LLMs + Vector Databases: Building Memory Architectures for AI Agents

This story was originally published on HackerNoon at: https://hackernoon.com/llms-vector-databases-building-memory-architectures-for-ai-agents . Why AI agents need vector databases and smarter memory architectures—not just bigger context windows—to handle real-world tasks like academic research Check more stories related to programming at: https://hackernoon.com/c/programming . You can also check exclusive content about #python , #ai , #llms , #vector-database , #feature-engineering , #semantic-embeddings , #vector-databases , #memory-architecture , and more. This story was written by: @hrlanreshittu . Learn more about this writer by checking @hrlanreshittu's about page, and for more stories, please visit hackernoon.com . The 128k token limit for GPT-4 is equivalent to about 96,000 words. This limitation becomes a major barrier for a research assistant dealing with whole academic libraries. Smarter memory architectures, not larger context windows, are the answer.