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185 lines
6.5 KiB
185 lines
6.5 KiB
# s06: Context Compact (上下文压缩)
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`s01 > s02 > s03 > s04 > s05 > [ s06 ] | s07 > s08 > s09 > s10 > s11 > s12`
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> *"上下文总会满, 要有办法腾地方"* -- 三层压缩策略, 换来无限会话。
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>
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> **Harness 层**: 压缩 -- 干净的记忆, 无限的会话。
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## 问题
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上下文窗口是有限的。读一个 1000 行的文件就吃掉 ~4000 token; 读 30 个文件、跑 20 条命令, 轻松突破 100k token。不压缩, 智能体根本没法在大项目里干活。
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## 解决方案
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三层压缩, 激进程度递增:
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```
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Every turn:
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+------------------+
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| Tool call result |
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+------------------+
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v
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[Layer 1: micro_compact] (silent, every turn)
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Replace tool_result > 3 turns old
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with "[Previous: used {tool_name}]"
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v
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[Check: tokens > 50000?]
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no yes
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v v
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continue [Layer 2: auto_compact]
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Save transcript to .transcripts/
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LLM summarizes conversation.
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Replace all messages with [summary].
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v
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[Layer 3: compact tool]
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Model calls compact explicitly.
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Same summarization as auto_compact.
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```
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## 工作原理
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1. **第一层 -- 上下文窗口管理**: Spring AI 的 ChatClient 自动管理工具循环, 无法在循环内插入压缩。Java 版通过限制注入系统提示的对话轮数（仅保留最近 N 轮）并截断内容来实现等价效果。
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```java
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/** 估算 token 数量: 粗略估计 4 字符 ≈ 1 token */
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public int estimateTokens() {
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int chars = history.stream().mapToInt(t -> t.content().length()).sum();
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return chars / 4;
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}
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/** 获取对话历史的摘要（用于注入系统提示, 仅保留最近几轮） */
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public String getContextSummary() {
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if (history.isEmpty()) return "";
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StringBuilder sb = new StringBuilder("\n<conversation-context>\n");
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int start = Math.max(0, history.size() - KEEP_RECENT * 2);
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for (int i = start; i < history.size(); i++) {
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ConversationTurn turn = history.get(i);
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sb.append("[").append(turn.role()).append("]: ")
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.append(turn.content(), 0, Math.min(500, turn.content().length()))
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.append("\n");
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}
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sb.append("</conversation-context>");
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return sb.toString();
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}
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```
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2. **第二层 -- auto_compact**: token 超过阈值时, 保存完整对话到磁盘, 让 LLM 做摘要。
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```java
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public String compact() {
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// 保存 transcript 到磁盘（完整历史不丢失）
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Files.createDirectories(transcriptDir);
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Path transcriptPath = transcriptDir.resolve(
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"transcript_" + System.currentTimeMillis() + ".jsonl");
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try (BufferedWriter writer = Files.newBufferedWriter(transcriptPath)) {
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for (ConversationTurn turn : history) {
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writer.write(objectMapper.writeValueAsString(turn));
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writer.newLine();
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}
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}
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// LLM 生成摘要
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String conversationText = history.stream()
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.map(t -> t.role() + ": " + t.content())
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.reduce("", (a, b) -> a + "\n" + b);
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if (conversationText.length() > 80000) {
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conversationText = conversationText.substring(0, 80000);
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}
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ChatClient summaryClient = ChatClient.builder(chatModel).build();
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String summary = summaryClient.prompt()
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.user("Summarize this conversation for continuity. Include: "
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+ "1) What was accomplished, 2) Current state, "
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+ "3) Key decisions.\n\n" + conversationText)
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.call().content();
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// 用摘要替换历史
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history.clear();
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history.add(new ConversationTurn("system",
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"[Conversation compressed. Transcript: " + transcriptPath
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+ "]\n\n" + summary));
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return summary;
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}
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```
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3. **第三层 -- manual compact**: `CompactTool` 工具按需触发同样的摘要机制。
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```java
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public class CompactTool {
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private final ContextCompactor compactor;
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public CompactTool(ContextCompactor compactor) {
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this.compactor = compactor;
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}
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@Tool(description = "Trigger manual conversation compression to free up context space.")
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public String compact(
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@ToolParam(description = "What to preserve in summary",
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required = false) String focus) {
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compactor.requestCompact();
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return "Compression triggered. Context will be summarized.";
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}
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}
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```
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4. REPL 层整合三层 (Spring AI 的 ChatClient 自动管理工具循环, 压缩在用户消息级别触发):
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```java
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AgentRunner.interactive("s06", userMessage -> {
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// Layer 2: 自动压缩检查（每次用户输入前）
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if (compactor.needsAutoCompact()) {
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System.out.println("[auto_compact triggered]");
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compactor.compact();
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}
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compactor.addTurn("user", userMessage);
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// 动态系统提示：包含对话上下文摘要
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String system = baseSystem + compactor.getContextSummary();
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ChatClient chatClient = ChatClient.builder(chatModel)
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.defaultSystem(system)
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.defaultTools(new BashTool(), new ReadFileTool(),
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new WriteFileTool(), new EditFileTool(), compactTool)
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.build();
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String response = chatClient.prompt()
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.user(userMessage).call().content();
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compactor.addTurn("assistant", response != null ? response : "");
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// Layer 3: 手动压缩（如果 Agent 调用了 compact 工具）
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if (compactor.isCompactRequested()) {
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compactor.compact();
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}
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return response;
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});
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```
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完整历史通过 transcript 保存在磁盘上。信息没有真正丢失, 只是移出了活跃上下文。
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## 相对 s05 的变更
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| 组件 | 之前 (s05) | 之后 (s06) |
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|----------------|------------------|--------------------------------|
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| Tools | 5 | 5 (基础 + compact) |
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| 上下文管理 | 无 | 三层压缩 |
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| 上下文窗口管理 | 无 | 限制注入轮数 + 内容截断 |
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| Auto-compact | 无 | token 阈值触发 |
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| Transcripts | 无 | 保存到 .transcripts/ |
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## 试一试
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```sh
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cd learn-claude-code
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mvn exec:java -Dexec.mainClass=io.mybatis.learn.s06.S06ContextCompact
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```
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试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
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1. `Read every Java file in the src/ directory one by one` (观察上下文窗口管理效果)
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2. `Keep reading files until compression triggers automatically`
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3. `Use the compact tool to manually compress the conversation`
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