Unlocking the Future of AI Memory: Lessons Learned at Scale
Building a digital brain, CORE, has revealed crucial insights about memory in AI. As we expand to 10 million nodes, challenges arose in:
- Query Variability: A question can yield inconsistent answers.
- Static Weights: Optimal search weights vary but our prior methods were too rigid.
- Latency Issues: Response times ballooned to several seconds, hindering user experience.
Why Memory Matters
We’re crafting a system that remembers context, contradictions, and historical changes. Our advanced memory layer distinguishes between evolving facts, ensuring accurate retrieval based on when something was true.
Key Strategies Implemented
- Reified triples for temporal accuracy
- Hybrid search techniques to cover all query types
- Sparse LLM outputs for efficient processing
Join us as we simplify AI memory management! If you’re excited about the intersection of AI and memory systems, let’s discuss! 💬👇
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