What is RAG? A Practical Overview of Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) is an approach that combines document retrieval with large language models to produce responses grounded in real-time, domain-specific data. Rather than relying solely on pre-trained knowledge, RAG allows AI systems to incorporate up-to-date information from your own documents, making answers more relevant and accurate.

‍

How RAG Works

1. Indexing Your Data:
   Documents such as PDFs, DOCs, images, code files, or CSVs are uploaded and processed. The content is split into smaller segments and stored in a searchable index, often using vector databases.
2. Retrieval and Generation:
   When a query is made, the system retrieves the most relevant pieces of your indexed data. A language model then uses this retrieved information to generate a response that is both context-aware and precise.

RAG for Open Source AI

Intellinode supports RAG with open-source models like Gemma, DeepSeek, and Llama. This flexibility lets you integrate your data with your chosen AI model without being locked into proprietary systems. The open-source approach enables a wide range of applications—from customer support chatbots to internal knowledge bases—while ensuring that answers remain accurate and tailored to your needs.

‍

Integrating RAG with Intellinode

Integrating Retrieval Augmented Generation (RAG) with Intellinode is straightforward. First, you use the Intellinode app to upload your documents. Once uploaded, the system processes your files and generates a unique “One Key.” This key acts as a bridge, connecting your data to any large language model (LLM).

Creating a Project

• Start a Project:
  Visit app.intellinode.ai to create a new document-based project.
• Upload Documents:
  Upload any PDFs, DOCs, images, or other data files. Once processed, you’ll receive a unique “One Key” that links your documents to the AI modules.

‍

Deploying with Docker

For those who prefer containerized deployments, run the IntelliServer with Docker:

docker pull intellinode/intelliserver:latest

ONE_KEY=<your-key>
docker run -p 80:80 -e ONE_KEY=$ONE_KEY intellinode/intelliserver:latest

‍

Python Integration Example

If you’re working with Python, integrate RAG into your application with minimal effort:

pip install intelli

Use the one key from intellinode app to connect to your data using the chatbot code:

from intelli.model.input.chatbot_input import ChatModelInput
from intelli.function.chatbot import Chatbot

# Initialize the chatbot with your API key and One Key
chatbot = Chatbot(api_key=YOUR_API_KEY, provider="mistral", options={"one_key": INTELLI_ONE_KEY})

# Prepare the input with reference to your document data
chat_input = ChatModelInput(system="You are a helpful assistant.", model="mistral-medium")
chat_input.attach_reference = True
chat_input.add_user_message("Explain the concept of relativity.")

response = chatbot.chat(chat_input)

‍

In summary, RAG is a practical method for enhancing AI responses by blending retrieval techniques with generative models. Whether you're leveraging open-source tools or proprietary systems, this approach ensures that your applications remain both accurate and relevant.

‍