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# RAG Memory Agent
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# Agent with RAG Memory
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A simple Retrieval-Augmented Generation (RAG) memory system built with Node.js, TypeScript, and Express.
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It stores user data in an in‑memory virtual file system and uses a language model (OpenAI or a mock) to answer queries based on stored memory.
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This project demonstrates a simple LangChain agent that uses Retrieval-Augmented Generation (RAG) to answer questions based on a small set of documents. The implementation is written in TypeScript and follows the latest LangChain initialization patterns.
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## Features
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- **Virtual File System** – CRUD operations for memory entries.
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- **LLM abstraction** – Uses OpenAI GPT‑3.5‑Turbo if `OPENAI_API_KEY` is set, otherwise falls back to a mock echo.
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- **RAG Agent** – Retrieves relevant memory, builds a prompt, and generates an answer.
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- **RESTful API** – Endpoints for managing memory and querying the agent.
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- **Unit tests** – Jest tests for VFS and Agent logic.
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- **Updated Agent Initialization**: Uses `initializeAgentExecutorWithOptions` from LangChain.
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- **Custom Text Splitter**: Configured with a chunk size of 1000 characters and an overlap of 200 characters.
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- **RAG Memory**: Embeddings are stored in a FAISS vector store and queried via a RetrievalQA chain.
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- **Simple Test Harness**: Runs a sample query and prints the agent's response.
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## Installation
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## Prerequisites
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- Node.js v18 or newer
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- npm
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## Setup
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```bash
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git clone https://git.brojs.ru/kuzakhmetovartur/prakticheskoe-zadanie-agent-s-rag-pamyat.git
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cd prakticheskoe-zadanie-agent-s-rag-pamyat
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# Clone the repository
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git clone https://github.com/your-username/agent-rag-memory.git
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cd agent-rag-memory
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# Install dependencies
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npm install
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# Create a .env file with your OpenAI API key
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echo "OPENAI_API_KEY=your_api_key_here" > .env
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```
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## Environment Variables
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Create a `.env` file based on `.env.example`:
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## Running the Agent
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```bash
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cp .env.example .env
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```
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- `OPENAI_API_KEY` – (optional) Your OpenAI API key. If omitted, the agent will use a mock LLM.
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- `PORT` – Port number for the server (default: 3000).
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## Running the Server
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```bash
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npm run dev # Development with ts-node
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# or
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npm run build
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npm start
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```
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The server will start on `http://localhost:<PORT>`.
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You should see output similar to:
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## API Endpoints
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| Method | Path | Description | Body (JSON) |
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|--------|-----------|---------------------------------------------|---------------------------------|
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| GET | `/memory` | List all memory entries (id, snippet). | – |
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| POST | `/memory` | Create a new memory entry. | `{ "content": "string" }` |
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| DELETE | `/memory/:id` | Delete a memory entry by ID. | – |
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| POST | `/query` | Query the agent. | `{ "query": "string" }` |
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### Example Requests
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```bash
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# Add memory
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curl -X POST http://localhost:3000/memory \
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-H "Content-Type: application/json" \
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-d '{"content":"I love programming in TypeScript."}'
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# Query
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curl -X POST http://localhost:3000/query \
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-H "Content-Type: application/json" \
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-d '{"query":"What do I like?"}'
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```
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## Testing
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Run unit tests with coverage:
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```bash
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npm test
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=== Agent Response ===
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Paris
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```
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## Project Structure
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```
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src/
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index.ts # Server entry point
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agent.ts # RAG agent logic
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llm.ts # LLM abstraction
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vfs.ts # Virtual file system
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utils.ts # Helpers
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routes.ts # Express routes
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middleware.ts # Error handling & validation
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tests/
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vfs.test.ts
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agent.test.ts
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agent-rag-memory/
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├── src/
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│ └── index.ts # Main implementation
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├── package.json
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├── tsconfig.json
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└── README.md
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```
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## License
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MIT © Your Name
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MIT License
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{
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"name": "rag-memory-agent",
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"name": "agent-rag-memory",
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"version": "1.0.0",
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"description": "Retrieval-Augmented Generation (RAG) memory system with virtual file system and RESTful API",
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"description": "A simple LangChain agent with RAG memory implemented in TypeScript",
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"main": "dist/index.js",
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"type": "commonjs",
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"scripts": {
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"build": "tsc",
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"start": "node dist/index.js",
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"dev": "ts-node src/index.ts",
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"test": "jest --coverage"
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"start": "ts-node src/index.ts"
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},
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"keywords": [
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"RAG",
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"LLM",
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"virtual-file-system",
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"express",
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"langchain",
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"rag",
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"agent",
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"typescript"
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],
