Building a LeetCode Chatbot Using AI: A Comprehensive Guide

Overview

DSA AI is your personalized Data Structures and Algorithms companion! This comprehensive tutorial shows you how to build an AI-powered chatbot that helps you solve LeetCode problems, explains algorithms, and provides personalized guidance.

What You’ll Learn

• RAG Architecture for Coding Problems
• Vector Embeddings for Code
• LLM Integration for Code Explanation
• Building a Conversational Interface
• Fine-tuning for DSA Domain

System Architecture

User Query
    ↓
Vector Search (FAISS)
    ↓
Retrieve Relevant Problems/Solutions
    ↓
LLM Context Generation
    ↓
Response with Code + Explanation

Key Features

1. Problem Recommendations

   • Based on difficulty level
   • Topic-wise grouping
   • Similar problems lookup

2. Code Explanation

   • Line-by-line breakdown
   • Complexity analysis
   • Optimization suggestions

3. Personalized Learning Path

   • Track solved problems
   • Identify weak areas
   • Suggest next problems

4. Interview Preparation

   • Company-specific questions
   • Pattern recognition
   • Common mistakes

Tech Stack

• Python
• LangChain
• OpenAI GPT-4
• FAISS Vector Database
• Streamlit
• LeetCode API (unofficial)

Implementation Highlights

1. Data Collection

# Scrape LeetCode problems
problems = scrape_leetcode_problems()
solutions = get_problem_solutions()

# Create embeddings
embeddings = create_embeddings(problems + solutions)
vector_store.add(embeddings)

2. RAG Pipeline

def answer_query(user_question):
    # Retrieve relevant context
    relevant_docs = vector_store.similarity_search(user_question, k=3)
    
    # Generate response
    response = llm.generate(
        context=relevant_docs,
        question=user_question
    )
    
    return response

3. Code Explanation

def explain_code(code_snippet, language):
    prompt = f"""
    Explain this {language} code step-by-step:
    {code_snippet}
    
    Include:
    - What the code does
    - Time and space complexity
    - Edge cases handled
    - Possible optimizations
    """
    return llm.complete(prompt)

Use Cases

• Interview Prep: Practice with AI feedback
• Learning: Understand algorithm patterns
• Code Review: Get instant explanations
• Problem Discovery: Find similar problems

Related Resources

• Blog Post
• GitHub Repository

Watch on YouTube

Video Thumbnail:

▶️ Watch Video: Building a LeetCode Chatbot Using AI

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More Resources:

• VayuX AI Channel - Full tutorial and code walkthrough