feat(asl): Implement full-text screening core LLM service and validation system (Day 1-3)
Core Components: - PDFStorageService with Dify/OSS adapters - LLM12FieldsService with Nougat-first + dual-model + 3-layer JSON parsing - PromptBuilder for dynamic prompt assembly - MedicalLogicValidator with 5 rules + fault tolerance - EvidenceChainValidator for citation integrity - ConflictDetectionService for dual-model comparison Prompt Engineering: - System Prompt (6601 chars, Section-Aware strategy) - User Prompt template (PICOS context injection) - JSON Schema (12 fields constraints) - Cochrane standards (not loaded in MVP) Key Innovations: - 3-layer JSON parsing (JSON.parse + json-repair + code block extraction) - Promise.allSettled for dual-model fault tolerance - safeGetFieldValue for robust field extraction - Mixed CN/EN token calculation Integration Tests: - integration-test.ts (full test) - quick-test.ts (quick test) - cached-result-test.ts (fault tolerance test) Documentation Updates: - Development record (Day 2-3 summary) - Quality assurance strategy (full-text screening) - Development plan (progress update) - Module status (v1.1 update) - Technical debt (10 new items) Test Results: - JSON parsing success rate: 100% - Medical logic validation: 5/5 passed - Dual-model parallel processing: OK - Cost per PDF: CNY 0.10 Files: 238 changed, 14383 insertions(+), 32 deletions(-) Docs: docs/03-涓氬姟妯″潡/ASL-AI鏅鸿兘鏂囩尞/05-寮€鍙戣褰?2025-11-22_Day2-Day3_LLM鏈嶅姟涓庨獙璇佺郴缁熷紑鍙?md
This commit is contained in:
546
backend/src/modules/asl/common/llm/LLM12FieldsService.ts
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546
backend/src/modules/asl/common/llm/LLM12FieldsService.ts
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import { logger } from '../../../../common/logging/index.js';
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import { LLMFactory } from '../../../../common/llm/adapters/LLMFactory.js';
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import { ILLMAdapter, ModelType } from '../../../../common/llm/adapters/types.js';
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import { cache } from '../../../../common/cache/index.js';
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import { PromptBuilder, PICOSContext, DEFAULT_MVP_CONFIG } from './PromptBuilder.js';
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import { ExtractionClient } from '../../../../common/document/ExtractionClient.js';
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import { calculateTokens } from '../utils/tokenCalculator.js';
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import { jsonrepair } from 'jsonrepair';
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import * as crypto from 'crypto';
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/**
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* 模型名称映射：从用户友好的名称映射到内部ModelType
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* 与标题摘要初筛保持一致
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*/
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const MODEL_NAME_MAP: Record<string, ModelType> = {
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'deepseek-chat': 'deepseek-v3',
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'deepseek-v3': 'deepseek-v3',
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'qwen-max': 'qwen3-72b', // ⭐ qwen-max = Qwen最新最强模型
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'qwen-plus': 'qwen3-72b', // qwen-plus = Qwen2.5-72B (次选)
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'qwen3-72b': 'qwen3-72b',
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'qwen-long': 'qwen-long',
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'gpt-4o': 'gpt-5', // ⭐ gpt-4o 映射到 gpt-5
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'gpt-5-pro': 'gpt-5',
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'gpt-5': 'gpt-5',
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'claude-sonnet-4.5': 'claude-4.5', // ⭐ claude-sonnet-4.5 映射
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'claude-sonnet-4-5-20250929': 'claude-4.5',
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'claude-4.5': 'claude-4.5',
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};
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/**
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* LLM处理模式
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*/
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export enum LLM12FieldsMode {
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SCREENING = '12fields-screening', // 评估模式（全文复筛）
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EXTRACTION = '12fields-extraction', // 提取模式（全文提取，未来）
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}
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/**
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* LLM处理结果
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*/
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export interface LLMResult {
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result: any; // 解析后的JSON结果
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processingTime: number; // 处理时间（毫秒）
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tokenUsage: number; // Token使用量
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cost: number; // 成本（人民币）
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extractionMethod: string; // 'nougat' | 'pymupdf'
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structuredFormat: boolean; // 是否为结构化格式（Markdown）
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rawResponse: string; // 原始响应（用于调试）
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}
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/**
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* Nougat提取选项
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*/
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interface NougatExtractionOptions {
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preferNougat: boolean; // 是否优先使用Nougat（英文论文）
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nougatQualityThreshold: number; // Nougat质量阈值（0.0-1.0，低于此值降级到PyMuPDF）
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}
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/**
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* LLM 12字段处理服务
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*
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* 功能：
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* 1. 全文提取（Nougat优先）
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* 2. Prompt动态组装
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* 3. LLM调用（支持DeepSeek-V3、Qwen3-Max等）
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* 4. 结果缓存
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* 5. 双模型并行调用
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*/
