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chore: bump version to 2.80.17 and refine agent streaming logic

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g1nation
2026-05-07 18:12:57 +09:00
parent cf6f33dd5c
commit 6894152892
2 changed files with 49 additions and 310 deletions
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package.json
+1 -1
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@@ -2,7 +2,7 @@
"name": "astra", "name": "astra",
"displayName": "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.", "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": "2.80.16", "version": "2.80.17",
"publisher": "g1nation", "publisher": "g1nation",
"license": "MIT", "license": "MIT",
"icon": "assets/icon.png", "icon": "assets/icon.png",
src/agent.ts
+48 -309
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@@ -451,74 +451,35 @@ export class AgentExecutor {
if (this.isStaleRun(runId)) return; if (this.isStaleRun(runId)) return;
let aiResponseText = ''; let aiResponseText = '';
const body = response.body as any; const reader = response.body?.getReader();
if (!body) throw new Error("Response body is null."); if (!reader) throw new Error("Response body is not readable.");
if (loopDepth === 0) this.webview?.postMessage({ type: 'streamStart' }); if (loopDepth === 0) this.webview.postMessage({ type: 'streamStart' });
let buffer = ''; let buffer = '';
const decoder = new TextDecoder(); const decoder = new TextDecoder();
const processChunk = (value: any) => {
if (this.isStaleRun(runId)) return false;
buffer += decoder.decode(value, { stream: true });
const lines = buffer.split('\n');
buffer = lines.pop() || '';
for (const line of lines) {
const trimmed = line.trim();
if (!trimmed || trimmed === 'data: [DONE]') continue;
try {
let raw = trimmed;
if (trimmed.startsWith('data:')) {
raw = trimmed.replace(/^data:\s*/, '');
}
if (!raw || raw === '[DONE]') continue;
const json = JSON.parse(raw);
if (json.error) {
const errMsg = typeof json.error === 'string' ? json.error : (json.error.message || JSON.stringify(json.error));
throw new Error(`AI Engine Error: ${errMsg}`);
}
let token = '';
if (json.choices?.[0]) {
const choice = json.choices[0];
token = choice.delta?.content || choice.message?.content || choice.text || '';
} else if (json.message?.content) {
token = json.message.content;
} else if (json.response) {
token = json.response;
}
if (token) {
aiResponseText += token;
if (loopDepth === 0) {
this.webview?.postMessage({ type: 'streamUpdate', value: token });
}
}
} catch (e: any) {
// Silent fail for non-JSON lines unless it's an AI Engine Error
if (e.message.startsWith('AI Engine Error:')) throw e;
}
}
return true;
};
try { try {
if (typeof body[Symbol.asyncIterator] === 'function') { while (true) {
for await (const chunk of body) { const { done, value } = await reader.read();
if (!processChunk(chunk)) break; if (done) break;
} if (this.isStaleRun(runId)) return;
} else {
const reader = body.getReader(); buffer += decoder.decode(value, { stream: true });
while (true) { const lines = buffer.split('\n');
const { done, value } = await reader.read(); buffer = lines.pop() || '';
if (done) break; for (const line of lines) {
if (!processChunk(value)) break; const trimmed = line.trim();
if (!trimmed || trimmed === 'data: [DONE]') continue;
try {
const raw = trimmed.startsWith('data: ') ? trimmed.slice(6) : trimmed;
const json = JSON.parse(raw);
const token = engine === 'lmstudio' ? json.choices?.[0]?.delta?.content || '' : json.message?.content || json.response || '';
if (token) {
aiResponseText += token;
}
} catch (e: any) {
logError('Failed to parse streaming chunk.', { engine, apiUrl, chunk: summarizeText(trimmed, 300), error: e?.message || String(e) });
}
} }
} }
} catch (err: any) { } catch (err: any) {
@@ -527,7 +488,6 @@ export class AgentExecutor {
} else { } else {
logError('Stream reading error.', { engine, apiUrl, error: err?.message || String(err) }); logError('Stream reading error.', { engine, apiUrl, error: err?.message || String(err) });
this.webview?.postMessage({ type: 'error', value: `Connection lost: ${err.message}` }); this.webview?.postMessage({ type: 'error', value: `Connection lost: ${err.message}` });
} }
} }
@@ -535,87 +495,17 @@ export class AgentExecutor {
if (buffer.trim() && buffer.trim() !== 'data: [DONE]') { if (buffer.trim() && buffer.trim() !== 'data: [DONE]') {
try { try {
const trimmed = buffer.trim(); const trimmed = buffer.trim();
let raw = trimmed; const raw = trimmed.startsWith('data: ') ? trimmed.slice(6) : trimmed;
