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690606:1538 ADR-035-135 #05
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admin
2026-06-06 15:38:10 +07:00
parent 33c3935164
commit 15dec6c3fc
5 changed files with 195 additions and 87 deletions
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backend/src/modules/ai/processors/ai-batch.processor.spec.ts
+64
View File
@@ -84,6 +84,7 @@ describe('AiBatchProcessor', () => {
};
const mockRedis = {
setex: jest.fn().mockResolvedValue('OK'),
get: jest.fn().mockResolvedValue(null),
};
const mockAttachmentRepo = {
findOne: jest.fn().mockResolvedValue({
@@ -143,6 +144,7 @@ describe('AiBatchProcessor', () => {
resolvedPrompt: 'Resolved test prompt with OCR text',
versionNumber: 2,
}),
findByVersion: jest.fn().mockResolvedValue(null),
saveTestResult: jest.fn().mockResolvedValue(undefined),
};
beforeEach(async () => {
@@ -315,6 +317,68 @@ describe('AiBatchProcessor', () => {
expect.stringContaining('completed')
);
});
it('sandbox-ai-extract ควร regenerate response ใหม่เมื่อ parse JSON ครั้งแรกล้มเหลว', async () => {
const cachedOcrPayload = {
ocrText: 'OCR text for retry test\u0002\u0000',
ocrUsed: true,
engineUsed: 'typhoon-np-dms-ocr',
fallbackUsed: false,
timestamp: '2026-06-06T15:00:00.000Z',
};
mockRedis.get = jest
.fn()
.mockResolvedValueOnce(JSON.stringify(cachedOcrPayload));
mockAiPromptsService.findByVersion = jest.fn().mockResolvedValue({
id: 1,
promptType: 'ocr_extraction',
versionNumber: 2,
template:
'Resolved test prompt with OCR text {{ocr_text}} and context {{master_data_context}}',
isActive: true,
contextConfig: { filter: {} },
});
mockOllamaService.generate
.mockResolvedValueOnce('{\u0002\u0000')
.mockResolvedValueOnce(
JSON.stringify({
subject: 'Recovered after retry',
confidence: 0.91,
tags: ['retry'],
})
);
const job = {
id: 'job-ai-extract-retry',
data: {
jobType: 'sandbox-ai-extract',
documentPublicId: 'idem-ai-extract-123',
projectPublicId: 'default',
payload: { promptVersion: 2 },
idempotencyKey: 'idem-ai-extract-123',
},
} as unknown as Job<AiBatchJobData>;
await processor.process(job);
expect(mockOllamaService.generate).toHaveBeenCalledTimes(2);
expect(mockOllamaService.generate).toHaveBeenNthCalledWith(
1,
expect.not.stringContaining('\u0002'),
expect.objectContaining({
timeoutMs: 120000,
})
);
expect(mockAiPromptsService.saveTestResult).toHaveBeenCalledWith(
'ocr_extraction',
2,
expect.objectContaining({
subject: 'Recovered after retry',
confidence: 0.91,
})
);
expect(mockRedis.setex).toHaveBeenLastCalledWith(
'ai:rag:result:idem-ai-extract-123',
3600,
expect.stringContaining('"llmPrompt"')
);
});
it('EC-001: ควรบันทึก aiIssues เมื่อ AI สกัด Tag ใหม่ที่ไม่มีในระบบ', async () => {
mockTagsService.findOrSuggestTags.mockResolvedValueOnce([
{
backend/src/modules/ai/processors/ai-batch.processor.ts
+107 -87
View File
@@ -78,6 +78,21 @@ export interface AiBatchJobData {
/** OCR text สูงสุดที่ส่งเข้า LLM prompt — ป้องกัน context overflow (num_ctx 8192, Thai ~3 chars/token) */
const MAX_OCR_TEXT_CHARS = 15000;
const MAX_JSON_PARSE_ATTEMPTS = 2;
const removeControlCharacters = (
value: string,
includeDeleteCharacter = false
): string =>
Array.from(value)
.filter((character) => {
const code = character.charCodeAt(0);
const isAsciiControl =
(code >= 0 && code <= 8) || code === 11 || code === 12;
const isAdditionalControl = code >= 14 && code <= 31;
const isDeleteCharacter = includeDeleteCharacter && code === 127;
return !isAsciiControl && !isAdditionalControl && !isDeleteCharacter;
})
.join('');
const readString = (value: unknown): string | undefined =>
typeof value === 'string' && value.trim().length > 0 ? value : undefined;
@@ -145,6 +160,14 @@ const parseMigrateDocumentMetadata = (
};
};
const sanitizeLlmJsonResponse = (response: string): string =>
removeControlCharacters(
