povtornyy-ekzamen-2-graf-s-refleksiey-na/README.md

Graph with Reflection on Code

This repository contains a simple Python implementation of a graph that performs reflection on code snippets using LangGraph and an OpenAI LLM.

Requirements

• Python 3.10+
• langgraph
• langchain-openai
• openai

Setup

1. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\\Scripts\\activate

2. Install dependencies:

pip install -r requirements.txt

3. Set your OpenAI API key:

export OPENAI_API_KEY="your_api_key_here"

Running the Graph

The graph is defined in src/main.py. To run it with a sample code snippet:

python src/main.py

You should see a reflection printed to the console.

Using the Graph Programmatically

You can import the run_graph function from src/main.py and pass any code snippet:

from src.main import run_graph

code = """
def add(a, b):
    return a + b
"""

reflection = run_graph(code)
print(reflection)

How Reflection Works

The graph has three nodes:

1. Input Node – Receives the code snippet.
2. Reflection Node – Uses an OpenAI LLM to analyze the code and produce a reflection.
3. Output Node – Returns the reflection.

The LLM prompt is designed to ask for a concise reflection on structure, improvements, and patterns.

License

MIT License