面试题库

30 天从前端工程师到 Agent 工程师

D12 长期记忆：pgvector、embedding、chunking、memory_search 工具

• 为什么 Agent 需要额外的长期记忆，而不是把历史全部塞进上下文？Why does an agent need a separate long-term memory instead of stuffing all history into the context window?

  国内高频海外高频基础#long-term-memory#rag#cost

  分析过程 · 先想清楚再作答

  1. 这题最容易答成「因为窗口装不下」。那只答对了一半，而且是不值钱的那一半——窗口一年比一年大，光靠这条理由，面试官会追问「等窗口到一百万 token 呢」，你就没词了。
  2. 先把两个问题拆开：上下文压缩解决的是「同一次会话里这一轮塞不下」，长期记忆解决的是「上个月说过的事想不起来」。前者在组装请求时做减法，后者做加法，触发时机、数据去向、失败后果都不同。能主动区分这两件事，是这题最大的区分度。
  3. 然后给成本账：200 条记忆、每条约 400 token 就是 8 万 token，按输入价 0.15 美元每百万 token 算，每一轮多付 0.012 美元；一天 20 轮就是 0.24 美元一个用户。只检索最相关的 5 条是 2000 token、每轮 0.0003 美元，差 40 倍。而且这笔钱是每轮重复付的，不是一次性的。
  4. 再给比钱更硬的理由：无关信息会降低命中率。200 条里跟这一轮相关的可能只有 1 条，剩下 199 条是噪声，模型会被带偏去回答一个用户没问的问题。**所以哪怕窗口无限大、token 免费，也该检索而不是全塞。** 这一句是这题的最优解。
  5. 落到做法上：把跨会话的用户事实与偏好抽成陈述句存进向量库，每轮按语义检索最相关的三五条注入请求——这就是 RAG 最小的一环。
  6. 可以预期的追问：什么信息该进长期记忆？答三问——跨会话之后还需要吗、会不会随时间失效、能不能靠检索捞回来。「用户住上海」三条都满足，「把刚才那段改成三句话」一条都不满足。

  How to reason about it · think before answering

  1. The tempting answer is 'the window is too small'. That is half right and it is the cheap half — windows keep growing, and the interviewer will ask what you would do at a million tokens.
  2. Separate the two problems first: context compression solves 'this turn does not fit in one session', long-term memory solves 'I cannot recall what was said last month'. One subtracts at request-assembly time, the other adds. Naming that distinction unprompted is where the signal is.
  3. Then quantify: 200 memories at roughly 400 tokens each is 80k tokens; at 0.15 USD per million input tokens that is 0.012 USD every single turn, about 0.24 USD per user per day at 20 turns. Retrieving the top 5 is 2k tokens, 0.0003 USD per turn — a 40x gap, and it repeats every turn.
  4. Give the reason that beats cost: irrelevant context lowers accuracy. If one of 200 memories is relevant, the other 199 are noise that pull the model toward answering something nobody asked. So even with an infinite free window, you would still retrieve rather than dump.
  5. Land on practice: distil cross-session user facts and preferences into standalone statements, store them as vectors, and inject the three to five most relevant per turn — the minimal form of RAG.
  6. Expect the follow-up: what belongs in long-term memory? Three tests — is it still needed across sessions, does it expire, can retrieval find it again. 'Lives in Shanghai' passes all three; 'shorten that paragraph' passes none.

  答题要点

  • 压缩管「这一轮塞不下」，长期记忆管「上个月说过的事想不起来」，是两个问题、两套机制
  • 全塞的成本是每轮重复付的：200 条约 8 万 token，每轮多 0.012 美元；检索 5 条只要 0.0003 美元
  • 更硬的理由是准确率：无关记忆是噪声，会把模型带偏，所以窗口再大也该检索而不是全塞
  • 做法是把跨会话的事实抽成陈述句、向量化存储，每轮按语义检索最相关的三五条注入
  • 判断一条信息该不该进长期记忆：跨会话还需要吗、会不会失效、能不能被检索到

  Key points

  • Compression handles 'this turn does not fit'; long-term memory handles 'what did they say last month' — different problems, different machinery
  • Dumping everything costs on every turn: 200 memories is about 80k tokens and 0.012 USD per turn versus 0.0003 USD for five retrieved ones
  • The stronger reason is accuracy — irrelevant memories are noise, so you would retrieve even with an infinite window
  • Practice: distil cross-session facts into statements, embed them, inject the top three to five per turn
  • Admission test for a memory: still needed across sessions, does not expire, and is findable by retrieval
• chunking 的切分策略会怎么影响检索效果？切多大合适？How does the chunking strategy affect retrieval quality, and how do you pick a chunk size?

