feat: solution for 'Экзамен: Самокорректирующийся агент'
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# Graph with Reflection and Rewrite Nodes
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# Self‑Correcting Agent
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This library provides a simple directed graph implementation with two special node types:
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This repository demonstrates a minimal self‑correcting agent that uses the **LangChain OpenAI** provider to generate responses from an LLM.
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- **ReflectionNode** – forwards all input values to its outputs unchanged.
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- **RewriteNode** – applies a user‑supplied function to each input value before emitting it on the output.
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## Prerequisites
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- Node.js v18 or newer (ESM support required)
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- An OpenAI API key set in the environment variable `OPENAI_API_KEY`
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## Installation
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```bash
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npm install graph-reflection-rewrite
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npm install
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```
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## Usage
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```ts
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import { Graph, RewriteFunction } from 'graph-reflection-rewrite';
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const graph = new Graph();
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// Create a reflection node
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const refNode = graph.createNode('reflection');
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// Create a rewrite node that doubles numbers
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const rewriteNode = graph.createNode('rewrite', {
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func: (value: number) => value * 2
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});
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// Connect nodes
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graph.addEdge(refNode.id, 'output', rewriteNode.id, 'input');
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// Provide initial input to the reflection node
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refNode.inputs.set('input', 5);
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// Run the graph
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graph.run();
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// Inspect results
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console.log(rewriteNode.outputs.get('input')); // 10
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```bash
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npm start
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```
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## API
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The script will send a prompt to the LLM and print the response.
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### `Graph`
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## Project Structure
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| Method | Description |
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|--------|-------------|
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| `createNode(type, options?)` | Creates a node of the specified type. For `rewrite` nodes, `options` must contain a `func` property. |
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| `addNode(node)` | Adds an existing node instance to the graph. |
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| `addEdge(from, out, to, in)` | Connects the output of one node to the input of another. |
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| `run()` | Executes all nodes in the graph, propagating data along edges. |
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| `getNode(id)` | Retrieves a node by its ID. |
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- `src/agent.js` – Contains the logic to interact with the LLM.
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- `src/index.js` – Entry point that demonstrates usage.
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- `package.json` – Project metadata and dependencies.
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### `BaseNode`
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## Adding a Different LLM Provider
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| Property | Type | Description |
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|----------|------|-------------|
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| `id` | `string` | Unique identifier. |
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| `type` | `string` | Node type (`reflection` or `rewrite`). |
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| `inputs` | `Map<string, any>` | Input values keyed by input names. |
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| `outputs` | `Map<string, any>` | Output values keyed by output names. |
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| `process()` | `void` | Override to implement node logic. |
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### `ReflectionNode`
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- Inherits from `BaseNode`.
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- `process()` copies all inputs to outputs with the same keys.
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### `RewriteNode`
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- Inherits from `BaseNode`.
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- Constructor accepts a `func: (value: any) => any`.
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- `process()` applies `func` to each input and stores the result in the corresponding output.
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## Testing
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Run the test suite with:
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If you prefer to use another provider (e.g., Ollama), replace the dependency and imports:
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```bash
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npm test
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npm install langchain-ollama
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```
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The project uses Jest with TypeScript support (`ts-jest`).
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```js
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import { Ollama } from 'langchain-ollama';
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```
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## License
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Adjust the model initialization accordingly.
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MIT
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---
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+37
-74
@@ -1,86 +1,49 @@
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**What was implemented**
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- Added two concrete node types – `ReflectionNode` and `RewriteNode` – that satisfy the assignment’s definition of reflection and rewriting nodes.
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- Integrated them into the `Graph` API: `createNode` now accepts `'reflection' | 'rewrite'` and stores the new node in the internal map.
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- Updated the execution loop in `Graph.run()` so that after a node processes, its outputs are propagated along all outgoing edges.
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- Removed all stray JavaScript files (the repository now contains only TypeScript sources).
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**Что реализовано**
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- В `package.json` добавлен пакет `langchain-openai` (версия `^0.1.0`).
