index stage. add nlp stage 2. Choose the annotator type (OpenNLP or SparkNLP). which model If you select the `sparknlp` model, you need to download and install one or more models: .. Download the models at [https://github.com/JohnSnowLabs/spark-nlp#models](https://github.com/JohnSnowLabs/spark-nlp#models). .. Rename the downloaded models to something easy to identify, then upload them to Fusion’s [blob store](/docs/4/fusion-server/concepts/indexing/blob-storage). * add_blob 3\. Configure the index pipeline stage: 1. Specify the model to use (fill the box with `model id` in the blob store). model_id_blob fill_index_stage 2. Specify the source, label pattern, and target (destination) fields: * source field: the raw text with name entities to be extracted. * label pattern: regex pattern that matches the NER/POS labels: for example, `PER.` will match extracted name entities with label `PERSON`, while `NN.` will match tagged nouns. * target field: the outcome extraction/tagging and so on. source_target result ## Configuration When entering configuration values in the UI, use *unescaped* characters, such as `\t` for the tab character. When entering configuration values in the API, use *escaped* characters, such as `\\t` for the tab character.