nlqcat 0.1.0

Natural Language Question Answering Toolkit (Hybrid NLP + GenAI)

NLQcat: Natural Language Question Answering Toolkit

NLQcat is a production-ready, hybrid NLP + GenAI library that unifies classic linguistic analysis (spaCy) with modern semantic search (Vector Databases) and Large Language Models (LLMs) into a single, easy-to-use pipeline.

📦 Installation

pip install nlqcat
python -m spacy download en_core_web_sm

🚀 Quick Start (3-Line Example)

from nlqcat.core.pipeline import Pipeline

pipe = Pipeline(vector_store_type="chroma")
pipe.add_documents(["NLQcat combines linguistic NLP with semantic RAG."])
print(pipe.query("What does NLQcat do?")['answer'])

✨ Features

• Hybrid Analysis: Seamlessly mixes spaCy's linguistic features (NER, POS tagging) with semantic embeddings.
• Unified RAG Pipeline: Built-in Retrieve-Augmented Generation flow (Query -> Retrieve -> Answer).
• Vector Store Support: Integrated ChromaDB client for local vector storage.
• Modular Design: Plug-and-play components for Loaders, Preprocessors, and Agents.
• LLM Ready: Simple OpenAIAgent integration for generative answers.

🗺️ Roadmap

• Support for FAISS and Pinecone vector stores.
• Integration with HuggingFace and Anthropic LLMs.
• Advanced RAG techniques (Re-ranking, HyDE).
• Async support for high-throughput pipelines.