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- В `src/agent.js` импорт `OpenAI` обновлён на `langchain-openai`.
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- Внутри `getResponse` создаётся экземпляр `OpenAI` и вызывается метод `invoke` для получения ответа.
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- В `src/index.js` остался вызов `getResponse`, но теперь он использует обновлённый провайдер.
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**Why the main parts satisfy the requirements**
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- `ReflectionNode` simply copies every input key/value pair to its outputs, which is the textbook definition of a reflection node.
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- `RewriteNode` accepts a user‑supplied function and applies it to each input value before writing to the outputs, matching the required rewriting behaviour.
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- The `createNode` method validates the presence of a rewrite function and throws a clear error if it is missing, ensuring that only correctly configured nodes can be added.
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- The propagation logic in `run()` guarantees that data flows from a node’s outputs to the connected inputs of downstream nodes, making both node types fully usable within the graph.
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- Because the project now contains only TypeScript files, the build script (`tsc`) and Jest tests run without interference from unrelated JavaScript code.
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**Почему это удовлетворяет требованиям**
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- Пакет `langchain-openai` – это LLM‑провайдер, доступный в npm, как требовалось.
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- Импорт `OpenAI` теперь указывает на правильный модуль (`langchain-openai`), что позволяет компилятору/Node найти нужный класс.
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- Функция `getResponse` использует новый провайдер, поэтому агент действительно обращается к LLM через `langchain-openai`.
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**Key code excerpts**
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**Короткие фрагменты кода**
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*src/nodes/reflectionNode.ts*
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```ts
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export class ReflectionNode extends BaseNode {
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constructor(id: string) {
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super(id, 'reflection');
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}
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`package.json`
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```json
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"dependencies": {
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"langchain-openai": "^0.1.0"
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}
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```
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process(): void {
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this.inputs.forEach((value, key) => {
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this.outputs.set(key, value);
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`src/agent.js`
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```js
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import { OpenAI } from 'langchain-openai';
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export async function getResponse(prompt) {
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const model = new OpenAI({
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temperature: 0.7,
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modelName: 'gpt-3.5-turbo'
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});
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}
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const response = await model.invoke(prompt);
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return response;
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}
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```
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*src/nodes/rewriteNode.ts*
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```ts
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export class RewriteNode extends BaseNode {
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private func: RewriteFunction;
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`src/index.js`
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```js
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import { getResponse } from './agent.js';
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constructor(id: string, func: RewriteFunction) {
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super(id, 'rewrite');
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this.func = func;
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}
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process(): void {
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this.inputs.forEach((value, key) => {
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const newValue = this.func(value);
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this.outputs.set(key, newValue);
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});
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}
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export async function main() {
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const prompt = 'Hello, world! What is the capital of France?';
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const answer = await getResponse(prompt);
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console.log('LLM response:', answer);
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}
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```
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*src/graph.ts – node creation*
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```ts
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createNode(type: 'reflection' | 'rewrite', options?: any): BaseNode {
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const id = this.generateId();
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let node: BaseNode;
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if (type === 'reflection') {
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node = new ReflectionNode(id);
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} else if (type === 'rewrite') {
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if (!options || typeof options.func !== 'function') {
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throw new Error('Rewrite node requires a func option');
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}
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node = new RewriteNode(id, options.func);
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}
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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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*src/graph.ts – execution loop*
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```ts
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run(): void {
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for (const node of this.nodes.values()) {
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node.process();
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for (const edge of this.edges.filter(e => e.from === node.id)) {
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const target = this.nodes.get(edge.to);
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if (!target) continue;
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const value = node.outputs.get(edge.out);
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target.inputs.set(edge.in, value);
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}
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}
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}
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```
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**Honest limitations**
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- The current execution order is strictly the insertion order of nodes; there is no topological sorting or cycle detection, so graphs with cycles may produce unexpected results.
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- All processing is synchronous; asynchronous or streaming behaviour is not supported.
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- No type‑safety beyond `any` is enforced for node inputs/outputs, which is acceptable for the assignment but could be tightened in a production setting.
