Original Summary

I built the data side of a small review analyser. It pulls a business's reviews from Google Maps, the Play Store, the App Store and CSV files, groups them into themes, and uses Hindsight as memory so each week's analysis can compare against past weeks. The first time we ran it on a Hyderabad cafe, it said the cafe's pricing complaints were "a recurring trend from previous weeks." The cafe had never been analysed before. Those "previous weeks" belonged to a telecom provider. Every memory sat in one shared bank with no business attached, so semantic recall returned 26 Telco memories for "overpriced chai." What fixed it: - Tag every memory with its business and recall with strict tag matching. Old untagged memories stop showing up, so we didn't need a migration. - Use the review dates as timestamps, not the time we stored them. We also filter recall to weeks before the one being analysed, so replayed runs don't treat later weeks as history. - Give each memory a stable document ID with replace semantics. Re-running a week no longer creates fake "3 runs in a row" trends. - Never store fallback scores. Rate-limited runs were saving zeros, and the model later cited them as real trends. - Pass competitor reviews to the model as context and keep them out of memory. The main lesson: memory fails by confidently remembering the wrong things, not just by forgetting. The full write-up (with code) is at the link. I'd like to hear how others scope memory in multi-tenant agents.   submitted by   /u/Famous-Position9362 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/30 01:07:57