Pythonaibrain-NLP 0.2.0 Is Now on PyPI - A Structured NLU/NLG Architecture for Python

Today I'm releasing Pythonaibrain-NLP 0.2.0, the latest public release of my Python NLP framework. The package is now available on PyPI, and the complete source code, documentation, architecture notes, examples, and tests are available on GitHub.

• PyPI: https://pypi.org/project/Pythonaibrain-NLP/
• GitHub: https://github.com/DivyanshuSinha136/Pythonaibrain-NLP

Install it with:

pip install pythonaibrain-nlp

Why another NLP framework?

Pythonaibrain-NLP was built around a different idea. Instead of making a transformer the center of everything, I wanted to build a more structured NLP system where understanding, dialogue state, retrieval, and generation are explicit components of the architecture.

The current system combines:

• Neural intent classification
• Slot filling
• Dialogue context
• Retrieval-augmented responses
• Neural language generation
• A controllable NLG architecture
• Standalone NLU and NLG APIs

The goal isn't to replace every modern NLP architecture. The goal is to provide a structured, understandable, trainable NLP pipeline that can be integrated into Python applications.

The architecture

The core pipeline is:

User Input
│
▼
┌─────────────┐
│ NLU         │
│             │
│ Intent      │
│ + Slots     │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│ Dialogue State  │
│ + Context       │
└────────┬────────┘
         │
    ┌────┴────┐
    ▼         ▼
Function/API  RAG
Dispatch      Retrieval
    │         │
    └────┬────┘
         ▼
┌─────────────┐
│ NLG         │
│ SC-LSTM     │
└──────┬──────┘
       │
       ▼
Response

This separation makes each stage independently accessible and easier to experiment with.

NLU

The NLU subsystem uses a joint neural architecture for:

• Intent classification + slot tagging

The model is designed to understand both what the user wants and which pieces of information