This is a RAG (Retrieval-Augmented Generation) example using Node.js with markdown files. To run the project locally, follow the steps below:

• Node.js v22 and Express.js.
• Ollama with the following models:
  • nomic-embed-text for text embedding during ingestion
  • TinyLlama for generating responses
• qDrant for vector storage and search

First, make sure you have the following requirements installed:

• Node.js v22 or higher
• NPM (Node.js package manager)
• Docker installed
• At least 4GB of RAM available

1. Clone the repository:

git clone git@github.com:odanieldcs/rag-nodejs.git
cd rag-nodejs

2. Install the project dependencies:

npm install

3. Configure Docker to have at least 4GB of RAM available. You can do this by editing the Docker Desktop settings or using the command:

4. Start the Ollama and qDrant services using Docker Compose:

docker-compose up -d

5. Install the Ollama models:

docker exec -it ollama ollama pull nomic-embed-text
docker exec -it ollama ollama pull TinyLlama

6. Start the application:

npm start

7. Add some markdown files to the source folder. You can use the provided example files or create your own.

8. Execute the ingestion script.

npm run ingest

9. Now you can test the application by sending a POST request to the /chat endpoint with a JSON body containing your question:

curl -X POST http://localhost:3000/chat -H "Content-Type: application/json" -d '{"question": "What is RAG?"}'