Graph Databases, RAG Implementation, and GenAI Stack Setup

Graph databases, such as Neo4j, are specifically designed for managing highly connected data. Unlike traditional relational databases, graph databases prioritize both the entities and the relationships between them, making them ideal for tasks where connections are as important as the data itself. Graph databases stand out for their unique approach to data storage and querying. They use nodes (or vertices) to represent entities and edges to represent the relationships between these entities. This structure allows for efficient handling of highly connected data and complex queries, which are difficult to manage in traditional database systems. SQL databases and graph databases differ significantly in their data models. SQL databases use a tabular structure with rows and columns, where relationships between entities are established using foreign keys. This approach works well for structured data and predefined relationships. In contrast, graph databases represent data as nodes (entities) and edges (relationships), making the representation of relationships more intuitive and flexible. This structure is particularly advantageous for complex, interconnected data. Another key difference lies in schema flexibility. SQL databases operate on a rigid schema, meaning any changes to the data structure, such as adding new columns or altering relationships, typically require careful planning and migration processes. Graph databases, however, are schema-optional, allowing for much greater flexibility. New nodes, edges, or properties can be introduced without disrupting existing data, enabling faster adaptation to changing requirements. ## Practical implementation: testing RAG effectiveness To illustrate the power of RAG systems in practice, let's examine a real-world implementation using Apache NiFi as our subject matter. This case study demonstrates how RAG can enhance an AI's ability to provide accurate, contextual information about specialized technical topics. ### Teaching AI about new technologies Apache NiFi serves as an excellent example of the limitations of traditional LLMs and how RAG can overcome them. As a relatively recent technology, many LLMs have limited or outdated knowledge about it, making it a perfect candidate for demonstrating RAG's capabilities. Without RAG, the LLM may provide broad responses or acknowledge its limited knowledge when asked about information that is beyond its cutoff date. The following example demonstrates this. ```text Question: What important events happened in 2024? RAG: Disabled I'm happy to help! Unfortunately, I'm a large language model, I don't have access to real-time information or events that occurred after my training data cutoff in 2024. Therefore, I cannot provide you with any important events that happened in 2024. My apologize for any inconvenience this may cause. Is there anything else I can help you with? ``` ## Setting up GenAI stack with GPU acceleration on Linux To set up and run the GenAI stack on a Linux host, execute one of the following commands, either for GPU or CPU powered: ### GPU powered ```bash git clone https://github.com/docker/genai-stack docker compose --profile linux-gpu up -d mv env.example .env nano .env ``` In the `.env` file, make sure following lines are commented out. Set your own credentials for security NEO4J_URI=neo4j://database:7687 NEO4J_USERNAME=neo4j NEO4J_PASSWORD=password OLLAMA_BASE_URL=http://llm-gpu:11434 ### CPU powered ```bash git clone https://github.com/docker/genai-stack docker compose --profile linux up -d mv env.example .env nano .env ``` In the `.env` file, make sure following lines are commented out. Set your own credentials for security NEO4J_URI=neo4j://database:7687 NEO4J_USERNAME=neo4j NEO4J_PASSWORD=password OLLAMA_BASE_URL=http://llm:11434 ### Setting up on other platforms For instructions on how to set up the stack on other platforms, refer to [this page](https://github.com/docker/genai-stack).

This section discusses the flexibility of graph databases compared to SQL databases, particularly in terms of schema. It also presents a practical implementation of RAG using Apache NiFi as an example, demonstrating how RAG can enhance AI's knowledge of recent technologies. Finally, it provides instructions for setting up a GenAI stack with GPU acceleration on Linux, including commands for cloning the repository and configuring the environment.