ragpy-core 1.0.10

A modular Retrieval-Augmented Generation (RAG) pipeline for Python.

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, PDF, and DOCX documents • Deterministic 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

From PyPI: pip install ragpy

Quickstart Example

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

Ingestion

Load text, PDF, or DOCX using FileLoader

Chunk text using TextChunker

Batch chunks using ChunkBatcher

Generate embeddings with Azure OpenAI

Store vectors and metadata in ChromaDB

Retrieval

Embed the user query

Retrieve top-K candidates from the vector database

Reranking

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

Compression

Summarize top chunks into a compact context block

Reduce token usage while preserving grounding

Answer Generation

Build a prompt using compressed context

Generate a grounded answer using Azure OpenAI

Project Structure

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 • python-docx • 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: 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.