if (trimmed.startsWith('data:')) { const json = JSON.parse(raw);
raw = trimmed.replace(/^data:\s*/, ''); const token = engine === 'lmstudio' ? json.choices?.[0]?.delta?.content || '' : json.message?.content || json.response || '';
} if (token) {
aiResponseText += token;
if (raw && raw !== '[DONE]') {
const json = JSON.parse(raw);
if (json.error) {
const errMsg = typeof json.error === 'string' ? json.error : (json.error.message || JSON.stringify(json.error));
throw new Error(`AI Engine Error: ${errMsg}`);
}
let token = '';
if (json.choices?.[0]) {
const choice = json.choices[0];
token = choice.delta?.content || choice.message?.content || choice.text || '';
} else if (json.message?.content) {
token = json.message.content;
} else if (json.response) {
token = json.response;
}
if (token) {
aiResponseText += token;
}
} }
} catch (e: any) { } catch (e: any) {
logError('Failed to parse final streaming buffer.', { engine, apiUrl, buffer: summarizeText(buffer, 300), error: e?.message || String(e) }); logError('Failed to parse final streaming buffer.', { engine, apiUrl, buffer: summarizeText(buffer, 300), error: e?.message || String(e) });
} }
} }
// 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 (this.isStaleRun(runId)) return;
if (requestTimeoutHandle) { if (requestTimeoutHandle) {
clearTimeout(requestTimeoutHandle); clearTimeout(requestTimeoutHandle);
@@ -673,33 +563,14 @@ export class AgentExecutor {
this.statusBarManager.updateStatus(AgentStatus.Executing); this.statusBarManager.updateStatus(AgentStatus.Executing);
const report = await this.executeActions(aiResponseText, rootPath, activeBrain); const report = await this.executeActions(aiResponseText, rootPath, activeBrain);
if (!assistantContent.trim() && report.length === 0) { if (!assistantContent.trim() && report.length === 0) {
// 실제 전송에 사용된 메시지(request.finalMessages)를 기준으로 토큰 재계산 logError('Model returned an empty response without actions.', { model: actualModel, engine, apiUrl, loopDepth });
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 > 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({ this.webview.postMessage({
type: 'error', type: 'error',
value: [ value: [
'AI engine returned an empty response.', 'AI engine returned an empty response.',
`Engine: ${request.engine} | Model: ${actualModel}`, `Engine: ${engine}`,
isContextOverflow `Model: ${actualModel}`,
? `Context overflow: ~${estimatedTokens2.toLocaleString()} tokens (actually sent). The model context window was likely exceeded even after compression.` 'The request reached the local LLM server, but no usable content was returned. Try another model, restart the local server, or reduce the prompt/context size.'
: 'The request reached the LLM server, but no content was returned.',
'',
'**해결 방법:**',
isContextOverflow
? '1. Brain 비활성화 후 재시도 2. 더 큰 모델(7B+) 사용 3. 대화 기록 초기화 후 재시도'
: '1. LM Studio에서 해당 모델이 로드되어 있는지 확인 2. 모델 재시작 후 재시도 3. 다른 모델로 전환'
].join('\n') ].join('\n')
}); });
return; return;
@@ -2069,12 +1940,11 @@ export class AgentExecutor {
modelName: string; modelName: string;
reqMessages: ChatMessage[]; reqMessages: ChatMessage[];
temperature: number; temperature: number;
}): Promise<{ response: Response; engine: 'lmstudio' | 'ollama'; apiUrl: string; finalMessages: ChatMessage[] }> { }): Promise<{ response: Response; engine: 'lmstudio' | 'ollama'; apiUrl: string }> {
const { baseUrl, modelName, reqMessages, temperature } = params; const { baseUrl, modelName, reqMessages, temperature } = params;
const primaryEngine = resolveEngine(baseUrl); const primaryEngine = resolveEngine(baseUrl);
const engines = primaryEngine === 'lmstudio' ? ['lmstudio', 'ollama'] as const : ['ollama', 'lmstudio'] as const; const engines = primaryEngine === 'lmstudio' ? ['lmstudio', 'ollama'] as const : ['ollama', 'lmstudio'] as const;
let lastError: Error | null = null; let lastError: Error | null = null;
let nCtxRetried = false; // n_ctx 재시도 1회 제한
for (const engine of engines) { for (const engine of engines) {
const apiUrl = buildApiUrl(baseUrl, engine, 'chat'); const apiUrl = buildApiUrl(baseUrl, engine, 'chat');
@@ -2083,95 +1953,25 @@ export class AgentExecutor {
for (const candidateModel of modelCandidates) { for (const candidateModel of modelCandidates) {
for (const variant of messageVariants) { for (const variant of messageVariants) {
// 실제 전송할 메시지
let finalMessages = variant.messages;
// ── LM Studio 선제적 컨텍스트 압축 ──
// 소형 모델(4B 등)은 GPU 메모리 부족으로 n_ctx가 설정값보다 크게 줄어들 수 있고,
// 이때 LM Studio는 에러 대신 200 OK + 빈 스트림을 반환하여 재시도 불가.
