feat(asl): Complete Deep Research V2.0 core development
Backend: - Add SSE streaming client (unifuncsSseClient) replacing async polling - Add paragraph-based reasoning parser with mergeConsecutiveThinking - Add requirement expansion service (DeepSeek-V3 PICOS+MeSH) - Add Word export service with Pandoc, inline hyperlinks, reference link expansion - Add deep research V2 worker with 2s log flush and Chinese source prompt - Add 5 curated data sources config (PubMed/ClinicalTrials/Cochrane/CNKI/MedJournals) - Add 4 API endpoints (generate-requirement/tasks/task-status/export-word) - Update Prisma schema with 6 new V2.0 fields on AslResearchTask - Add DB migration for V2.0 fields - Simplify ASL_DEEP_RESEARCH_EXPANSION prompt (remove strategy section) Frontend: - Add waterfall-flow DeepResearchPage (phase 0-4 progressive reveal) - Add LandingView, SetupPanel, StrategyConfirm, AgentTerminal, ResultsView - Add react-markdown + remark-gfm for report rendering - Add custom link component showing visible URLs after references - Add useDeepResearchTask polling hook - Add deep research TypeScript types Tests: - Add E2E test, smoke test, and Chinese data source test scripts Docs: - Update ASL module status (v2.0 - core features complete) - Update system status (v6.1 - ASL V2.0 milestone) - Update Unifuncs DeepSearch API guide (v2.0 - SSE mode + Chinese source results) - Update module auth specification (test script guidelines) - Update V2.0 development plan Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
@@ -0,0 +1,10 @@
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-- Deep Research V2.0: Add 6 new fields to research_tasks
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-- Backward compatible: all new columns are nullable
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ALTER TABLE "asl_schema"."research_tasks"
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ADD COLUMN IF NOT EXISTS "target_sources" JSONB,
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ADD COLUMN IF NOT EXISTS "confirmed_requirement" TEXT,
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ADD COLUMN IF NOT EXISTS "ai_intent_summary" JSONB,
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ADD COLUMN IF NOT EXISTS "execution_logs" JSONB,
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ADD COLUMN IF NOT EXISTS "synthesis_report" TEXT,
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ADD COLUMN IF NOT EXISTS "result_list" JSONB;
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@@ -1,100 +0,0 @@
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-- =====================================================
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-- Phase 2A: SSA 智能化核心 - 数据库迁移脚本
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-- 日期: 2026-02-20
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-- 描述: 添加工作流表和数据画像字段
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-- 注意: ssa_sessions.id 是 TEXT 类型（存储 UUID 字符串）
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-- =====================================================
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-- 1. 给 ssa_sessions 表添加 data_profile 字段（如果不存在）
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ALTER TABLE ssa_schema.ssa_sessions
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ADD COLUMN IF NOT EXISTS data_profile JSONB;
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COMMENT ON COLUMN ssa_schema.ssa_sessions.data_profile IS 'Python Tool C 生成的数据画像 (Phase 2A)';
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-- 2. 创建 ssa_workflows 表（多步骤分析流程）
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CREATE TABLE IF NOT EXISTS ssa_schema.ssa_workflows (
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id TEXT PRIMARY KEY DEFAULT gen_random_uuid()::TEXT,
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session_id TEXT NOT NULL,
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message_id TEXT,
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status VARCHAR(20) NOT NULL DEFAULT 'pending',
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total_steps INTEGER NOT NULL,
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completed_steps INTEGER NOT NULL DEFAULT 0,
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workflow_plan JSONB NOT NULL,
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reasoning TEXT,
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created_at TIMESTAMP WITHOUT TIME ZONE NOT NULL DEFAULT NOW(),
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started_at TIMESTAMP WITHOUT TIME ZONE,
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completed_at TIMESTAMP WITHOUT TIME ZONE,
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CONSTRAINT fk_ssa_workflow_session
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FOREIGN KEY (session_id)
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REFERENCES ssa_schema.ssa_sessions(id)
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ON DELETE CASCADE
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);
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-- ssa_workflows 索引
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CREATE INDEX IF NOT EXISTS idx_ssa_workflow_session
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ON ssa_schema.ssa_workflows(session_id);
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CREATE INDEX IF NOT EXISTS idx_ssa_workflow_status
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ON ssa_schema.ssa_workflows(status);
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-- ssa_workflows 字段注释
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COMMENT ON TABLE ssa_schema.ssa_workflows IS 'SSA 多步骤分析工作流 (Phase 2A)';
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COMMENT ON COLUMN ssa_schema.ssa_workflows.status IS 'pending | running | completed | partial | error';
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COMMENT ON COLUMN ssa_schema.ssa_workflows.workflow_plan IS 'LLM 生成的原始工作流计划 JSON';
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COMMENT ON COLUMN ssa_schema.ssa_workflows.reasoning IS 'LLM 规划理由说明';
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-- 3. 创建 ssa_workflow_steps 表（流程中的每个步骤）
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CREATE TABLE IF NOT EXISTS ssa_schema.ssa_workflow_steps (
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id TEXT PRIMARY KEY DEFAULT gen_random_uuid()::TEXT,
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workflow_id TEXT NOT NULL,
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step_order INTEGER NOT NULL,
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tool_code VARCHAR(50) NOT NULL,
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tool_name VARCHAR(100) NOT NULL,
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status VARCHAR(20) NOT NULL DEFAULT 'pending',
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input_params JSONB,
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guardrail_checks JSONB,
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output_result JSONB,
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error_info JSONB,
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execution_ms INTEGER,
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started_at TIMESTAMP WITHOUT TIME ZONE,
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completed_at TIMESTAMP WITHOUT TIME ZONE,
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CONSTRAINT fk_ssa_workflow_step_workflow
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FOREIGN KEY (workflow_id)
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REFERENCES ssa_schema.ssa_workflows(id)
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ON DELETE CASCADE
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);
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-- ssa_workflow_steps 索引
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CREATE INDEX IF NOT EXISTS idx_ssa_workflow_step_workflow
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ON ssa_schema.ssa_workflow_steps(workflow_id);
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CREATE INDEX IF NOT EXISTS idx_ssa_workflow_step_status
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ON ssa_schema.ssa_workflow_steps(status);
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-- ssa_workflow_steps 字段注释
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COMMENT ON TABLE ssa_schema.ssa_workflow_steps IS 'SSA 工作流单步执行记录 (Phase 2A)';
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COMMENT ON COLUMN ssa_schema.ssa_workflow_steps.status IS 'pending | running | success | warning | error | skipped';
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COMMENT ON COLUMN ssa_schema.ssa_workflow_steps.guardrail_checks IS 'R Service JIT 护栏检验结果 (正态性、方差齐性等)';
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COMMENT ON COLUMN ssa_schema.ssa_workflow_steps.output_result IS '工具执行结果 (已裁剪，符合 LLM 上下文限制)';
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COMMENT ON COLUMN ssa_schema.ssa_workflow_steps.error_info IS '错误信息 (用于容错管道的部分成功场景)';
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-- =====================================================
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-- 验证脚本
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-- =====================================================
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SELECT 'ssa_sessions.data_profile 字段' as item,
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CASE WHEN EXISTS (
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SELECT 1 FROM information_schema.columns
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WHERE table_schema = 'ssa_schema' AND table_name = 'ssa_sessions' AND column_name = 'data_profile'
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) THEN '✅ 已创建' ELSE '❌ 未创建' END as status;
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SELECT 'ssa_workflows 表' as item,
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CASE WHEN EXISTS (
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SELECT 1 FROM information_schema.tables
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WHERE table_schema = 'ssa_schema' AND table_name = 'ssa_workflows'
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) THEN '✅ 已创建' ELSE '❌ 未创建' END as status;
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SELECT 'ssa_workflow_steps 表' as item,
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CASE WHEN EXISTS (
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SELECT 1 FROM information_schema.tables
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WHERE table_schema = 'ssa_schema' AND table_name = 'ssa_workflow_steps'
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) THEN '✅ 已创建' ELSE '❌ 未创建' END as status;
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@@ -477,7 +477,7 @@ model AslFulltextScreeningTask {
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@@schema("asl_schema")
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}
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/// 智能文献检索任务（DeepSearch）
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/// 智能文献检索任务（DeepSearch V1.x + V2.0）
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model AslResearchTask {
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id String @id @default(uuid())
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@@ -486,23 +486,23 @@ model AslResearchTask {
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userId String @map("user_id")
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// 检索输入
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query String // 用户的自然语言查询
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filters Json? // 🔜 后续：高级筛选 { yearFrom, yearTo, articleTypes }
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query String // 用户的自然语言查询（V1.x 原始输入 / V2.0 Step 1 粗略想法）
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filters Json? // 高级筛选 { yearRange, targetCount, requireOpenAccess }
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// unifuncs 任务
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externalTaskId String? @map("external_task_id")
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// 状态
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status String @default("pending") // pending/processing/completed/failed
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// 状态: draft → pending → running → completed / failed
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status String @default("pending")
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errorMessage String? @map("error_message")
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// 结果
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// V1.x 结果字段（保留向后兼容）
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resultCount Int? @map("result_count")
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rawResult String? @map("raw_result") @db.Text
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reasoningContent String? @map("reasoning_content") @db.Text // AI思考过程
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literatures Json? // 解析后的文献列表
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reasoningContent String? @map("reasoning_content") @db.Text
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literatures Json?
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// 统计（🔜 后续展示）
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// 统计
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tokenUsage Json? @map("token_usage")
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searchCount Int? @map("search_count")
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readCount Int? @map("read_count")
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@@ -513,6 +513,15 @@ model AslResearchTask {
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updatedAt DateTime @updatedAt @map("updated_at")
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completedAt DateTime? @map("completed_at")
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// ── V2.0 新增字段 ──────────────────────────────
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targetSources Json? @map("target_sources") // 选中的数据源 ["https://pubmed.ncbi.nlm.nih.gov/", ...]
