I published my first Python package — a reusable RAG core library
Vlad has released his first Python package, pyragcore, a reusable and modular RAG (Retrieval-Augmented Generation) library designed to reduce repetitive coding tasks. Built on FAISS and Ollama, the library supports local execution with no external APIs, enabling semantic search and document ingestion with metadata filtering. The package is open source and available on PyPI and GitHub, with active development and community contributions welcomed.
- ▪Vlad published pyragcore, a reusable RAG core library for Python.
- ▪The library runs entirely locally using FAISS for vector storage and Ollama for local LLM inference.
- ▪pyragcore supports semantic search with MMR, metadata filtering, and modular installation options.
- ▪It is currently at version 0.1.11 and is already being used in document chatbot projects.
- ▪The package is open source and available on both PyPI and GitHub for contributions and feedback.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3906919) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Vlad Posted on May 1 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.
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