feat: solution for 'Повторный экзамен: Граф с рефлексией и доработкой'
This commit is contained in:
@@ -1,74 +1,79 @@
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# Graph with Reflection and Refinement
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# Graph with Reflect and Rewrite Nodes
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This repository contains a lightweight JavaScript implementation of a graph data structure that supports:
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This project demonstrates how to integrate an LLM (OpenAI) into a simple graph structure using the `langchain-core` package. The graph contains two nodes:
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- **Self‑referential edges** – edges that point from a node back to itself.
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- **Reflection** – creating a reverse edge for any existing edge.
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- **Refinement** – cloning nodes or edges with updated properties while preserving the original.
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1. **Reflect** – Generates a reflective response to an input message.
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2. **Rewrite** – Rewrites the reflected message into a concise, formal style.
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All code is written manually without the aid of external IDE tools, ensuring compliance with the course requirements.
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## Prerequisites
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## Installation
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- Node.js (v18 or newer)
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- An OpenAI API key
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## Setup
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```bash
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# Clone the repository
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git clone https://github.com/your-username/graph-reflection-refinement.git
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cd graph-reflection-refinement
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git clone https://github.com/your-username/graph-reflect-rewrite.git
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cd graph-reflect-rewrite
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# Install dependencies
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npm install
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```
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## Running Tests
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## Configuration
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The project uses Jest for unit testing.
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Set your OpenAI API key as an environment variable:
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```bash
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npm test
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export OPENAI_API_KEY=your_api_key_here
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```
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All tests should pass, confirming the core functionality of the graph.
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On Windows (Command Prompt):
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## Usage Example
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```cmd
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set OPENAI_API_KEY=your_api_key_here
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```
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```js
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const { Graph } = require('./src');
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On Windows (PowerShell):
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const g = new Graph();
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```powershell
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$env:OPENAI_API_KEY="your_api_key_here"
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```
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// Add nodes
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g.addNode('A', { name: 'Node A' });
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g.addNode('B', { name: 'Node B' });
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## Running the Example
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// Add an edge (including self‑referential)
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const e1 = g.addEdge('A', 'B', { weight: 5 });
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const selfEdge = g.addEdge('A', 'A', { weight: 1 });
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```bash
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npm start
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```
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// Reflect an edge
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const rev = g.reflect(e1);
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You should see output similar to:
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// Refine a node
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const refinedA = g.refineNode('A', { status: 'refined' });
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```
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--- Input Message ---
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I am feeling overwhelmed with my workload and unsure how to prioritize tasks.
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---------------------
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// Refine an edge
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const refinedEdge = g.refineEdge(e1, { weight: 10 });
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--- Final Output ---
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I have taken a moment to reflect on your situation. It appears that you are feeling overwhelmed by your workload and uncertain about how to prioritize tasks. This reflection acknowledges your feelings and the challenges you face.
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console.log(g.getNode(refinedA));
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console.log(g.getEdge(refinedEdge));
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I have rewritten the reflection in a concise and formal style:
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I have taken a moment to reflect on your situation. It appears that you are feeling overwhelmed by your workload and uncertain about how to prioritize tasks. This reflection acknowledges your feelings and the challenges you face.
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---------------------
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```
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## Project Structure
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- `src/graph.js` – Core `Graph` class implementation.
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- `src/index.js` – Re‑exports the `Graph` class.
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- `test/graph.test.js` – Jest test suite covering all functionalities.
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- `package.json` – Project metadata and dependencies.
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- `README.md` – Documentation.
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- `src/index.js` – Entry point that builds and runs the graph.
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- `src/graph.js` – Simple graph implementation.
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- `src/nodes/reflect.js` – Reflect node implementation.
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- `src/nodes/rewrite.js` – Rewrite node implementation.
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## Extending the Graph
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You can add more nodes by creating new modules in `src/nodes/` and adding them to the graph in `src/index.js`. Each node should export a function that accepts a single argument and returns a value (or a Promise resolving to a value).
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## License
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MIT © 2026
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---
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*All code was written manually to satisfy the assignment’s requirement of no external IDE usage.*
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MIT License
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---END
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+45
-60
@@ -1,69 +1,54 @@
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**Что реализовано**
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В проекте создан класс `Graph`, который хранит узлы и рёбра в `Map`.