// 따라서 전송 전에 선제적으로 메시지를 n_ctx에 맞게 압축합니다.
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 = 3000; // 4096 n_ctx 기준 더 보수적인 안전 마진
if (estimatedTokensRaw > LM_CTX_SAFE_LIMIT) {
logInfo('LM Studio proactive compression triggered.', {
estimatedTokens: estimatedTokensRaw,
limit: LM_CTX_SAFE_LIMIT,
originalMessageCount: finalMessages.length
});
// 1. system 메시지에서 [CONTEXT] 이후 부분을 우선 제거
const sysIdx = finalMessages.findIndex(m => m.role === 'system');
if (sysIdx >= 0) {
const sysContent = String(finalMessages[sysIdx].content || '');
const contextSplit = sysContent.indexOf('[CONTEXT]');
if (contextSplit > 0) {
// [CONTEXT] 이전까지만 유지 (기본 시스템 프롬프트 + 핵심 지시)
const trimmedSys = sysContent.slice(0, contextSplit).trimEnd();
finalMessages = finalMessages.map((m, i) =>
i === sysIdx ? { ...m, content: trimmedSys + '\n[Context omitted: model context limit]' } : m
);
}
}
// 2. 그래도 크면 시스템 프롬프트를 max 글자로 강제 잘라냄
const afterTrimChars = finalMessages.reduce((acc, m) => acc + String(m.content || '').length, 0);
const afterTrimTokens = Math.ceil(afterTrimChars / 4);
if (afterTrimTokens > LM_CTX_SAFE_LIMIT && sysIdx >= 0) {
// 유저 메시지 토큰 계산
const nonSysTokens = finalMessages
.filter((_, i) => i !== sysIdx)
.reduce((acc, m) => acc + String(m.content || '').length, 0) / 4;
const maxSysChars = Math.max(2000, (LM_CTX_SAFE_LIMIT - Math.ceil(nonSysTokens) - 512)) * 4;
const sysContent = String(finalMessages[sysIdx].content || '');
if (sysContent.length > maxSysChars) {
finalMessages = finalMessages.map((m, i) =>
i === sysIdx ? { ...m, content: sysContent.slice(0, maxSysChars) + '\n[Truncated for model context limit]' } : m
);
}
}
// 3. 히스토리 메시지 정리: system + 마지막 user만 유지
const finalCheck = finalMessages.reduce((acc, m) => acc + String(m.content || '').length, 0) / 4;
if (finalCheck > LM_CTX_SAFE_LIMIT) {
const sysMsg = finalMessages.find(m => m.role === 'system');
const lastUserMsg = [...finalMessages].reverse().find(m => m.role === 'user');
finalMessages = [
...(sysMsg ? [sysMsg] : []),
...(lastUserMsg ? [lastUserMsg] : [])
];
}
logInfo('LM Studio compression result.', {
originalTokens: estimatedTokensRaw,
compressedTokens: Math.ceil(finalMessages.reduce((a, m) => a + String(m.content || '').length, 0) / 4),
messageCount: finalMessages.length
});
}
}
const totalChars = finalMessages.reduce((acc, m) => acc + String(m.content || '').length, 0);
const estimatedTokens = Math.ceil(totalChars / 4);
const streamBody = { const streamBody = {
model: candidateModel, model: candidateModel,
messages: finalMessages.map(m => ({ role: m.role, content: m.content })), messages: variant.messages,
stream: true, stream: true,
...(engine === 'lmstudio' ...(engine === 'lmstudio'
? { max_tokens: Math.min(4096, Math.max(256, 3000 - estimatedTokens)), temperature } ? { max_tokens: 4096, temperature }
: { options: { num_ctx: 32768, num_predict: 4096, temperature } }), : { options: { num_ctx: 32768, num_predict: 4096, temperature } }),
}; };
logInfo('AI streaming request started.', {
engine, apiUrl, model: candidateModel,
variant: variant.name,
messageCount: finalMessages.length,
estimatedTokens,
roles: finalMessages.map(message => message.role),
firstUserPreview: summarizeText(String(finalMessages.find(message => message.role === 'user')?.content || ''), 300)
});
try { try {
logInfo('AI streaming request started.', {
engine,
apiUrl,
model: candidateModel,
variant: variant.name,
messageCount: variant.messages.length,
roles: variant.messages.map(message => message.role),
firstUserPreview: summarizeText(String(variant.messages.find(message => message.role === 'user')?.content || ''), 300)
});
const response = await fetch(apiUrl, { const response = await fetch(apiUrl, {
method: 'POST', method: 'POST',
