I Built a Local AI Study Assistant Using Ollama, ChromaDB, FastAPI and React

Apoorva Chittapur — Fri, 21 Aug 2026 18:13:08 +0000

I built ExamAI, a local AI study assistant that lets students upload their study PDFs and ask questions about them.

The main goal was simple:

Upload your notes → ask a question → get an answer based on your documents.

I wanted to build something that could actually be useful for studying while also learning how AI applications work behind the scenes.

🚀What ExamAI Does

ExamAI allows you to:

Upload PDF study material
Extract text from the documents
Split the content into searchable chunks
Generate embeddings
Store the chunks in ChromaDB
Search for relevant information when a question is asked
Send the relevant context to a local LLM
Get an answer through a React interface
Continue a conversation with follow-up questions

The important part is that the AI runs locally using Ollama, rather than requiring a paid cloud AI API.

🧠 Tech Stack

Frontend

React
Vite
Axios
CSS

Backend

Python
FastAPI
Uvicorn

AI / RAG

Ollama
ChromaDB
Sentence Transformers
PyMuPDF

The application follows a basic RAG (Retrieval-Augmented Generation) architecture.

🔎 How It Works

The flow is roughly:

PDF
 ↓
Text Extraction
 ↓
Chunking
 ↓
Embeddings
 ↓
ChromaDB
 ↓
User Question
 ↓
Similarity Search
 ↓
Relevant Context
 ↓
Ollama
 ↓
AI Answer

Instead of sending the entire PDF to the model every time, ExamAI retrieves the most relevant parts of the document and uses those as context.

💻 Running It Locally

The project includes Windows setup and startup scripts to make running it easier.

After setting up the required dependencies, the application can start the FastAPI backend and React frontend together.

The project is available on GitHub:

https://github.com/apoorvaapoorva0160-droid/ExamAI

🛠️ What I Learned

Building this project taught me much more than just connecting an LLM to a frontend.

I learned about:

RAG architecture
Vector databases
Embeddings
PDF processing
FastAPI APIs
React frontend/backend communication
Local LLMs with Ollama
Conversation context
Git and GitHub
Windows automation with .bat scripts

I also ran into plenty of bugs along the way.

Some of the biggest lessons came from fixing those bugs rather than writing the initial code.

🔐 Why Local AI?

One of the things I like about this project is that the AI processing can happen locally.

That means students can experiment with the application without needing an API key for a cloud LLM.

It also made the project a good way for me to understand what actually happens inside a local AI application.

📌 What's Next?

ExamAI is still a work in progress.

Some things I want to improve include:

Better document management
Improved retrieval quality
More accurate answers
Better multi-document support
Cleaner UI
Better error handling
More robust conversation/session management
Easier installation

I'd also like to eventually make the project easier for other students to install and use.

🙌 Feedback Welcome

This is one of my first serious AI projects, and I'm sharing it because I'd genuinely like feedback from other developers.

If you have suggestions about the RAG architecture, code structure, retrieval approach, UI, or anything else, I'd love to hear them.

GitHub:

https://github.com/apoorvaapoorva0160-droid/ExamAI

Thanks for reading!

DEV Community: Apoorva Chittapur

I Built a Local AI Study Assistant Using Ollama, ChromaDB, FastAPI and React

Frontend

Backend

AI / RAG