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confirmedRequirement String? @map("confirmed_requirement") @db.Text // 用户核验后的自然语言检索指令书
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aiIntentSummary Json? @map("ai_intent_summary") // PICOS + MeSH 结构化摘要
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executionLogs Json? @map("execution_logs") // 终端日志数组 [{type, title, text, ts}]
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synthesisReport String? @map("synthesis_report") @db.Text // AI综合报告（Markdown）
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resultList Json? @map("result_list") // 结构化文献元数据列表
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// ── 索引 ────────────────────────────
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@@index([projectId], map: "idx_research_tasks_project_id")
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@@index([userId], map: "idx_research_tasks_user_id")
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@@index([status], map: "idx_research_tasks_status")
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@@ -55,7 +55,7 @@ const RVW_FALLBACKS: Record<string, FallbackPrompt> = {
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};
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/**
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* ASL 模块兜底 Prompt（预留）
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* ASL 模块兜底 Prompt
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*/
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const ASL_FALLBACKS: Record<string, FallbackPrompt> = {
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ASL_SCREENING: {
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@@ -64,6 +64,49 @@ const ASL_FALLBACKS: Record<string, FallbackPrompt> = {
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请根据提供的标准对文献进行筛选，输出JSON格式的结果。`,
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modelConfig: { model: 'deepseek-v3', temperature: 0.2 },
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},
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ASL_DEEP_RESEARCH_EXPANSION: {
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content: `你是一位经验丰富的医学信息官，擅长将研究者的模糊想法转化为精准的文献检索需求指令。
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## 任务
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根据用户输入的粗略研究想法，生成一份简洁的深度文献检索指令书。
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## 输出规则
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1. 自然语言风格：像写邮件一样，口语化但专业，方便研究者直接阅读和编辑
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2. PICOS 拆解：明确 Population / Intervention / Comparison / Outcome / Study Design
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3. MeSH 扩展：为关键术语补充 MeSH 同义词（用括号标注英文 MeSH 术语）
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4. 研究设计偏好：若用户未指定，默认优先 RCT、Systematic Review/Meta-Analysis、Cohort Study
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5. 不要输出"检索策略建议"章节
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6. 不要使用 Markdown 加粗标记（即不要用 ** 符号）
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7. 不得自行添加约束：不要擅自限定"仅开放获取"或"仅英文文献"
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## 用户输入
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- 研究想法：{{originalQuery}}
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- 选择的数据源：{{targetSources}}
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- 时间范围：{{yearRange}}
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- 目标数量：{{targetCount}}
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## 输出格式
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请严格按以下两部分输出，不要添加额外内容：
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### Part 1: 自然语言检索指令书
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（简洁的检索需求描述，包含研究背景、PICOS要素、MeSH术语，不要包含检索策略建议）
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### Part 2: 结构化摘要（JSON）
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\`\`\`json
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{
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"objective": "研究目标一句话描述",
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"population": "研究人群",
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"intervention": "干预措施（含英文MeSH）",
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"comparison": "对照组",
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"outcome": "主要结局指标",
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"studyDesign": ["RCT", "Meta-analysis"],
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"meshTerms": ["MeSH术语1", "MeSH术语2"],
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"condition": "疾病/状况"
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}
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\`\`\``,
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modelConfig: { model: 'deepseek-v3', temperature: 0.4 },
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},
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};
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/**
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@@ -0,0 +1,179 @@
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/**
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* 中文数据源专项测试
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*
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* 只搜索 CNKI + 中华医学期刊网，验证 Unifuncs API 能否检索中文文献。
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*
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* 运行: npx tsx src/modules/asl/__tests__/deep-research-chinese-sources.ts
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*/
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const UNIFUNCS_BASE_URL = 'https://api.unifuncs.com/deepsearch/v1';
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const UNIFUNCS_API_KEY = process.env.UNIFUNCS_API_KEY || 'sk-2fNwqUH73elGq0aDKJEM4ReqP7Ry0iqHo4OXyidDe2WpQ9XQ';
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const CHINESE_SOURCES = [
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'https://www.cnki.net/',
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'https://medjournals.cn/',
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];
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const TEST_QUERIES = [
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'2型糖尿病患者SGLT2抑制剂的肾脏保护作用',
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'非小细胞肺癌免疫治疗的中国临床研究进展',
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];
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async function runTest(query: string, domainScope: string[]) {
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console.log('\n' + '='.repeat(80));
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console.log(`查询: ${query}`);
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console.log(`数据源: ${domainScope.join(', ')}`);
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console.log('='.repeat(80));
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// --- Test 1: SSE 流式模式 ---
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console.log('\n--- SSE 流式模式测试 ---');
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try {
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const payload = {
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model: 's2',
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messages: [{ role: 'user', content: query }],
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stream: true,
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introduction: '你是一名专业的中国临床研究文献检索专家，擅长从中国学术数据库中检索中文医学文献。请使用中文关键词进行检索。',
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max_depth: 10,
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domain_scope: domainScope,
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reference_style: 'link',
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output_prompt: '请使用中文输出。列出所有检索到的文献，包含标题、作者、期刊、年份和链接。如果文献是中文的，请保留中文信息。',
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};
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const response = await fetch(`${UNIFUNCS_BASE_URL}/chat/completions`, {
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method: 'POST',
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headers: {
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'Authorization': `Bearer ${UNIFUNCS_API_KEY}`,
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'Content-Type': 'application/json',
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'Accept': 'text/event-stream',
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},
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body: JSON.stringify(payload),
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});
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if (!response.ok) {
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const text = await response.text();
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console.error(`❌ SSE 请求失败 HTTP ${response.status}: ${text}`);
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return;
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}
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console.log(`✅ SSE 连接成功 (HTTP ${response.status})`);
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const reader = (response.body as any).getReader() as ReadableStreamDefaultReader<Uint8Array>;
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const decoder = new TextDecoder();
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let buffer = '';
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let reasoningContent = '';
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let content = '';
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let chunkCount = 0;
|
||||
let reasoningChunks = 0;
|
||||
let contentChunks = 0;
|
||||
const startTime = Date.now();
|
||||
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||||
while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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buffer += decoder.decode(value, { stream: true });
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const lines = buffer.split('\n');
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buffer = lines.pop() || '';
