RAG

Phoenix introduced the RAG (Retrieval-Augmented Generation) memory system to provide DePin with real-time knowledge retrieval and generation capabilities, significantly improving document interpretation and dynamic adaptation during task execution. This system enables DePin to respond more accurately to tasks and provide optimized decision support in changing environments.

Core Functions:

Real-time knowledge retrieval

DePin can access on- and off-chain data sources to retrieve up-to-date information in real time, such as market data, progress on governance proposals or community feedback. Data filtering and contextual enhancements ensure that the content generated is accurate and relevant to actual needs.

Combining short-term and long-term memory

Memory Manager combines short-term memory, which stores real-time data for the duration of a task, and long-term memory, which stores a record of key events and decisions. dePin recalls historical data across tasks to provide a more consistent user experience and decision support.

Personalized Knowledge Model

Each DePin can customize its knowledge base based on its role and mission to support deep optimization in specific areas (e.g., investment analysis, governance proposal evaluation, etc.). Machine learning models dynamically adjust the content of the knowledge base to improve the accuracy and applicability of the DePin.

By introducing the RAG memory system, Phoenix can provide DePin with higher task adaptability and accuracy, and enhance its ability to handle complex dynamic tasks. In applications such as on-chain governance, asset management, and market forecasting, DePin can access the latest data in real time, generate targeted content, and optimize decision-making based on historical data, thus promoting the intelligent upgrading of the Web3 ecosystem. In addition, the personalized knowledge model ensures that each DePin can provide tailor-made solutions according to specific needs, bringing precise services to users.