instruction: Create a Dockerfile for Node.js with production best practices
output: ```dockerfile
  from external sources.The system operates as follows:Questions get turned into mathematical patterns that capture their meaningThese patterns help find matching information in a databaseThe LLM generates responses that blend the model's inherent knowledge with the this extra information.To hold this vector information in an efficient manner, we need a special type of database.Introduction to Graph databasesGraph 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 effectivenessTo 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 technologiesApache 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.
  
  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 LinuxTo 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
  
  git clone https://github.com/docker/genai-stack
  docker compose --profile linux-gpu up -d
  mv env.example .env
  nano .envIn 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:11434CPU powered
  
  git clone https://github.com/docker/genai-stack
  docker compose --profile linux up -d
  mv env.example .env
  nano .envIn 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:11434Setting up on other platformsFor instructions on how to set up the stack on other platforms, refer to this page.Initial startupThe first startup may take some time because the system needs to download a large language model.Monitoring progressWe can monitor the download and initialization progress by viewing the logs. Run the following command to view the logs:
  
  docker compose logsWait for specific lines in the logs indicating that the download is complete and the stack is ready. These lines typically confirm successful setup and initialization.
  
   pull-model-1 exited with code 0
   database-1 | 2024-12-29 09:35:53.269+0000 INFO Started.
   pdf_bot-1 | You can now view your Streamlit app in your browser.
   loader-1 | You can now view your Streamlit app in your browser.
   bot-1 | You can now view your Streamlit app in your browser.You can now access the interface at http://localhost:8501/ to ask questions. For example, you can try the sample question:When we see those lines in the logs, web apps are ready to be used.Since our goal is to teach AI about things it does not yet know, we begin by asking it a simple question about Nifi at
  http://localhost:8501/.
  
  Question: What is Apache Nifi?
  RAG: Disabled
  Hello! I'm here to help you with your question about Apache NiFi. Unfortunately, I don't know the answer to that question. I'm just an AI and my knowledge cutoff is December 2022, so I may not be familiar with the latest technologies or software. Can you please provide more context or details about Apache NiFi? Maybe there's something I can help you with related to it.As we can see, AI does not know anything about this subject because it did not exist during the time of its training, also known as the information cutoff point.Now it's time to teach the AI some new tricks. First, connect to http://localhost:8502/. Instead of using the "neo4j" tag, change it to the "apache-nifi" tag, then select the Import button.
  After the import is successful, we can access Neo4j to verify the data.After logging in to http://localhost:7474/ using the credentials
  ```