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim();
|
||||
if (!trimmed || trimmed.startsWith(':')) continue;
|
||||
|
||||
if (trimmed.startsWith('data: ')) {
|
||||
const data = trimmed.slice(6);
|
||||
if (data === '[DONE]') {
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||||
console.log('\n✅ 流式传输完成 [DONE]');
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break;
|
||||
}
|
||||
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||||
try {
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||||
const json = JSON.parse(data);
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||||
const delta = json.choices?.[0]?.delta;
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||||
chunkCount++;
|
||||
|
||||
if (delta?.reasoning_content) {
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||||
reasoningContent += delta.reasoning_content;
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||||
reasoningChunks++;
|
||||
if (reasoningChunks % 50 === 0) {
|
||||
process.stdout.write(`\r reasoning chunks: ${reasoningChunks}, content chunks: ${contentChunks}`);
|
||||
}
|
||||
} else if (delta?.content) {
|
||||
content += delta.content;
|
||||
contentChunks++;
|
||||
if (contentChunks % 20 === 0) {
|
||||
process.stdout.write(`\r reasoning chunks: ${reasoningChunks}, content chunks: ${contentChunks}`);
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
// Skip unparseable
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
reader.releaseLock();
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
console.log(`\n\n📊 统计:`);
|
||||
console.log(` 耗时: ${elapsed}s`);
|
||||
console.log(` 总 chunks: ${chunkCount}`);
|
||||
console.log(` reasoning chunks: ${reasoningChunks} (${reasoningContent.length} chars)`);
|
||||
console.log(` content chunks: ${contentChunks} (${content.length} chars)`);
|
||||
|
||||
// 分析结果
|
||||
console.log(`\n📝 Reasoning 内容（前 500 字）:`);
|
||||
console.log(reasoningContent.slice(0, 500));
|
||||
|
||||
console.log(`\n📝 Content 结果（前 1000 字）:`);
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||||
console.log(content.slice(0, 1000));
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||||
|
||||
// 检查是否包含中文内容
|
||||
const chineseCharCount = (content.match(/[\u4e00-\u9fa5]/g) || []).length;
|
||||
const hasChineseLinks = content.includes('cnki.net') || content.includes('medjournals.cn');
|
||||
const hasPubMedLinks = content.includes('pubmed.ncbi.nlm.nih.gov');
|
||||
|
||||
console.log(`\n🔍 中文分析:`);
|
||||
console.log(` 中文字符数: ${chineseCharCount}`);
|
||||
console.log(` 包含 CNKI 链接: ${hasChineseLinks ? '✅ 是' : '❌ 否'}`);
|
||||
console.log(` 包含 PubMed 链接: ${hasPubMedLinks ? '⚠️ 是（非中文源）' : '✅ 否'}`);
|
||||
|
||||
// 统计所有 URL
|
||||
const urls = content.match(/https?:\/\/[^\s)]+/g) || [];
|
||||
console.log(` 结果中的链接 (共 ${urls.length} 个):`);
|
||||
const domainCounts: Record<string, number> = {};
|
||||
for (const url of urls) {
|
||||
try {
|
||||
const domain = new URL(url).hostname;
|
||||
domainCounts[domain] = (domainCounts[domain] || 0) + 1;
|
||||
} catch { /* skip */ }
|
||||
}
|
||||
for (const [domain, count] of Object.entries(domainCounts).sort((a, b) => b[1] - a[1])) {
|
||||
console.log(` ${domain}: ${count} 个`);
|
||||
}
|
||||
|
||||
} catch (err: any) {
|
||||
console.error(`❌ SSE 测试失败: ${err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
async function main() {
|
||||
console.log('🔬 中文数据源专项测试');
|
||||
console.log(`API Key: ${UNIFUNCS_API_KEY.slice(0, 10)}...`);
|
||||
console.log(`测试数据源: ${CHINESE_SOURCES.join(', ')}`);
|
||||
|
||||
for (const query of TEST_QUERIES) {
|
||||
await runTest(query, CHINESE_SOURCES);
|
||||
}
|
||||
|
||||
// 额外测试: 同时包含中英文数据源
|
||||
console.log('\n\n' + '🔬'.repeat(20));
|
||||
console.log('附加测试: 中英文混合数据源');
|
||||
await runTest(
|
||||
'他汀类药物在心血管疾病一级预防中的最新RCT证据',
|
||||
['https://pubmed.ncbi.nlm.nih.gov/', ...CHINESE_SOURCES]
|
||||
);
|
||||
|
||||
console.log('\n\n✅ 所有测试完成');
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
345
backend/src/modules/asl/__tests__/deep-research-v2-e2e.ts
Normal file
345
backend/src/modules/asl/__tests__/deep-research-v2-e2e.ts
Normal file
@@ -0,0 +1,345 @@
|
||||
/**
|
||||
* Deep Research V2.0 — 端到端集成测试
|
||||
*
|
||||
* 测试全流程：
|
||||
* Step 1: 登录获取 Token
|
||||
* Step 2: 获取数据源列表
|
||||
* Step 3: 需求扩写（LLM 调用）
|
||||
* Step 4: 查看 draft 任务
|
||||
* Step 5: HITL 确认 → 启动执行
|
||||
* Step 6: 轮询任务直到 completed/failed
|
||||
* Step 7: 验证结果完整性
|
||||
* Step 8: Word 导出（可选，需要 Pandoc 微服务）
|
||||
* Step 9: 清理测试数据
|
||||
*
|
||||
* 运行方式：
|
||||
* npx tsx src/modules/asl/__tests__/deep-research-v2-e2e.ts
|
||||
*
|
||||
* 前置条件：
|
||||
* - 后端服务运行在 localhost:3001
|
||||
* - PostgreSQL 运行中
|
||||
* - UNIFUNCS_API_KEY 环境变量已设置
|
||||
* - （可选）Python 微服务运行在 localhost:8000（Word 导出）
|
||||
*
|
||||
* 预计耗时：2-5 分钟（取决于 Unifuncs 搜索深度）
|
||||
* 预计成本：约 ¥0.05-0.2（LLM 需求扩写 + Unifuncs DeepSearch）
|
||||
*/
|
||||
|
||||
// ─── 配置 ───────────────────────────────────────
|
||||
|
||||
const BASE_URL = process.env.TEST_BASE_URL || 'http://localhost:3001';
|
||||
const API_PREFIX = `${BASE_URL}/api/v1`;
|
||||
|
||||
const TEST_PHONE = process.env.TEST_PHONE || '13800000001';
|
||||
const TEST_PASSWORD = process.env.TEST_PASSWORD || '123456';
|
||||
|
||||
const TEST_QUERY = '他汀类药物在心血管疾病一级预防中的最新 RCT 证据';
|
||||
|
||||
const MAX_POLL_ATTEMPTS = 60;
|
||||
const POLL_INTERVAL_MS = 5000;
|
||||
|
||||
const SKIP_WORD_EXPORT = process.env.SKIP_WORD_EXPORT === 'true';
|
||||
const SKIP_CLEANUP = process.env.SKIP_CLEANUP === 'true';
|
||||
|
||||
// ─── 工具函数 ───────────────────────────────────
|
||||
|
||||
const sleep = (ms: number) => new Promise(r => setTimeout(r, ms));
|
||||
|
||||
let authToken = '';
|
||||
|
||||
async function api(
|
||||
path: string,
|
||||
options: RequestInit = {}
|
||||
): Promise<{ status: number; data: any }> {
|
||||
const url = `${API_PREFIX}${path}`;
|
||||
const res = await fetch(url, {
|
||||
...options,
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
...(authToken ? { Authorization: `Bearer ${authToken}` } : {}),
|
||||
...(options.headers || {}),
|
||||
},
|
||||
});
|
||||
|
||||
const contentType = res.headers.get('content-type') || '';
|
||||
let data: any;
|
||||
|
||||
if (contentType.includes('application/json')) {
|
||||
data = await res.json();
|
||||
} else if (contentType.includes('application/vnd.openxmlformats')) {
|
||||
const buf = await res.arrayBuffer();
|
||||
data = { _binary: true, byteLength: buf.byteLength };
|
||||
} else {
|
||||
data = await res.text();
|
||||
}
|
||||
|
||||
return { status: res.status, data };
|
||||
}
|
||||
|
||||
function assert(condition: boolean, message: string) {
|
||||
if (!condition) {
|
||||
throw new Error(`❌ 断言失败: ${message}`);
|
||||
}
|
||||
console.log(` ✅ ${message}`);
|
||||
}
|
||||
|
||||
function printDivider(title: string) {
|
||||
console.log(`\n${'═'.repeat(70)}`);
|
||||
console.log(` ${title}`);
|
||||
console.log(`${'═'.repeat(70)}\n`);
|
||||
}
|
||||
|
||||
// ─── 测试流程 ───────────────────────────────────
|
||||
|
||||
async function runE2E() {
|
||||
console.log('🧪 Deep Research V2.0 端到端集成测试');
|
||||
console.log(`⏰ 时间: ${new Date().toLocaleString('zh-CN')}`);
|
||||
console.log(`📍 后端: ${BASE_URL}`);
|
||||
console.log(`📝 查询: ${TEST_QUERY}`);
|
||||
console.log('');
|
||||
|
||||
let taskId: string | null = null;
|
||||
const startTime = Date.now();
|
||||
|
||||
try {
|
||||
// ═══════════ Step 1: 登录 ═══════════
|
||||
printDivider('Step 1: 登录获取 Token');
|
||||
|
||||
const loginRes = await api('/auth/login/password', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ phone: TEST_PHONE, password: TEST_PASSWORD }),
|
||||
});
|
||||
|
||||
if (loginRes.status !== 200 || !loginRes.data.success) {
|
||||
console.log('⚠️ 登录返回:', JSON.stringify(loginRes.data, null, 2));
|
||||
throw new Error(
|
||||
`登录失败 (HTTP ${loginRes.status}): ${loginRes.data.error || loginRes.data.message || '未知错误'}\n` +
|
||||
`请确认测试账号存在：phone=${TEST_PHONE}, password=${TEST_PASSWORD}\n` +
|
||||
`或通过环境变量指定：TEST_PHONE=xxx TEST_PASSWORD=yyy`
|
||||
);
|
||||
}
|
||||
|
||||
authToken = loginRes.data.data.tokens?.accessToken || loginRes.data.data.accessToken || loginRes.data.data.token;
|
||||
assert(!!authToken, `获取到 Token (${authToken.slice(0, 20)}...)`);
|
||||
|
||||
// ═══════════ Step 2: 获取数据源列表 ═══════════
|
||||
printDivider('Step 2: 获取数据源列表');
|
||||
|
||||
const sourcesRes = await api('/asl/research/data-sources');
|
||||
assert(sourcesRes.status === 200, `HTTP 200 (实际: ${sourcesRes.status})`);
|
||||
assert(sourcesRes.data.success === true, 'success=true');
|
||||
|
||||
const sources = sourcesRes.data.data;
|
||||
assert(Array.isArray(sources), `返回数组 (${sources.length} 个数据源)`);
|
||||
assert(sources.length >= 3, `至少 3 个数据源`);
|
||||
|
||||
console.log('\n 数据源列表:');
|
||||
sources.forEach((s: any) => {
|
||||
console.log(` ${s.defaultChecked ? '☑' : '☐'} [${s.category}] ${s.label} → ${s.domainScope}`);
|
||||
});
|
||||
|
||||
const defaultIds = sources.filter((s: any) => s.defaultChecked).map((s: any) => s.domainScope);
|
||||
console.log(`\n 默认选中: ${defaultIds.join(', ')}`);
|
||||
|
||||
// ═══════════ Step 3: 需求扩写 ═══════════
|
||||
printDivider('Step 3: 需求扩写（LLM 调用）');
|
||||
console.log(' ⏳ 调用 LLM 生成检索指令书，请稍候...\n');
|
||||
|
||||
const expandStart = Date.now();
|
||||
const expandRes = await api('/asl/research/generate-requirement', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({
|
||||
originalQuery: TEST_QUERY,
|
||||
targetSources: defaultIds,
|
||||
filters: { yearRange: '近5年', targetCount: '约20篇' },
|
||||
}),
|
||||
});
|
||||
|
||||
const expandMs = Date.now() - expandStart;
|
||||
assert(expandRes.status === 200, `HTTP 200 (实际: ${expandRes.status})`);
|
||||
assert(expandRes.data.success === true, 'success=true');
|
||||
|
||||
const expandData = expandRes.data.data;
|
||||
taskId = expandData.taskId;
|
||||
assert(!!taskId, `创建 draft 任务: ${taskId}`);
|
||||
assert(!!expandData.generatedRequirement, `生成检索指令书 (${expandData.generatedRequirement.length} 字)`);
|
||||
assert(!!expandData.intentSummary, 'PICOS 结构化摘要已生成');
|
||||
|
||||
console.log(`\n ⏱️ 耗时: ${(expandMs / 1000).toFixed(1)}s`);
|
||||
console.log(`\n 📋 PICOS 摘要:`);
|
||||
