Metadata-Version: 2.4
Name: ragpy-core
Version: 1.0.0
Summary: A modular Retrieval-Augmented Generation (RAG) pipeline for Python.
Author: William Klusman
Author-email: William Klusman <klusmannwilliam@gmail.com>
License: MIT License
        
        Copyright (c) 2026 William Klusman
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the “Software”), to deal
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in  
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Keywords: RAG,retrieval-augmented-generation,LLM,vector-database,azure-openai,machine-learning,nlp
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: chromadb>=0.4.0
Requires-Dist: numpy>=1.20
Requires-Dist: tiktoken>=0.5.0
Requires-Dist: requests>=2.0
Requires-Dist: openai>=1.0.0
Requires-Dist: pypdf>=4.0.0
Dynamic: author
Dynamic: license-file
Dynamic: requires-python

RAGpy
	RAGpy is a lightweight, modular Retrieval-Augmented Generation (RAG) pipeline for Python. It provides a clear and testable architecture for document ingestion, chunking, embedding, retrieval, reranking, context compression, and grounded answer generation using Azure OpenAI and ChromaDB.

	RAGpy is designed for developers who want a transparent, hackable RAG system without the complexity of large frameworks.

Features
	Modular ingestion pipeline for text and PDF documents

	Chunking and batching utilities for efficient embedding

	Azure OpenAI embeddings and chat completions

ChromaDB vector database integration

LLM-based reranking for improved retrieval quality

Context compression to reduce token usage

Fully monkeypatch-friendly design for offline testing

Clean architecture suitable for extension and customization

Installation
Once published to PyPI:

Code
pip install ragpy
For development:

Code
git clone https://github.com/yourusername/ragpy
cd ragpy
pip install -e .
Quickstart Example
python
from ragpy.RAGOrchestrator import IngestFile, GenerateAnswer
from ragpy.VectorDatabase import OpenDatabase

OpenDatabase("AeroDB", "./vectorDB")
IngestFile("engine_vibration.pdf", "AeroDB")

answer = GenerateAnswer("What causes engine vibration?", "AeroDB")
print(answer)
How RAGpy Works
1. Ingestion
Load text or PDF using FileLoader

Chunk text using TextChunker

Batch chunks using ChunkBatcher

Generate embeddings with Azure OpenAI

Store vectors and metadata in ChromaDB

2. Retrieval
Embed the user query

Retrieve top-K candidates from the vector database

3. Reranking
Use an LLM-based reranker to reorder retrieved chunks by relevance

4. Compression
Summarize top chunks into a compact context block

5. Answer Generation
Build a prompt using compressed context

Generate a grounded answer using Azure OpenAI

Project Structure
Code
ragpy/
    AzureOpenAIRelay.py
    RAGOrchestrator.py
    VectorDatabase.py
    Reranker.py
    ChunkCompressor.py
    loaders/
        FileLoader.py
        TextChunker.py
    batching/
        ChunkBatcher.py
tests/
docs/
Requirements
Python 3.9+

ChromaDB

numpy

tiktoken

pypdf

openai (Azure OpenAI SDK)

Testing
RAGpy includes a full pytest suite. All Azure calls are monkeypatch-friendly, allowing offline testing with mock LLMs.

Run tests:

Code
pytest -q
Contributing
Contributions are welcome.
Please open an issue or submit a pull request on GitHub.

Planned enhancements include:

Local embedding support (sentence-transformers)

Hybrid retrieval (vector + keyword)

Multimodal RAG (image + text)

Evaluation tools for relevance and faithfulness

Agentic RAG extensions

License
RAGpy is released under the MIT License.
