Enhancing Automated Code Generation with GraphRAG - Project Showcase

GDG Surrey

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.

Mar 26, 12:30 – 1:30 AM (UTC)

48 RSVP'd

Key Themes

AICloud

About this event

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

    Hunyo

    CTO and Co-Founder

Host

  • Priti Yadav

    Full Stack Engineer | AI/ML

Partner

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Human in loop podcasts

Organizers

  • Priti yadav

    GDG Organizer

  • Yashi Girdhar

    Software Developer, Volunteer

  • Sam Huo

    Senior Software Developer, Co-Organizer

  • Sowndarya Venkateswaran

    Data Scientist, Instructor

  • Riya Eliza

    The University of British Columbia

    Data Scientist Co-Organizer GDG Surrey

  • Darshan Parikh

    Autodesk Inc.

    Android Engineer | Co - Organizer

  • Justin Xiao

    British Columbia Institute of Technology

    Outreach Coordinator

  • Bishneet Rekhi

    Volunteer

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