const summary = expandData.intentSummary;
|
||||
console.log(` 目标: ${summary.objective || '—'}`);
|
||||
console.log(` P: ${summary.population || '—'}`);
|
||||
console.log(` I: ${summary.intervention || '—'}`);
|
||||
console.log(` C: ${summary.comparison || '—'}`);
|
||||
console.log(` O: ${summary.outcome || '—'}`);
|
||||
console.log(` 研究设计: ${(summary.studyDesign || []).join(', ')}`);
|
||||
console.log(` MeSH: ${(summary.meshTerms || []).join(', ')}`);
|
||||
|
||||
console.log(`\n 📝 指令书预览 (前 200 字):`);
|
||||
console.log(` ${expandData.generatedRequirement.slice(0, 200).replace(/\n/g, '\n ')}...`);
|
||||
|
||||
// ═══════════ Step 4: 查看 draft 任务 ═══════════
|
||||
printDivider('Step 4: 查看 draft 状态任务');
|
||||
|
||||
const draftRes = await api(`/asl/research/tasks/${taskId}`);
|
||||
assert(draftRes.status === 200, `HTTP 200`);
|
||||
assert(draftRes.data.data.status === 'draft', `状态为 draft`);
|
||||
assert(draftRes.data.data.query === TEST_QUERY, `query 匹配`);
|
||||
|
||||
// ═══════════ Step 5: HITL 确认 → 启动 ═══════════
|
||||
printDivider('Step 5: HITL 确认 → 启动执行');
|
||||
|
||||
const confirmedReq = expandData.generatedRequirement;
|
||||
|
||||
const executeRes = await api(`/asl/research/tasks/${taskId}/execute`, {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ confirmedRequirement: confirmedReq }),
|
||||
});
|
||||
|
||||
assert(executeRes.status === 200, `HTTP 200 (实际: ${executeRes.status})`);
|
||||
assert(executeRes.data.success === true, '任务已入队');
|
||||
console.log(' 🚀 Deep Research 任务已启动！');
|
||||
|
||||
// ═══════════ Step 6: 轮询 ═══════════
|
||||
printDivider('Step 6: 轮询任务进度');
|
||||
|
||||
let lastLogCount = 0;
|
||||
let finalStatus = '';
|
||||
|
||||
for (let i = 1; i <= MAX_POLL_ATTEMPTS; i++) {
|
||||
await sleep(POLL_INTERVAL_MS);
|
||||
|
||||
const pollRes = await api(`/asl/research/tasks/${taskId}`);
|
||||
const task = pollRes.data.data;
|
||||
|
||||
const logs = task.executionLogs || [];
|
||||
const newLogs = logs.slice(lastLogCount);
|
||||
|
||||
if (newLogs.length > 0) {
|
||||
newLogs.forEach((log: any) => {
|
||||
const icon: Record<string, string> = {
|
||||
thinking: '💭', searching: '🔍', reading: '📖',
|
||||
analyzing: '🧪', summary: '📊', info: 'ℹ️',
|
||||
};
|
||||
console.log(` ${icon[log.type] || '•'} [${log.title}] ${log.text.slice(0, 80)}`);
|
||||
});
|
||||
lastLogCount = logs.length;
|
||||
}
|
||||
|
||||
console.log(` [${i}/${MAX_POLL_ATTEMPTS}] status=${task.status} | logs=${logs.length} | elapsed=${((Date.now() - startTime) / 1000).toFixed(0)}s`);
|
||||
|
||||
if (task.status === 'completed' || task.status === 'failed') {
|
||||
finalStatus = task.status;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
assert(finalStatus === 'completed', `任务完成 (实际: ${finalStatus || 'timeout'})`);
|
||||
|
||||
// ═══════════ Step 7: 验证结果 ═══════════
|
||||
printDivider('Step 7: 验证结果完整性');
|
||||
|
||||
const resultRes = await api(`/asl/research/tasks/${taskId}`);
|
||||
const result = resultRes.data.data;
|
||||
|
||||
assert(result.status === 'completed', '状态: completed');
|
||||
assert(!!result.synthesisReport, `AI 综合报告已生成 (${(result.synthesisReport || '').length} 字)`);
|
||||
assert(!!result.completedAt, `completedAt 已设置`);
|
||||
|
||||
const hasResultList = Array.isArray(result.resultList) && result.resultList.length > 0;
|
||||
if (hasResultList) {
|
||||
console.log(` ✅ 结构化文献列表: ${result.resultList.length} 篇`);
|
||||
console.log('\n 📚 文献样例 (前 3 篇):');
|
||||
result.resultList.slice(0, 3).forEach((item: any, i: number) => {
|
||||
console.log(` ${i + 1}. ${item.title || '(无标题)'}`);
|
||||
if (item.authors) console.log(` 作者: ${item.authors}`);
|
||||
if (item.journal) console.log(` 期刊: ${item.journal}`);
|
||||
if (item.pmid) console.log(` PMID: ${item.pmid}`);
|
||||
});
|
||||
} else {
|
||||
console.log(' ⚠️ 结构化文献列表为空（降级到报告展示模式）');
|
||||
}
|
||||
|
||||
const logs = result.executionLogs || [];
|
||||
console.log(`\n 📊 执行日志统计: ${logs.length} 条`);
|
||||
const typeCounts: Record<string, number> = {};
|
||||
logs.forEach((l: any) => { typeCounts[l.type] = (typeCounts[l.type] || 0) + 1; });
|
||||
Object.entries(typeCounts).forEach(([type, count]) => {
|
||||
console.log(` ${type}: ${count}`);
|
||||
});
|
||||
|
||||
console.log(`\n 📝 报告预览 (前 300 字):`);
|
||||
console.log(` ${result.synthesisReport.slice(0, 300).replace(/\n/g, '\n ')}...`);
|
||||
|
||||
// ═══════════ Step 8: Word 导出 ═══════════
|
||||
if (!SKIP_WORD_EXPORT) {
|
||||
printDivider('Step 8: Word 导出（需要 Pandoc 微服务）');
|
||||
|
||||
try {
|
||||
const exportRes = await api(`/asl/research/tasks/${taskId}/export-word`);
|
||||
|
||||
if (exportRes.status === 200 && exportRes.data._binary) {
|
||||
assert(true, `Word 导出成功 (${exportRes.data.byteLength} bytes)`);
|
||||
} else {
|
||||
console.log(` ⚠️ Word 导出返回异常 (HTTP ${exportRes.status})`);
|
||||
console.log(` 这通常是因为 Python 微服务（Pandoc）未运行，可忽略。`);
|
||||
}
|
||||
} catch (e: any) {
|
||||
console.log(` ⚠️ Word 导出跳过: ${e.message}`);
|
||||
console.log(` 如需测试，请启动 Python 微服务: cd extraction-service && python app.py`);
|
||||
}
|
||||
} else {
|
||||
console.log('\n ⏭️ 跳过 Word 导出测试 (SKIP_WORD_EXPORT=true)');
|
||||
}
|
||||
|
||||
// ═══════════ Step 9: 清理 ═══════════
|
||||
if (!SKIP_CLEANUP && taskId) {
|
||||
printDivider('Step 9: 清理测试数据');
|
||||
try {
|
||||
const { PrismaClient } = await import('@prisma/client');
|
||||
const prisma = new PrismaClient();
|
||||
await prisma.aslResearchTask.delete({ where: { id: taskId } });
|
||||
await prisma.$disconnect();
|
||||
console.log(` 🗑️ 已删除测试任务: ${taskId}`);
|
||||
} catch (e: any) {
|
||||
console.log(` ⚠️ 清理失败: ${e.message} (可手动删除)`);
|
||||
}
|
||||
} else {
|
||||
console.log(`\n ⏭️ 保留测试数据 taskId=${taskId}`);
|
||||
}
|
||||
|
||||
// ═══════════ 总结 ═══════════
|
||||
printDivider('🎉 测试通过！');
|
||||
const totalMs = Date.now() - startTime;
|
||||
console.log(` 总耗时: ${(totalMs / 1000).toFixed(1)}s`);
|
||||
console.log(` 任务 ID: ${taskId}`);
|
||||
console.log(` 综合报告: ${(result.synthesisReport || '').length} 字`);
|
||||
console.log(` 文献数量: ${hasResultList ? result.resultList.length : 0} 篇`);
|
||||
console.log(` 执行日志: ${logs.length} 条`);
|
||||
console.log('');
|
||||
|
||||
} catch (error: any) {
|
||||
printDivider('💥 测试失败');
|
||||
console.error(` 错误: ${error.message}`);
|
||||
console.error(` 任务 ID: ${taskId || '未创建'}`);
|
||||
console.error(` 耗时: ${((Date.now() - startTime) / 1000).toFixed(1)}s`);
|
||||
console.error('');
|
||||
|
||||
if (error.message.includes('登录失败')) {
|
||||
console.error(' 💡 提示: 请检查测试账号配置');
|
||||
console.error(' 可通过环境变量指定: TEST_PHONE=xxx TEST_PASSWORD=yyy npx tsx ...');
|
||||
}
|
||||
|
||||
if (error.message.includes('fetch failed') || error.message.includes('ECONNREFUSED')) {
|
||||
console.error(' 💡 提示: 后端服务未启动，请先运行:');
|
||||
console.error(' cd backend && npm run dev');
|
||||
}
|
||||
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
// ─── 入口 ───────────────────────────────────────
|
||||
|
||||
runE2E();
|
||||
161
backend/src/modules/asl/__tests__/deep-research-v2-smoke.ts
Normal file
161
backend/src/modules/asl/__tests__/deep-research-v2-smoke.ts
Normal file
@@ -0,0 +1,161 @@
|
||||
/**
|
||||
* Deep Research V2.0 — 冒烟测试（Smoke Test）
|
||||
*
|
||||
* 仅测试 API 接口连通性和基本参数校验，
|
||||
* 不调用 LLM / Unifuncs，无外部依赖，几秒完成。
|
||||
*
|
||||
* 运行方式：
|
||||
* npx tsx src/modules/asl/__tests__/deep-research-v2-smoke.ts
|
||||
*
|
||||
* 前置条件：
|
||||
* - 后端服务运行在 localhost:3001
|
||||
* - PostgreSQL 运行中
|
||||
*/
|
||||
|
||||
const BASE_URL = process.env.TEST_BASE_URL || 'http://localhost:3001';
|
||||
const API_PREFIX = `${BASE_URL}/api/v1`;
|
||||
|
||||
const TEST_PHONE = process.env.TEST_PHONE || '13800000001';
|
||||
const TEST_PASSWORD = process.env.TEST_PASSWORD || '123456';
|
||||
|
||||
let authToken = '';
|
||||
let passed = 0;
|
||||
let failed = 0;
|
||||
|
||||
async function api(path: string, options: RequestInit = {}): Promise<{ status: number; data: any }> {
|
||||
const res = await fetch(`${API_PREFIX}${path}`, {
|
||||
...options,
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
...(authToken ? { Authorization: `Bearer ${authToken}` } : {}),
|
||||
...(options.headers || {}),
|
||||
},
|
||||
});
|
||||
|
||||
const contentType = res.headers.get('content-type') || '';
|
||||
const data = contentType.includes('json') ? await res.json() : await res.text();
|
||||
return { status: res.status, data };
|
||||
}
|
||||
|
||||
function check(ok: boolean, label: string) {
|
||||
if (ok) {
|
||||
console.log(` ✅ ${label}`);
|
||||
passed++;
|
||||
} else {
|
||||
console.log(` ❌ ${label}`);
|
||||
failed++;
|
||||
}
|
||||
}
|
||||
|
||||
async function run() {
|
||||
console.log('🔥 Deep Research V2.0 冒烟测试\n');
|
||||
|
||||
// ─── 1. 登录 ───
|
||||
console.log('[1] 登录');
|
||||
try {
|
||||
const res = await api('/auth/login/password', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ phone: TEST_PHONE, password: TEST_PASSWORD }),
|
||||
});
|
||||
const ok = res.status === 200 && res.data.success;
|
||||
check(ok, `POST /auth/login/password → ${res.status}`);
|
||||
if (ok) {
|
||||
authToken = res.data.data.tokens?.accessToken || res.data.data.accessToken || res.data.data.token;
|
||||
} else {
|
||||
console.log(' ⚠️ 登录失败，后续需要认证的测试将跳过');
|
||||
console.log(` 返回: ${JSON.stringify(res.data).slice(0, 200)}`);
|
||||
}
|
||||
} catch (e: any) {
|
||||
check(false, `连接后端失败: ${e.message}`);
|
||||
console.log('\n💡 请先启动后端: cd backend && npm run dev\n');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
// ─── 2. GET /data-sources ───
|
||||
console.log('\n[2] GET /asl/research/data-sources');
|
||||
{
|
||||
const res = await api('/asl/research/data-sources');
|
||||
check(res.status === 200, `HTTP ${res.status} === 200`);
|
||||
check(res.data.success === true, 'success=true');
|
||||
check(Array.isArray(res.data.data) && res.data.data.length >= 3, `返回 ${res.data.data?.length} 个数据源`);
|
||||
|
||||
const hasPubmed = res.data.data?.some((s: any) => s.id === 'pubmed');
|
||||
check(hasPubmed, 'PubMed 存在于数据源列表');
|
||||
|
||||
const defaultChecked = res.data.data?.filter((s: any) => s.defaultChecked);
|
||||
check(defaultChecked?.length >= 1, `默认选中 ${defaultChecked?.length} 个`);
|
||||
}
|
||||
|
||||
// ─── 3. POST /generate-requirement (参数校验) ───