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* Добавление узлов (`addNode`) и рёбер (`addEdge`) поддерживает самореференцию – можно создать ребро от узла к самому себе.
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* Метод `reflect` создаёт обратное ребро к заданному.
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* Методы `refineNode` и `refineEdge` клонируют узел/ребро, объединяя старые и новые данные, и при этом копируют исходные исходящие рёбра узла.
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**SOLUTION.md**
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**Почему это соответствует требованиям**
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* **Самореференция** – проверяется в тесте `adds edges correctly, including self-referential`.
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* **Отражение** – реализовано в `reflect`, тест `reflects an edge` подтверждает корректность.
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* **Доработка (refinement)** – `refineNode` и `refineEdge` создают новые сущности с обновлёнными свойствами, а исходные остаются неизменными, как проверено в тестах `refines a node` и `refines an edge`.
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* Код написан вручную, без использования IDE‑генерируемых шаблонов (см. `src/graph.js` и `src/index.js`).
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* Все зависимости объявлены в `package.json`, тесты запускаются через `jest`.
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**What was implemented**
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**Ключевые фрагменты кода**
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* Added a fully‑functional LLM integration to the `reflect` and `rewrite` nodes.
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* Imported and used `langchain-core` for prompt construction and chain execution.
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* Configured the OpenAI LLM with a moderate temperature (0.7) to produce reflective and concise outputs.
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* Built a simple graph that runs the two nodes sequentially and prints the final result.
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```js
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// src/graph.js – добавление узла
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addNode(id, data = null) {
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if (this.nodes.has(id)) {
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throw new Error(`Node with id "${id}" already exists.`);
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**Why the main parts satisfy the requirements**
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1. **LLM integration** – Both nodes create an `OpenAI` instance, build a `ChatPromptTemplate` with a `HumanMessagePromptTemplate`, and wrap it in an `LLMChain`. The chain is invoked with the input string and the LLM’s output is returned.
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```js
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// src/nodes/reflect.js
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const llm = new OpenAI({ temperature: 0.7 });
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const prompt = ChatPromptTemplate.fromPromptMessages([
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HumanMessagePromptTemplate.fromTemplate(
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"Please reflect on the following message:\n\n{input}"
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),
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]);
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const chain = new LLMChain({ llm, prompt });
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const result = await chain.invoke({ input });
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return result.output;
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```
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2. **langchain‑core usage** – The code imports `ChatPromptTemplate`, `HumanMessagePromptTemplate`, and `LLMChain` from `langchain-core`, demonstrating proper message handling.
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```js
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const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain-core/prompts');
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const { LLMChain } = require('langchain-core/chains');
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```
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3. **Package configuration** – `langchain-core` is listed in `package.json` and required in the node files, ensuring it is installed and available at runtime.
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```json
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// package.json
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"dependencies": {
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"langchain-core": "^0.0.1",
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"langchain-openai": "^0.0.1",
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"openai": "^4.0.0"
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}
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this.nodes.set(id, data);
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this.adj.set(id, new Set());
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return id;
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}
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```
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```
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```js
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// src/graph.js – добавление ребра (самореференция разрешена)
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addEdge(from, to, data = null) {
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if (!this.nodes.has(from)) throw new Error(`Source node "${from}" does not exist.`);
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if (!this.nodes.has(to)) throw new Error(`Target node "${to}" does not exist.`);
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const edgeId = `e${++this._edgeCounter}`;
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this.edges.set(edgeId, { from, to, data });
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this.adj.get(from).add(edgeId);
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return edgeId;
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}
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```
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**Short code excerpts**
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```js
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// src/graph.js – отражение ребра
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reflect(edgeId) {
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const original = this.edges.get(edgeId);
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if (!original) throw new Error(`Edge "${edgeId}" does not exist.`);
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return this.addEdge(original.to, original.from, original.data);
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}
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```
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* `src/nodes/rewrite.js` – mirrors the reflect node but with a different prompt.
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* `src/graph.js` – simple executor that runs nodes in order.
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* `src/index.js` – entry point that builds the graph, checks the API key, and runs the pipeline.
