116 lines
2.9 KiB
Markdown
116 lines
2.9 KiB
Markdown
# Graph with Reflection on Code
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A lightweight Python project that demonstrates how to build a **LangGraph** workflow powered by **LangChain** and the **OpenAI** API.
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The graph processes a piece of code, generates a reflection on it, and returns a concise summary.
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> **Repository**: <https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-graf-s-refleksiey-na>
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---
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## 📌 Overview
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- **LangGraph** – orchestrates the flow of data between nodes.
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- **LangChain** – provides the language model wrappers and utilities.
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- **OpenAI** – the LLM that performs code analysis and reflection.
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The workflow consists of three main nodes:
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1. **Input Node** – receives raw code.
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2. **Analysis Node** – calls the OpenAI model to analyze the code.
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3. **Reflection Node** – generates a reflection and summary.
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The graph is defined in `graph.py` and can be executed via the CLI or imported as a library.
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---
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## 🚀 Features
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- **Code Analysis** – extracts key functions, classes, and comments.
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- **Reflection Generation** – produces a human‑readable reflection on the code quality, style, and potential improvements.
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- **Modular Design** – each node can be replaced or extended independently.
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- **OpenAI Integration** – uses the `gpt-4o-mini` model by default (configurable).
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---
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## 🛠️ Installation
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```bash
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# Clone the repository
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git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-2-graf-s-refleksiey-na.git
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cd povtornyy-ekzamen-2-graf-s-refleksiey-na
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# Create a virtual environment (optional but recommended)
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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# Install dependencies
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pip install -r requirements.txt
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```
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> **Requirements**
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> - Python 3.10+
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> - `langgraph`, `langchain`, `openai` (listed in `requirements.txt`)
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> - An OpenAI API key set as the environment variable `OPENAI_API_KEY`.
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---
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## 📦 Usage
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### Command‑Line
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```bash
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python main.py --file path/to/your_code.py
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```
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The script will:
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1. Load the file content.
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2. Run it through the LangGraph workflow.
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3. Print the reflection and summary to the console.
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### Programmatic
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```python
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from graph import CodeReflectionGraph
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graph = CodeReflectionGraph()
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result = graph.run(code="def hello():\n print('Hello, world!')")
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print(result["reflection"])
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```
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---
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## 📁 Project Structure
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```
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povtornyy-ekzamen-2-graf-s-refleksiey-na/
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├── graph.py # LangGraph workflow definition
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├── main.py # CLI entry point
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├── requirements.txt # Python dependencies
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├── README.md # This file
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└── tests/
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└── test_graph.py # Unit tests
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```
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---
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## 🤝 Contributing
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Feel free to open issues or submit pull requests.
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Please follow the existing coding style and add tests for new features.
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---
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## 📄 License
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MIT License – see the [LICENSE](LICENSE) file for details.
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---
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## 📞 Contact
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- **Author**: Artur Kuzakhmetov
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- **Email**: artur.kuzakhmetov@example.com
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---
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END |