We’re launching Lyria 3.5 in Google Flow Music, with advances across musicality, lyrics, vocals, and creative control
deepmind.google·6d ago
AI Engineering
Hacker News·Jun 18·5 min read
We built a persistent agent memory layer on Elasticsearch with 0.89 recall
TL;DR
Smart agents often lack persistent memory, leading to repeated failures in user interactions. A persistent agent memory layer was built on Elasticsearch, utilizing three indices and hybrid retrieval techniques.
✦ Why It Matters
Engineers can implement persistent memory in agents to enhance user experience and context retention.
Key Takeaways
- Explore Elasticsearch for building memory layers in applications.
- Utilize hybrid retrieval methods to improve information recall.
- Implement per-user data isolation to prevent tenant data leaks.
- Leverage open-source implementations for rapid development.
Full Summary
Smart agents frequently struggle with memory retention, causing them to forget important user context between sessions. To address this, a persistent memory layer was developed using Elasticsearch, which involved creating three indices and employing hybrid retrieval methods, including a reranker and decay strategies.
The architecture also incorporated per-user Data Loss Prevention (DLP) to ensure data isolation across multiple tenants. The system achieved a recall rate of 0.89 at rank 10 (R@10) when tested with 168 questions, demonstrating its effectiveness in retaining relevant information.
This implementation allows agents to remember user-specific histories, improving their ability to provide contextually relevant assistance. The open-source nature of the project enables other developers to leverage this technology in their applications.