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"author": "Your Name",
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"license": "MIT",
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"dependencies": {
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"dotenv": "^16.4.5",
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"express": "^4.18.2",
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"uuid": "^9.0.0"
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},
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"devDependencies": {
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"@types/express": "^4.17.21",
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"@types/jest": "^29.5.12",
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"@types/node": "^20.11.5",
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"@types/supertest": "^2.0.12",
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"jest": "^29.7.0",
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"supertest": "^6.3.3",
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"ts-jest": "^29.1.1",
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"ts-node": "^10.9.2",
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"@types/node": "^20.11.0",
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"langchain": "^0.0.202",
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"openai": "^4.20.0",
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"ts-node": "^10.9.1",
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"typescript": "^5.3.3"
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}
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}
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+74
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import express from 'express';
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import dotenv from 'dotenv';
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import bodyParser from 'body-parser';
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import { VirtualFileSystem } from './vfs';
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import { LLM } from './llm';
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import { Agent } from './agent';
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import { createRoutes } from './routes';
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import { errorHandler } from './middleware';
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import { OpenAI } from "langchain/llms/openai";
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import { OpenAIEmbeddings } from "langchain/embeddings/openai";
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import { FAISS } from "langchain/vectorstores/faiss";
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import { RetrievalQA } from "langchain/chains/retrieval-qa";
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import { Tool } from "langchain/tools/base";
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import { initializeAgentExecutorWithOptions } from "langchain/agents";
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import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
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import * as dotenv from "dotenv";
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dotenv.config();
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const app = express();
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const port = process.env.PORT ? parseInt(process.env.PORT, 10) : 3000;
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async function main() {
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// Ensure API key is set
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) {
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console.error("Error: OPENAI_API_KEY environment variable is not set.");
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process.exit(1);
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}
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app.use(bodyParser.json());
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// Sample document
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const sampleText = `
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The quick brown fox jumps over the lazy dog. This sentence is often used to test typing and fonts.
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The capital of France is Paris. Paris is known for its art, gastronomy, and culture.
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The Earth revolves around the Sun every 365.25 days. The Moon orbits the Earth approximately every 27.3 days.
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`;
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const vfs = new VirtualFileSystem();
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const llm = new LLM();
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const agent = new Agent(vfs, llm);
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app.use('/', createRoutes(agent));
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app.use(errorHandler);
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app.listen(port, () => {
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console.log(`RAG Memory Agent listening on port ${port}`);
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// Text splitter configuration (chunk size 1000, overlap 200)
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const splitter = new RecursiveCharacterTextSplitter({
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chunkSize: 1000,
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chunkOverlap: 200,
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});
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// Split the document into chunks
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const docs = await splitter.splitText(sampleText);
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// Initialize embeddings and vector store
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const embeddings = new OpenAIEmbeddings({ openAIApiKey: apiKey });
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const vectorStore = await FAISS.fromTexts(docs, [], embeddings);
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// Initialize LLM
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const llm = new OpenAI({
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openAIApiKey: apiKey,
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temperature: 0,
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});
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// Create RetrievalQA chain
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const qaChain = RetrievalQA.fromLLM(llm, vectorStore);
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// Define a tool that uses the QA chain
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const ragTool = new Tool({
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name: "RAG",
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description: "Answer questions based on the provided documents using Retrieval-Augmented Generation.",
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func: async (input: string) => {
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const result = await qaChain.invoke({ query: input });
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return result.output as string;
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},
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});
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// Initialize the agent with the updated method
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const agent = await initializeAgentExecutorWithOptions(
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[ragTool],
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llm,
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{
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agentType: "zero-shot-react-description",
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verbose: true,
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}
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);
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// Run a sample query
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const query = "What is the capital of France?";
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const response = await agent.invoke({ input: query });
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console.log("\n=== Agent Response ===");
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console.log(response.output);
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}
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main().catch((err) => {
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console.error("Error in main execution:", err);
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process.exit(1);
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});
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+4
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"compilerOptions": {
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"target": "ES2020",
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"module": "CommonJS",
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"outDir": "dist",
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"rootDir": "src",
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"strict": true,
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"esModuleInterop": true,
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"skipLibCheck": true,
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"forceConsistentCasingInFileNames": true,
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"skipLibCheck": true
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"outDir": "dist",
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"rootDir": "src"
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},
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"include": ["src/**/*"],
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"exclude": ["node_modules", "**/*.test.ts"]
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"include": ["src"]
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}
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