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 - Advanced retrieval - HyDE, hybrid search (BM25 + vector), cross-encoder reranking - Smart chunking - semantic, contextual, hierarchical strategies - OpenAI compatible - swap in GPT/Claude whenever you want Quick examples: # Filter by metadata answer = kb.filter(file_type="pdf").ask("What's in the contracts?") #Enable advanced retrieval kb = Ragi("./docs", config={ "retrieval": { "use_hyde": True, "use_hybrid_search": True, "use_cross_encoder": True } }) # Use OpenAI instead kb = Ragi("./docs", config={"llm": {"model": "gpt-4o-mini", "api_key": "sk-..."}}) Install: pip install piragi PyPI: https://pypi.org/project/piragi/ Would love feedback. What's missing? What would make this actually useful for your projects?