Tag: API

Fast Copy over network

Posted on by lechuck park

With a Claude
This image illustrates a system architecture diagram for “Fast Copy over network”. Here’s a detailed breakdown:

  1. Main Sections:
  • Fast Copy over network
  • Minimize Copy stacks
  • Minimize Computing
  • API optimization for read/write
  1. System Components:
  • Basic computing layer including OS (Operating System) and CPU
  • RAM (memory) layer
  • Hardware device layer
  1. Key Features:
  • The purple area on the left focuses on minimizing Count & Copy with API
  • The blue center area represents minimized computing works (Program Code)
  • The orange area on the right shows programmable API implementation
  1. Data Flow:
  • Arrows indicating bi-directional communication between systems
  • Vertical data flow from OS to RAM to hardware
  • Horizontal data exchange between systems

The architecture demonstrates a design aimed at optimizing data copying operations over networks while efficiently utilizing system resources.

personalized RAG

Posted on by lechuck park

from Claude with some prompting
This diagram illustrates a personalized RAG (Retrieval-Augmented Generation) system that allows individuals to use their personal data with various LLM (Large Language Model) implementations. Key aspects include:

  1. User input: Represented by a person icon and notebook on the left, indicating personal data or queries.
  2. On-Premise storage: Contains LLM models that can be managed and run locally by the user.
  3. Cloud integration: An API connects to cloud-based LLM services, represented by icons in the “on cloud” section. These also symbolize different cloud-based LLM models.
  4. Flexible model utilization: The structure enables users to leverage both on-premise and cloud-based LLM models, allowing for combination of different models’ strengths or selection of the most suitable model for specific tasks.
  5. Privacy protection: A “Control a privacy Filter” icon emphasizes the importance of managing privacy filters to prevent inappropriate exposure of sensitive information to LLMs.
  6. Model selection: The “Use proper Foundation models” icon stresses the importance of choosing appropriate base models for different tasks.

This system empowers individual users to safely manage their data while flexibly utilizing various LLM models, both on-premise and cloud-based. It places a strong emphasis on privacy protection, which is crucial in RAG systems dealing with personal data.

The diagram effectively showcases how personal data can be integrated with advanced LLM technologies while maintaining control over privacy and model selection.

Data Center Management Upgrade

Posted on by lechuck park

From Claude with some prompting
explain the image in more detail from the data collection perspective and how the data analysis platform facilitates the expansion of AI services.

First, we can see the data collection stage where data is gathered from various systems within the data center building, such as electrical, mechanical, security, and so on, through subsystems like EPMS, BAS, ETC. This collected data is stored in the Data Gathering DB.

Next, this accumulated data is transmitted to the Data Analysis Platform via an API. The necessity of the data analysis platform arises from the need to process the vast amount of collected data and derive meaningful insights.

Within the Data Analysis Platform, tools like Query, Program, and Visualization are utilized for data analysis and monitoring purposes. Based on this, services such as Energy Optimization and Predictive Failure Detection are provided.

Furthermore, by integrating AI technology, data-driven insights can be enhanced. AI models can leverage the data and services from the data analysis platform to perform advanced analytics, automated decision-making, and more.

In summary, the flow is as follows: Data Collection -> Data Processing/Analysis on the Data Analysis Platform -> Provision of services like Energy Optimization and Failure Prediction -> Integration of AI technology for advanced analysis and automation, all contributing to effective data center management.