I published my first Python package — a reusable RAG core library

Source: Dev.to

Overview

If you’ve ever built a Retrieval‑Augmented Generation (RAG) system, you know the pain: every new project means rewriting the same boilerplate—vector store setup, embeddings, chunking, LLM wiring. I got tired of it, so I packaged it up.

Features

• FAISS vector store with persistence, deduplication, and metadata filtering
• Semantic search with MMR support
• Local LLM inference via Ollama
• Modular installs — grab only what you need
• Abstract base classes so you can extend it your way

Installation

pip install pyragcore[all]

Usage

from pyragcore import BasePipeline

class MyPipeline(BasePipeline):
    def ingest(self, source: str) -> str:
        # your ingestion logic here
        ...

pipeline = MyPipeline(persist_dir="./memory", output_folder="./output")
source_id = pipeline.ingest("./my_document.pdf")
answer = pipeline.ask("What is this document about?", source_id=source_id)
print(answer)

Status

It’s still early days (v0.1.11) and I’m actively working on it, but it’s already powering a couple of projects I built—a document chat bot.

Links

• PyPI: https://pypi.org/project/pyragcore/
• GitHub: https://github.com/yourusername/pyragcore

Feel free to share feedback, ideas, or contributions. If you build something with it, let me know!