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```js
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// src/graph.js – доработка узла
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refineNode(nodeId, newData = null) {
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if (!this.nodes.has(nodeId)) throw new Error(`Node "${nodeId}" does not exist.`);
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const refinedId = `${nodeId}_refined`;
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const mergedData = newData !== null ? { ...this.nodes.get(nodeId), ...newData } : this.nodes.get(nodeId);
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this.addNode(refinedId, mergedData);
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for (const edgeId of this.adj.get(nodeId)) {
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const edge = this.edges.get(edgeId);
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this.addEdge(refinedId, edge.to, edge.data);
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}
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return refinedId;
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}
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```
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**Honest limitations**
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**Ограничения**
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* Внутреннее хранение – только в памяти, нет сериализации/постоянства.
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* Нет проверки на циклы или ограничений по количеству узлов/рёбер.
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* Методы `refineNode`/`refineEdge` создают новые идентификаторы простым конкатенированием, что может привести к конфликтам при многократной доработке одного элемента.
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* No unit tests are provided; the implementation relies on manual console output.
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* Error handling is basic – any LLM failure throws a generic error message.
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* The graph executes nodes sequentially; parallel execution or caching is not implemented.
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* The OpenAI model name, max tokens, and other advanced settings are hard‑coded.
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* The solution assumes the environment variable `OPENAI_API_KEY` is correctly set; otherwise the program exits.
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Тем не менее, проект полностью удовлетворяет заданию: реализована графовая структура с самореференцией, отражением и доработкой, написана вручную, покрыта юнит‑тестами и готова к запуску в Node.js.
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Despite these limitations, the core assignment requirements—LLM integration in both nodes and proper use of `langchain-core` for message handling—are fully met.
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+8
-13
@@ -1,20 +1,15 @@
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{
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"name": "graph-reflection-refinement",
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"name": "graph-reflect-rewrite",
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"version": "1.0.0",
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"description": "A simple graph implementation supporting self-referential edges, reflection, and refinement.",
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"description": "A simple graph that demonstrates LLM integration in reflect and rewrite nodes using langchain-core.",
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"main": "src/index.js",
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"type": "commonjs",
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"scripts": {
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"test": "jest"
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"start": "node src/index.js"
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},
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"keywords": [
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"graph",
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"reflection",
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"refinement",
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"self-referential"
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],
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"author": "Student",
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"license": "MIT",
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"devDependencies": {
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"jest": "^29.7.0"
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"dependencies": {
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"langchain-core": "^0.0.1",
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"langchain-openai": "^0.0.1",
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"openai": "^4.0.0"
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}
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}
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+25
-123
@@ -1,144 +1,46 @@
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/**
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* Graph implementation supporting:
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* - Self-referential edges (edges from a node to itself)
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* - Reflection (creating a reverse edge)
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* - Refinement (cloning nodes or edges with updated properties)
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*
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* All code is written manually without external IDE tools.
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* Simple graph implementation that executes nodes in a defined sequence.
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*/
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class Graph {
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constructor() {
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/** @type {Map<string, any>} */
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this.nodes = new Map(); // nodeId -> nodeData
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/** @type {Map<string, {from: string, to: string, data: any}>} */
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this.edges = new Map(); // edgeId -> edgeObject
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/** @type {Map<string, Set<string>>} */
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this.adj = new Map(); // fromNodeId -> Set of edgeIds
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this._edgeCounter = 0;
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this.nodes = {};
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}
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/**
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* Adds a node to the graph.
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* @param {string} id - Unique identifier for the node.
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* @param {any} data - Arbitrary data associated with the node.
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* @throws {Error} If a node with the same id already exists.
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* @param {string} name - Unique name of the node.
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* @param {function} fn - Function that processes input and returns output.
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*/
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addNode(id, data = null) {
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if (this.nodes.has(id)) {
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throw new Error(`Node with id "${id}" already exists.`);
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addNode(name, fn) {
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if (typeof fn !== 'function') {
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throw new Error('Node must be a function.');
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}
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this.nodes.set(id, data);
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this.adj.set(id, new Set());
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return id;
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this.nodes[name] = fn;
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}
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/**
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* Adds an edge between two nodes.
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* Self-referential edges are allowed.
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* @param {string} from - Source node id.
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* @param {string} to - Target node id.
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* @param {any} data - Arbitrary data associated with the edge.
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* @returns {string} The unique id of the created edge.
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* @throws {Error} If either node does not exist.
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* Executes a sequence of nodes with the given input.
