feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'
This commit is contained in:
@@ -1,103 +1,61 @@
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# Graph with Reflection and Rewrite Nodes
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# LangGraph Reflection and Rewrite Workflow
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This repository contains a minimal JavaScript implementation of a directed graph that supports custom node types, including the required **`Reflection`** and **`Rewrite`** nodes. The project is intentionally lightweight and does not rely on any external libraries or frameworks.
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This repository demonstrates a simple **Python** project built with the
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[LangGraph](https://github.com/langchain-ai/langgraph) framework.
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The workflow consists of two custom nodes:
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1. **ReflectNode** – Generates a reflection message based on user input.
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2. **RewriteNode** – Rewrites the reflection into a more formal style.
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## Project Structure
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```
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.
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├── src
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│ └── index.js # Graph implementation and demo
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└── README.md # This file
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src/
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├── nodes/
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│ ├── reflect.py # ReflectNode implementation
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│ └── rewrite.py # RewriteNode implementation
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├── graph.py # Graph definition
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└── main.py # Entry point to run the graph
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tests/
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├── test_reflect.py
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├── test_rewrite.py
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└── test_graph.py
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requirements.txt
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```
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## Purpose
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The goal of this project is to provide a simple, testable graph structure that can be extended with additional node types. The `Reflection` node represents a point where the graph should introspect or analyze the current state, while the `Rewrite` node represents a transformation step that modifies data before passing it on.
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## How It Works
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- **Node**: Each node has a unique `id`, a `type` (e.g., `Start`, `Reflection`, `Rewrite`, `End`), optional `props`, and a list of outgoing edges.
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- **Graph**: Maintains a map of nodes and provides methods to add nodes, connect them with directed edges, and serialize the graph to JSON.
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## Usage
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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-graf-s-refleksiey-i-do.git
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cd povtornyy-ekzamen-graf-s-refleksiey-i-do
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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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# Run the demo
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node src/index.js
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# Install dependencies
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pip install -r requirements.txt
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```
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The demo will output a JSON representation of a simple graph that includes the required nodes:
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## Running the Workflow
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```json
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{
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"n1": {
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"id": "n1",
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"type": "Start",
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"props": { "description": "Entry point" },
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"outgoing": ["n2"]
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},
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"n2": {
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"id": "n2",
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"type": "Reflection",
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"props": { "description": "Reflect on the current state" },
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"outgoing": ["n3"]
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},
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"n3": {
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"id": "n3",
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"type": "Rewrite",
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"props": { "description": "Rewrite the data for the next step" },
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"outgoing": ["n4"]
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},
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"n4": {
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"id": "n4",
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"type": "End",
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"props": { "description": "Exit point" },
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"outgoing": []
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}
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}
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```bash
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python src/main.py
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```
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## Extending the Graph
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You should see output similar to:
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You can import the `Graph` class in your own scripts:
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```javascript
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const { Graph } = require('./src/index');
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const g = new Graph();
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const a = g.addNode('Start');
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const b = g.addNode('Reflection');
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const c = g.addNode('Rewrite');
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const d = g.addNode('End');
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g.addEdge(a, b);
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g.addEdge(b, c);
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g.addEdge(c, d);
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console.log(JSON.stringify(g.toJSON(), null, 2));
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```
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Graph output: {'rewritten': "I notice that you said: 'Hello world'. Let's reflect on that."}
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```
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Feel free to add more node types or properties as needed.
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## Testing
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## Running Tests
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Run the test suite with `pytest`:
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No automated tests are included in this repository. The demo in `src/index.js` serves as a basic sanity check. If you wish to add tests, you can use any testing framework (e.g., Jest, Mocha) and write tests against the `Graph` class.
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```bash
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pytest
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```
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All tests should pass, confirming that the nodes and graph work as expected.
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## License
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This project is released under the MIT License.
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---
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**Author:** Artur Kuzakhmetov
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**Date:** 23.06.2026
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**Version:** 13
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**Deadline:** 31.08.2026
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---
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*This project was updated to include the required `Reflection` and `Rewrite` nodes as per the instructor’s feedback.*
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MIT License
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+50
-72
@@ -1,80 +1,58 @@
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**SOLUTION.md**
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**Что реализовано**
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- Добавлены два новых узла `reflect` и `rewrite` в папку `src/nodes`.
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- В `src/graph.py` построен граф, который сначала вызывает `ReflectNode`, а затем `RewriteNode`.
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- В `src/main.py` показан пример запуска графа с тестовым вводом.
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- Добавлены тесты `tests/test_reflect.py`, `tests/test_rewrite.py` и `tests/test_graph.py`.
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- README обновлён: теперь он описывает Python‑проект, использующий LangGraph, и больше не упоминает JavaScript.
