feat: solution for 'Повторный экзамен #2: Граф с рефлексией на код'

README.md

@@ -1,38 +1,26 @@
 # Graph with Reflection on Code
 
-A lightweight Python project that demonstrates how to build a **LangGraph** workflow powered by **LangChain** and the **OpenAI** API.  
+This repository demonstrates how to build a conversational agent that can analyze and reflect on Python code using **LangGraph** and **LangChain OpenAI**. The agent can parse code, generate explanations, and answer questions about the code structure.
-The graph processes a piece of code, generates a reflection on it, and returns a concise summary.
 
-> **Repository**: <https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-graf-s-refleksiey-na>
+## Features
 
----
+- **LangGraph**: Orchestrates the conversation flow and manages state across multiple turns.
+- **LangChain OpenAI**: Provides language model capabilities via OpenAI’s GPT-4 (or any compatible model).
+- Code parsing and analysis using the `ast` module.
+- Interactive CLI for asking questions about a Python file.
 
-## 📌 Overview
+## Getting Started
 
-- **LangGraph** – orchestrates the flow of data between nodes.
+### Prerequisites
-- **LangChain** – provides the language model wrappers and utilities.
-- **OpenAI** – the LLM that performs code analysis and reflection.
 
-The workflow consists of three main nodes:
+- Python 3.10+
+- An OpenAI API key. Set it in your environment:
 
-1. **Input Node** – receives raw code.
+```bash
-2. **Analysis Node** – calls the OpenAI model to analyze the code.
+export OPENAI_API_KEY="your_api_key_here"
-3. **Reflection Node** – generates a reflection and summary.
+```
 
-The graph is defined in `graph.py` and can be executed via the CLI or imported as a library.
+### Installation
-
----
-
-## 🚀 Features
-
-- **Code Analysis** – extracts key functions, classes, and comments.
-- **Reflection Generation** – produces a human‑readable reflection on the code quality, style, and potential improvements.
-- **Modular Design** – each node can be replaced or extended independently.
-- **OpenAI Integration** – uses the `gpt-4o-mini` model by default (configurable).
-
----
-
-## 🛠️ Installation
 
 ```bash
 # Clone the repository
@@ -41,76 +29,53 @@ cd povtornyy-ekzamen-2-graf-s-refleksiey-na
 
 # Create a virtual environment (optional but recommended)
 python -m venv .venv
-source .venv/bin/activate   # On Windows: .venv\Scripts\activate
+source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
 
 # Install dependencies
 pip install -r requirements.txt
 ```
 
-> **Requirements**  
+`requirements.txt` contains:
-> - Python 3.10+  
-> - `langgraph`, `langchain`, `openai` (listed in `requirements.txt`)  
-> - An OpenAI API key set as the environment variable `OPENAI_API_KEY`.
 
----
+```
+langchain==0.2.0
+langgraph==0.1.0
+openai==1.0.0
+```
 
-## 📦 Usage
+### Usage
 
-### Command‑Line
+Run the main script and provide the path to a Python file you want to analyze:
 
 ```bash
-python main.py --file path/to/your_code.py
+python main.py path/to/your_script.py
 ```
 
-The script will:
+You will be prompted to ask questions about the code. The agent will respond using the OpenAI model and the conversation graph.
 
-1. Load the file content.
+### Example
-2. Run it through the LangGraph workflow.
-3. Print the reflection and summary to the console.
 
-### Programmatic
+```bash
-
+$ python main.py example.py
-```python
+Enter your question (or type 'exit' to quit): What does the `add` function do?
-from graph import CodeReflectionGraph
+The `add` function takes two numbers, `a` and `b`, and returns their sum.
-
-graph = CodeReflectionGraph()
-result = graph.run(code="def hello():\n    print('Hello, world!')")
-print(result["reflection"])
 ```
 
----
+## Project Structure
-
-## 📁 Project Structure
 
 ```
 povtornyy-ekzamen-2-graf-s-refleksiey-na/
-├── graph.py          # LangGraph workflow definition
+├── main.py          # Entry point
-├── main.py           # CLI entry point
+├── code_analyzer.py # Code parsing utilities
-├── requirements.txt  # Python dependencies
+├── graph.py         # LangGraph definition
-├── README.md         # This file
+├── requirements.txt
-└── tests/
+└── README.md
-    └── test_graph.py # Unit tests
 ```
 
----
+## License
 
-## 🤝 Contributing
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
-
-Feel free to open issues or submit pull requests.  
-Please follow the existing coding style and add tests for new features.
 
 ---
 
-## 📄 License
+*This project was developed as part of a coursework assignment. It showcases the integration of LangGraph and LangChain OpenAI for code analysis and reflection.*
-
-MIT License – see the [LICENSE](LICENSE) file for details.
-
----
-
-## 📞 Contact
-
-- **Author**: Artur Kuzakhmetov  
-- **Email**: artur.kuzakhmetov@example.com
-
---- 
-END