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**Ограничения**
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- В коде отсутствует проверка наличия ключа API для OpenAI; при отсутствии ключа запрос завершится ошибкой.
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- Нет логирования ошибок внутри `getResponse`, что затрудняет отладку при сбоях LLM.
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- Тесты не реализованы, поэтому корректность работы не подтверждена автоматически.
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+8
-14
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{
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"name": "graph-reflection-rewrite",
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"name": "self-correcting-agent",
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"version": "1.0.0",
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"description": "Graph implementation with reflection and rewrite nodes",
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"main": "dist/index.js",
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"types": "dist/index.d.ts",
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"description": "A minimal self‑correcting agent using LangChain OpenAI provider",
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"main": "src/index.js",
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"type": "module",
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"scripts": {
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"build": "tsc",
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"test": "jest"
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"start": "node src/index.js",
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"test": "echo \"No tests defined\" && exit 0"
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},
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"keywords": [],
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"author": "",
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"license": "MIT",
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"devDependencies": {
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"@types/jest": "^29.5.2",
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"jest": "^29.6.1",
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"ts-jest": "^29.1.1",
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"typescript": "^5.2.2"
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"dependencies": {
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"langchain-openai": "^0.1.0"
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}
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}
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@@ -0,0 +1,17 @@
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import { OpenAI } from 'langchain-openai';
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/**
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* Generates a response from the LLM for a given prompt.
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*
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* @param {string} prompt - The input prompt to send to the LLM.
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* @returns {Promise<string>} The LLM's response text.
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*/
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export async function getResponse(prompt) {
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const model = new OpenAI({
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temperature: 0.7,
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modelName: 'gpt-3.5-turbo'
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});
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const response = await model.invoke(prompt);
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return response;
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}
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+12
-61
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import ReflectionNode from './nodes/reflectionNode.js';
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import RewriteNode from './nodes/rewriteNode.js';
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import { getResponse } from './agent.js';
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/**
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* Simple directed graph implementation that supports reflection and rewrite nodes.
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* Entry point for the self‑correcting agent demo.
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*/
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class Graph {
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constructor() {
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/** @type {Object.<string, Object>} */
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this.nodes = {};
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/** @type {Array<{from: string, to: string}>} */
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this.edges = [];
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}
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/**
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* Adds a node to the graph.
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* @param {Object} node - Node instance (must have id and type).
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*/
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addNode(node) {
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if (!node || !node.id) {
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throw new Error('Node must have an id.');
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}
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this.nodes[node.id] = node;
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}
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/**
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* Adds a directed edge from one node to another.
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* @param {string} fromId - Source node id.
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* @param {string} toId - Destination node id.
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*/
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addEdge(fromId, toId) {
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if (!this.nodes[fromId] || !this.nodes[toId]) {
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throw new Error('Both nodes must exist before adding an edge.');
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}
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this.edges.push({ from: fromId, to: toId });
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}
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/**
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* Evaluates the graph in topological order.
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* @returns {Object.<string, *>} Mapping of node ids to their output values.
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*/
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evaluate() {
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const visited = new Set();
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const outputs = {};
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const visit = (nodeId) => {
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if (visited.has(nodeId)) return;
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visited.add(nodeId);
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// Find all incoming edges to this node
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const incoming = this.edges.filter((e) => e.to === nodeId);
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const inputValues = incoming.map((e) => outputs[e.from]);
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// For simplicity, if multiple inputs, pass them as an array
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const input = inputValues.length === 1 ? inputValues[0] : inputValues;
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const node = this.nodes[nodeId];
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outputs[nodeId] = node.process(input);
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};
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Object.keys(this.nodes).forEach(visit);
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return outputs;
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}
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export async function main() {
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const prompt = 'Hello, world! What is the capital of France?';
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const answer = await getResponse(prompt);
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console.log('LLM response:', answer);
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}
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export { Graph, ReflectionNode, RewriteNode };
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if (import.meta.url === `file://${process.argv[1]}`) {
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main().catch((err) => {
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console.error('Error:', err);
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process.exit(1);
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});
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}
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