feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'

README.md

@@ -1,80 +1,46 @@
-# Graph Reflection and Refinement Demo
+# Graph with Reflection and Rewriting Nodes
 
-This repository demonstrates how to integrate **LangChain LLMs** (OpenAI or Ollama) into a simple Python script that explains graph theory concepts. The project is intentionally minimal to focus on the LLM integration.
+This project implements a simple directed graph data structure in JavaScript that supports three types of nodes:
 
-## Features
+- **Generic Node** – the base node type.
+- **Reflection Node** – represents a node that reflects on itself.
+- **Rewriting Node** – represents a node that rewrites or transforms data.
 
-- **OpenAI LLM** support via `langchain-openai`.
-- **Ollama LLM** support via `langchain-ollama`.
-- Environment variable configuration using `.env` or system variables.
-- Simple prompt chain that explains graph reflection and refinement.
+## Installation
 
-## Setup
+```bash
+npm install
+```
 
-1. **Clone the repository**
+## Running Tests
 
-   ```bash
-   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-graf-s-refleksiey-i-do
-   cd povtornyy-ekzamen-graf-s-refleksiey-i-do
-   ```
-
-2. **Create a virtual environment (recommended)**
-
-   ```bash
-   python3 -m venv .venv
-   source .venv/bin/activate
-   ```
-
-3. **Install dependencies**
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-4. **Configure environment variables**
-
-   Create a `.env` file in the project root (or set system variables) with one of the following:
-
-   ```dotenv
-   # For OpenAI
-   OPENAI_API_KEY=your_openai_api_key
-   OPENAI_MODEL=gpt-3.5-turbo
-   OPENAI_TEMPERATURE=0.7
-
-   # OR for Ollama
-   OLLAMA_HOST=http://localhost:11434
-   OLLAMA_MODEL=llama2
-   OLLAMA_TEMPERATURE=0.7
-   ```
-
-   Only one of the two configurations is required.
+```bash
+npm test
+```
 
 ## Usage
 
-Run the script:
+```js
+import { Graph, Node, ReflectionNode, RewritingNode } from './src/index.js';
 
-```bash
-python src/main.py
+const graph = new Graph();
+
+const n1 = new Node('n1');
+const r1 = new ReflectionNode('r1');
+const w1 = new RewritingNode('w1');
+
+graph.addNode(n1);
+graph.addNode(r1);
+graph.addNode(w1);
+
+graph.addEdge('n1', 'r1');
+graph.addEdge('r1', 'w1');
+
+graph.traverse('n1', (node) => {
+  console.log(node.id, node.type);
+});
 ```
 
-You should see an LLM-generated explanation of graph reflection and refinement printed to the console.
+## License
 
-## Project Structure
-
-```
-povtornyy-ekzamen-graf-s-refleksiey-i-do/
-├── src/
-│   └── main.py          # Core script with LangChain integration
-├── requirements.txt     # All required Python packages
-└── README.md            # Project documentation
-```
-
-## Notes
-
-- The script automatically selects the LLM based on the presence of environment variables.
-- If neither `OPENAI_API_KEY` nor `OLLAMA_HOST` is set, the script will raise an error.
-- Feel free to extend the prompt or chain logic to suit more complex use cases.
-
---- 
-
-Happy coding!
+MIT