Applying RAG for Working with Wazuh Documentation: A Step-by-Step Guide (Part 2)

Preparing for Code Development

For local code development for RAG, you will need to install the following tools:

• Ollama
• Python v3.9+
• Basic Python knowledge
• Wazuh documentation in PDF format

Running and Configuring Ollama

1. Install Ollama
2. Obtain the necessary models: llama3.2 and nomic-embed-text.

Developing a Mechanism for Loading PDF Documentation

For development, we will use the following tools:

• LangChain - for creating data processing chains.
• Ollama - for running and configuring models.
• Python - as the main programming language.
• ChromaDB - as a vector store.

Install the dependencies:

Applying RAG for Wazuh Documentation: A Step-by-Step Guide (Part 1)

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a method that allows the use of information from various sources to generate more accurate and useful responses to questions.

In the context of Wazuh, RAG can be used to automate data processing, optimize access to information, and improve information retrieval.

Preparing for RAG Integration

Before integrating RAG with the Wazuh documentation, you need to complete the following steps: