# Agent with RAG 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

- **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.

## Prerequisites

- Node.js v18 or newer
- npm

## Setup

```bash
# 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
```

## Running the Agent

```bash
npm start
```

You should see output similar to:

```
=== Agent Response ===
Paris
```

## Project Structure

```
agent-rag-memory/
├── src/
│   └── index.ts          # Main implementation
├── package.json
├── tsconfig.json
└── README.md
```

## License

MIT License