Teaching an AI to Understand Your Codebase (File Exploration Tools) - Part 2, Section 1

Build an Agentic Bug Hunter AI - Part 2, Section 1 | Python Tutorial In this tutorial, we build an agentic bug hunter AI. This is Part 2, Section 1, where we set up the project structure and implement core functions. What You'll Learn: Setting up a Python project with UV (modern package installer) Configuring Ollama for local LLM inference Implementing the get_files_info() function with security checks Environment configuration and project structure Technologies Used: UV - Fast Python package installer Ollama - Local LLM inference (OpenAI-compatible API) Qwen2.5:14b - Large language model Python 3.13+ In This Video: We start by installing UV and setting up Ollama, then create the project structure with a functions folder. We configure environment variables for the LLM model, working directory, and API settings. Finally, we implement get_files_info() with path traversal protection. 💻 Code Repository: https://github.com/cholakovit/bug-hunter-ai 🌐 Visit my website for more tutorials and projects: www.cholakovit.com 👍 If you found this video helpful, please like and subscribe for more AI and coding tutorials! 📝 Full code and documentation available in the repository. --- #AI #MachineLearning #Python #Ollama #FunctionCalling #AgenticAI #AIAgent #BugFixing #CodingTutorial #PythonTutorial #LLM #LocalLLM #Qwen #OpenAI #AIDevelopment #SoftwareEngineering #CodeReview #Automation #AITools #TechTutorial #Programming #Developer #Coding #AIProgramming #AutonomousAI #CodeAgent #TechEducation