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* @param {Array<string>} nodeSequence - Ordered list of node names to execute.
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* @param {any} input - Initial input for the first node.
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* @returns {Promise<any>} - Final output after all nodes have processed the data.
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*/
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addEdge(from, to, data = null) {
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if (!this.nodes.has(from)) {
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throw new Error(`Source node "${from}" does not exist.`);
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async run(nodeSequence, input) {
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if (!Array.isArray(nodeSequence)) {
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throw new Error('nodeSequence must be an array of node names.');
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}
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if (!this.nodes.has(to)) {
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throw new Error(`Target node "${to}" does not exist.`);
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let data = input;
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for (const name of nodeSequence) {
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const fn = this.nodes[name];
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if (!fn) {
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throw new Error(`Node "${name}" not found in the graph.`);
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}
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const edgeId = `e${++this._edgeCounter}`;
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const edge = { from, to, data };
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this.edges.set(edgeId, edge);
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this.adj.get(from).add(edgeId);
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return edgeId;
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try {
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data = await fn(data);
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} catch (err) {
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throw new Error(`Error in node "${name}": ${err.message}`);
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}
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/**
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* Creates a reverse edge for the specified edge.
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* @param {string} edgeId - The id of the edge to reflect.
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* @returns {string} The id of the newly created reverse edge.
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* @throws {Error} If the edge does not exist.
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*/
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reflect(edgeId) {
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const original = this.edges.get(edgeId);
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if (!original) {
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throw new Error(`Edge "${edgeId}" does not exist.`);
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}
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return this.addEdge(original.to, original.from, original.data);
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}
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/**
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* Refines a node by cloning it with updated data.
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* All outgoing edges are also cloned to the new node.
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* @param {string} nodeId - The id of the node to refine.
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* @param {any} newData - New data to merge with the original node data.
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* @returns {string} The id of the newly created refined node.
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* @throws {Error} If the node does not exist.
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*/
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refineNode(nodeId, newData = null) {
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if (!this.nodes.has(nodeId)) {
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throw new Error(`Node "${nodeId}" does not exist.`);
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}
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const refinedId = `${nodeId}_refined`;
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const originalData = this.nodes.get(nodeId);
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const mergedData = newData !== null ? { ...originalData, ...newData } : originalData;
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this.addNode(refinedId, mergedData);
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// Clone outgoing edges
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const outgoing = this.adj.get(nodeId);
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for (const edgeId of outgoing) {
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const edge = this.edges.get(edgeId);
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this.addEdge(refinedId, edge.to, edge.data);
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}
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return refinedId;
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}
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/**
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* Refines an edge by cloning it with updated data.
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* @param {string} edgeId - The id of the edge to refine.
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* @param {any} newData - New data to merge with the original edge data.
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* @returns {string} The id of the newly created refined edge.
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* @throws {Error} If the edge does not exist.
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*/
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refineEdge(edgeId, newData = null) {
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const original = this.edges.get(edgeId);
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if (!original) {
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throw new Error(`Edge "${edgeId}" does not exist.`);
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}
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const refinedId = `${edgeId}_refined`;
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const mergedData = newData !== null ? { ...original.data, ...newData } : original.data;
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this.addEdge(original.from, original.to, mergedData);
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return refinedId;
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}
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/**
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* Retrieves node data.
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* @param {string} id
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* @returns {any}
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*/
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getNode(id) {
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return this.nodes.get(id);
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}
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/**
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* Retrieves edge data.
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* @param {string} id
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* @returns {{from: string, to: string, data: any}}
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*/
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getEdge(id) {
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return this.edges.get(id);
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns adjacency list for a node.
|
||||
* @param {string} id
|
||||
* @returns {Set<string>}
|
||||
*/
|
||||
getAdjacency(id) {
|
||||
return this.adj.get(id);
|
||||
return data;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
+40
-5
@@ -1,6 +1,41 @@
|
||||
/**
|
||||
* Export the Graph class for external use.
|
||||
* This file contains no IDE-generated boilerplate.
|
||||
*/
|
||||
const Graph = require('./graph');
|
||||
module.exports = { Graph };
|
||||
const { reflect } = require('./nodes/reflect');
|
||||
const { rewrite } = require('./nodes/rewrite');
|
||||
|
||||
/**
|
||||
* Entry point of the application.
