🦙 LeetLLama

Welcome to LeetLLama

LeetLLama is a lightweight yet forward-thinking proof of concept (PoC) for an interactive, AI-augmented platform designed to empower aspiring software engineers through self-directed learning.

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🎯 Purpose & Vision

The primary goal of this project—beyond “reinventing the wheel for academic purposes”—is to demonstrate a core philosophy in modern tech:

Artificial Intelligence should augment, not replace, human developers.

In this PoC, we explore how AI-driven tools can enhance the way programmers learn complex topics like algorithms and data structures, providing a more personalized, adaptive, and contextual learning experience.

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💡 Concept Overview

At its core, LeetLLama enables learners to:

• Interact with a fine-tuned language model trained on solved LeetCode-style problems.
• Receive guidance as if from a more experienced developer or mentor.
• Customize their learning journey based on:
  • ✏️ Coding style
  • 🧠 Skill level
  • 🎯 Problem preferences (difficulty, topic, and more)

The goal is to simulate the benefits of a pair-programming session with a senior engineer—one who not only knows the answers but can explain why they work, and tailor their advice to your unique thinking process.

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🧱 Tech Stack & Architecture

• 🐍 Python
• 📦 Dockerized microservice architecture
• 🧵 Message queue for asynchronous communication
• 🤖 LLM backend
• 💾 Vector store
• ☁️ Cloud-deployable with Kubernetes

Software Requirements

Software	Version
Python	3.11
pip	25.1
Docker	28.1.1
Docker-Compose	2.36.0

Hardware Requirements

These are minimun requirements

Component	Vendor	Model
CPU	Intel	8th Gen
GPU	NVIDIA	GeForce 4070 RTX
RAM	Any	DDR4
Secondary Memory	SSD	100 GB

Setup & Configuration

1. Start by building the entire project

docker compose build

2. Then up the project with

docker compose up -d

3. Connect to ollama to download the required models:

llama3.2:3b
qwen3:4b
phi3:3.8b
gemma3:4b

Using the commands:

(Connect to the ollama container)

docker exec -it leetllama_ollama /bin/bash

(Pull the models)

ollama pull llama3.2:3b && ollama pull qwen3:4b && ollama pull phi3:3.8b && ollama pull gemma3:4b

Then exit

4. Then, download all the Python dependencies required to populate the Milvus Knowledge Base with the Leetcode Dataset, running this script:

(Download the requirements)

python -m pip install -r requirements.txt

(Populate the Milvus Vector Store)

cd ~/data
python load_milvus_kb.py
cd ../

5. Now you may connect to the Frontend UI at the following URL:

http://localhost:1291/

Some Samples

This is the frontend UI you will see upon upping the docker-compose file.

The gear button aids with the selection of the models to be used for the inference:

Then once you have chosen the model you seek, press the confirm button

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1. In this simple example, I asked how I could invert a linked list

2. In this more articulated example, I gave the model the entire question for the infamous two sum probelm

Disclaimer

This repo is still an early PoC and not ready for being forked, once the Cloud Setup and Better Architecture will be implemented, then you may proceed with forking the repo, and to know for sure, this message will no longer appear in the README

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