DSPyG is a new optimization, based on DSPy, extended w/ graph theory insights. Real world example of a Multi Hop RAG implementation w/ Graph optimization. New DSPy integration with a Graph Optimizer (PyG) to enhance AI contributions in research without the need for extensive Compute Cloud resources. The focus is on the multi-hop DSPy program's ability to decompose complex queries into simpler questions, using various retrieval methods. This method's efficacy in addressing intricate questions like, eg the environmental impact of solar panels is showcased. The process involves splitting a complex query into manageable parts, retrieving answers through diverse sources, and synthesizing these into a comprehensive response. The speaker elaborates on leveraging graph structures for optimizing question-answer pathways, suggesting a novel approach for academic exploration in AI with limited infrastructure. This methodology, unexplored by major institutions, offers vast research opportunities in specialized knowledge domains without requiring significant computational power, making it an ideal topic for academic projects or PhD research. https://github.com/stanfordnlp/dspy all rights with authors: DSPY: COMPILING DECLARATIVE LANGUAGE MODEL CALLS INTO SELF-IMPROVING PIPELINES by Stanford, UC Berkeley, et al https://arxiv.org/pdf/2310.03714.pdf