# Agent with RAG Memory

This project implements a simple command‑line agent that uses **Ollama embeddings** for a Retrieval‑Augmented Generation (RAG) style knowledge base.  
The agent supports two main tools:

- **`search_knowledge_base`** – find the most relevant documents for a query.  
- **`add_to_knowledge_base`** – add new content to the knowledge base.

## Setup

```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/agent-s-rag-pamyatyu.git
cd agent-s-rag-pamyatyu

# Install dependencies
npm install
```

> **Note**: The project uses the `ollama-embeddings` package.  
> Make sure you have an Ollama server running locally (default `http://localhost:11434`).  
> You can change the host or model via environment variables:

```bash
# Example .env file
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=all-minilm
```

## Running the Agent

```bash
npm start
```

You will see a prompt:

```
Agent> 
```

### Commands

- `/search <query>` – Search the knowledge base for the most relevant documents.
- `/add <content>` – Add new content to the knowledge base.
- `/exit` – Exit the program.

Example:

```
Agent> /add The quick brown fox jumps over the lazy dog.
Content added with id 3f1c2e4b-...

Agent> /search fox
Searching for "fox"...
Top results:
1. [3f1c2e4b-...] (0.9123)
   The quick brown fox jumps over the lazy dog.
```

## Project Structure

- `src/embeddings.js` – Wrapper around `ollama-embeddings`.  
- `src/tools/searchKnowledgeBase.js` – Implements the search tool.  
- `src/tools/addToKnowledgeBase.js` – Implements the add tool.  
- `src/index.js` – CLI entry point and agent logic.  
- `package.json` – Dependencies and scripts.

## Extending

The current implementation uses an in‑memory vector store.  
To persist data or use a more sophisticated vector database, replace the `knowledgeBase` array in `searchKnowledgeBase.js` with your preferred storage solution.

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