feat: solution for 'Агент с RAG-памятью'

README.md

@@ -1,93 +1,57 @@
-# RAG Memory Agent
+# Agent with RAG Memory
 
-A simple Retrieval-Augmented Generation (RAG) memory system built with Node.js, TypeScript, and Express.  
-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.
+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.
 
 ## Features
 
-- **Virtual File System** – CRUD operations for memory entries.
-- **LLM abstraction** – Uses OpenAI GPT‑3.5‑Turbo if `OPENAI_API_KEY` is set, otherwise falls back to a mock echo.
-- **RAG Agent** – Retrieves relevant memory, builds a prompt, and generates an answer.
-- **RESTful API** – Endpoints for managing memory and querying the agent.
-- **Unit tests** – Jest tests for VFS and Agent logic.
+- **Updated Agent Initialization**: Uses `initializeAgentExecutorWithOptions` from LangChain.
+- **Custom Text Splitter**: Configured with a chunk size of 1000 characters and an overlap of 200 characters.
+- **RAG Memory**: Embeddings are stored in a FAISS vector store and queried via a RetrievalQA chain.
+- **Simple Test Harness**: Runs a sample query and prints the agent's response.
 
-## Installation
+## Prerequisites
+
+- Node.js v18 or newer
+- npm
+
+## Setup
 
 ```bash
-git clone https://git.brojs.ru/kuzakhmetovartur/prakticheskoe-zadanie-agent-s-rag-pamyat.git
-cd prakticheskoe-zadanie-agent-s-rag-pamyat
+# Clone the repository
+git clone https://github.com/your-username/agent-rag-memory.git
+cd agent-rag-memory
+
+# Install dependencies
 npm install
+
+# Create a .env file with your OpenAI API key
+echo "OPENAI_API_KEY=your_api_key_here" > .env
 ```
 
-## Environment Variables
-
-Create a `.env` file based on `.env.example`:
+## Running the Agent
 
 ```bash
-cp .env.example .env
-```
-
-- `OPENAI_API_KEY` – (optional) Your OpenAI API key. If omitted, the agent will use a mock LLM.
-- `PORT` – Port number for the server (default: 3000).
-
-## Running the Server
-
-```bash
-npm run dev   # Development with ts-node
-# or
-npm run build
 npm start
 ```
 
-The server will start on `http://localhost:<PORT>`.
+You should see output similar to:
 
-## API Endpoints
-
-| Method | Path      | Description                                 | Body (JSON)                     |
-|--------|-----------|---------------------------------------------|---------------------------------|
-| GET    | `/memory` | List all memory entries (id, snippet).      | –                               |
-| POST   | `/memory` | Create a new memory entry.                  | `{ "content": "string" }`       |
-| DELETE | `/memory/:id` | Delete a memory entry by ID.            | –                               |
-| POST   | `/query`  | Query the agent.                            | `{ "query": "string" }`         |
-
-### Example Requests
-
-```bash
-# Add memory
-curl -X POST http://localhost:3000/memory \
-  -H "Content-Type: application/json" \
-  -d '{"content":"I love programming in TypeScript."}'
-
-# Query
-curl -X POST http://localhost:3000/query \
-  -H "Content-Type: application/json" \
-  -d '{"query":"What do I like?"}'
 ```
-
-## Testing
-
-Run unit tests with coverage:
-
-```bash
-npm test
+=== Agent Response ===
+Paris
 ```
 
 ## Project Structure
 
 ```
-src/
-  index.ts          # Server entry point
-  agent.ts          # RAG agent logic
-  llm.ts            # LLM abstraction
-  vfs.ts            # Virtual file system
-  utils.ts          # Helpers
-  routes.ts         # Express routes
-  middleware.ts     # Error handling & validation
-  tests/
-    vfs.test.ts
-    agent.test.ts
+agent-rag-memory/
+├── src/
+│   └── index.ts          # Main implementation
+├── package.json
+├── tsconfig.json
+└── README.md
 ```
 
 ## License
 
-MIT © Your Name
+MIT License