chore: Spring AI 重构
This commit is contained in:
abel533
+107
-49
@@ -44,61 +44,119 @@ continue [Layer 2: auto_compact]
|
||||
|
||||
## 工作原理
|
||||
|
||||
1. **第一层 -- micro_compact**: 每次 LLM 调用前, 将旧的 tool result 替换为占位符。
|
||||
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。
|
||||
|
||||
```python
|
||||
def micro_compact(messages: list) -> list:
|
||||
tool_results = []
|
||||
for i, msg in enumerate(messages):
|
||||
if msg["role"] == "user" and isinstance(msg.get("content"), list):
|
||||
for j, part in enumerate(msg["content"]):
|
||||
if isinstance(part, dict) and part.get("type") == "tool_result":
|
||||
tool_results.append((i, j, part))
|
||||
if len(tool_results) <= KEEP_RECENT:
|
||||
return messages
|
||||
for _, _, part in tool_results[:-KEEP_RECENT]:
|
||||
if len(part.get("content", "")) > 100:
|
||||
part["content"] = f"[Previous: used {tool_name}]"
|
||||
return messages
|
||||
```java
|
||||
/** 估算 token 数量: 粗略估计 4 字符 ≈ 1 token */
|
||||
public int estimateTokens() {
|
||||
int chars = history.stream().mapToInt(t -> t.content().length()).sum();
|
||||
return chars / 4;
|
||||
}
|
||||
|
||||
/** 获取对话历史的摘要（用于注入系统提示, 仅保留最近几轮） */
|
||||
public String getContextSummary() {
|
||||
if (history.isEmpty()) return "";
|
||||
StringBuilder sb = new StringBuilder("\n<conversation-context>\n");
|
||||
int start = Math.max(0, history.size() - KEEP_RECENT * 2);
|
||||
for (int i = start; i < history.size(); i++) {
|
||||
ConversationTurn turn = history.get(i);
|
||||
sb.append("[").append(turn.role()).append("]: ")
|
||||
.append(turn.content(), 0, Math.min(500, turn.content().length()))
|
||||
.append("\n");
|
||||
}
|
||||
sb.append("</conversation-context>");
|
||||
return sb.toString();
|
||||
}
|
||||
```
|
||||
|
||||
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。
|
||||
|
||||
```python
|
||||
def auto_compact(messages: list) -> list:
|
||||
# Save transcript for recovery
|
||||
transcript_path = TRANSCRIPT_DIR / f"transcript_{int(time.time())}.jsonl"
|
||||
with open(transcript_path, "w") as f:
|
||||
for msg in messages:
|
||||
f.write(json.dumps(msg, default=str) + "\n")
|
||||
# LLM summarizes
|
||||
response = client.messages.create(
|
||||
model=MODEL,
|
||||
messages=[{"role": "user", "content":
|
||||
"Summarize this conversation for continuity..."
|
||||
+ json.dumps(messages, default=str)[:80000]}],
|
||||
max_tokens=2000,
|
||||
)
|
||||
return [
|
||||
{"role": "user", "content": f"[Compressed]\n\n{response.content[0].text}"},
|
||||
{"role": "assistant", "content": "Understood. Continuing."},
|
||||
]
|
||||
```java
|
||||
public String compact() {
|
||||
// 保存 transcript 到磁盘（完整历史不丢失）
|
||||
Files.createDirectories(transcriptDir);
|
||||
Path transcriptPath = transcriptDir.resolve(
|
||||
"transcript_" + System.currentTimeMillis() + ".jsonl");
|
||||
try (BufferedWriter writer = Files.newBufferedWriter(transcriptPath)) {
|
||||
for (ConversationTurn turn : history) {
|
||||
writer.write(objectMapper.writeValueAsString(turn));
|
||||
writer.newLine();
|
||||
}
|
||||
}
|
||||
|
||||
// LLM 生成摘要
|
||||
String conversationText = history.stream()
|
||||
.map(t -> t.role() + ": " + t.content())
|
||||
.reduce("", (a, b) -> a + "\n" + b);
|
||||
if (conversationText.length() > 80000) {
|
||||
conversationText = conversationText.substring(0, 80000);
|
||||
}
|
||||
|
||||
ChatClient summaryClient = ChatClient.builder(chatModel).build();
|
||||
String summary = summaryClient.prompt()
|
||||
.user("Summarize this conversation for continuity. Include: "
|
||||
+ "1) What was accomplished, 2) Current state, "
|
||||
+ "3) Key decisions.\n\n" + conversationText)
|
||||
.call().content();
|
||||
|
||||
// 用摘要替换历史
|
||||
history.clear();
|
||||
history.add(new ConversationTurn("system",
|
||||
"[Conversation compressed. Transcript: " + transcriptPath
|
||||
+ "]\n\n" + summary));
|
||||
return summary;
|
||||
}
|
||||
```
|
||||
|
||||
3. **第三层 -- manual compact**: `compact` 工具按需触发同样的摘要机制。
|
||||
3. **第三层 -- manual compact**: `CompactTool` 工具按需触发同样的摘要机制。
|
||||
|
||||
4. 循环整合三层:
|
||||
```java
|
||||
public class CompactTool {
|
||||
private final ContextCompactor compactor;
|
||||
|
||||
```python
|
||||
def agent_loop(messages: list):
|
||||
while True:
|
||||
micro_compact(messages) # Layer 1
|
||||
if estimate_tokens(messages) > THRESHOLD:
|
||||
messages[:] = auto_compact(messages) # Layer 2
|
||||
response = client.messages.create(...)