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export class LLM12FieldsService {
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private promptBuilder: PromptBuilder;
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private extractionClient: ExtractionClient;
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private nougatOptions: NougatExtractionOptions;
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constructor(options?: {
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promptBuilder?: PromptBuilder;
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extractionClient?: ExtractionClient;
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nougatOptions?: Partial<NougatExtractionOptions>;
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}) {
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this.promptBuilder = options?.promptBuilder || new PromptBuilder();
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this.extractionClient = options?.extractionClient || new ExtractionClient();
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this.nougatOptions = {
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preferNougat: true,
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nougatQualityThreshold: 0.8,
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...options?.nougatOptions,
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};
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}
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/**
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* 处理12字段（screening or extraction）
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*
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* 策略：全文一次性输入，通过Prompt工程优化
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*/
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async process12Fields(
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mode: LLM12FieldsMode,
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model: string, // 'deepseek-v3' | 'qwen-max' | 'deepseek-chat' 等用户友好名称
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pdfBuffer: Buffer,
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filename: string,
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picosContext: PICOSContext
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): Promise<LLMResult> {
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const startTime = Date.now();
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logger.info(`Starting 12-fields processing with model: ${model}, mode: ${mode}`);
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// 映射模型名称到ModelType
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const modelType = MODEL_NAME_MAP[model];
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if (!modelType) {
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throw new Error(
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`Unsupported model name: ${model}. Supported models: ${Object.keys(MODEL_NAME_MAP).join(', ')}`
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);
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}
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// Step 1: 提取全文（Nougat优先）
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const { fullTextMarkdown, extractionMethod, structuredFormat } =
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await this.extractFullTextStructured(pdfBuffer, filename);
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logger.info(
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`Full-text extracted, method: ${extractionMethod}, structured: ${structuredFormat}, length: ${fullTextMarkdown.length} chars`
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);
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// Step 2: 检查缓存
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const cacheKey = this.generateCacheKey(mode, model, fullTextMarkdown, picosContext);
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const cached = await this.checkCache(cacheKey);
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if (cached) {
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logger.info('Cache hit, returning cached result');
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return cached;
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}
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// Step 3: 构建Prompt
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const { systemPrompt, userPrompt } = await this.promptBuilder.buildFullPrompt({
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picosContext,
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fullTextContent: fullTextMarkdown,
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documentFormat: structuredFormat ? 'markdown' : 'plaintext',
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estimatedWordCount: Math.floor(fullTextMarkdown.length / 1.5), // 粗略估算字数
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modelName: model,
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includeCochraneStandards: DEFAULT_MVP_CONFIG.cochraneStandards,
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includeFewShotExamples: DEFAULT_MVP_CONFIG.fewShotExamples,
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});
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logger.info(
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`Prompt built, system: ${systemPrompt.length} chars, user: ${userPrompt.length} chars`
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);
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// Step 4: 调用LLM
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const llmAdapter = LLMFactory.getAdapter(modelType);
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const llmResponse = await this.callLLMWithRetry(
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llmAdapter,
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systemPrompt,
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userPrompt,
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mode
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);
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// Step 5: 解析结果
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const parsedResult = this.parseResponse(llmResponse);
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// Step 6: 计算Token和成本
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const tokenUsage = calculateTokens(systemPrompt + userPrompt + llmResponse);
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const cost = this.calculateCost(model, tokenUsage);
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const result: LLMResult = {
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result: parsedResult,
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processingTime: Date.now() - startTime,
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tokenUsage,
