A Dive into MemGPT
Large Language Models (LLMs) face a limitation in terms of their context window, which restricts their effectiveness in extended conversations and document analysis. To overcome this constraint, researchers have introduced a concept called virtual context management, inspired by hierarchical memory systems in traditional operating systems. This technique is implemented through MemGPT (Memory-GPT), which enables the analysis of extensive documents surpassing the inherent context window of the base model. Additionally, MemGPT is capable of facilitating multi-session chat interactions, creating conversational agents that can remember, reflect, and evolve dynamically during long-term interactions with users.
MemGPT (Memory-GPT)
The key idea behind virtual context management is to provide the illusion of unlimited context while still using fixed-context models. This approach draws inspiration from virtual memory paging, which allows applications to operate on datasets larger than available memory, and it is implemented as an operating system-inspired LLM system for managing virtual context.
As illustrated in the image above, a fixed-context LLM is enhanced with a hierarchical memory system and self-memory management capabilities. The LLM processor takes the Main context and External context.
