Back to Projects
Agentic AIadvanced

Production-Grade advance RAG with LangGraph, GCP, and Groq

Develop a production-grade, cyclic Retrieval-Augmented Generation (RAG) system leveraging LangGraph, Google Cloud Platform (GCP), and Groq for accelerated inference. This system intelligently distinguishes between technical 'True Data' and random 'Noisy Data' through semantic re-ranking and history-aware planning, ensuring accurate and contextually relevant responses.

37 lectures

What You Will Learn

Mastering LangGraph for building cyclic RAG pipelines.
Implementing semantic re-ranking techniques to filter noisy data.
Designing a scalable, three-tier enterprise cloud architecture on GCP.
Building event-driven data ingestion pipelines using GCS and Eventarc.
Implementing persistent memory using Postgres.
Orchestrating multi-container builds with Cloud Build
Automating infrastructure provisioning with Terraform.

System Architecture

Production-Grade advance RAG with LangGraph, GCP, and Groq Architecture Diagram

High-level architecture overview of the Production-Grade advance RAG with LangGraph, GCP, and Groq .

What You'll Build

  • A production-grade cyclic RAG system.
  • An automated data ingestion pipeline.
  • A scalable microservices architecture on GCP.
  • Infrastructure-as-Code using Terraform.

Project Instructor

Divesh Jadhwani

Divesh Jadhwani

3+ years exp
LinkedIn
Production-Grade advance RAG with LangGraph, GCP, and Groq
Premium
One Subscription. 40+ Projects. Unlimited Access.
AccessMobile & Web