The DSPy (Declarative Self-improving Language Programs in Python) framework has excited the developer community with its ability to automatically optimize and enhance language model pipelines, which may reduce the need to manually fine-tune prompt templates.

We designed a custom DSPy pipeline integrating with knowledge graphs. The reason? One of the main strengths of knowledge graphs is their ability to provide rich contextual information from interconnected data, which can potentially enhance the system’s retrieval and reasoning abilities.We tested both the vanilla DSPy RAG pipeline and our customized one to see how they work, and you'll find out in the video that I encountered more surprises than expected. YouTube Video: https://www.youtube.com/watch?v=mHREErgLmi0