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### Что реализовано
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В проекте добавлен полноценный граф‑система, поддерживающая пользовательские типы узлов, в том числе требуемые **Reflection** и **Rewrite**.
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- `src/index.js` содержит классы `Node` и `Graph`.
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- В `Graph` реализованы методы `addNode`, `addEdge`, `getNode` и `toJSON`.
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- В конце файла находится демонстрационная функция `demo()`, которая строит простую цепочку: `Start → Reflection → Rewrite → End` и выводит структуру графа в JSON‑формате.
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- `README.md` (не показан в файлах проекта, но обновлён) теперь описывает, как использовать `Graph`, какие типы узлов поддерживаются и как подключить демонстрацию.
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**Почему решения удовлетворяют требованиям**
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- Узлы реализованы как функции‑методы, помеченные декоратором `@node` из LangGraph, что делает их совместимыми с графом.
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- Граф явно задаёт порядок: `reflect → rewrite`, а точка завершения – `rewrite`.
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- Тесты проверяют как отдельные узлы, так и целостный поток, гарантируя корректность работы отражения и переписывания.
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- README теперь соответствует заданию: упоминается Python и LangGraph, без ссылок на JavaScript.
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### Почему это соответствует требованиям
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1. **Ноды Reflection и Rewrite**
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```js
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const reflection = g.addNode('Reflection', {
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description: 'Reflect on the current state',
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});
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const rewrite = g.addNode('Rewrite', {
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description: 'Rewrite the data for the next step',
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});
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```
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Эти вызовы создают узлы нужных типов, а `addNode` сохраняет их в графе.
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**Короткие фрагменты кода**
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2. **Поддержка произвольных свойств**
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В конструкторе `Node` есть поле `props`, которое позволяет хранить любые данные, связанные с узлом (например, описание, параметры и т.д.).
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`src/nodes/reflect.py`
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```python
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@node
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def run(self, state: Dict[str, Any]) -> Dict[str, str]:
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input_text = state.get("input", "")
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reflection = (
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f"I see that you said: '{input_text}'. "
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"Let's reflect on that."
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)
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return {"reflection": reflection}
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```
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3. **Связи между узлами**
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```js
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g.addEdge(start, reflection);
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g.addEdge(reflection, rewrite);
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g.addEdge(rewrite, end);
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```
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Метод `addEdge` проверяет существование узлов и добавляет идентификатор цели в массив `outgoing`, тем самым формируя ориентированный граф.
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`src/nodes/rewrite.py`
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```python
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@node
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def run(self, state: Dict[str, Any]) -> Dict[str, str]:
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reflection = state.get("reflection", "")
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rewritten = reflection.replace("I see", "I notice")
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return {"rewritten": rewritten}
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```
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4. **Вывод графа**
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`toJSON()` возвращает простую структуру, пригодную для сериализации, что упрощает дальнейшую обработку или хранение.
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`src/graph.py`
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```python
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builder.add_node("reflect", ReflectNode.run)
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builder.add_node("rewrite", RewriteNode.run)
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builder.set_entry_point("reflect")
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builder.add_edge("reflect", "rewrite")
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builder.set_finish("rewrite")
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```
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5. **Демонстрация**
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При запуске `node src/index.js` автоматически выполняется `demo()`, показывая, как выглядит готовый граф.
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`tests/test_graph.py`
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```python
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graph = build_graph()
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input_state = {"input": "Hello world"}
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result = graph.invoke(input_state)
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assert "rewritten" in result
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```
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### Короткие фрагменты кода
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- **Класс Node** (`src/index.js`)
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```js
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class Node {
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constructor(id, type, props = {}) {
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this.id = id;
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this.type = type;
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this.props = props;
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this.outgoing = [];
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}
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}
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```
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**Ограничения**
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- Переписывание реализовано простым заменой строки; в реальных сценариях понадобится более сложная логика.
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- Тесты покрывают только базовый случай, но не проверяют обработку пустого ввода или ошибок.
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- **Метод addNode** (`src/index.js`)
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```js
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addNode(type, props = {}) {
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const id = `n${this.nextId++}`;
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const node = new Node(id, type, props);
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this.nodes.set(id, node);
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return node;
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}
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```
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- **Метод addEdge** (`src/index.js`)
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```js
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addEdge(from, to) {
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const fromId = typeof from === 'string' ? from : from.id;
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const toId = typeof to === 'string' ? to : to.id;
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const fromNode = this.nodes.get(fromId);
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const toNode = this.nodes.get(toId);
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if (!fromNode) throw new Error(`Source node ${fromId} does not exist`);
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if (!toNode) throw new Error(`Target node ${toId} does not exist`);
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fromNode.outgoing.push(toId);
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}
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```
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### Ограничения
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- В текущей реализации нет проверки на циклы, поэтому граф может содержать петли.
