feat: Ghost Response retry and PID logging for bridge (v2.80.16)

This commit is contained in:
2026-05-07 16:04:54 +09:00
parent d9a2ebfedd
commit cf6f33dd5c
3 changed files with 75 additions and 14 deletions
+1 -1
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@@ -2,7 +2,7 @@
"name": "astra",
"displayName": "Astra",
"description": "The personal intelligence layer for Antigravity and VS Code. A private cognitive partner for deep project context, memory, and proactive strategic decision-making.",
"version": "2.80.15",
"version": "2.80.16",
"publisher": "g1nation",
"license": "MIT",
"icon": "assets/icon.png",
+71 -10
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@@ -565,6 +565,57 @@ export class AgentExecutor {
}
}
// 4.1 Check for Ghost Response (Empty response from LM Studio/Ollama despite 200 OK)
if (!aiResponseText.trim() && request.engine === 'lmstudio' && loopDepth === 0) {
logInfo('Empty response detected from LM Studio. Retrying with extreme compression...', { model: actualModel });
// Force extreme compression: system + last user only
const sysMsg = messagesForRequest.find(m => m.role === 'system');
const lastUserMsg = [...messagesForRequest].reverse().find(m => m.role === 'user');
const extremeMessages = [
...(sysMsg ? [sysMsg] : []),
...(lastUserMsg ? [lastUserMsg] : [])
];
const retryRequest = await this.createStreamingRequest({
baseUrl: ollamaUrl,
modelName: actualModel,
reqMessages: extremeMessages,
temperature
});
if (retryRequest.response.ok) {
const retryBody = retryRequest.response.body as any;
const retryDecoder = new TextDecoder();
let retryBuffer = '';
// Simple stream reader for retry
const reader = retryBody.getReader();
while (true) {
const { done, value } = await reader.read();
if (done) break;
retryBuffer += retryDecoder.decode(value, { stream: true });
// ... simplified parsing for retry ...
const lines = retryBuffer.split('\n');
retryBuffer = lines.pop() || '';
for (const line of lines) {
const trimmed = line.trim();
if (!trimmed || trimmed === 'data: [DONE]') continue;
try {
const raw = trimmed.startsWith('data:') ? trimmed.replace(/^data:\s*/, '') : trimmed;
if (!raw || raw === '[DONE]') continue;
const json = JSON.parse(raw);
const token = json.choices?.[0]?.delta?.content || json.message?.content || json.response || '';
if (token) {
aiResponseText += token;
this.webview?.postMessage({ type: 'streamUpdate', value: token });
}
} catch {}
}
}
}
}
if (this.isStaleRun(runId)) return;
if (requestTimeoutHandle) {
clearTimeout(requestTimeoutHandle);
@@ -622,17 +673,27 @@ export class AgentExecutor {
this.statusBarManager.updateStatus(AgentStatus.Executing);
const report = await this.executeActions(aiResponseText, rootPath, activeBrain);
if (!assistantContent.trim() && report.length === 0) {
const totalChars2 = messagesForRequest.reduce((acc, m) => acc + String(m.content || '').length, 0);
// 실제 전송에 사용된 메시지(request.finalMessages)를 기준으로 토큰 재계산
const usedMessages = request.finalMessages || messagesForRequest;
const totalChars2 = usedMessages.reduce((acc, m) => acc + String(m.content || '').length, 0);
const estimatedTokens2 = Math.ceil(totalChars2 / 4);
const isContextOverflow = estimatedTokens2 > 5000;
logError('Model returned an empty response without actions.', { model: actualModel, engine, apiUrl, loopDepth, estimatedTokens: estimatedTokens2 });
const isContextOverflow = estimatedTokens2 > 2500; // 3000 한도에 근접하면 오버플로우로 간주
logError('Model returned an empty response without actions.', {
model: actualModel,
engine: request.engine,
apiUrl: request.apiUrl,
loopDepth,
estimatedTokens: estimatedTokens2,
wasCompressed: usedMessages.length !== messagesForRequest.length || totalChars2 !== (messagesForRequest.reduce((a, m) => a + String(m.content || '').length, 0))
});
this.webview.postMessage({