headers: { headers: {
@@ -2181,80 +1981,19 @@ export class AgentExecutor {
'Connection': 'keep-alive' 'Connection': 'keep-alive'
}, },
body: JSON.stringify(streamBody), body: JSON.stringify(streamBody),
signal: this.abortController?.signal signal: this.abortController?.signal,
keepalive: true
}); });
if (!response.ok) { if (!response.ok) {
const errText = await response.text(); const errText = await response.text();
// ── LM Studio n_keep >= n_ctx 에러 감지 및 자동 재시도 ──
const nCtxMatch = errText.match(/n_keep\s*:\s*(\d+)\s*>=?\s*n_ctx\s*:\s*(\d+)/);
if (nCtxMatch && engine === 'lmstudio' && !nCtxRetried) {
nCtxRetried = true;
const nCtx = parseInt(nCtxMatch[2], 10);
logInfo(`n_ctx overflow detected (n_ctx=${nCtx}). Compressing messages and retrying...`);
// system 메시지를 n_ctx 크기에 맞게 강제 압축
const maxResponseTokens = 512;
const maxSysTokens = Math.max(500, nCtx - maxResponseTokens);
const maxSysChars = maxSysTokens * 4;
// 히스토리는 마지막 user 메시지만 유지
const sysMsg = finalMessages.find(m => m.role === 'system');
const lastUserMsg = [...finalMessages].reverse().find(m => m.role === 'user');
const compressedMessages: ChatMessage[] = [];
if (sysMsg) {
const sysContent = String(sysMsg.content || '');
compressedMessages.push({
role: 'system',
content: sysContent.length > maxSysChars
? sysContent.slice(0, maxSysChars) + `\n[Compressed for n_ctx=${nCtx}]`
: sysContent,
internal: true
});
}
if (lastUserMsg) {
compressedMessages.push(lastUserMsg);
}
// 압축된 메시지로 즉시 재요청
const retryBody = {
model: candidateModel,
messages: compressedMessages.map(m => ({ role: m.role, content: m.content })),
stream: true,
max_tokens: Math.min(1024, maxResponseTokens),
temperature,
};
logInfo('Retrying with compressed context.', {
originalTokens: estimatedTokens,
compressedTokens: Math.ceil(compressedMessages.reduce((a, m) => a + String(m.content || '').length, 0) / 4),
nCtx,
messageCount: compressedMessages.length
});
const retryResponse = await fetch(apiUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json', 'Accept': 'text/event-stream', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive' },
body: JSON.stringify(retryBody),
signal: this.abortController?.signal
});
if (retryResponse.ok) {
logInfo('n_ctx retry succeeded.', { apiUrl });
return { response: retryResponse, engine, apiUrl, finalMessages: compressedMessages };
}
logError('n_ctx retry also failed.', { status: retryResponse.status });
}
lastError = new Error(`AI Engine error (${engine}/${variant.name}): ${response.status} - ${summarizeText(errText, 300)}`); lastError = new Error(`AI Engine error (${engine}/${variant.name}): ${response.status} - ${summarizeText(errText, 300)}`);
logError('AI streaming request returned non-OK status.', { engine, variant: variant.name, apiUrl, status: response.status, body: summarizeText(errText, 500) }); logError('AI streaming request returned non-OK status.', { engine, variant: variant.name, apiUrl, status: response.status, body: summarizeText(errText, 500) });
continue; continue;
} }
logInfo('AI streaming request connected.', { engine, variant: variant.name, apiUrl }); logInfo('AI streaming request connected.', { engine, variant: variant.name, apiUrl });
return { response, engine, apiUrl, finalMessages }; return { response, engine, apiUrl };
} catch (error: any) { } catch (error: any) {
lastError = error instanceof Error ? error : new Error(String(error)); lastError = error instanceof Error ? error : new Error(String(error));
logError('AI streaming request failed.', { engine, variant: variant.name, apiUrl, model: candidateModel, error: lastError.message }); logError('AI streaming request failed.', { engine, variant: variant.name, apiUrl, model: candidateModel, error: lastError.message });