Hypothesis Sage demonstrates the implementation of an intelligent statistical analysis assistant using LlamaIndex workflows and RAG architecture. This command-line tool combines a curated statistical database with advanced language models to provide contextual recommendations for hypothesis testing and statistical analysis scenarios. The project showcases practical applications of AI agents in educational and analytical domains.
Key Features
• Multi-Modal Statistical Assistance: Query database retrieval, test recommendation engine, concept explanation, and example generation capabilities • Intelligent Agent Architecture: LlamaIndex workflow implementation enabling complex statistical question resolution and comprehensive guidance • CLI-First Design: Typer-powered command-line interface providing direct access to individual tools and integrated agent functionality • Curated Knowledge Base: Structured statistical information database supporting contextual recommendations and educational content
Technical Implementation
The project demonstrates advanced RAG implementation patterns using LlamaIndex workflows for building domain-specific AI assistants. The architecture combines structured statistical knowledge with language model capabilities, showcasing practical approaches to creating educational AI tools. The modular CLI design illustrates scalable patterns for AI agent deployment with plans for web interface expansion.