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

#ai #rag #python #opensource

If you've ever built a RAG (Retrieval-Augmented Generation) 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.
pyragcore is a modular RAG library built on FAISS and Ollama. The idea is simple: give you a solid foundation so you can focus on your actual use case instead of reinventing the plumbing every time.
What I'm most proud of is that it runs entirely locally — no external APIs, no data leaving your machine. Just Ollama for the LLM and SentenceTransformers for embeddings.

What's inside

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

How to install it:

pip install pyragcore[all]

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)