Navigating the impact of graph databases and how AI agents address the limitations of static graphs with continuous knowledge base expansion and enrichment.
This sounds great in theory, but what if we are brand new to LLMs and AI Agents and don't have a foundation of our own foundational LLM? While this suggests that I don't have to manage the complexity of a GraphDB, I need to navigate the complexity to build my foundation for AI Agents that can interact with and manage the GraphDB to infer relationships and learn/relearn my data, right? Any guidance on where to get started?
If you are new to LLMs and Building agents and looking to build a foundational infrastructure to develop your own agents then it would make sense to start with platforms like Amazon Bedrock, Google Vertex AI etc. If you want to explore these areas in depth then I am happy to collaborate with you
I haven't had a chance to read this yet but I'm also working on a kind of self-evolving wiki, not quite a knowledge graph https://github.com/micseydel/tinker-casting
This sounds great in theory, but what if we are brand new to LLMs and AI Agents and don't have a foundation of our own foundational LLM? While this suggests that I don't have to manage the complexity of a GraphDB, I need to navigate the complexity to build my foundation for AI Agents that can interact with and manage the GraphDB to infer relationships and learn/relearn my data, right? Any guidance on where to get started?
Hey Bryan,
If you are new to LLMs and Building agents and looking to build a foundational infrastructure to develop your own agents then it would make sense to start with platforms like Amazon Bedrock, Google Vertex AI etc. If you want to explore these areas in depth then I am happy to collaborate with you