response.replace(/```json/g, '').replace(/```/g, '')
).trim();
const sanitizeOcrText = (text: string): string =>
removeControlCharacters(text.replace(/\r\n/g, '\n'), true).trim();
/** Processor สำหรับงาน AI batch ที่รันทีละงานเพื่อคุม VRAM
* lockDuration: 150000ms — รองรับ Ollama sandbox ที่ใช้เวลาสูงสุด 120s (ADR-029 FR-008)
* ค่า default ของ BullMQ คือ 30000ms ซึ่งน้อยกว่า timeout → job stall
@@ -174,6 +197,51 @@ export class AiBatchProcessor extends WorkerHost {
super();
}
/** เรียก LLM แล้ว parse JSON แบบ retry จริงเมื่อได้ผลลัพธ์ไม่สมบูรณ์ */
private async generateStructuredJson(
prompt: string,
options: { timeoutMs: number; model?: string; system?: string }
): Promise<{
extractedMetadata: Record<string, unknown>;
rawResponse: string;
cleanedResponse: string;
}> {
let lastRawResponse = '';
let lastCleanedResponse = '';
for (let attempt = 1; attempt <= MAX_JSON_PARSE_ATTEMPTS; attempt += 1) {
const rawResponse = await this.ollamaService.generate(prompt, options);
const cleanedResponse = sanitizeLlmJsonResponse(rawResponse);
lastRawResponse = rawResponse;
lastCleanedResponse = cleanedResponse;
this.logger.debug(`Raw LLM response: ${rawResponse}`);
try {
return {
extractedMetadata: JSON.parse(cleanedResponse) as Record<
string,
unknown
>,
rawResponse,
cleanedResponse,
};
} catch {
if (attempt >= MAX_JSON_PARSE_ATTEMPTS) {
this.logger.error(
`Failed to parse LLM response as JSON after ${MAX_JSON_PARSE_ATTEMPTS} attempts. Raw: ${lastRawResponse}, Cleaned: ${lastCleanedResponse}`
);
throw new Error(
`Failed to parse LLM response as JSON after ${MAX_JSON_PARSE_ATTEMPTS} attempts. Raw: ${lastRawResponse.substring(0, 200)}, Cleaned: ${lastCleanedResponse.substring(0, 200)}`
);
}
this.logger.warn(
`JSON parse attempt ${attempt} failed, regenerating response...`
);
}
}
throw new Error(
`Failed to parse LLM response as JSON after ${MAX_JSON_PARSE_ATTEMPTS} attempts`
);
}
/** Dispatch งาน batch ตาม jobType */
async process(job: Job<AiBatchJobData>): Promise<void> {
const isSandbox =
@@ -410,6 +478,12 @@ export class AiBatchProcessor extends WorkerHost {
pdfPath,
engineType
);
const sanitizedOcrText = sanitizeOcrText(ocrResult.text);
if (sanitizedOcrText.length !== ocrResult.text.length) {
this.logger.warn(
`OCR text sanitized before LLM: raw=${ocrResult.text.length} chars, sanitized=${sanitizedOcrText.length} chars`
);
}
const activePrompt =
await this.aiPromptsService.getActive('ocr_extraction');
@@ -426,12 +500,12 @@ export class AiBatchProcessor extends WorkerHost {
);
const ocrTextSafe =
ocrResult.text.length > MAX_OCR_TEXT_CHARS
sanitizedOcrText.length > MAX_OCR_TEXT_CHARS
? (this.logger.warn(
`OCR text truncated: ${ocrResult.text.length} chars > ${MAX_OCR_TEXT_CHARS} limit (context overflow protection)`
`OCR text truncated: ${sanitizedOcrText.length} chars > ${MAX_OCR_TEXT_CHARS} limit (context overflow protection)`
),
ocrResult.text.substring(0, MAX_OCR_TEXT_CHARS))
: ocrResult.text;
sanitizedOcrText.substring(0, MAX_OCR_TEXT_CHARS))
: sanitizedOcrText;
const resolvedPrompt = activePrompt.template
.replace('{{ocr_text}}', ocrTextSafe)
@@ -444,45 +518,12 @@ export class AiBatchProcessor extends WorkerHost {
`Prompt stats: OCR=${ocrTextSafe.length} chars, MasterData=${JSON.stringify(masterDataContext, null, 2).length} chars, Total=${resolvedPrompt.length} chars`
);
const response = await this.ollamaService.generate(resolvedPrompt, {
timeoutMs: 120000,
});
this.logger.debug(`Raw LLM response: ${response}`);
const cleanedResponse = response
.replace(/```json/g, '')
.replace(/```/g, '')
.trim();
let extractedMetadata: Record<string, unknown> | null = null;
let parseAttempts = 0;
const maxParseAttempts = 2;
while (parseAttempts < maxParseAttempts) {
try {
extractedMetadata = JSON.parse(cleanedResponse) as Record<
string,
unknown
