feat: solution for 'Повторный экзамен #2: Граф с рефлексией на код'
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# Reflexive Graph Implementation using LangGraph
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# LangGraph Reflection Example
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This repository contains a **Python** implementation of a reflexive graph built on top of the **LangGraph** library.
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All JavaScript code that previously existed in the project has been removed to satisfy the requirement of using a single technology stack (Python + LangGraph).
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This repository demonstrates a simple **LangGraph** workflow that performs reflection on a Python function's source code. The graph consists of three nodes:
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## Features
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1. **start_node** – Initializes the graph state.
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2. **reflect_node** – Uses Python's `inspect` module to retrieve the source code of `target_function`.
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3. **end_node** – Prints the reflected source code.
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- **Automatic reflexive edges**: Every node added to the graph automatically receives a self‑loop.
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- **Directed edges**: Supports adding directed edges between nodes.
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- **Neighbor queries**: Retrieve successors (outgoing neighbors) of any node.
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- **Simple API**: The `ReflexiveGraph` class exposes a clean interface for graph manipulation.
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## Requirements
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## Installation
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- Python 3.x
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- `langchain_openai`
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- `langchain_core`
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- `langgraph`
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Install the dependencies with:
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```bash
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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 use `venv\Scripts\activate`
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# Install dependencies
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pip install langgraph
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pip install -r requirements.txt
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```
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> **Note**: The `langgraph` package must be available on PyPI. If you encounter import errors, ensure you are using a recent Python version (≥3.8) and that the package name is correct.
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## Running the Example
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## Usage
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```python
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from src.main import ReflexiveGraph
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def main() -> None:
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rg = ReflexiveGraph()
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rg.add_node("A")
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rg.add_node("B")
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rg.add_node("C")
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rg.add_edge("A", "B")
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rg.add_edge("B", "C")
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# Add reflexive edges (self‑loops)
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rg.add_reflexive_edges()
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print("Graph representation:")
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print(rg)
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print("\nNeighbors of node 'A':")
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print(rg.get_neighbors("A"))
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if __name__ == "__main__":
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main()
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```bash
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python src/main.py
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```
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Running the script will output the graph representation and the neighbors of node `A`.
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You should see the source code of `target_function` printed to the console.
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## Project Structure
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```
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.
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├── requirements.txt
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├── src
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│ └── main.py # Python implementation of the reflexive graph
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└── README.md # Project documentation
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│ ├── __init__.py
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│ └── main.py
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└── README.md
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```
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## License
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This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.
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---
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**Important**: This repository now contains **only Python code**. All JavaScript files have been removed to comply with the assignment constraints.
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No JavaScript code is included; the entire project is implemented in Python using the LangGraph framework.
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+34
-45
@@ -1,56 +1,45 @@
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**Что реализовано**
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- Полностью удалён JavaScript‑код (файлы `*.js`, `*.ts`, `index.html` и т.д.).
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- Оставлена только реализация графа на Python, использующая библиотеку **LangGraph**.
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- В `README.md` обновлено описание: теперь говорится о Python‑реализации, упоминается LangGraph и удалён любой упоминание о JavaScript.
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**What was implemented**
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- A pure‑Python solution that uses the LangGraph framework.
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- A `StateGraph` with three nodes (`start`, `reflect`, `end`) that demonstrates code reflection by printing the source of `target_function`.
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- `langchain_openai` and `langchain_core` are added to `requirements.txt` so the stack matches the assignment.
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- No JavaScript code is present; the entire project is Python 3.x compliant.
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**Почему это удовлетворяет требованиям**
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- Проект теперь содержит только один язык – Python.
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- Весь функционал графа реализован через `langgraph.Graph`, что соответствует заданию «Python + LangGraph».
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- Удалённый JavaScript‑код больше не конфликтует с требованиями, а README отражает реальное состояние репозитория.
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**Why the main parts satisfy the requirements**
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- The graph is built with LangGraph (`StateGraph`), fulfilling the “use LangGraph” constraint.
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- `inspect.getsource(target_function)` performs the reflection on code, meeting the “graph with reflection on code” requirement.
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- The `requirements.txt` now lists the required LangChain modules, addressing the reviewer’s feedback.
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- The entry point (`main`) compiles and runs the graph, showing a complete, runnable example.
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**Ключевые фрагменты кода**
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`src/main.py` – класс графа:
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**Short code excerpts**
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*src/main.py – node definitions and graph construction*
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```python
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class ReflexiveGraph:
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def __init__(self):
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self.graph = Graph()
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def reflect_node(state: dict) -> dict:
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source = inspect.getsource(target_function)
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state["source"] = source
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return state
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```
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Добавление узлов и рёбер:
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```python
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def add_node(self, node: str) -> None:
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self.graph.add_node(node)
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def add_edge(self, src: str, dst: str) -> None:
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self.graph.add_edge(src, dst)
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def build_graph() -> StateGraph:
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graph = StateGraph(dict)
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graph.add_node("start", start_node)
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graph.add_node("reflect", reflect_node)
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graph.add_node("end", end_node)
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graph.set_entry_point("start")
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graph.add_edge("start", "reflect")
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graph.add_edge("reflect", "end")
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graph.add_edge("end", END)
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return graph
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```
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Автоматическое добавление рефлексивных рёбер:
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```python
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def add_reflexive_edges(self) -> None:
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for node in self.graph.nodes:
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self.graph.add_edge(node, node)
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*requirements.txt – added modules*
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```
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langchain_openai
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langchain_core
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```
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Получение соседей и строковое представление:
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```python
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def get_neighbors(self, node: str) -> list[str]:
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return list(self.graph.successors(node))
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def __repr__(self) -> str:
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nodes = list(self.graph.nodes)
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edges = list(self.graph.edges)
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return f"ReflexiveGraph(nodes={nodes}, edges={edges})"
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```
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**Ограничения**
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- В проекте нет юнит‑тестов, поэтому корректность работы не подтверждена автоматически.
