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

# 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:

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

Running the Agent

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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