text-classifier
Text classification and labeling microservice: assign the best label to a piece of text from a caller-supplied label set, detect conversational intent, extract topics, and identify language. Tools...
text-classifier — text classification + labeling microservice
Assign the best label to text from a caller-supplied label set, detect conversational intent, extract topics, do zero-shot labeling, suggest tags, and identify language. A paid "intelligent microservice": every tool has a deterministic bag-of-words / cosine / trigram core that runs fully offline; classify and zeroShot additionally enrich with an LLM when reachable and silently fall back. Results carry { mode: 'heuristic' | 'llm' }.
Tools
| Tool | Args | Returns | ||
|---|---|---|---|---|
classify | { text, labels, options?:{threshold,multiLabel} } | best label + ranked scores (softmax probs). labels = array \ | comma-string \ | {label:[keywords]} map (supervised, stronger). |
intent | { text } | intent ∈ question/request/complaint/praise/greeting/farewell/statement, confidence, per-intent scores. | ||
topics | { text, top? } | salient topics (phrases + terms) and top terms with counts. | ||
zeroShot | { text, labels, hypothesis?, options? } | zero-shot best label + scores against arbitrary labels with an NL hypothesis template ({} placeholder). | ||
tag | { text, top? } | suggested keyword tags + hashtags. | ||
language | { text } | language code + name + confidence via character-trigram profile (en/es/fr/de/it/pt/nl/sv/pl/la). |
Usage
import pack from './index.js';
await pack.adapters.classify({ text: 'my payment failed again', labels: ['billing', 'shipping', 'account'] });
await pack.adapters.classify({ text: 'reset my password', labels: { auth: ['password', 'login', 'reset'], billing: ['invoice', 'charge'] } });
pack.adapters.intent({ text: 'Could you please refund my order?' });
pack.adapters.topics({ text: 'machine learning models train on large datasets ...' });
pack.adapters.language({ text: 'Bonjour, comment allez-vous aujourd\'hui?' });Every tool takes ONE args object (maps 1:1 onto an HTTP POST body). Bad input throws TypeError.
Hybrid intelligence
Set OPENAI_BASE_URL+OPENAI_API_KEY or run a local Ollama to enable the AI path on classify and zeroShot. With no model the heuristic core is used. A down model never throws.
DRY boundary
nlpowns generic corpus stats (sentiment, RAKE keywords, ngrams, readability, stopword-based
language, word frequency). text-classifier is the classification workflow: map text → a caller's label set, intent, topics, zero-shot, tags, and trigram language ID (deliberately a different method than nlp.languageDetect).
embeddingsowns real vector cosine over learned vectors; here cosine is over surface term/trigram
bags, self-contained.
Pure ESM, Node built-ins only, zero npm deps (only ../_shared/llm.js for the optional AI path).