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cost,
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extractionMethod,
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structuredFormat,
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rawResponse: llmResponse,
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};
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// Step 7: 缓存结果
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await this.cacheResult(cacheKey, result);
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logger.info(
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`12-fields processing completed, time: ${result.processingTime}ms, tokens: ${tokenUsage}, cost: ¥${cost.toFixed(4)}`
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);
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return result;
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}
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/**
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* 双模型并行调用（容错版本）
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*
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* 使用Promise.allSettled确保单个模型失败不影响另一个
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*
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* 容错策略：
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* - 双模型成功：正常返回
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* - 单模型失败：返回成功的模型结果，标记降级模式
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* - 双模型失败：抛出异常
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*/
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async processDualModels(
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mode: LLM12FieldsMode,
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modelA: string = 'deepseek-v3',
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modelB: string = 'qwen-max',
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pdfBuffer: Buffer,
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filename: string,
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picosContext: PICOSContext
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): Promise<{
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resultA: LLMResult | null;
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resultB: LLMResult | null;
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degradedMode: boolean;
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failedModel?: string;
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}> {
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logger.info(`Starting dual-model processing: ${modelA} + ${modelB}`);
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// 使用allSettled确保一个失败不影响另一个
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const [settledA, settledB] = await Promise.allSettled([
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this.process12Fields(mode, modelA, pdfBuffer, filename, picosContext),
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this.process12Fields(mode, modelB, pdfBuffer, filename, picosContext),
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]);
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// 提取结果
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const resultA = settledA.status === 'fulfilled' ? settledA.value : null;
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const resultB = settledB.status === 'fulfilled' ? settledB.value : null;
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// ========================================
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// 容错逻辑
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// ========================================
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// 情况1：双模型都失败 ❌
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if (!resultA && !resultB) {
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const errorA = settledA.status === 'rejected' ? settledA.reason : 'unknown';
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const errorB = settledB.status === 'rejected' ? settledB.reason : 'unknown';
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logger.error('Both models failed', {
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modelA,
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modelB,
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errorA: errorA?.message || String(errorA),
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errorB: errorB?.message || String(errorB)
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});
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throw new Error(
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`Both models (${modelA} and ${modelB}) failed to process. ` +
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`${modelA} error: ${errorA?.message || errorA}. ` +
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`${modelB} error: ${errorB?.message || errorB}.`
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);
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}
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// 情况2：模型A失败，使用模型B ⚠️
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if (!resultA && resultB) {
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const errorA = settledA.status === 'rejected' ? settledA.reason : 'unknown';
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logger.warn(`Model ${modelA} failed, using ${modelB} only (degraded mode)`, {
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failedModel: modelA,
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error: errorA?.message || String(errorA),
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successModelCost: resultB.cost
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});
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return {
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resultA: null,
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resultB,
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degradedMode: true,
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failedModel: modelA
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};
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}
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// 情况3：模型B失败，使用模型A ⚠️
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if (resultA && !resultB) {
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const errorB = settledB.status === 'rejected' ? settledB.reason : 'unknown';
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logger.warn(`Model ${modelB} failed, using ${modelA} only (degraded mode)`, {
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failedModel: modelB,
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error: errorB?.message || String(errorB),
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successModelCost: resultA.cost
