Welcome to the first lesson of the Database Setup and Code Ingestion course! In this lesson, you will learn how to set up the foundation for storing and managing code data using Python data structures that will later be ingested into a database. By the end of this lesson, you will understand the purpose of the project, what each part of the initial setup code does, and how these data structures prepare us for database storage. This knowledge will prepare you for hands-on practice with database setup and code ingestion in the rest of the course.

Before we dive into the code, let's talk about what we are building and why.

This course is the first step in building an LLM Code Review Assistant - a system that can analyze and review code using large language models. However, before we can build an intelligent code review system, we need a solid foundation for storing and managing code data. That's exactly what this course covers: database setup and code ingestion.

An LLM Code Review Assistant needs access to code files, changes, commit history, and metadata. All this information must be stored, organized, and easily retrievable. This course focuses on creating that data foundation by teaching you how to structure code data before ingesting it into a database.

Throughout this course, you will learn how to:

• Define Python data structures to represent code files and their metadata.
• Prepare code data for database ingestion.
• Set up database schemas to store code information.
• Implement processes to ingest structured code data into databases.

By the end of the course, you will have a working system that can take code information, structure it properly, and store it in a database. This database will serve as the foundation for the LLM Code Review Assistant you'll build in subsequent courses.

Let's break down the initial setup code step by step. We will use Python's dataclass feature to define data structures that represent the code information we want to store in our database. These structures will serve as the foundation for our database schema design and data ingestion processes.

First, we need to import some modules that will help us define our data structures:

• dataclass helps us create classes that are mainly used to store data.
• datetime allows us to work with dates and times, which is useful for tracking when files are updated or commits are made.
• List from the typing module lets us specify that a variable should be a list of items.
• os.path provides utilities for working with file paths, such as checking if files exist, getting file extensions, and manipulating path strings. This will be useful when working with code file paths in our data structures.

Next, let’s define a class to represent a code file:

• @dataclass is a decorator that tells Python to automatically add special methods to the class, like __init__ (for creating new objects) and __repr__ (for printing objects).
• file_path is a string that stores the location of the file (for example, "src/main.py").
• content is a string that holds the actual code inside the file.
• language is a string that tells us what programming language the file uses (like "python" or "javascript").
• last_updated is a datetime object that records when the file was last changed.

Example:

Output:

This creates a CodeFile object and prints its details.

Now, let’s define a class to represent a commit (a saved change in the project):

• hash is a unique string that identifies the commit (like "a1b2c3d4").
• message is a short description of what the commit does.
• author is the name of the person who made the commit.
• date is when the commit was made.

Example:

Output:

Finally, let’s define a class to represent a change made to a file:

• file_path is the location of the file that was changed.
• commit_hash links this change to a specific commit.
• diff_content is a string that shows what was changed in the file (for example, lines that were added or removed).

Example:

Output:

At the end of the file, you will see this code:

• This block checks if the script is being run directly (not imported as a module).
• If it is, it prints a message to let you know the setup is complete.

Output:

This is a common pattern in Python to make sure certain code only runs when you execute the file directly.

In this lesson, you learned about the main building blocks of the LLM Code Review Assistant project. We covered how to use Python data classes to represent code files, commits, and file changes, and explained the purpose of each part of the setup code. You also saw examples of how to create and use these classes.

Next, you will get a chance to practice working with these data classes in hands-on exercises. This will help you become more comfortable with the concepts and prepare you for building more advanced features in the project. Good luck, and let’s get started!