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LangChain 拆解智能体检索三大坑：搜索笨、结果大、文本旧

Agents can't hold everything in context, so they retrieve what each task needs. This can go wrong ...

官方账号LangChain@LangChainAI
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LangChain 发的 RAG 踩坑总结，讲智能体检索最常见的三种翻车，做 Agent 的可以照着排查自己的系统。

LangChain 发布一篇关于智能体检索的博客，指出 RAG 在 Agent 中常见三类问题：Clumsy searches（查询写法笨拙）、Oversized results（检索结果过大撑爆上下文）、Stale text（索引文本过期）。作者 jess_weng18 给出对应的应对方法，并讨论设计 agent harness 时需要注意的要点。文章属于工程实践层面的检索优化指南。

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Agents can't hold everything in context, so they retrieve what each task needs. This can go wrong ...

Agents can't hold everything in context, so they retrieve what each task needs. This can go wrong in a few ways: ✅ Clumsy searches ✅ Oversized results ✅ Stale text @jess_weng18 on how to combat these challenges + what to keep in mind when designing an agent harness ⤵️ langchain.com/blog/context-e… 💬 9 🔄 1 ❤️ 8 👀 1654 📊 10 ⚡

#RAG#LangChain#智能体#agent harness

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