  国内高频海外高频进阶#chunking#rag#retrieval-quality

  分析过程 · 先想清楚再作答

  1. 题眼在「怎么影响」。只回答一个数字（比如「切 500 字」）会被追着问为什么，所以要先把两个方向的失效模式讲出来，数字才有落点。
  2. 切太碎的失效模式：单张卡片脱离上下文。「他说要 42 码」检索命中了也没用，代词失去指代，模型拿到一句悬空的话反而更容易编。
  3. 切太整的失效模式更反直觉，也是这题真正的区分点：一块横跨三个主题时，它的向量是这几个主题的平均值，结果对哪个 query 都不太像，命中率反而下降。**块越大信息越全，却越难被检索到**——能说出这句话基本就过了。
  4. 然后给可操作的口径：目标 400 字符、相邻块重叠 80 字符，并优先在句号、换行这类自然边界收尾。重叠的作用要说清楚——一句关键的话被切口劈开时，两块各拿半句，重叠保证它至少在其中一块里是完整的。
  5. 补上代价，这是工程视角：重叠 80 除以 400 等于 20% 的存储放大，向量也跟着多一份；内容高度重叠的两块可能一起被检索出来，白占返回名额，所以要按内容去重。
  6. 可以预期的追问：怎么验证切分策略好不好？答案是准备一批 query 与标注好的期望命中，量召回率和 top-k 命中率，改切分参数后重跑对比——切分是可以被度量的，不该靠感觉调。第二个追问是「对话数据要不要原样切」，答不要：先让模型抽成陈述句再切，否则大量寒暄句会把向量拉平。

  How to reason about it · think before answering

  1. The hinge is 'how does it affect'. Naming a number alone invites a why, so describe both failure modes first and let the number follow.
  2. Too small: a chunk loses its context. 'He wants size 42' retrieves fine but resolves to nothing — pronouns dangle and the model is more likely to fabricate.
  3. Too large is the counter-intuitive half and the real discriminator: a chunk spanning three topics gets a vector that averages them, so it looks only vaguely like any query and recall drops. Bigger chunks carry more information yet are harder to retrieve.
  4. Give an operational default: target 400 characters with 80 characters of overlap, ending on natural boundaries such as sentence stops or newlines. Explain the overlap — when a key sentence lands on a cut, each side holds half of it, and the overlap guarantees at least one chunk holds it whole.
  5. Add the costs: 80 over 400 is 20% storage amplification plus an extra vector per duplicated span, and near-duplicate chunks can both surface and waste result slots, so deduplicate by content before returning.
  6. Expect: how do you validate a chunking strategy? Build a query set with labelled expected hits and measure recall and top-k hit rate, then re-run after changing parameters — chunking is measurable, not a matter of taste. Second follow-up: should raw dialogue be chunked as-is? No — have the model distil it into standalone statements first, or filler turns flatten the vectors.

  答题要点

  • 切太碎：单块脱离上下文，代词失去指代，命中了也用不上
  • 切太整：一块横跨多个主题，向量被平均，对任何 query 都不够像，命中率反而下降
  • 可操作口径：目标 400 字符、重叠 80 字符，优先在句号或换行这类自然边界收尾
  • 重叠的作用是保证被切口劈开的句子至少在一块里完整；代价是约 20% 的存储放大和可能的重复命中
  • 别直接切对话原文，先抽成陈述句；切分效果要用标注好的 query 集测召回率，而不是凭感觉

  Key points

  • Too small: chunks lose context, pronouns dangle, and a hit is useless
  • Too large: one chunk spans several topics, its vector averages them, and recall drops for every query
  • Working default: target 400 characters with 80 characters of overlap, cutting on sentence or newline boundaries
  • Overlap keeps a split sentence whole in at least one chunk, at roughly 20% storage amplification plus possible duplicate hits
  • Distil dialogue into standalone statements before chunking, and validate with a labelled query set measuring recall