Engram 新视频解释如何解决 AI 记忆问题
AI memory gets worse when you treat it as an ever-growing pile of chat history. Every turn adds mor...
朋友，@weaviate_io 的视频讲得特别清楚，Engram 怎么解决 AI 记忆问题，比单纯堆聊天历史好多了。
AI 记忆会变差，因为每轮对话都会增加令牌，导致响应变慢和信息的过时或矛盾。Engram 通过提取关键事实、比较现有记忆并解决矛盾来处理这个问题。开发者可以使用 Topics 控制提取，并设置项目范围、用户范围或用户和会话 ID 组合的范围。
AI memory gets worse when you treat it as an ever-growing pile of chat history. Every turn adds mor...
AI memory gets worse when you treat it as an ever-growing pile of chat history. Every turn adds more tokens, slower responses, and more outdated or contradictory information for the model to figure out at query time. People change their minds. Facts go stale. Eventually you have 50 memories about the same thing, all competing for space in the context window. @victorialslocum 's new video explains how 𝗘𝗻𝗴𝗿𝗮𝗺 handles this differently. Raw messages are sent to Engram, then asynchronous pipelines: 1️⃣ Extract the facts that matter 2️⃣ Compare them with existing memories 3️⃣ Reconcile contradictions, duplicates, and updates 4️⃣ Store a relevant, pruned set of memories for retrieval Developers control extraction with 𝗧𝗼𝗽𝗶𝗰𝘀, which are plain-language descriptions of what matters to the application. Memories are also fully scoped: • 𝗣𝗿𝗼𝗷𝗲𝗰𝘁-𝘄𝗶𝗱𝗲 for memories shared across a team of humans or agents • 𝗨𝘀𝗲𝗿-𝘀𝗰𝗼𝗽𝗲𝗱 for memories isolated to an individual • 𝗨𝘀𝗲𝗿-𝗮𝗻𝗱-𝗽𝗿𝗼𝗽𝗲𝗿𝘁𝘆 𝘀𝗰𝗼𝗽𝗲𝗱 for combinations like `user_id` + `conversation_id` The video also walks through setting up a basic user-scoped memory project: choose the personalization template, use the default pipeline, create an API key, and manage m youtu.be/kX9IhyltvuQ ns from the dashbo weaviate.io/product/engram… co/zfTE engram.playground.weaviate.io/?utm_source=ch… https://t.co/P4bsqEFCVQ Demo: https://t.co/Vp9PaG4Vfh Your browser does not support the video tag. 🔗 View on Twitter 💬 0 🔄 0 ❤️ 1 👀 109 📊 1 ⚡