>;
break;
} catch {
parseAttempts++;
if (parseAttempts >= maxParseAttempts) {
this.logger.error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts. Raw: ${response}, Cleaned: ${cleanedResponse}`
);
throw new Error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts. Raw: ${response.substring(0, 200)}, Cleaned: ${cleanedResponse.substring(0, 200)}`
);
}
this.logger.warn(
`JSON parse attempt ${parseAttempts} failed, retrying...`
);
await new Promise((resolve) => setTimeout(resolve, 1000));
const { extractedMetadata } = await this.generateStructuredJson(
resolvedPrompt,
{
timeoutMs: 120000,
}
}
if (!extractedMetadata) {
throw new Error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts`
);
}
);
await this.aiPromptsService.saveTestResult(
'ocr_extraction',
activePrompt.versionNumber,
@@ -495,7 +536,7 @@ export class AiBatchProcessor extends WorkerHost {
requestPublicId: idempotencyKey,
status: 'completed',
answer: JSON.stringify(extractedMetadata, null, 2),
ocrText: ocrResult.text,
ocrText: sanitizedOcrText,
ocrUsed: ocrResult.ocrUsed,
engineUsed: ocrResult.engineUsed,
fallbackUsed: ocrResult.fallbackUsed,
@@ -549,13 +590,19 @@ export class AiBatchProcessor extends WorkerHost {
engineType,
typhoonOptions
);
const sanitizedOcrText = sanitizeOcrText(ocrResult.text);
if (sanitizedOcrText.length !== ocrResult.text.length) {
this.logger.warn(
`OCR text sanitized before cache: raw=${ocrResult.text.length} chars, sanitized=${sanitizedOcrText.length} chars`
);
}
// Cache OCR text สำหรับ Step 2
await this.redis.setex(
`ai:sandbox:ocr:${idempotencyKey}`,
3600,
JSON.stringify({
ocrText: ocrResult.text,
ocrText: sanitizedOcrText,
ocrUsed: ocrResult.ocrUsed,
engineUsed: ocrResult.engineUsed,
fallbackUsed: ocrResult.fallbackUsed,
@@ -569,7 +616,7 @@ export class AiBatchProcessor extends WorkerHost {
JSON.stringify({
requestPublicId: idempotencyKey,
status: 'completed',
ocrText: ocrResult.text,
ocrText: sanitizedOcrText,
ocrUsed: ocrResult.ocrUsed,
engineUsed: ocrResult.engineUsed,
fallbackUsed: ocrResult.fallbackUsed,
@@ -624,7 +671,12 @@ export class AiBatchProcessor extends WorkerHost {
fallbackUsed?: boolean;
timestamp: string;
};
const { ocrText } = parsedOcr;
const ocrText = sanitizeOcrText(parsedOcr.ocrText);
if (ocrText.length !== parsedOcr.ocrText.length) {
this.logger.warn(
`Cached OCR text sanitized before AI extraction: raw=${parsedOcr.ocrText.length} chars, sanitized=${ocrText.length} chars`
);
}
// ดึง prompt version
const activePrompt =
@@ -667,48 +719,15 @@ export class AiBatchProcessor extends WorkerHost {
'{{master_data_context}}',
JSON.stringify(masterDataContext, null, 2)
);
const response = await this.ollamaService.generate(resolvedPrompt, {
timeoutMs: 120000,
});
this.logger.debug(`Raw LLM response: ${response}`);
const cleanedResponse = response
.replace(/```json/g, '')
.replace(/```/g, '')
.trim();
let extractedMetadata: Record<string, unknown> | null = null;
let parseAttempts = 0;
const maxParseAttempts = 2;
while (parseAttempts < maxParseAttempts) {
try {
extractedMetadata = JSON.parse(cleanedResponse) as Record<
string,
unknown
>;
break;
} catch {
parseAttempts++;
if (parseAttempts >= maxParseAttempts) {
this.logger.error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts. Raw: ${response}, Cleaned: ${cleanedResponse}`
);
throw new Error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts. Raw: ${response.substring(0, 200)}, Cleaned: ${cleanedResponse.substring(0, 200)}`
);
}
this.logger.warn(
`JSON parse attempt ${parseAttempts} failed, retrying...`
);
await new Promise((resolve) => setTimeout(resolve, 1000));
this.logger.debug(
`Prompt stats: OCR=${ocrTextSafe.length} chars, MasterData=${JSON.stringify(masterDataContext, null, 2).length} chars, Total=${resolvedPrompt.length} chars`
);
