contentintelpy 0.1.0

Production-grade NLP library for unified content intelligence.

contentintelpy

Production-grade NLP library for unified content intelligence.

contentintelpy provides a unified, DAG-based engine for multilingual sentiment analysis, NER, translation, and summarization using real transformer models (RoBERTa, GLiNER, NLLB).

Features

• Real Models: No heuristics. Uses State-of-the-Art Transformers.
  • Sentiment: RoBERTa
  • NER: GLiNER
  • Translation: NLLB (GPU) + ArgosTranslate (Offline CPU)
• Hybrid Execution: Models download on first run (lazy-loaded). Offline fallback available.
• Deterministic Pipelines: DAG-based execution guarantees order.
• Dual API:
  • Pipeline-first for complex workflows.
  • Service-first for quick scripts.
• Production Ready: Thread-safe, standard error handling, sparse outputs.

Installation

Install the base library:

pip install contentintelpy

Optional Dependencies (Recommended)

Since the library uses heavy ML models, you should install the specific components you need:

# For all core features
pip install "contentintelpy[core,ner,translation,summarization]"

# For development
pip install "contentintelpy[dev]"

[!IMPORTANT] spaCy Model Requirement If you use NER or language features, you must install a spaCy model manually:

python -m spacy download en_core_web_sm

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Quick Start

Ideal for simple tasks in notebooks or scripts.

from contentintelpy import SentimentService, TranslationService

# Sentiment
service = SentimentService()
result = service.analyze("This library is amazing!")
print(result) 
# {'value': 'positive', 'confidence': 0.99, ...}

# Translation
translator = TranslationService()
text = translator.translate("Hola mundo", target="en")
print(text)
# "Hello world"

Production Usage (Pipeline-First)

Recommended for Backends, APIs, and Data Pipelines.

import contentintelpy as ci

# 1. Create the canonical pipeline
pipeline = ci.create_default_pipeline()

# 2. Run it (Thread-safe)
result = pipeline.run({
    "text": "गूगल ने बेंगलुरु में नया कार्यालय खोला"
})

# 3. Access Sparse Output
print(result)

Output Example:

{
  "text": "...",
  "text_translated": "Google opened a new office in Bengaluru",
  "language": "hi",
  "entities": [
    {"text": "Google", "label": "ORG"},
    {"text": "Bengaluru", "label": "LOC"}
  ],
  "sentiment": {
    "value": "neutral",
    "value_en": "neutral",
    "confidence": 0.95
  },
  "summary": "..."
}

Error Handling

Nodes never crash the pipeline. Errors are collected in errors dict.

{
    "text": "...",
    "errors": {
        "TranslationNode": "Model download failed: Connection error"
    }
}

Architecture

This library is pure logic. It does NOT contain:

• Flask / FastAPI routes
• Database models
• Authentication

It is designed to be consumed by your backend application.