Cloud-Native AI Document Assistant with Full-Stack MLOps
Author: Anonymous Builder
Status: Draft
Duration: 1 Week Deep Dive
Executive Summary
This project develops an AI-powered document Q&A system leveraging Retrieval-Augmented Generation (RAG) with LangChain and a FastAPI backend. It integrates a full MLOps pipeline, covering cloud infrastructure provisioning on AWS/Azure with Terraform, containerization with Docker, and orchestrated deployment via Kubernetes, all monitored through Prometheus and Grafana.
Key Skills
Project Execution Log
Stage 1: Design & Develop RAG API with LangChain
This stage successfully established the core intelligence of our AI document assistant. We leveraged LangChain to orchestrate document processing (loading, chunking, embedding, vector storage) and the RAG workflow. FastAPI was used to expose this functionality as a robust, scalable API, laying the essential groundwork for integrating with front-end applications and cloud infrastructure in subsequent stages.
Deliverables
- [x] `rag_api/main.py`: The main FastAPI application file, defining all API endpoints (e.g., `/ingest_document`, `/query`).
Stage 2: Containerize Application & Configure Local Kubernetes
Deliverables
Stage 3: Provision Cloud Infrastructure with Terraform
Deliverables
Stage 4: Build CI/CD Pipeline for Kubernetes Deployment
Deliverables
Stage 5: Implement Monitoring & MLOps Best Practices
Deliverables