|
||||
# ... tool execution ...
|
||||
if manual_compact:
|
||||
messages[:] = auto_compact(messages) # Layer 3
|
||||
public CompactTool(ContextCompactor compactor) {
|
||||
this.compactor = compactor;
|
||||
}
|
||||
|
||||
@Tool(description = "Trigger manual conversation compression to free up context space.")
|
||||
public String compact(
|
||||
@ToolParam(description = "What to preserve in summary",
|
||||
required = false) String focus) {
|
||||
compactor.requestCompact();
|
||||
return "Compression triggered. Context will be summarized.";
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
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):
|
||||
|
||||
```java
|
||||
AgentRunner.interactive("s06", userMessage -> {
|
||||
// Layer 2: 自动压缩检查（每次用户输入前）
|
||||
if (compactor.needsAutoCompact()) {
|
||||
System.out.println("[auto_compact triggered]");
|
||||
compactor.compact();
|
||||
}
|
||||
compactor.addTurn("user", userMessage);
|
||||
|
||||
// 动态系统提示：包含对话上下文摘要
|
||||
String system = baseSystem + compactor.getContextSummary();
|
||||
ChatClient chatClient = ChatClient.builder(chatModel)
|
||||
.defaultSystem(system)
|
||||
.defaultTools(new BashTool(), new ReadFileTool(),
|
||||
new WriteFileTool(), new EditFileTool(), compactTool)
|
||||
.build();
|
||||
|
||||
String response = chatClient.prompt()
|
||||
.user(userMessage).call().content();
|
||||
compactor.addTurn("assistant", response != null ? response : "");
|
||||
|
||||
// Layer 3: 手动压缩（如果 Agent 调用了 compact 工具）
|
||||
if (compactor.isCompactRequested()) {
|
||||
compactor.compact();
|
||||
}
|
||||
return response;
|
||||
});
|
||||
```
|
||||
|
||||
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。
|
||||
@@ -109,7 +167,7 @@ def agent_loop(messages: list):
|
||||
|----------------|------------------|--------------------------------|
|
||||
| Tools | 5 | 5 (基础 + compact) |
|
||||
| 上下文管理 | 无 | 三层压缩 |
|
||||
| Micro-compact | 无 | 旧结果 -> 占位符 |
|
||||
| 上下文窗口管理 | 无 | 限制注入轮数 + 内容截断 |
|
||||
| Auto-compact | 无 | token 阈值触发 |
|
||||
| Transcripts | 无 | 保存到 .transcripts/ |
|
||||
|
||||
@@ -117,11 +175,11 @@ def agent_loop(messages: list):
|
||||
|
||||
```sh
|
||||
cd learn-claude-code
|
||||
python agents/s06_context_compact.py
|
||||
mvn exec:java -Dexec.mainClass=io.mybatis.learn.s06.S06ContextCompact
|
||||
```
|
||||
|
||||
试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):
|
||||
|
||||
1. `Read every Python file in the agents/ directory one by one` (观察 micro-compact 替换旧结果)
|
||||
1. `Read every Java file in the src/ directory one by one` (观察上下文窗口管理效果)
|
||||
2. `Keep reading files until compression triggers automatically`
|
||||
3. `Use the compact tool to manually compress the conversation`
|
||||
|
||||
Reference in New Issue
Block a user