|
||||
console.log('\n[3] POST /asl/research/generate-requirement — 参数校验');
|
||||
{
|
||||
const res = await api('/asl/research/generate-requirement', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({ originalQuery: '' }),
|
||||
});
|
||||
check(res.status === 400, `空 query → HTTP ${res.status} === 400`);
|
||||
}
|
||||
|
||||
{
|
||||
const res = await api('/asl/research/generate-requirement', {
|
||||
method: 'POST',
|
||||
body: JSON.stringify({}),
|
||||
});
|
||||
check(res.status === 400, `缺少 query → HTTP ${res.status} === 400`);
|
||||
}
|
||||
|
||||
// ─── 4. PUT /tasks/:taskId/execute (参数校验) ───
|
||||
console.log('\n[4] PUT /asl/research/tasks/:taskId/execute — 参数校验');
|
||||
{
|
||||
const res = await api('/asl/research/tasks/nonexistent-id/execute', {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ confirmedRequirement: 'test' }),
|
||||
});
|
||||
check(res.status === 404, `不存在的 taskId → HTTP ${res.status} === 404`);
|
||||
}
|
||||
|
||||
{
|
||||
const res = await api('/asl/research/tasks/some-id/execute', {
|
||||
method: 'PUT',
|
||||
body: JSON.stringify({ confirmedRequirement: '' }),
|
||||
});
|
||||
check(res.status === 400, `空 confirmedRequirement → HTTP ${res.status} === 400`);
|
||||
}
|
||||
|
||||
// ─── 5. GET /tasks/:taskId (不存在) ───
|
||||
console.log('\n[5] GET /asl/research/tasks/:taskId — 不存在');
|
||||
{
|
||||
const res = await api('/asl/research/tasks/nonexistent-id');
|
||||
check(res.status === 404, `不存在 → HTTP ${res.status} === 404`);
|
||||
}
|
||||
|
||||
// ─── 6. GET /tasks/:taskId/export-word (不存在) ───
|
||||
console.log('\n[6] GET /asl/research/tasks/:taskId/export-word — 不存在');
|
||||
{
|
||||
const res = await api('/asl/research/tasks/nonexistent-id/export-word');
|
||||
check(res.status === 500 || res.status === 404, `不存在 → HTTP ${res.status}`);
|
||||
}
|
||||
|
||||
// ─── 7. 未认证访问 ───
|
||||
console.log('\n[7] 未认证访问（无 Token）');
|
||||
{
|
||||
const savedToken = authToken;
|
||||
authToken = '';
|
||||
|
||||
const res = await api('/asl/research/data-sources');
|
||||
check(res.status === 401, `无 Token → HTTP ${res.status} === 401`);
|
||||
|
||||
authToken = savedToken;
|
||||
}
|
||||
|
||||
// ─── 结果汇总 ───
|
||||
console.log(`\n${'═'.repeat(50)}`);
|
||||
console.log(` 🏁 冒烟测试完成: ${passed} 通过, ${failed} 失败 (共 ${passed + failed})`);
|
||||
console.log(`${'═'.repeat(50)}\n`);
|
||||
|
||||
if (failed > 0) {
|
||||
process.exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
run();
|
||||
75
backend/src/modules/asl/config/dataSources.ts
Normal file
75
backend/src/modules/asl/config/dataSources.ts
Normal file
@@ -0,0 +1,75 @@
|
||||
/**
|
||||
* Deep Research V2.0 — 精选数据源配置
|
||||
*
|
||||
* 基于 Unifuncs API 18 站实测结果（2026-02-22）精选的 5 个数据源。
|
||||
* 前端 SetupPanel 直接消费此配置渲染 Checkbox 列表。
|
||||
*/
|
||||
|
||||
export interface DataSourceConfig {
|
||||
id: string;
|
||||
label: string;
|
||||
labelEn: string;
|
||||
domainScope: string;
|
||||
category: 'english' | 'chinese';
|
||||
defaultChecked: boolean;
|
||||
note?: string;
|
||||
}
|
||||
|
||||
export const DEEP_RESEARCH_DATA_SOURCES: DataSourceConfig[] = [
|
||||
{
|
||||
id: 'pubmed',
|
||||
label: 'PubMed',
|
||||
labelEn: 'PubMed',
|
||||
domainScope: 'https://pubmed.ncbi.nlm.nih.gov/',
|
||||
category: 'english',
|
||||
defaultChecked: true,
|
||||
},
|
||||
{
|
||||
id: 'clinicaltrials',
|
||||
label: 'ClinicalTrials.gov',
|
||||
labelEn: 'ClinicalTrials.gov',
|
||||
domainScope: 'https://clinicaltrials.gov/',
|
||||
category: 'english',
|
||||
defaultChecked: false,
|
||||
},
|
||||
{
|
||||
id: 'cochrane',
|
||||
label: 'Cochrane Library',
|
||||
labelEn: 'Cochrane Library',
|
||||
domainScope: 'https://www.cochranelibrary.com/',
|
||||
category: 'english',
|
||||
defaultChecked: false,
|
||||
},
|
||||
{
|
||||
id: 'cnki',
|
||||
label: '中国知网 CNKI',
|
||||
labelEn: 'CNKI',
|
||||
domainScope: 'https://www.cnki.net/',
|
||||
category: 'chinese',
|
||||
defaultChecked: false,
|
||||
},
|
||||
{
|
||||
id: 'medjournals',
|
||||
label: '中华医学期刊网',
|
||||
labelEn: 'Chinese Medical Journals',
|
||||
domainScope: 'https://medjournals.cn/',
|
||||
category: 'chinese',
|
||||
defaultChecked: false,
|
||||
},
|
||||
];
|
||||
|
||||
export function getDefaultDataSources(): string[] {
|
||||
return DEEP_RESEARCH_DATA_SOURCES
|
||||
.filter(ds => ds.defaultChecked)
|
||||
.map(ds => ds.domainScope);
|
||||
}
|
||||
|
||||
export function getDomainScopes(ids: string[]): string[] {
|
||||
return DEEP_RESEARCH_DATA_SOURCES
|
||||
.filter(ds => ids.includes(ds.id))
|
||||
.map(ds => ds.domainScope);
|
||||
}
|
||||
|
||||
export function hasEnglishOnlySource(ids: string[]): boolean {
|
||||
return ids.includes('clinicaltrials');
|
||||
}
|
||||
210
backend/src/modules/asl/controllers/deepResearchController.ts
Normal file
210
backend/src/modules/asl/controllers/deepResearchController.ts
Normal file
@@ -0,0 +1,210 @@
|
||||
/**
|
||||
* Deep Research V2.0 Controller
|
||||
*
|
||||
* 新增端点（保留 V1.x 不动）：
|
||||
* - POST /generate-requirement — 需求扩写
|
||||
* - PUT /tasks/:taskId/execute — 启动异步执行
|
||||
* - GET /tasks/:taskId — 状态 + 日志 + 结果
|
||||
*/
|
||||
|
||||
import { FastifyRequest, FastifyReply } from 'fastify';
|
||||
import { prisma } from '../../../config/database.js';
|
||||
import { jobQueue } from '../../../common/jobs/index.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import { requirementExpansionService } from '../services/requirementExpansionService.js';
|
||||
import { wordExportService } from '../services/wordExportService.js';
|
||||
import { DEEP_RESEARCH_DATA_SOURCES } from '../config/dataSources.js';
|
||||
|
||||
// ─── Request types ──────────────────────────────
|
||||
|
||||
interface GenerateRequirementBody {
|
||||
originalQuery: string;
|
||||
targetSources?: string[];
|
||||
filters?: {
|
||||
yearRange?: string;
|
||||
targetCount?: string;
|
||||
requireOpenAccess?: boolean;
|
||||
};
|
||||
}
|
||||
|
||||
interface ExecuteBody {
|
||||
confirmedRequirement: string;
|
||||
}
|
||||
|
||||
interface TaskParams {
|
||||
taskId: string;
|
||||
}
|
||||
|
||||
// ─── POST /research/generate-requirement ────────
|
||||
|
||||
export async function generateRequirement(
|
||||
request: FastifyRequest<{ Body: GenerateRequirementBody }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const userId = request.user?.userId;
|
||||
if (!userId) {
|
||||
return reply.code(401).send({ success: false, error: '用户未认证' });
|
||||
}
|
||||
|
||||
const { originalQuery, targetSources, filters } = request.body;
|
||||
|
||||
if (!originalQuery?.trim()) {
|
||||
return reply.code(400).send({ success: false, error: '请输入研究想法' });
|
||||
}
|
||||
|
||||
const projectId = 'default';
|
||||
|
||||
const result = await requirementExpansionService.generateRequirement({
|
||||
projectId,
|
||||
userId,
|
||||
originalQuery: originalQuery.trim(),
|
||||
targetSources,
|
||||
filters,
|
||||
});
|
||||
|
||||
return reply.send({ success: true, data: result });
|
||||
} catch (error: any) {
|
||||
logger.error('[DeepResearchController] generateRequirement failed', {
|
||||
error: error.message,
|
||||
});
|
||||
return reply.code(500).send({ success: false, error: error.message });
|
||||
}
|
||||
}
|
||||
|
||||
// ─── PUT /research/tasks/:taskId/execute ────────
|
||||
|
||||
export async function executeTask(
|
||||
request: FastifyRequest<{ Params: TaskParams; Body: ExecuteBody }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const userId = request.user?.userId;
|
||||
if (!userId) {
|
||||
return reply.code(401).send({ success: false, error: '用户未认证' });
|
||||
}
|
||||
|
||||
const { taskId } = request.params;
|
||||
const { confirmedRequirement } = request.body;
|
||||
|
||||
if (!confirmedRequirement?.trim()) {
|
||||
return reply.code(400).send({ success: false, error: '检索指令不能为空' });
|
||||
}
|
||||
|
||||
const task = await prisma.aslResearchTask.findUnique({
|
||||
where: { id: taskId },
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return reply.code(404).send({ success: false, error: '任务不存在' });
|
||||
}
|
||||
|
||||
if (task.userId !== userId) {
|
||||
return reply.code(403).send({ success: false, error: '无权操作此任务' });
|
||||
}
|
||||
|
||||
if (task.status !== 'draft') {
|
||||
return reply.code(400).send({
|
||||
success: false,
|
||||
error: `任务状态为 ${task.status}，只有 draft 状态可启动`,
|
||||
});
|
||||
}
|
||||
|
||||
await prisma.aslResearchTask.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
confirmedRequirement: confirmedRequirement.trim(),
|
||||
status: 'pending',
|
||||
},
|
||||
});
|
||||
|
||||
await jobQueue.push('asl_deep_research_v2', { taskId });
|
||||
|
||||
logger.info('[DeepResearchController] Task pushed to queue', { taskId });
|
||||
|
||||
return reply.send({ success: true });
|
||||
} catch (error: any) {
|
||||
logger.error('[DeepResearchController] executeTask failed', {
|
||||
error: error.message,
|
||||
});
|
||||
return reply.code(500).send({ success: false, error: error.message });
|
||||
}
|
||||
}
|
||||
|
||||
// ─── GET /research/tasks/:taskId ────────────────
|
||||
|
||||
export async function getTask(
|
||||
request: FastifyRequest<{ Params: TaskParams }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { taskId } = request.params;
|
||||
|
||||
const task = await prisma.aslResearchTask.findUnique({
|
||||
where: { id: taskId },
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
return reply.code(404).send({ success: false, error: '任务不存在' });
|
||||
}
|
||||
|
||||
return reply.send({
|
||||
success: true,
|
||||
data: {
|
||||
taskId: task.id,
|
||||
status: task.status,
|
||||
query: task.query,
|
||||
targetSources: task.targetSources,
|
||||
confirmedRequirement: task.confirmedRequirement,
|
||||
aiIntentSummary: task.aiIntentSummary,
|
||||
executionLogs: task.executionLogs || [],
|
||||
synthesisReport: task.synthesisReport,
|
||||
resultList: task.resultList,
|
||||
resultCount: task.resultCount,
|
||||
errorMessage: task.errorMessage,
|
||||
createdAt: task.createdAt,
|
||||
completedAt: task.completedAt,
|
||||
},
|
||||
});
|
||||
} catch (error: any) {
|
||||
logger.error('[DeepResearchController] getTask failed', {
|
||||
error: error.message,
|
||||
});
|
||||
