PyBlend: Towards an AI Food Scientist for Nutritional Product Design

Imagine having a “food scientist” built in Python who, instead of wearing a lab coat, uses DAGs, embeddings, and LLMs to help you design nutritious powder blends. In this talk I’ll present PyBlend, an AI agent that takes a nutritional brief in natural language (for example: “I want a vegan, high‑protein, low‑sugar blend that’s suitable for dehydration”) and turns it into a quantitative formulation ready for the lab: ingredients, raw and dehydrated proportions, nutritional profile, and estimated cost. We’ll walk step by step through how to combine intelligent ingredient search (starting from one‑hot encodings and tabular features, all the way to text and nutrition embeddings), hybrid retrieval over food databases, and LLM agents orchestrated in a directed acyclic graph. Everything is implemented in Python, built on open-source libraries, and designed to be reproducible and extensible for anyone who wants to push language models beyond the classic “chatbot” use case. If you’re interested in building Python agents that do real scientific/applied work, not just answer questions, if you work with tabular data, search, optimization, or simply want to see how an LLM can end up designing a functional food formulation, this talk is for you. You’ll leave with concrete ideas, architecture patterns, and code examples you can adapt to your own domains.