Custom embedding and memory plugins – the evolutionary aspects of Auto-GPT
Auto-GPT is architected with a vision that extends beyond traditional AI models. One of its revolutionary design choices is treating the GPT not as a monolithic core but rather as a foundational plugin. This design philosophy ensures that while GPT offers a robust starting point, it is merely a part, one that can be replaced or augmented. Such a modular approach positions Auto-GPT for future advancements, ensuring that it stays adaptable, extensible, and perennially relevant. This section delves deep into two pivotal dimensions of this modularity: custom embedding plugins and custom memory plugins.
Having embeddings checked as a topic, we can now move a bit toward the base of Auto-GPT, which is the LLM we use as our thinking core, so to speak. Although GPT-4 from OpenAI is currently the go-to LLM, Auto-GPT presents some very user-friendly ideas.