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});
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return {
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resultA,
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resultB: null,
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degradedMode: true,
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failedModel: modelB
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};
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}
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// 情况4：双模型都成功 ✅
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logger.info(
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`Dual-model processing completed successfully, total cost: ¥${(resultA!.cost + resultB!.cost).toFixed(4)}`
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);
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return {
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resultA,
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resultB,
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degradedMode: false
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};
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}
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/**
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* 提取全文（Nougat优先策略）
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*/
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private async extractFullTextStructured(
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pdfBuffer: Buffer,
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filename: string
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): Promise<{
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fullTextMarkdown: string;
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extractionMethod: 'nougat' | 'pymupdf';
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structuredFormat: boolean;
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}> {
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logger.info('Extracting full-text with Nougat-first strategy...');
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// Step 1: 检测语言（通过Python microservice）
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// 注意：这里简化了，实际可能需要先用PyMuPDF提取少量文本检测语言
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// 为了性能，我们直接尝试Nougat，失败则降级
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// Step 2: 优先尝试Nougat（英文论文效果最好）
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if (this.nougatOptions.preferNougat) {
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try {
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const nougatResult = await this.extractionClient.extractPdf(pdfBuffer, filename);
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// 检查Nougat质量
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if (
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nougatResult.method === 'nougat' &&
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(nougatResult.quality || 0) >= this.nougatOptions.nougatQualityThreshold
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) {
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logger.info('✅ Using Nougat extraction (structured Markdown)');
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return {
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fullTextMarkdown: nougatResult.text,
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extractionMethod: 'nougat',
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structuredFormat: true, // Nougat输出Markdown
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};
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} else {
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logger.warn(
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`⚠️ Nougat quality too low (${nougatResult.quality}), falling back to PyMuPDF`
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);
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}
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} catch (error) {
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logger.warn(`⚠️ Nougat extraction failed: ${(error as Error).message}, falling back to PyMuPDF`);
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}
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}
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// Step 3: 降级使用PyMuPDF
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logger.info('Using PyMuPDF extraction (plaintext)');
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const pymupdfResult = await this.extractionClient.extractPdf(pdfBuffer, filename);
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return {
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fullTextMarkdown: pymupdfResult.text,
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extractionMethod: 'pymupdf',
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structuredFormat: false, // PyMuPDF输出纯文本
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};
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}
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/**
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* 调用LLM（带重试）
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*/
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private async callLLMWithRetry(
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adapter: ILLMAdapter,
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systemPrompt: string,
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userPrompt: string,
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_mode: LLM12FieldsMode,
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maxRetries: number = 2
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): Promise<string> {
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let lastError: Error | null = null;
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for (let attempt = 0; attempt <= maxRetries; attempt++) {
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try {
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logger.info(`LLM call attempt ${attempt + 1}/${maxRetries + 1}`);
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const response = await adapter.chat(
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[
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: userPrompt },
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],
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{
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temperature: 0.1, // 低温度，提高一致性
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maxTokens: 8000, // 足够输出12字段+处理日志
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}
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);
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return response.content;