return reply.code(500).send({ success: false, error: error.message });
|
||||
}
|
||||
}
|
||||
|
||||
// ─── GET /research/tasks/:taskId/export-word ────
|
||||
|
||||
export async function exportWord(
|
||||
request: FastifyRequest<{ Params: TaskParams }>,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
try {
|
||||
const { taskId } = request.params;
|
||||
|
||||
const { buffer, filename } = await wordExportService.exportTaskToWord(taskId);
|
||||
|
||||
reply
|
||||
.header('Content-Type', 'application/vnd.openxmlformats-officedocument.wordprocessingml.document')
|
||||
.header('Content-Disposition', `attachment; filename*=UTF-8''${encodeURIComponent(filename)}`)
|
||||
.send(buffer);
|
||||
} catch (error: any) {
|
||||
logger.error('[DeepResearchController] exportWord failed', {
|
||||
error: error.message,
|
||||
});
|
||||
return reply.code(500).send({ success: false, error: error.message });
|
||||
}
|
||||
}
|
||||
|
||||
// ─── GET /research/data-sources ─────────────────
|
||||
|
||||
export async function getDataSources(
|
||||
_request: FastifyRequest,
|
||||
reply: FastifyReply
|
||||
) {
|
||||
return reply.send({
|
||||
success: true,
|
||||
data: DEEP_RESEARCH_DATA_SOURCES,
|
||||
});
|
||||
}
|
||||
@@ -8,6 +8,7 @@ import * as literatureController from '../controllers/literatureController.js';
|
||||
import * as screeningController from '../controllers/screeningController.js';
|
||||
import * as fulltextScreeningController from '../fulltext-screening/controllers/FulltextScreeningController.js';
|
||||
import * as researchController from '../controllers/researchController.js';
|
||||
import * as deepResearchController from '../controllers/deepResearchController.js';
|
||||
import { authenticate, requireModule } from '../../../common/auth/auth.middleware.js';
|
||||
|
||||
export async function aslRoutes(fastify: FastifyInstance) {
|
||||
@@ -79,16 +80,33 @@ export async function aslRoutes(fastify: FastifyInstance) {
|
||||
// 导出Excel
|
||||
fastify.get('/fulltext-screening/tasks/:taskId/export', { preHandler: [authenticate, requireModule('ASL')] }, fulltextScreeningController.exportExcel);
|
||||
|
||||
// ==================== 智能文献检索路由 (DeepSearch) ====================
|
||||
// ==================== 智能文献检索路由 (DeepSearch V1.x — 保留兼容) ====================
|
||||
|
||||
// SSE 流式检索（推荐，实时显示思考过程）
|
||||
// SSE 流式检索
|
||||
fastify.post('/research/stream', { preHandler: [authenticate, requireModule('ASL')] }, researchController.streamSearch);
|
||||
|
||||
// 创建检索任务（异步模式，备用）
|
||||
// 创建检索任务（V1.x 异步模式）
|
||||
fastify.post('/research/tasks', { preHandler: [authenticate, requireModule('ASL')] }, researchController.createTask);
|
||||
|
||||
// 获取任务状态（轮询）
|
||||
// 获取任务状态（V1.x 轮询）
|
||||
fastify.get('/research/tasks/:taskId/status', { preHandler: [authenticate, requireModule('ASL')] }, researchController.getTaskStatus);
|
||||
|
||||
// ==================== Deep Research V2.0 路由 ====================
|
||||
|
||||
// 获取可选数据源列表
|
||||
fastify.get('/research/data-sources', { preHandler: [authenticate, requireModule('ASL')] }, deepResearchController.getDataSources);
|
||||
|
||||
// 需求扩写（PICOS + MeSH）
|
||||
fastify.post('/research/generate-requirement', { preHandler: [authenticate, requireModule('ASL')] }, deepResearchController.generateRequirement);
|
||||
|
||||
// 启动异步执行
|
||||
fastify.put('/research/tasks/:taskId/execute', { preHandler: [authenticate, requireModule('ASL')] }, deepResearchController.executeTask);
|
||||
|
||||
// V2.0 任务详情（状态 + 日志 + 结果）
|
||||
fastify.get('/research/tasks/:taskId', { preHandler: [authenticate, requireModule('ASL')] }, deepResearchController.getTask);
|
||||
|
||||
// V2.0 导出 Word
|
||||
fastify.get('/research/tasks/:taskId/export-word', { preHandler: [authenticate, requireModule('ASL')] }, deepResearchController.exportWord);
|
||||
}
|
||||
|
||||
|
||||
|
||||
180
backend/src/modules/asl/services/requirementExpansionService.ts
Normal file
180
backend/src/modules/asl/services/requirementExpansionService.ts
Normal file
@@ -0,0 +1,180 @@
|
||||
/**
|
||||
* Deep Research V2.0 — 需求扩写服务
|
||||
*
|
||||
* 职责：
|
||||
* 1. 通过 Prompt 管理服务获取 ASL_DEEP_RESEARCH_EXPANSION 模板
|
||||
* 2. 调用 LLM 将用户粗略想法扩写为 PICOS 结构化检索指令
|
||||
* 3. 解析 LLM 输出为 generatedRequirement + intentSummary
|
||||
* 4. 创建 DB 记录（status=draft）
|
||||
*/
|
||||
|
||||
import { prisma } from '../../../config/database.js';
|
||||
import { getPromptService } from '../../../common/prompt/index.js';
|
||||
import { LLMFactory } from '../../../common/llm/adapters/LLMFactory.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import { getDefaultDataSources, hasEnglishOnlySource } from '../config/dataSources.js';
|
||||
import type { Message } from '../../../common/llm/adapters/types.js';
|
||||
|
||||
export interface GenerateRequirementInput {
|
||||
projectId: string;
|
||||
userId: string;
|
||||
originalQuery: string;
|
||||
targetSources?: string[];
|
||||
filters?: {
|
||||
yearRange?: string;
|
||||
targetCount?: string;
|
||||
requireOpenAccess?: boolean;
|
||||
};
|
||||
}
|
||||
|
||||
export interface IntentSummary {
|
||||
objective: string;
|
||||
population: string;
|
||||
intervention: string;
|
||||
comparison: string;
|
||||
outcome: string;
|
||||
studyDesign: string[];
|
||||
meshTerms: string[];
|
||||
condition: string;
|
||||
}
|
||||
|
||||
export interface GenerateRequirementResult {
|
||||
taskId: string;
|
||||
generatedRequirement: string;
|
||||
intentSummary: IntentSummary;
|
||||
}
|
||||
|
||||
class RequirementExpansionService {
|
||||
|
||||
async generateRequirement(input: GenerateRequirementInput): Promise<GenerateRequirementResult> {
|
||||
const {
|
||||
projectId,
|
||||
userId,
|
||||
originalQuery,
|
||||
targetSources,
|
||||
filters,
|
||||
} = input;
|
||||
|
||||
const sources = targetSources && targetSources.length > 0
|
||||
? targetSources
|
||||
: getDefaultDataSources();
|
||||
|
||||
logger.info('[RequirementExpansion] Starting expansion', {
|
||||
userId,
|
||||
queryLength: originalQuery.length,
|
||||
sources,
|
||||
});
|
||||
|
||||
const promptService = getPromptService(prisma);
|
||||
const rendered = await promptService.get(
|
||||
'ASL_DEEP_RESEARCH_EXPANSION',
|
||||
{
|
||||
originalQuery,
|
||||
targetSources: sources.join(', '),
|
||||
yearRange: filters?.yearRange || '不限',
|
||||
targetCount: filters?.targetCount || '全面检索',
|
||||
},
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const adapter = LLMFactory.getAdapter(
|
||||
(rendered.modelConfig.model as any) || 'deepseek-v3'
|
||||
);
|
||||
|
||||
const messages: Message[] = [
|
||||
{ role: 'system', content: rendered.content },
|
||||
{ role: 'user', content: originalQuery },
|
||||
];
|
||||
|
||||
const llmResponse = await adapter.chat(messages, {
|
||||
temperature: rendered.modelConfig.temperature ?? 0.4,
|
||||
maxTokens: rendered.modelConfig.maxTokens ?? 4096,
|
||||
});
|
||||
|
||||
const rawOutput = llmResponse.content;
|
||||
|
||||
const { requirement, intentSummary } = this.parseOutput(rawOutput);
|
||||
|
||||
const task = await prisma.aslResearchTask.create({
|
||||
data: {
|
||||
projectId,
|
||||
userId,
|
||||
query: originalQuery,
|
||||
status: 'draft',
|
||||
targetSources: sources as any,
|
||||
confirmedRequirement: requirement,
|
||||
aiIntentSummary: intentSummary as any,
|
||||
filters: filters as any,
|
||||
},
|
||||
});
|
||||
|
||||
logger.info('[RequirementExpansion] Task created', {
|
||||
taskId: task.id,
|
||||
meshTerms: intentSummary.meshTerms?.length || 0,
|
||||
});
|
||||
|
||||
return {
|
||||
taskId: task.id,
|
||||
generatedRequirement: requirement,
|
||||
intentSummary,
|
||||
};
|
||||
}
|
||||
|
||||
private parseOutput(raw: string): {
|
||||
requirement: string;
|
||||
intentSummary: IntentSummary;
|
||||
} {
|
||||
let requirement = '';
|
||||
let intentSummary: IntentSummary = {
|
||||
objective: '',
|
||||
population: '',
|
||||
intervention: '',
|
||||
comparison: '',
|
||||
outcome: '',
|
||||
studyDesign: [],
|
||||
meshTerms: [],
|
||||
condition: '',
|
||||
};
|
||||
|
||||
const part1Match = raw.match(
|
||||
/### Part 1[：:][^\n]*\n([\s\S]*?)(?=### Part 2|$)/i
|
||||
);
|
||||
if (part1Match) {
|
||||
requirement = part1Match[1].trim();
|
||||
} else {
|
||||
const jsonBlockStart = raw.indexOf('```json');
|
||||
if (jsonBlockStart > 0) {
|
||||
requirement = raw.slice(0, jsonBlockStart).trim();
|
||||
} else {
|
||||
requirement = raw.trim();
|
||||
}
|
||||
}
|
||||
|
||||
const jsonMatch = raw.match(/```json\s*([\s\S]*?)```/);
|
||||
if (jsonMatch) {
|
||||
try {
|
||||
let cleaned = jsonMatch[1].trim();
|
||||
cleaned = cleaned.replace(/,\s*([}\]])/g, '$1');
|
||||
const parsed = JSON.parse(cleaned);
|
||||
intentSummary = {
|
||||
objective: parsed.objective || '',
|
||||
population: parsed.population || '',
|
||||
intervention: parsed.intervention || '',
|
||||
comparison: parsed.comparison || '',
|
||||
outcome: parsed.outcome || '',
|
||||
studyDesign: Array.isArray(parsed.studyDesign) ? parsed.studyDesign : [],
|
||||
meshTerms: Array.isArray(parsed.meshTerms) ? parsed.meshTerms : [],
|
||||
condition: parsed.condition || '',
|
||||
};
|
||||
} catch (e) {
|
||||
logger.warn('[RequirementExpansion] Failed to parse intent summary JSON', {
|
||||
error: (e as Error).message,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return { requirement, intentSummary };
|
||||
}
|
||||
}
|
||||
|
||||
export const requirementExpansionService = new RequirementExpansionService();
|
||||
155
backend/src/modules/asl/services/unifuncsAsyncClient.ts
Normal file
155
backend/src/modules/asl/services/unifuncsAsyncClient.ts
Normal file
@@ -0,0 +1,155 @@
|
||||
/**
|
||||
* Unifuncs DeepSearch 异步客户端
|
||||
*
|
||||
* 封装 create_task / query_task 两个 API。
|
||||
* Worker 通过此客户端与 Unifuncs 服务交互。
|
||||
*/
|
||||
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
|
||||
const UNIFUNCS_BASE_URL = 'https://api.unifuncs.com/deepsearch/v1';
|
||||
const UNIFUNCS_API_KEY = process.env.UNIFUNCS_API_KEY;
|
||||
|
||||
export interface CreateTaskParams {
|
||||
query: string;
|
||||
introduction?: string;
|
||||
maxDepth?: number;
|
||||
domainScope?: string[];
|
||||
domainBlacklist?: string[];
|
||||
referenceStyle?: 'link' | 'character';
