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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.
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By HackerNoonThis 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.