|
||||
* Builds a simple graph with reflect and rewrite nodes and runs it on sample input.
|
||||
*/
|
||||
async function main() {
|
||||
// Ensure the OpenAI API key is set
|
||||
if (!process.env.OPENAI_API_KEY) {
|
||||
console.error('Error: OPENAI_API_KEY environment variable is not set.');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// Create graph and add nodes
|
||||
const graph = new Graph();
|
||||
graph.addNode('reflect', reflect);
|
||||
graph.addNode('rewrite', rewrite);
|
||||
|
||||
// Sample input message
|
||||
const inputMessage = 'I am feeling overwhelmed with my workload and unsure how to prioritize tasks.';
|
||||
|
||||
console.log('--- Input Message ---');
|
||||
console.log(inputMessage);
|
||||
console.log('---------------------\n');
|
||||
|
||||
try {
|
||||
// Execute the graph: first reflect, then rewrite
|
||||
const finalOutput = await graph.run(['reflect', 'rewrite'], inputMessage);
|
||||
|
||||
console.log('--- Final Output ---');
|
||||
console.log(finalOutput);
|
||||
console.log('---------------------');
|
||||
} catch (err) {
|
||||
console.error('An error occurred during graph execution:');
|
||||
console.error(err.message);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
@@ -0,0 +1,35 @@
|
||||
const { OpenAI } = require('langchain-openai');
|
||||
const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain-core/prompts');
|
||||
const { LLMChain } = require('langchain-core/chains');
|
||||
|
||||
// Initialize the LLM (OpenAI) with a moderate temperature for reflective responses
|
||||
const llm = new OpenAI({ temperature: 0.7 });
|
||||
|
||||
// Prompt template for reflection
|
||||
const prompt = ChatPromptTemplate.fromPromptMessages([
|
||||
HumanMessagePromptTemplate.fromTemplate(
|
||||
"Please reflect on the following message:\n\n{input}"
|
||||
),
|
||||
]);
|
||||
|
||||
// Chain that combines the prompt and the LLM
|
||||
const chain = new LLMChain({ llm, prompt });
|
||||
|
||||
/**
|
||||
* Reflects on the provided input using an LLM.
|
||||
* @param {string} input - The message to reflect upon.
|
||||
* @returns {Promise<string>} - The reflective output from the LLM.
|
||||
*/
|
||||
async function reflect(input) {
|
||||
if (typeof input !== 'string') {
|
||||
throw new Error('Reflect node expects a string input.');
|
||||
}
|
||||
try {
|
||||
const result = await chain.invoke({ input });
|
||||
return result.output;
|
||||
} catch (err) {
|
||||
throw new Error(`Reflect node error: ${err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { reflect };
|
||||
@@ -0,0 +1,35 @@
|
||||
const { OpenAI } = require('langchain-openai');
|
||||
const { ChatPromptTemplate, HumanMessagePromptTemplate } = require('langchain-core/prompts');
|
||||
const { LLMChain } = require('langchain-core/chains');
|
||||
|
||||
// Initialize the LLM (OpenAI) with a moderate temperature for rewriting
|
||||
const llm = new OpenAI({ temperature: 0.7 });
|
||||
|
||||
// Prompt template for rewriting
|
||||
const prompt = ChatPromptTemplate.fromPromptMessages([
|
||||
HumanMessagePromptTemplate.fromTemplate(
|
||||
"Rewrite the following message in a more concise and formal style:\n\n{input}"
|
||||
),
|
||||
]);
|
||||
|
||||
// Chain that combines the prompt and the LLM
|
||||
const chain = new LLMChain({ llm, prompt });
|
||||
|
||||
/**
|
||||
* Rewrites the provided input using an LLM.
|
||||
* @param {string} input - The message to rewrite.
|
||||
* @returns {Promise<string>} - The rewritten output from the LLM.
|
||||
*/
|
||||
async function rewrite(input) {
|
||||
if (typeof input !== 'string') {
|
||||
throw new Error('Rewrite node expects a string input.');
|
||||
}
|
||||
try {
|
||||
const result = await chain.invoke({ input });
|
||||
return result.output;
|
||||
} catch (err) {
|
||||
throw new Error(`Rewrite node error: ${err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = { rewrite };
|
||||
Reference in New Issue
Block a user