|
||||
generateSummary?: boolean;
|
||||
outputPrompt?: string;
|
||||
}
|
||||
|
||||
export interface CreateTaskResponse {
|
||||
code: number;
|
||||
message: string;
|
||||
data: {
|
||||
task_id: string;
|
||||
status: string;
|
||||
created_at: string;
|
||||
};
|
||||
}
|
||||
|
||||
export interface QueryTaskResponse {
|
||||
code: number;
|
||||
message: string;
|
||||
data: {
|
||||
task_id: string;
|
||||
status: 'pending' | 'running' | 'completed' | 'failed';
|
||||
result?: {
|
||||
content: string;
|
||||
reasoning_content: string;
|
||||
};
|
||||
created_at: string;
|
||||
updated_at: string;
|
||||
progress?: {
|
||||
current: number;
|
||||
total: number;
|
||||
message: string;
|
||||
};
|
||||
statistics?: {
|
||||
iterations: number;
|
||||
search_count: number;
|
||||
read_count: number;
|
||||
token_usage?: {
|
||||
prompt_tokens: number;
|
||||
completion_tokens: number;
|
||||
total_tokens: number;
|
||||
};
|
||||
};
|
||||
};
|
||||
}
|
||||
|
||||
class UnifuncsAsyncClient {
|
||||
private apiKey: string;
|
||||
private baseUrl: string;
|
||||
|
||||
constructor() {
|
||||
this.apiKey = UNIFUNCS_API_KEY || '';
|
||||
this.baseUrl = UNIFUNCS_BASE_URL;
|
||||
}
|
||||
|
||||
async createTask(params: CreateTaskParams): Promise<CreateTaskResponse> {
|
||||
if (!this.apiKey) {
|
||||
throw new Error('UNIFUNCS_API_KEY not configured');
|
||||
}
|
||||
|
||||
const payload = {
|
||||
model: 's2',
|
||||
messages: [{ role: 'user', content: params.query }],
|
||||
introduction: params.introduction || this.defaultIntroduction(),
|
||||
max_depth: params.maxDepth ?? 25,
|
||||
domain_scope: params.domainScope || ['https://pubmed.ncbi.nlm.nih.gov/'],
|
||||
domain_blacklist: params.domainBlacklist || [],
|
||||
reference_style: params.referenceStyle || 'link',
|
||||
generate_summary: params.generateSummary ?? true,
|
||||
...(params.outputPrompt ? { output_prompt: params.outputPrompt } : {}),
|
||||
};
|
||||
|
||||
logger.info('[UnifuncsClient] Creating task', {
|
||||
queryLen: params.query.length,
|
||||
domainScope: payload.domain_scope,
|
||||
maxDepth: payload.max_depth,
|
||||
});
|
||||
|
||||
const res = await fetch(`${this.baseUrl}/create_task`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.apiKey}`,
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
body: JSON.stringify(payload),
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const text = await res.text();
|
||||
throw new Error(`create_task HTTP ${res.status}: ${text}`);
|
||||
}
|
||||
|
||||
const json = await res.json() as CreateTaskResponse;
|
||||
|
||||
if (json.code !== 0) {
|
||||
throw new Error(`create_task error: ${json.message}`);
|
||||
}
|
||||
|
||||
logger.info('[UnifuncsClient] Task created', {
|
||||
externalTaskId: json.data.task_id,
|
||||
});
|
||||
|
||||
return json;
|
||||
}
|
||||
|
||||
async queryTask(taskId: string): Promise<QueryTaskResponse> {
|
||||
if (!this.apiKey) {
|
||||
throw new Error('UNIFUNCS_API_KEY not configured');
|
||||
}
|
||||
|
||||
const params = new URLSearchParams({ task_id: taskId });
|
||||
|
||||
const res = await fetch(`${this.baseUrl}/query_task?${params.toString()}`, {
|
||||
headers: {
|
||||
'Authorization': `Bearer ${this.apiKey}`,
|
||||
},
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const text = await res.text();
|
||||
throw new Error(`query_task HTTP ${res.status}: ${text}`);
|
||||
}
|
||||
|
||||
const json = await res.json() as QueryTaskResponse;
|
||||
|
||||
if (json.code !== 0) {
|
||||
throw new Error(`query_task error: ${json.message}`);
|
||||
}
|
||||
|
||||
return json;
|
||||
}
|
||||
|
||||
private defaultIntroduction(): string {
|
||||
return '你是一名专业的临床研究文献检索专家，擅长从多个学术数据库中检索高质量的医学文献。请根据用户的检索需求，系统性地搜索并返回相关文献的详细信息。';
|
||||
}
|
||||
}
|
||||
|
||||
export const unifuncsAsyncClient = new UnifuncsAsyncClient();
|
||||
122
backend/src/modules/asl/services/unifuncsSseClient.ts
Normal file
122
backend/src/modules/asl/services/unifuncsSseClient.ts
Normal file
@@ -0,0 +1,122 @@
|
||||
/**
|
||||
* Unifuncs DeepSearch SSE 流式客户端
|
||||
*
|
||||
* 通过 OpenAI 兼容协议的 SSE 流获取实时 reasoning_content 和 content。
|
||||
* Worker 消费此流可实现逐条实时写入 executionLogs。
|
||||
*/
|
||||
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
|
||||
const UNIFUNCS_BASE_URL = 'https://api.unifuncs.com/deepsearch/v1';
|
||||
const UNIFUNCS_API_KEY = process.env.UNIFUNCS_API_KEY;
|
||||
|
||||
export interface SseStreamParams {
|
||||
query: string;
|
||||
introduction?: string;
|
||||
maxDepth?: number;
|
||||
domainScope?: string[];
|
||||
domainBlacklist?: string[];
|
||||
referenceStyle?: 'link' | 'character';
|
||||
outputPrompt?: string;
|
||||
}
|
||||
|
||||
export interface SseChunk {
|
||||
type: 'reasoning' | 'content' | 'done' | 'error';
|
||||
text: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* 以 AsyncGenerator 形式逐 chunk 返回 Unifuncs SSE 流数据
|
||||
*/
|
||||
export async function* streamDeepSearch(
|
||||
params: SseStreamParams
|
||||
): AsyncGenerator<SseChunk> {
|
||||
const apiKey = UNIFUNCS_API_KEY;
|
||||
if (!apiKey) throw new Error('UNIFUNCS_API_KEY not configured');
|
||||
|
||||
const payload: Record<string, any> = {
|
||||
model: 's2',
|
||||
messages: [{ role: 'user', content: params.query }],
|
||||
stream: true,
|
||||
introduction: params.introduction || defaultIntroduction(),
|
||||
max_depth: params.maxDepth ?? 25,
|
||||
domain_scope: params.domainScope || ['https://pubmed.ncbi.nlm.nih.gov/'],
|
||||
domain_blacklist: params.domainBlacklist || [],
|
||||
reference_style: params.referenceStyle || 'link',
|
||||
};
|
||||
|
||||
if (params.outputPrompt) {
|
||||
payload.output_prompt = params.outputPrompt;
|
||||
}
|
||||
|
||||
logger.info('[UnifuncsSse] Starting SSE stream', {
|
||||
queryLen: params.query.length,
|
||||
domainScope: payload.domain_scope,
|
||||
maxDepth: payload.max_depth,
|
||||
});
|
||||
|
||||
const response = await fetch(`${UNIFUNCS_BASE_URL}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Authorization': `Bearer ${apiKey}`,
|
||||
'Content-Type': 'application/json',
|
||||
'Accept': 'text/event-stream',
|
||||
},
|
||||
body: JSON.stringify(payload),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const text = await response.text();
|
||||
throw new Error(`SSE request failed HTTP ${response.status}: ${text}`);
|
||||
}
|
||||
|
||||
if (!response.body) {
|
||||
throw new Error('Response body is null');
|
||||
}
|
||||
|
||||
const reader = (response.body as any).getReader() as ReadableStreamDefaultReader<Uint8Array>;
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = '';
|
||||
|
||||
try {
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
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.startsWith(':')) continue;
|
||||
|
||||
if (trimmed.startsWith('data: ')) {
|
||||
const data = trimmed.slice(6);
|
||||
if (data === '[DONE]') {
|
||||
yield { type: 'done' as const, text: '' };
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const json = JSON.parse(data);
|
||||
const delta = json.choices?.[0]?.delta;
|
||||
if (delta?.reasoning_content) {
|
||||
yield { type: 'reasoning' as const, text: delta.reasoning_content };
|
||||
} else if (delta?.content) {
|
||||
yield { type: 'content' as const, text: delta.content };
|
||||
}
|
||||
} catch {
|
||||
// Skip unparseable chunks
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
reader.releaseLock();
|
||||
}
|
||||
}
|
||||
|
||||
function defaultIntroduction(): string {
|
||||
return '你是一名专业的临床研究文献检索专家，擅长从多个学术数据库中检索高质量的医学文献。请根据用户的检索需求，系统性地搜索并返回相关文献的详细信息。';
|
||||
}
|
||||
140
backend/src/modules/asl/services/wordExportService.ts
Normal file
140
backend/src/modules/asl/services/wordExportService.ts
Normal file
@@ -0,0 +1,140 @@
|
||||
/**
|
||||
* Deep Research V2.0 — Word 导出服务
|
||||
*
|
||||
* 将 synthesisReport + resultList 拼接为完整 Markdown，
|
||||
* 调用 Python 微服务（Pandoc）转换为 .docx。
|
||||
*
|
||||
* 文献清单中标题直接作为超链接，确保 Word 中可点击。
|
||||
*/
|
||||
|
||||
import axios from 'axios';
|
||||
import { prisma } from '../../../config/database.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import type { LiteratureItem } from '../utils/resultParser.js';
|
||||
|
||||
const EXTRACTION_SERVICE_URL = process.env.EXTRACTION_SERVICE_URL || 'http://localhost:8000';
|
||||
|
||||
class WordExportService {
|
||||
|
||||
async exportTaskToWord(taskId: string): Promise<{
|
||||
buffer: Buffer;
|
||||
filename: string;
|
||||
}> {
|
||||
const task = await prisma.aslResearchTask.findUnique({
|
||||
where: { id: taskId },
|
||||
});
|
||||
|
||||
if (!task) throw new Error('任务不存在');
|
||||
if (task.status !== 'completed') throw new Error('任务尚未完成');
|
||||
|
||||
const markdown = this.buildMarkdown(
|
||||
task.query,
|
||||
task.synthesisReport,
|
||||
task.resultList as LiteratureItem[] | null,
|
||||
task.completedAt,
|
||||
);
|
||||
|
||||
const docxBuffer = await this.convertToDocx(markdown, task.query);
|
||||
|
||||
const safeQuery = task.query.replace(/[^\u4e00-\u9fa5a-zA-Z0-9]/g, '').slice(0, 30);
|
||||
const dateStr = new Date().toISOString().slice(0, 10).replace(/-/g, '');
|
||||
const filename = `DeepResearch_${safeQuery}_${dateStr}.docx`;
|
||||
|
||||
return { buffer: docxBuffer, filename };
|
||||
}
|
||||
|
||||
private buildMarkdown(
|
||||
query: string,
|
||||
report: string | null,
|
||||
resultList: LiteratureItem[] | null,
|
||||
completedAt: Date | null,
|
||||
): string {
|
||||
const parts: string[] = [];
|
||||
|
||||
parts.push(`# Deep Research 报告\n`);
|
||||
parts.push(`检索主题： ${query}\n`);
|
||||
parts.push(`生成时间： ${completedAt ? new Date(completedAt).toLocaleString('zh-CN') : new Date().toLocaleString('zh-CN')}\n`);
|
||||
parts.push('---\n');
|
||||
|
||||
if (report) {
|
||||
parts.push('## 综合分析报告\n');
|
||||
let cleaned = report.replace(/\*\*([^*]+)\*\*/g, '$1');
|
||||
cleaned = this.expandReferenceLinks(cleaned);
|
||||
parts.push(cleaned);
|
||||
parts.push('\n');
|
||||
}
|
||||
|
||||
if (resultList && resultList.length > 0) {
|
||||
parts.push('---\n');
|
||||
parts.push(`## 文献清单（共 ${resultList.length} 篇）\n`);
|
||||
|
||||
resultList.forEach((item, idx) => {
|
||||
const title = item.title || '(无标题)';
|
||||
const url = item.url || (item.pmid ? `https://pubmed.ncbi.nlm.nih.gov/${item.pmid}/` : '');