type: 'error',
value: [
'AI engine returned an empty response.',
`Engine: ${engine} | Model: ${actualModel}`,
`Engine: ${request.engine} | Model: ${actualModel}`,
isContextOverflow
? `Context overflow: ~${estimatedTokens2.toLocaleString()} tokens estimated. This model likely has a smaller context window.`
? `Context overflow: ~${estimatedTokens2.toLocaleString()} tokens (actually sent). The model context window was likely exceeded even after compression.`
: 'The request reached the LLM server, but no content was returned.',
'',
'**해결 방법:**',
@@ -2008,7 +2069,7 @@ export class AgentExecutor {
modelName: string;
reqMessages: ChatMessage[];
temperature: number;
}): Promise<{ response: Response; engine: 'lmstudio' | 'ollama'; apiUrl: string }> {
}): Promise<{ response: Response; engine: 'lmstudio' | 'ollama'; apiUrl: string; finalMessages: ChatMessage[] }> {
const { baseUrl, modelName, reqMessages, temperature } = params;
const primaryEngine = resolveEngine(baseUrl);
const engines = primaryEngine === 'lmstudio' ? ['lmstudio', 'ollama'] as const : ['ollama', 'lmstudio'] as const;
@@ -2032,7 +2093,7 @@ export class AgentExecutor {
if (engine === 'lmstudio') {
const totalCharsRaw = finalMessages.reduce((acc, m) => acc + String(m.content || '').length, 0);
const estimatedTokensRaw = Math.ceil(totalCharsRaw / 4);
const LM_CTX_SAFE_LIMIT = 3500; // 4096 n_ctx 기준 안전 마진
const LM_CTX_SAFE_LIMIT = 3000; // 4096 n_ctx 기준 더 보수적인 안전 마진
if (estimatedTokensRaw > LM_CTX_SAFE_LIMIT) {
logInfo('LM Studio proactive compression triggered.', {
@@ -2098,7 +2159,7 @@ export class AgentExecutor {
messages: finalMessages.map(m => ({ role: m.role, content: m.content })),
stream: true,
...(engine === 'lmstudio'
? { max_tokens: Math.min(4096, Math.max(256, 3500 - estimatedTokens)), temperature }
? { max_tokens: Math.min(4096, Math.max(256, 3000 - estimatedTokens)), temperature }
: { options: { num_ctx: 32768, num_predict: 4096, temperature } }),
};
logInfo('AI streaming request started.', {
@@ -2182,7 +2243,7 @@ export class AgentExecutor {
if (retryResponse.ok) {
logInfo('n_ctx retry succeeded.', { apiUrl });
return { response: retryResponse, engine, apiUrl };
return { response: retryResponse, engine, apiUrl, finalMessages: compressedMessages };
}
logError('n_ctx retry also failed.', { status: retryResponse.status });
}
@@ -2193,7 +2254,7 @@ export class AgentExecutor {
}
logInfo('AI streaming request connected.', { engine, variant: variant.name, apiUrl });
return { response, engine, apiUrl };
return { response, engine, apiUrl, finalMessages };
} catch (error: any) {
lastError = error instanceof Error ? error : new Error(String(error));
logError('AI streaming request failed.', { engine, variant: variant.name, apiUrl, model: candidateModel, error: lastError.message });
+3 -3
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@@ -74,7 +74,7 @@ export class BridgeServer {
server.once('error', (err: any) => {
if (err.code === 'EADDRINUSE') {
// INFO 레벨: ERR 콘솔 오염 방지 (Extension Host가 console.error를 ERR로 표시)
logInfo(`Bridge Port ${port} already in use. Trying port ${port + 1}...`);
logInfo(`Bridge Port ${port} already in use. Trying port ${port + 1}... (Current PID: ${process.pid})`);
server.close();
if (this.server === server) {
this.server = null;
@@ -82,14 +82,14 @@ export class BridgeServer {
this.start(port + 1);
} else {
// EADDRINUSE 외 진짜 에러만 logError
logInfo(`Bridge server non-fatal error on port ${port}: ${err.code || err.message}`);
logInfo(`Bridge server non-fatal error on port ${port}: ${err.code || err.message} (PID: ${process.pid})`);
}
});
// 성공 시 서버 참조 저장
server.listen(port, '127.0.0.1', () => {
this.server = server;
logInfo(`Bridge server active on 127.0.0.1:${port}.`);
logInfo(`Bridge server active on 127.0.0.1:${port} (PID: ${process.pid}).`);
});
}