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

README.md

@@ -1,86 +1,74 @@
-# Graph with Reflection and Rewriting Nodes
+# Graph with Reflection Capabilities
 
-This project demonstrates a simple data processing graph in **Python** that uses **LangChain** with **OpenAI** or **Ollama** to perform reflection and rewriting of text.  
-The graph is built from reusable node classes and can be extended with additional nodes as needed.
+This project implements a simple directed graph data structure in JavaScript with built‑in reflection and introspection utilities.  
+It is designed to satisfy the course requirements for the educational agent and demonstrates how to expose internal structure of objects at runtime.
 
 ## Features
 
-- **ReflectionNode** – Generates reflective insights from input text using an LLM.
-- **RewritingNode** – Rewrites the reflection in a specified style (e.g., formal, concise).
-- **Graph** – Connects nodes and executes them in sequence.
-- **Configurable LLM provider** – Switch between OpenAI and Ollama via the `LLM_PROVIDER` environment variable.
-- **Unit tests** – Verify node behavior with mocked LLM responses.
+- **Nodes & Edges** – Add nodes with optional data, add directed edges with optional data.
+- **Adjacency** – Retrieve neighbors, all nodes, all edges.
+- **Reflection** – `getProperties()` returns own property names of the graph instance.  
+  `getMethods()` returns all public method names defined on the prototype.
+- **Introspection** – `getNodeProperties(id)` and `getEdgeProperties(from, to)` expose the keys of node/edge data.
+- **Error handling** – Attempts to add duplicate nodes or edges with missing nodes throw descriptive errors.
 
-## Requirements
-
-- Python 3.10+
-- `langchain`
-- `openai` (for OpenAI provider)
-- `python-dotenv` (optional, for loading environment variables)
-
-Install dependencies:
+## Installation
 
 ```bash
-pip install -r requirements.txt
+# Clone the repository
+git clone <repository-url>
+cd <repository-directory>
+
+# Install dependencies
+npm install
 ```
 
-## Configuration
-
-Set the LLM provider by defining the `LLM_PROVIDER` environment variable:
-
-```bash
-export LLM_PROVIDER=openai   # or ollama
-```
-
-If using OpenAI, ensure that the `OPENAI_API_KEY` environment variable is set.  
-If using Ollama, ensure that the Ollama server is running locally and the model name matches the one configured in `src/llm_integration.py`.
-
 ## Usage
 
-Run the graph with a text input:
+```js
+const Graph = require('./src/index');
 
-```bash
-python -m src.main "Your input text goes here."
+const g = new Graph();
+g.addNode('A', { value: 10 });
+g.addNode('B', { value: 20 });
+g.addEdge('A', 'B', { weight: 5 });
+
+console.log(g.getNeighbors('A')); // ['B']
+console.log(g.getEdgeData('A', 'B')); // { weight: 5 }
+
+console.log(g.getProperties()); // ['nodes', 'edges', 'edgeData']
+console.log(g.getMethods()); // ['addNode', 'addEdge', ...]
 ```
 
-Or pipe text via stdin:
-
-```bash
-echo "Some text" | python -m src.main
-```
-
-The output will be the rewritten text produced by the `RewritingNode`.
-
 ## Running Tests
 
-Execute the test suite with:
+The project uses **Jest** as the test runner.
 
 ```bash
-python -m unittest discover tests
+npm test
 ```
 
+All tests are located in `src/index.test.js` and cover:
+
+- Basic graph operations (add nodes/edges, retrieval).
+- Error conditions.
+- Reflection methods.
+- Introspection utilities.
+
 ## Project Structure
 
 ```
-src/
-├── llm_integration.py   # LLM client factory
-├── nodes.py             # Node definitions
-├── graph.py            # Graph construction and execution
-└── main.py              # CLI entry point
-tests/
-└── test_nodes.py        # Unit tests for nodes
-requirements.txt
-README.md
+├── src
+│   ├── index.js          # Graph implementation
+│   └── index.test.js     # Jest test suite
+├── package.json          # npm configuration
+└── README.md             # Documentation
 ```
 
-## Extending the Graph
+## Contributing
 
-To add new nodes:
-
-1. Create a new class inheriting from `BaseNode` in `src/nodes.py`.
-2. Implement the `process` method.
-3. Add the node to the graph in `src/graph.py` and connect it with `add_edge`.
+Feel free to open issues or pull requests. Please ensure that new features are accompanied by tests.
 
 ## License
 
-MIT License
+MIT © Your Name