Honegumi RAG Assistant

Agentic Code Generation for Bayesian Optimization

Honegumi RAG Assistant Pipeline

An intelligent AI assistant that converts natural language problem descriptions into ready-to-run Bayesian optimization code using Meta’s Ax Platform

Overview

Honegumi RAG Assistant is an advanced agentic AI system that automatically generates high-quality, executable Python code for Bayesian optimization experiments. Built on top of Honegumi, it uses LangGraph and OpenAI GPT models to orchestrate multiple specialized agents that collaborate to understand your optimization problem, retrieve relevant documentation, and generate production-ready code using the Ax Platform.

Simply describe your optimization problem in plain English, and the assistant produces complete, runnable code tailored to your specific requirements.

Key Capabilities

  • Natural language to code: Describe optimization problems conversationally

  • Intelligent RAG: Parallel retrieval of relevant Ax documentation to supplement skeleton code

  • Built on Honegumi: Leverages Honegumi for deterministic skeleton generation

  • Multi-agent architecture: Specialized agents for parameter extraction, retrieval planning, and code writing

  • Flexible model selection: Mix GPT-o1 and GPT-4o models for cost-performance optimization

Quick Start

Install via pip:

pip install honegumi-rag-assistant

Run the CLI:

honegumi-rag

The assistant will prompt you:

Please describe your Bayesian optimization problem.
(Press Enter when finished)

Your problem:

Press Enter after describing your problem, and the code will be generated!

Or try it in Google Colab!

Documentation