NLP Tools - Sentiment, NER, Toxicity & Language Detection

Analyze text for toxic content. Returns scores for 6 categories: toxic, severe_toxic, obscene, threat, insult, identity_hate. Each score is 0.0-1.0. BERT-based classifier with sub-15ms latency on GPU. Args: text: Text to analyze for toxicity (hate speech, insults, threats). Returns: dict with keys: - toxic (float 0-1): Overall toxicity score - severe_toxic (float 0-1): Severe toxicity score - obscene (float 0-1): Obscenity score - threat (float 0-1): Threat score - insult (float 0-1): Insult score - identity_hate (float 0-1): Identity-based hate score - is_toxic (bool): Whether text exceeds toxicity threshold

{
  "type": "object",
  "properties": {
    "text": {
      "description": "Text to analyze for toxicity (hate speech, insults, threats)",
      "maxLength": 100000,
      "type": "string"
    }
  },
  "required": [
    "text"
  ]
}

Analyze text sentiment. Returns positive/negative classification with confidence scores. DistilBERT-based with sub-10ms latency. Multiple domain-specific model variants available. Args: text: Text to analyze for sentiment (positive/negative). model: Model variant -- 'general' (default), 'financial', 'twitter'. Returns: dict with keys: - label (str): 'positive' or 'negative' - score (float 0-1): Confidence score for the predicted label - scores (dict): All label scores (positive, negative)

{
  "type": "object",
  "properties": {
    "text": {
      "description": "Text to analyze for sentiment (positive/negative)",
      "maxLength": 100000,
      "type": "string"
    },
    "model": {
      "default": "general",
      "description": "Model variant: 'general' (default), 'financial', 'twitter'",
      "type": "string"
    }
  },
  "required": [
    "text"
  ]
}

Extract named entities (NER) from text. Identifies persons, organizations, locations, and miscellaneous entities with span offsets and confidence scores. BERT-NER based with sub-50ms latency. Args: text: Text to extract named entities from. Returns: dict with keys: - entities (list): Detected entities, each containing: - text (str): Entity text - label (str): Entity type (PER, ORG, LOC, MISC) - start (int): Character offset start - end (int): Character offset end - score (float 0-1): Confidence score - count (int): Total number of entities found

{
  "type": "object",
  "properties": {
    "text": {
      "description": "Text to extract named entities from (persons, organizations, locations)",
      "maxLength": 100000,
      "type": "string"
    }
  },
  "required": [
    "text"
  ]
}

Detect personally identifiable information (PII) in text. Finds emails, phone numbers, SSNs, credit cards, IP addresses, and person names. Optionally returns redacted text with PII replaced by type labels (e.g. [EMAIL], [PHONE]). BERT-NER + regex ensemble. Args: text: Text to scan for personally identifiable information. redact: If true, return redacted text with PII replaced by [TYPE]. Returns: dict with keys: - pii_found (list): Detected PII items, each containing: - text (str): The PII value found - type (str): PII type (EMAIL, PHONE, SSN, CREDIT_CARD, IP, PERSON) - start (int): Character offset start - end (int): Character offset end - score (float 0-1): Detection confidence - count (int): Total PII items found - redacted_text (str|null): Text with PII replaced (when redact=true) - has_pii (bool): Whether any PII was detected

{
  "type": "object",
  "properties": {
    "text": {
      "description": "Text to scan for personally identifiable information",
      "maxLength": 100000,
      "type": "string"
    },
    "redact": {
      "default": false,
      "description": "If true, return redacted text with PII replaced by [TYPE]",
      "type": "boolean"
    }
  },
  "required": [
    "text"
  ]
}

Detect the language of text. Supports 176 languages using fastText. Sub-1ms inference latency. Returns ISO 639-1 codes with confidence scores. Args: text: Text to identify the language of. top_k: Number of top language predictions to return (default: 3). Returns: dict with keys: - language (str): Top predicted language ISO 639-1 code - confidence (float 0-1): Confidence for top prediction - predictions (list): Top-k predictions, each with: - language (str): ISO 639-1 code - confidence (float 0-1): Prediction confidence

{
  "type": "object",
  "properties": {
    "text": {
      "description": "Text to identify the language of",
      "maxLength": 100000,
      "type": "string"
    },
    "top_k": {
      "default": 3,
      "description": "Number of top language predictions to return",
      "type": "integer"
    }
  },
  "required": [
    "text"
  ]
}

Check health status of NLP API services and loaded models. Returns: dict with keys: - status (str): 'healthy' or error state - models (dict): Loaded model status per capability - version (str): API version

{
  "type": "object",
  "properties": {}
}