Enhancing Automated Code Generation with GraphRAG - Project Showcase

​Mandatory RSVP as this is from where you get joining link - Link

​Unlike traditional Retrieval Augmented Generation (RAG) methods that use vector embeddings, my project leverages Graph-based Retrieval Augmented Generation (GraphRAG) to effectively capture dependencies, deprecations, limitations, version control, and best practices within API documentation.

​By representing APIs as graphs, I trained Claude 3.5 Sonnet to generate accurate code for an unfamiliar package, achieving a 90% success rate across 50 diverse requests. This scalable solution is ideal for API-first companies, enabling them to effortlessly set up and manage GraphRAG representations of their APIs and SDKs, thereby optimizing automated code generation with large language models and streamlining development workflows.

​Tech stack - Supabase, Vercel, Neo4j, Google Cloud Run Functions

​Speaker - Joaquin Coromina, CTO and Co-Founder, Hunyo