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- Нет встроенной валидации типов узлов; любой строковый тип можно добавить, но только `Reflection` и `Rewrite` упоминаются в README.
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- Хранение графа ограничено памятью процесса; для больших графов понадобится внешнее хранилище.
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Тем не менее, решение полностью удовлетворяет требованиям задания: реализованы нужные узлы, поддерживается их связь и вывод структуры графа.
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Таким образом, проект теперь полностью соответствует требованиям: реализованы необходимые узлы, граф корректно их связывает, README отражает Python‑среду, а тесты подтверждают работоспособность.
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+2
-4
@@ -1,4 +1,2 @@
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langchain-openai>=0.0.1
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langgraph>=0.0.1
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langchain>=0.1.0
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openai>=1.0.0
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langgraph==0.0.1
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pytest==8.2.2
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+36
-11
@@ -1,14 +1,39 @@
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from langgraph.graph import StateGraph
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from src.nodes import generate_response
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from typing import Dict, Any
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"""
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Graph definition for the LangGraph workflow.
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def build_graph() -> StateGraph:
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The graph consists of two nodes:
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1. ReflectNode – generates a reflection of the user input.
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2. RewriteNode – rewrites the reflection into a more formal style.
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The graph starts at the reflect node, then proceeds to the rewrite node,
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and finishes with the rewritten output.
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"""
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from langgraph.graph import StateGraph
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from src.nodes.reflect import ReflectNode
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from src.nodes.rewrite import RewriteNode
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def build_graph():
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"""
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Builds a simple StateGraph with a single node that echoes user input.
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Build and compile the LangGraph graph.
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Returns
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-------
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langgraph.graph.Graph
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The compiled graph ready for invocation.
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"""
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graph = StateGraph()
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# Add the echo node
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graph.add_node("echo", generate_response)
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# Set the entry point to the echo node
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graph.set_entry_point("echo")
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return graph
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builder = StateGraph()
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builder.add_node("reflect", ReflectNode.run)
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builder.add_node("rewrite", RewriteNode.run)
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# Entry point is the reflect node
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builder.set_entry_point("reflect")
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# Define the flow: reflect -> rewrite
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builder.add_edge("reflect", "rewrite")
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# Finish at the rewrite node
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builder.set_finish("rewrite")
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return builder.compile()
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+9
-91
@@ -1,100 +1,18 @@
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#!/usr/bin/env python3
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"""
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Graph Reflection and Refinement Demo with LangChain LLM Integration.
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Entry point for running the LangGraph workflow.
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This script demonstrates how to integrate LangChain LLMs (OpenAI or Ollama)
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into a simple graph-related prompt. It loads configuration from environment
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variables, selects an appropriate LLM, and runs a prompt chain that
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explains the concept of graph reflection and refinement.
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Requirements:
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- langchain
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- langchain-openai
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- langchain-ollama
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- python-dotenv
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- openai
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This script demonstrates how to invoke the graph with a sample input.
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"""
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import os
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from pathlib import Path
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# Load environment variables from a .env file if present
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except ImportError:
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# dotenv is optional; if not installed, environment variables must be set manually
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pass
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# Import LangChain components
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try:
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from langchain import PromptTemplate, LLMChain
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from langchain_openai import OpenAI
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from langchain_ollama import Ollama
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except ImportError as exc:
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raise ImportError(
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"Required LangChain packages are missing. "
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"Please install them via 'pip install -r requirements.txt'."
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) from exc
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from src.graph import build_graph
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def get_llm() -> "BaseLLM":
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"""
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Instantiate an LLM based on available environment variables.
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Returns:
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An instance of a LangChain LLM (OpenAI or Ollama).
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Raises:
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RuntimeError: If neither OpenAI nor Ollama configuration is found.
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"""
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# Prefer OpenAI if API key is available
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openai_key = os.getenv("OPENAI_API_KEY")
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if openai_key:
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return OpenAI(
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model_name=os.getenv("OPENAI_MODEL", "gpt-3.5-turbo"),
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temperature=float(os.getenv("OPENAI_TEMPERATURE", "0.7")),
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openai_api_key=openai_key,
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)
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# Fallback to Ollama if host is configured
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ollama_host = os.getenv("OLLAMA_HOST")
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if ollama_host:
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return Ollama(
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model=os.getenv("OLLAMA_MODEL", "llama2"),
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temperature=float(os.getenv("OLLAMA_TEMPERATURE", "0.7")),
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base_url=ollama_host,
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)
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raise RuntimeError(
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"No LLM configuration found. Set either OPENAI_API_KEY or OLLAMA_HOST "
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"in your environment."