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} catch (error) {
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lastError = error as Error;
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logger.error(`LLM call attempt ${attempt + 1} failed: ${(error as Error).message}`);
|
||||
|
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if (attempt < maxRetries) {
|
||||
// 指数退避
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const waitTime = Math.pow(2, attempt) * 1000;
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logger.info(`Retrying in ${waitTime}ms...`);
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await new Promise((resolve) => setTimeout(resolve, waitTime));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error(`LLM call failed after ${maxRetries + 1} attempts: ${lastError?.message}`);
|
||||
}
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||||
|
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/**
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||||
* 解析LLM响应（3层容错策略）
|
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*
|
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* Layer 1: 严格JSON解析
|
||||
* Layer 2: JSON自动修复（jsonrepair）
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||||
* Layer 3: 提取代码块并解析
|
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*/
|
||||
private parseResponse(response: string): any {
|
||||
// ========================================
|
||||
// Layer 1: 严格JSON解析
|
||||
// ========================================
|
||||
try {
|
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const result = JSON.parse(response);
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logger.info('JSON parsed successfully (Layer 1: strict)');
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||||
return result;
|
||||
} catch (layer1Error) {
|
||||
logger.warn('Layer 1 failed: strict JSON parsing failed, trying Layer 2...');
|
||||
}
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||||
|
||||
// ========================================
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||||
// Layer 2: JSON自动修复
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||||
// ========================================
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||||
try {
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||||
const repaired = jsonrepair(response);
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||||
const result = JSON.parse(repaired);
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||||
|
||||
logger.warn('JSON auto-repaired (Layer 2)', {
|
||||
originalLength: response.length,
|
||||
repairedLength: repaired.length,
|
||||
message: 'LLM output had format issues, auto-repaired successfully'
|
||||
});
|
||||
|
||||
return result;
|
||||
} catch (layer2Error) {
|
||||
logger.warn('Layer 2 failed: JSON repair failed, trying Layer 3...');
|
||||
}
|
||||
|
||||
// ========================================
|
||||
// Layer 3: 提取代码块
|
||||
// ========================================
|
||||
let layer3Error: Error | null = null;
|
||||
try {
|
||||
// 匹配多种代码块格式
|
||||
const patterns = [
|
||||
/```json\s*\n([\s\S]*?)\n```/, // ```json ... ```
|
||||
/```\s*\n([\s\S]*?)\n```/, // ``` ... ```
|
||||
/\{[\s\S]*\}/, // 直接匹配 {...}
|
||||
];
|
||||
|
||||
for (const pattern of patterns) {
|
||||
const match = response.match(pattern);
|
||||
if (match) {
|
||||
const extracted = match[1] || match[0];
|
||||
|
||||
// 先尝试严格解析提取的内容
|
||||
try {
|
||||
const result = JSON.parse(extracted);
|
||||
logger.warn('JSON extracted from code block (Layer 3)', {
|
||||
pattern: pattern.source,
|
||||
message: 'LLM wrapped JSON in code block'
|
||||
});
|
||||
return result;
|
||||
} catch {
|
||||
// 尝试修复提取的内容
|
||||
const repaired = jsonrepair(extracted);
|
||||
const result = JSON.parse(repaired);
|
||||
logger.warn('JSON extracted and repaired (Layer 3)', {
|
||||
pattern: pattern.source,
|
||||
message: 'LLM wrapped JSON in code block with format issues'
|
||||
});
|
||||
return result;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
throw new Error('No valid JSON found in response');
|
||||
} catch (error) {
|
||||
layer3Error = error as Error;
|
||||
logger.error('All 3 layers failed to parse JSON');
|
||||
}
|
||||
|
||||
// ========================================
|
||||
// 最终失败：记录详细错误
|
||||
// ========================================
|
||||
const err = layer3Error || new Error('Unknown parsing error');
|
||||
logger.error('Failed to parse LLM response after all 3 layers', {
|
||||
error: err.message,
|
||||
responsePreview: response.substring(0, 500),
|
||||
responseLength: response.length
|
||||
});
|
||||
|
||||
throw new Error(
|
||||
`Invalid JSON response from LLM after 3 parsing attempts: ${err.message}. ` +
|
||||
`Please check logs for response preview.`
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* 生成缓存Key
|
||||
*/
|
||||
private generateCacheKey(
|
||||
mode: LLM12FieldsMode,
|
||||
model: string,
|
||||
fullText: string,
|
||||
picosContext: PICOSContext
|
||||
): string {
|
||||
const hash = crypto
|
||||
.createHash('sha256')
|
||||
.update(fullText + JSON.stringify(picosContext))
|
||||
.digest('hex')
|
||||
.substring(0, 16);
|
||||
|
||||
return `llm:${mode}:${model}:${hash}`;
|
||||
}
|
||||
|
||||
/**
|
||||
* 检查缓存
|
||||
*/
|
||||
private async checkCache(cacheKey: string): Promise<LLMResult | null> {
|
||||
try {
|
||||
const cached = await cache.get(cacheKey);
|
||||
return cached ? JSON.parse(cached) : null;
|
||||
} catch (error) {
|
||||
logger.warn(`Cache check failed: ${(error as Error).message}`);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 缓存结果
|
||||
*/
|
||||
private async cacheResult(cacheKey: string, result: LLMResult): Promise<void> {
|
||||
try {
|
||||
// 缓存1小时
|
||||
await cache.set(cacheKey, JSON.stringify(result), 3600);
|
||||
logger.info(`Result cached with key: ${cacheKey}`);
|
||||
} catch (error) {
|
||||
logger.warn(`Cache set failed: ${(error as Error).message}`);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 计算成本（人民币）
|
||||
*/
|
||||
private calculateCost(model: string, tokenUsage: number): number {
|
||||
// 成本表（人民币/1K tokens）
|
||||
const COST_TABLE: Record<string, number> = {
|
||||
'deepseek-v3': 0.001, // ¥0.001/1K tokens
|
||||
'qwen-max': 0.004, // ¥0.004/1K tokens
|
||||
'qwen-plus': 0.002, // ¥0.002/1K tokens
|
||||
'qwen-turbo': 0.0008, // ¥0.0008/1K tokens
|
||||
'gpt-4o': 0.03, // $0.005/1K tokens ≈ ¥0.03/1K tokens
|
||||
'claude-3.5-sonnet': 0.02, // $0.003/1K tokens ≈ ¥0.02/1K tokens
|
||||
};
|
||||
|
||||
const costPerK = COST_TABLE[model] || 0.01; // 默认值
|
||||
return (tokenUsage / 1000) * costPerK;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建LLM12FieldsService单例
|
||||
*/
|
||||
export const llm12FieldsService = new LLM12FieldsService();
|
||||
|
||||
Reference in New Issue
Block a user