- Designed and developed a production-ready Retrieval-Augmented Generation (RAG) web app with PDF ingestion and contextual AI chat capabilities.
- Implemented backend services in Python using FastAPI, LangChain, and Google Gemini Pro to support semantic search and response generation.
- Integrated Redis and Celery for asynchronous task queues, improving responsiveness and scalability.
- Built a responsive frontend using React.js and Bootstrap for file uploads, chat interaction, and displaying AI-generated insights.
- Deployed on a cloud VPS using Docker, Nginx, HTTPS (SSL), and environment-managed API keys.
- Created RESTful API endpoints for document processing and chat flow, supporting up to 3MB PDF uploads.
- Performed data preprocessing using exploratory data analysis (EDA) and KNN-based filtering.
PERCY PHAM
About Me
Hi, I am Phong (Percy)
I am currently developing my skills as a software developer by building personal projects and exploring new tools. My main focus right now is creating web applications, but I would also love to make a game since I enjoy gaming. I have always enjoyed creating things and building tools.
Outside of coding, I enjoy going to the gym, playing games, and watching movies. I’ve been especially into films lately, so feel free to check out my (Letterboxd ↗). You can also check out my (Pinterest ↗)or (Music ↗)to get a feel for interests and aesthetic. Oh! Food is also one of my biggest passions 🍜!
Experience
- Developed responsive web components using React.js, Node.js, TypeScript, Bootstrap, and Tailwind CSS for a client-facing platform.
- Collaborated with backend engineers to integrate and test RESTful APIs using Postman.
- Participated in sprints, design reviews, and deployment planning using Git.
- Gained hands-on exposure to backend infrastructure involving FastAPI and Uvicorn.
Projects
prcpham-dev@portfolio — /ChatDocument
projects$ ls
cd /chatdocument
name: ChatDocument
tech: #FastAPI#LangChain#Redis#Celery#React.js#Bootstrap#Docker#Nginx
Developed a Retrieval-Augmented Generation (RAG) app enabling users to upload and analyze PDFs with contextual AI chat. Integrated Google Gemini Pro (2M tokens) with LangChain for embeddings, semantic search, and long-context reasoning. Built a scalable backend with FastAPI, offloading heavy tasks asynchronously using Redis and Celery. Designed a responsive frontend in React.js and Bootstrap for file upload, chat interaction, and insights display. Deployed on a VPS with Docker, Nginx, and SSL certificates, securing API keys via environment management.
TEST GAMEPLAY
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