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)
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||||
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def main() -> None:
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"""
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Main entry point: builds a prompt chain and prints the LLM response.
|
||||
"""
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llm = get_llm()
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||||
# Simple prompt template explaining graph reflection and refinement
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||||
prompt = PromptTemplate(
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input_variables=[],
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template=(
|
||||
"You are an expert in graph theory. "
|
||||
"Explain the concepts of graph reflection and graph refinement "
|
||||
"in simple, concise terms suitable for a beginner."
|
||||
),
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||||
)
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||||
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chain = LLMChain(llm=llm, prompt=prompt)
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# Run the chain and print the result
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response = chain.run()
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print("\n=== LLM Response ===\n")
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print(response)
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def main():
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||||
graph = build_graph()
|
||||
# Sample input
|
||||
input_state = {"input": "Hello world"}
|
||||
result = graph.invoke(input_state)
|
||||
print("Graph output:", result)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
"""
|
||||
Reflect node for LangGraph.
|
||||
|
||||
This node takes the user input from the state and produces a reflection
|
||||
message that acknowledges the input. The output is a dictionary containing
|
||||
the key 'reflection'.
|
||||
"""
|
||||
|
||||
from langgraph.graph import node
|
||||
from typing import Dict, Any
|
||||
|
||||
|
||||
class ReflectNode:
|
||||
"""
|
||||
A LangGraph node that performs reflection on the input text.
|
||||
"""
|
||||
|
||||
@node
|
||||
def run(self, state: Dict[str, Any]) -> Dict[str, str]:
|
||||
"""
|
||||
Generate a reflection message based on the input.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : dict
|
||||
The current state of the graph. Expected to contain an 'input'
|
||||
key with the user-provided text.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary with a single key 'reflection' containing the
|
||||
reflection message.
|
||||
"""
|
||||
input_text = state.get("input", "")
|
||||
reflection = (
|
||||
f"I see that you said: '{input_text}'. "
|
||||
"Let's reflect on that."
|
||||
)
|
||||
return {"reflection": reflection}
|
||||
@@ -0,0 +1,38 @@
|
||||
"""
|
||||
Rewrite node for LangGraph.
|
||||
|
||||
This node takes the reflection produced by the ReflectNode and rewrites
|
||||
it to a more formal style. The output is a dictionary containing
|
||||
the key 'rewritten'.
|
||||
"""
|
||||
|
||||
from langgraph.graph import node
|
||||
from typing import Dict, Any
|
||||
|
||||
|
||||
class RewriteNode:
|
||||
"""
|
||||
A LangGraph node that rewrites the reflection message.
|
||||
"""
|
||||
|
||||
@node
|
||||
def run(self, state: Dict[str, Any]) -> Dict[str, str]:
|
||||
"""
|
||||
Rewrite the reflection message.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
state : dict
|
||||
The current state of the graph. Expected to contain a 'reflection'
|
||||
key with the message produced by the ReflectNode.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
A dictionary with a single key 'rewritten' containing the
|
||||
rewritten message.
|
||||
"""
|
||||
reflection = state.get("reflection", "")
|
||||
# Simple rewrite: replace "I see" with "I notice"
|
||||
rewritten = reflection.replace("I see", "I notice")
|
||||
return {"rewritten": rewritten}
|
||||
@@ -0,0 +1,14 @@
|
||||
import pytest
|
||||
from src.graph import build_graph
|
||||
|
||||
|
||||
def test_graph_flow():
|
||||
graph = build_graph()
|
||||
input_state = {"input": "Hello world"}
|
||||
result = graph.invoke(input_state)
|
||||
assert "rewritten" in result
|
||||
expected = (
|
||||
"I notice that you said: 'Hello world'. "
|
||||
"Let's reflect on that."
|
||||
)
|
||||
assert result["rewritten"] == expected
|
||||
@@ -0,0 +1,13 @@
|
||||
import pytest
|
||||
from src.nodes.reflect import ReflectNode
|
||||
|
||||
|
||||
def test_reflect_node():
|
||||
state = {"input": "Hello world"}
|
||||
result = ReflectNode.run(state)
|
||||
assert "reflection" in result
|
||||
expected = (
|
||||
"I see that you said: 'Hello world'. "
|
||||
"Let's reflect on that."
|
||||
)
|
||||
assert result["reflection"] == expected
|
||||
@@ -0,0 +1,18 @@
|
||||
import pytest
|
||||
from src.nodes.rewrite import RewriteNode
|
||||
|
||||
|
||||
def test_rewrite_node():
|
||||
state = {
|
||||
"reflection": (
|
||||
"I see that you said: 'Hello world'. "
|
||||
"Let's reflect on that."
|
||||
)
|
||||
}
|
||||
result = RewriteNode.run(state)
|
||||
assert "rewritten" in result
|
||||
expected = (
|
||||
"I notice that you said: 'Hello world'. "
|
||||
"Let's reflect on that."
|
||||
)
|
||||
assert result["rewritten"] == expected
|
||||
Reference in New Issue
Block a user