const { extractedMetadata } = await this.generateStructuredJson(
resolvedPrompt,
{
timeoutMs: 120000,
}
}
if (!extractedMetadata) {
throw new Error(
`Failed to parse LLM response as JSON after ${maxParseAttempts} attempts`
);
}
);
await this.aiPromptsService.saveTestResult(
'ocr_extraction',
@@ -728,6 +747,7 @@ export class AiBatchProcessor extends WorkerHost {
engineUsed: parsedOcr.engineUsed,
fallbackUsed: parsedOcr.fallbackUsed,
promptVersionUsed: targetPrompt.versionNumber,
llmPrompt: resolvedPrompt,
completedAt: new Date().toISOString(),
})
);
memory/agent-memory.md
+1
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@@ -20,6 +20,7 @@
- 2026-06-05 (Session 15): Feature 234 RAG Pipeline สมบูรณ์ — implement BGE-M3 embedding (dense 1024 + sparse), BGE-Reranker-Large, Semantic Chunking (typhoon2.5 + `<chunk topic>` tags + fallback), Hybrid Qdrant schema (drop+recreate), workflow hook `syncStatus()` → `enqueueRagPrepare()`, processRagPrepare pipeline ใน ai-batch.processor; แก้ CRITICAL 2 ประเด็นจาก speckit-analyze; ผ่าน speckit-tester (19/19 tests), speckit-validate (15/15 FR, ทุก SC); ปิด Gap ทั้ง 2 รายการ (jobId dedup confirmed + integration test 9 tests); สร้าง validation-report.md ใน specs/200-fullstacks/234-rag-pipeline-enhancements/
- 2026-06-06: เพิ่ม MCP MariaDB Tools section ใน memory/agent-memory.md, AGENTS.md, และ rule files (.agents/rules/08-development-flow.md, .devin/rules/08-development-flow.md) — รวม 8 tools (test_connection, show_databases, show_tables, describe_table, query, insert, update, delete), การใช้งานร่วมกับ Development Flow, และข้อควรระวังเรื่อง DDL operations
- 2026-06-06: เพิ่ม MCP Memory Tools section ใน memory/agent-memory.md — รวม 9 tools (create_entities, create_relations, add_observations, delete_entities, delete_relations, delete_observations, open_nodes, read_graph, search_nodes), การใช้งานร่วมกับ Development Flow สำหรับจัดการ Knowledge Graph และ Long-term Memory
- 2026-06-06 (Session 15): LLM JSON Parse Failure & VRAM Fix (ADR-035-135) — แก้ปัญหา JSON parse failure ใน Sandbox AI Extraction โดยเพิ่ม retry logic (2 attempts) + enhanced error logging ใน ai-batch.processor.ts, เพิ่ม system prompt support ใน ollama.service.ts, แก้ VRAM contention โดยเปลี่ยน keep_alive=0 ใน ocr-sidecar/app.py (Desk-5439), แก้ ESLint seg fault โดยลด heap size 8192 → 4096 ใน backend/package.json, แก้ Schema mismatch โดยสร้าง delta SQL 2026-06-06-add-ai-prompts-public-id.sql เพิ่ม public_id และ context_config columns ใน ai_prompts table, เพิ่ม prompt stats log + llmPrompt field ใน Sandbox Result, เพิ่ม LLM Prompt UI card (สีม่วง) ใน OcrSandboxPromptManager.tsx
-->
# 🧠 Agent Long-term Project Memory
specs/03-Data-and-Storage/deltas/2026-06-06-add-ai-prompts-public-id.sql
+23
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@@ -0,0 +1,23 @@
-- Delta: เพิ่ม public_id และ context_config columns ใน ai_prompts
-- Date: 2026-06-06
-- Related ADR: ADR-019 (UUID strategy), ADR-029 (Dynamic Prompts)
-- ------------------------------------------------------------
-- การเปลี่ยนแปลงโครงสร้างฐานข้อมูล (Schema changes)
-- ------------------------------------------------------------
-- เพิ่ม public_id column (UUIDv7) สำหรับ ADR-019 compliance
ALTER TABLE ai_prompts
ADD COLUMN public_id UUID UNIQUE COMMENT 'Public UUID สำหรับ API (ADR-019)';
-- เพิ่ม context_config column สำหรับ ADR-029 context filtering
ALTER TABLE ai_prompts
ADD COLUMN context_config JSON NULL COMMENT 'Configuration สำหรับ Master Data context filtering (project/contract scope)';
-- สร้าง UUID สำหรับ records ที่มีอยู่แล้ว
UPDATE ai_prompts
SET public_id = UUID()
WHERE public_id IS NULL;
-- ตั้ง public_id เป็น NOT NULL หลังจาก populate ครบแล้ว
ALTER TABLE ai_prompts
MODIFY COLUMN public_id UUID NOT NULL;
specs/88-logs/_backend_logs.md
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