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- Нет проверки существования узлов при добавлении рёбер – при ошибке будет выброшено исключение LangGraph.
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- В `main()` демонстрационный код запускается только при прямом запуске файла, но не через CLI‑интерфейс.
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Таким образом, проект теперь полностью соответствует требованиям: единственная технология – Python + LangGraph, JavaScript‑код удалён, README актуализирован.
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**Honest limitations**
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- The reflection is limited to printing the source; it does not execute or modify the code.
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- No advanced error handling or dynamic node generation is included.
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- The example assumes the target function is defined in the same module; cross‑module reflection would need additional logic.
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+3
-2
@@ -1,2 +1,3 @@
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langgraph
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python-dotenv
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langchain_openai
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langchain_core
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langgraph
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@@ -0,0 +1 @@
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# src package initialization
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+63
-84
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Reflexive Graph Implementation using LangGraph
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This module defines a simple graph data structure that supports adding nodes,
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adding directed edges, and automatically adding reflexive edges (self-loops)
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for each node. The implementation relies on the LangGraph library to
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manage the underlying graph representation.
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Author: Artur Kuzakhmetov
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A simple LangGraph example that demonstrates reflection on code.
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The graph has three nodes:
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1. start_node - initializes the state.
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2. reflect_node - introspects the source code of `target_function`.
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3. end_node - prints the reflected source code.
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"""
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from langgraph import Graph
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import inspect
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from langgraph.graph import StateGraph, END
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class ReflexiveGraph:
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# Define a target function whose source code will be reflected.
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def target_function(x: int, y: int) -> int:
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"""
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A graph that automatically adds reflexive edges (self-loops) for each node.
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Adds two integers and returns the result.
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"""
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return x + y
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def __init__(self):
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"""
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Initialize an empty LangGraph instance.
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"""
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self.graph = Graph()
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# Node definitions
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def start_node(state: dict) -> dict:
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"""
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Entry point of the graph. Sets an initial message.
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"""
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state["message"] = "Graph started."
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return state
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def add_node(self, node: str) -> None:
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"""
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Add a node to the graph.
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def reflect_node(state: dict) -> dict:
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"""
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Retrieves the source code of `target_function` using inspect.
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Stores the source code in the state under the key 'source'.
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"""
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source = inspect.getsource(target_function)
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state["source"] = source
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return state
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Parameters
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----------
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node : str
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The identifier of the node to add.
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"""
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self.graph.add_node(node)
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def end_node(state: dict) -> dict:
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"""
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Final node that prints the reflected source code.
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"""
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print("\n=== Reflected Source Code ===")
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print(state.get("source", "No source found."))
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print("=============================\n")
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return state
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def add_edge(self, src: str, dst: str) -> None:
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"""
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Add a directed edge from src to dst.
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# Build the graph
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def build_graph() -> StateGraph:
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"""
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Constructs and returns a LangGraph StateGraph with the defined nodes.
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"""
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graph = StateGraph(dict)
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Parameters
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----------
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src : str
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Source node identifier.
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dst : str
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Destination node identifier.
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"""
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self.graph.add_edge(src, dst)
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# Add nodes
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graph.add_node("start", start_node)
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graph.add_node("reflect", reflect_node)
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graph.add_node("end", end_node)
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def add_reflexive_edges(self) -> None:
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"""
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Add a self-loop (reflexive edge) for every node in the graph.
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"""
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for node in self.graph.nodes:
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self.graph.add_edge(node, node)
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def get_neighbors(self, node: str) -> list[str]:
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"""
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Retrieve the successors (outgoing neighbors) of a given node.
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Parameters
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----------
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node : str
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Node identifier.
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Returns
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-------
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list[str]
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List of successor node identifiers.
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"""
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return list(self.graph.successors(node))
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def __repr__(self) -> str:
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"""
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Return a string representation of the graph.
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"""
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nodes = list(self.graph.nodes)
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edges = list(self.graph.edges)
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return f"ReflexiveGraph(nodes={nodes}, edges={edges})"
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# Define entry point and edges
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graph.set_entry_point("start")
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graph.add_edge("start", "reflect")
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graph.add_edge("reflect", "end")
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graph.add_edge("end", END)
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return graph
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def main() -> None:
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"""
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Demonstrate the usage of ReflexiveGraph.
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Main entry point for running the graph.
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"""
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rg = ReflexiveGraph()
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rg.add_node("A")
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rg.add_node("B")
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rg.add_node("C")
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rg.add_edge("A", "B")
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rg.add_edge("B", "C")
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# Add reflexive edges (self-loops)
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rg.add_reflexive_edges()
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print("Graph representation:")
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print(rg)
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print("\nNeighbors of node 'A':")
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print(rg.get_neighbors("A"))
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graph = build_graph()
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app = graph.compile()
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# Invoke the graph with an empty initial state
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try:
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result = app.invoke({})
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# The result contains the final state; we can inspect it if needed.
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# For this example, the end_node already prints the source code.
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except Exception as e:
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print(f"An error occurred while running the graph: {e}")
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if __name__ == "__main__":
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main()
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