Chatbot with message summarization

Chatbot with message summarization Review We've covered how to customize graph state schema and reducer. We've also shown a number of ways to trim or filter messages in graph state. Goals Now, let's take it one step further! Rather than just trimming or filtering messages, we'll show how to use LLMs to produce a running summary of the conversation. This allows us to retain a compressed representation of the full conversation, rather than just removing it with trimming or filtering. We'll incorporate this summarization into a simple Chatbot. And we'll equip that Chatbot with memory, supporting long-running conversations without incurring high token cost / latency.