RAG in 3 Lines of Python
Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire setup. No API keys required - runs on Ollama + sentence-transformers locally. What it does: - All formats - PDF, Word, Excel, Markdown, code, URLs, images, audio - Auto-updates - watches sources, refreshes in background, zero query latency - Citations - every answer includes sources…
RAG in 3 Lines of Python launched on December 4, 2025 with 41 votes, #462 of 11,628 launches that month and more than 90% of that year's launches.
In plain words
Piragi is a Python library that simplifies retrieval-augmented generation by handling vector stores, embeddings, and document chunking automatically. It ingests PDFs, Word documents, Excel files, Markdown, code, URLs, images, and audio, then answers questions with source citations. The system runs locally using Ollama and sentence-transformers without requiring API keys, automatically updates documents in the background, and supports advanced retrieval techniques like hybrid search and cross-encoder reranking. It's designed for developers who need RAG functionality without complex setup.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire…
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