|
||||
|
||||
const titleLine = url ? `[${title}](${url})` : title;
|
||||
parts.push(`### ${idx + 1}. ${titleLine}\n`);
|
||||
|
||||
const details: string[] = [];
|
||||
if (item.authors) details.push(`作者: ${item.authors}`);
|
||||
if (item.journal) details.push(`期刊: ${item.journal}`);
|
||||
if (item.year) details.push(`年份: ${item.year}`);
|
||||
if (item.studyType) details.push(`研究类型: ${item.studyType}`);
|
||||
if (item.pmid) details.push(`PMID: ${item.pmid}`);
|
||||
|
||||
if (details.length > 0) {
|
||||
parts.push(details.join(' | '));
|
||||
}
|
||||
|
||||
if (url) {
|
||||
parts.push(`\n链接: ${url}`);
|
||||
}
|
||||
|
||||
parts.push('\n');
|
||||
});
|
||||
}
|
||||
|
||||
parts.push('---\n');
|
||||
parts.push('*本报告由 AI Clinical Research 平台 Deep Research 引擎自动生成*\n');
|
||||
|
||||
return parts.join('\n');
|
||||
}
|
||||
|
||||
/**
|
||||
* 将 [[N]](url) 格式的引用链接展开为 [N] url 形式，
|
||||
* 使 Word 中引用旁边可见完整 URL。
|
||||
*/
|
||||
private expandReferenceLinks(text: string): string {
|
||||
return text.replace(
|
||||
/\[\[(\d+)\]\]\((https?:\/\/[^\s)]+)\)/g,
|
||||
'[$1]($2) ($2)'
|
||||
);
|
||||
}
|
||||
|
||||
private async convertToDocx(markdown: string, title: string): Promise<Buffer> {
|
||||
try {
|
||||
logger.info('[WordExport] Converting Markdown → Word');
|
||||
|
||||
const response = await axios.post(
|
||||
`${EXTRACTION_SERVICE_URL}/api/convert/docx`,
|
||||
{
|
||||
content: markdown,
|
||||
use_template: true,
|
||||
title: `Deep Research: ${title.slice(0, 50)}`,
|
||||
},
|
||||
{
|
||||
responseType: 'arraybuffer',
|
||||
timeout: 30000,
|
||||
}
|
||||
);
|
||||
|
||||
logger.info(`[WordExport] Conversion success, size: ${response.data.length} bytes`);
|
||||
return Buffer.from(response.data);
|
||||
} catch (error) {
|
||||
logger.error('[WordExport] Conversion failed:', error);
|
||||
throw new Error(`Word 转换失败: ${error instanceof Error ? error.message : 'Unknown error'}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const wordExportService = new WordExportService();
|
||||
115
backend/src/modules/asl/utils/reasoningParser.ts
Normal file
115
backend/src/modules/asl/utils/reasoningParser.ts
Normal file
@@ -0,0 +1,115 @@
|
||||
/**
|
||||
* Reasoning Content 解析器
|
||||
*
|
||||
* 将 Unifuncs 返回的 reasoning_content 增量文本解析为结构化日志条目。
|
||||
*
|
||||
* 核心策略：按段落（\n\n）拆分，同一段落内的思考内容合并为一条日志，
|
||||
* 只有 搜索/阅读/分析 等动作才单独成条。
|
||||
*/
|
||||
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
|
||||
export interface ExecutionLogEntry {
|
||||
type: 'thinking' | 'searching' | 'reading' | 'analyzing' | 'summary' | 'info';
|
||||
title: string;
|
||||
text: string;
|
||||
ts: string;
|
||||
}
|
||||
|
||||
const SEARCH_PATTERN = /(?:搜索|searching|search(?:ing)?\s+for|查找|检索|looking\s+for)[：:\s]+(.+)/i;
|
||||
const READ_PATTERN = /(?:阅读|reading|read(?:ing)?|访问|打开|visiting|open(?:ing)?)\s*[：:\s]*(https?:\/\/\S+|\S+\.(?:com|org|net|gov|cn)\S*)/i;
|
||||
const ANALYZE_PATTERN = /(?:分析|analyz|发现|总结|归纳|结论|found|result|finding|conclud|summariz)/i;
|
||||
|
||||
/**
|
||||
* 将增量 reasoning 文本解析为段落级日志条目。
|
||||
* 连续的思考行合并为一段，动作行（搜索/阅读/分析）独立成条。
|
||||
*/
|
||||
export function parseReasoningIncrement(
|
||||
newText: string,
|
||||
_previousLength: number
|
||||
): ExecutionLogEntry[] {
|
||||
if (!newText) return [];
|
||||
|
||||
const entries: ExecutionLogEntry[] = [];
|
||||
const now = new Date().toISOString();
|
||||
|
||||
const paragraphs = newText.split(/\n{2,}/);
|
||||
|
||||
for (const para of paragraphs) {
|
||||
const lines = para.split('\n').filter(l => l.trim());
|
||||
if (lines.length === 0) continue;
|
||||
|
||||
let thinkingBuf: string[] = [];
|
||||
|
||||
const flushThinking = () => {
|
||||
if (thinkingBuf.length === 0) return;
|
||||
const text = thinkingBuf.join('').slice(0, 800);
|
||||
if (text.length > 10) {
|
||||
entries.push({ type: 'thinking', title: '思考', text, ts: now });
|
||||
}
|
||||
thinkingBuf = [];
|
||||
};
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim();
|
||||
if (!trimmed) continue;
|
||||
|
||||
const searchMatch = trimmed.match(SEARCH_PATTERN);
|
||||
if (searchMatch) {
|
||||
flushThinking();
|
||||
entries.push({ type: 'searching', title: '搜索', text: searchMatch[1].trim(), ts: now });
|
||||
continue;
|
||||
}
|
||||
|
||||
const readMatch = trimmed.match(READ_PATTERN);
|
||||
if (readMatch) {
|
||||
flushThinking();
|
||||
entries.push({ type: 'reading', title: '阅读页面', text: readMatch[1].trim(), ts: now });
|
||||
continue;
|
||||
}
|
||||
|
||||
if (ANALYZE_PATTERN.test(trimmed) && trimmed.length > 20) {
|
||||
flushThinking();
|
||||
entries.push({ type: 'analyzing', title: '分析', text: trimmed.slice(0, 500), ts: now });
|
||||
continue;
|
||||
}
|
||||
|
||||
thinkingBuf.push(trimmed);
|
||||
}
|
||||
|
||||
flushThinking();
|
||||
}
|
||||
|
||||
return entries;
|
||||
}
|
||||
|
||||
/**
|
||||
* 合并连续的同类型（thinking）条目为一段。
|
||||
* 在 Worker 写入 DB 前调用，减少碎片化。
|
||||
*/
|
||||
export function mergeConsecutiveThinking(entries: ExecutionLogEntry[]): ExecutionLogEntry[] {
|
||||
if (entries.length <= 1) return entries;
|
||||
|
||||
const merged: ExecutionLogEntry[] = [];
|
||||
let current = { ...entries[0] };
|
||||
|
||||
for (let i = 1; i < entries.length; i++) {
|
||||
if (entries[i].type === 'thinking' && current.type === 'thinking') {
|
||||
current.text = (current.text + ' ' + entries[i].text).slice(0, 800);
|
||||
} else {
|
||||
merged.push(current);
|
||||
current = { ...entries[i] };
|
||||
}
|
||||
}
|
||||
merged.push(current);
|
||||
|
||||
return merged;
|
||||
}
|
||||
|
||||
/**
|
||||
* 从完整的 reasoning_content 一次性提取摘要级日志
|
||||
*/
|
||||
export function parseFullReasoning(fullText: string): ExecutionLogEntry[] {
|
||||
if (!fullText) return [];
|
||||
return parseReasoningIncrement(fullText, 0);
|
||||
}
|
||||
113
backend/src/modules/asl/utils/resultParser.ts
Normal file
113
backend/src/modules/asl/utils/resultParser.ts
Normal file
@@ -0,0 +1,113 @@
|
||||
/**
|
||||
* Deep Research V2.0 — 结果解析器
|
||||
*
|
||||
* 职责：
|
||||
* 1. 从 Unifuncs content 中切割出 synthesisReport + resultList
|
||||
* 2. safeParseJsonList: 4 层防崩溃 JSON 解析
|
||||
*/
|
||||
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
|
||||
export interface LiteratureItem {
|
||||
title: string;
|
||||
authors?: string;
|
||||
journal?: string;
|
||||
year?: number | string;
|
||||
doi?: string;
|
||||
pmid?: string;
|
||||
url?: string;
|
||||
abstract?: string;
|
||||
studyType?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* 从 Unifuncs 返回的 content 中拆分报告和文献列表
|
||||
*/
|
||||
export function parseContent(content: string): {
|
||||
synthesisReport: string;
|
||||
resultList: LiteratureItem[] | null;
|
||||
} {
|
||||
if (!content) {
|
||||
return { synthesisReport: '', resultList: null };
|
||||
}
|
||||
|
||||
const jsonBlockMatch = content.match(/```json\s*([\s\S]*?)```/);
|
||||
|
||||
if (jsonBlockMatch) {
|
||||
const beforeJson = content.slice(0, content.indexOf('```json')).trim();
|
||||
const jsonRaw = jsonBlockMatch[1];
|
||||
|
||||
const resultList = safeParseJsonList(jsonRaw);
|
||||
|
||||
const afterJsonEnd = content.indexOf('```', content.indexOf('```json') + 7) + 3;
|
||||
const afterJson = content.slice(afterJsonEnd).trim();
|
||||
|
||||
const synthesisReport = (beforeJson + (afterJson ? '\n\n' + afterJson : '')).trim();
|
||||
|
||||
return { synthesisReport: synthesisReport || content, resultList };
|
||||
}
|
||||
|
||||
const links = extractPubMedLinks(content);
|
||||
if (links.length > 0) {
|
||||
const resultList: LiteratureItem[] = links.map(url => ({
|
||||
title: '',
|
||||
url,
|
||||
pmid: extractPmidFromUrl(url) || undefined,
|
||||
}));
|
||||
return { synthesisReport: content, resultList };
|
||||
}
|
||||
|
||||
return { synthesisReport: content, resultList: null };
|
||||
}
|
||||
|
||||
/**
|
||||
* 4 层防崩溃 JSON 解析
|
||||
*/
|
||||
export function safeParseJsonList(raw: string | null): LiteratureItem[] | null {
|
||||
if (!raw) return null;
|
||||
|
||||
let cleaned = raw.replace(/```json\s*/gi, '').replace(/```\s*/g, '');
|
||||
|
||||
cleaned = cleaned.replace(/,\s*([}\]])/g, '$1');
|
||||
|
||||
try {
|
||||
const parsed = JSON.parse(cleaned);
|
||||
return Array.isArray(parsed) ? parsed : [parsed];
|
||||
} catch {
|
||||
logger.warn('[resultParser] Standard JSON.parse failed, trying regex extraction');
|
||||
}
|
||||
|
||||
const objects: any[] = [];
|
||||
const regex = /\{[^{}]*\}/g;
|
||||
let match;
|
||||
while ((match = regex.exec(cleaned)) !== null) {
|
||||
try {
|
||||
objects.push(JSON.parse(match[0]));
|
||||
} catch {
|
||||
// skip unparseable fragment
|
||||
}
|
||||
}
|
||||
|
||||
if (objects.length > 0) {
|
||||
logger.info('[resultParser] Regex extraction recovered items', { count: objects.length });
|
||||
return objects;
|
||||
}
|
||||
|
||||
logger.warn('[resultParser] All parsing strategies failed');
|
||||
return null;
|
||||
}
|
||||
|
||||
function extractPubMedLinks(content: string): string[] {
|
||||
const linkSet = new Set<string>();
|
||||
const pattern = /https?:\/\/pubmed\.ncbi\.nlm\.nih\.gov\/(\d+)\/?/gi;
|
||||
let match;
|
||||
while ((match = pattern.exec(content)) !== null) {
|
||||
linkSet.add(`https://pubmed.ncbi.nlm.nih.gov/${match[1]}/`);
|
||||
}
|
||||
return Array.from(linkSet);
|
||||
}
|
||||
|
||||
function extractPmidFromUrl(url: string): string | null {
|
||||
const m = url.match(/pubmed\.ncbi\.nlm\.nih\.gov\/(\d+)/);
|
||||
return m ? m[1] : null;
|
||||
}
|
||||
223
backend/src/modules/asl/workers/deepResearchV2Worker.ts
Normal file
223
backend/src/modules/asl/workers/deepResearchV2Worker.ts
Normal file
@@ -0,0 +1,223 @@
|
||||
/**
|
||||
* Deep Research V2.0 Worker — SSE 流式架构
|
||||
*
|
||||
* 核心流程：
|
||||
* 1. 从 DB 读取任务（含 confirmedRequirement + targetSources）
|
||||
* 2. 通过 SSE 流连接 Unifuncs OpenAI 兼容接口
|
||||
* 3. 实时读取 reasoning_content 增量 → 解析为 executionLogs → 每 2s 刷写 DB
|
||||
* 4. 读取 content 增量 → 累积
|
||||
* 5. 流结束后解析 content → synthesisReport + resultList
|
||||
* 6. 超时保护: 15 分钟
|
||||
*/
|
||||
|
||||
import { prisma } from '../../../config/database.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import { streamDeepSearch } from '../services/unifuncsSseClient.js';
|
||||
import { parseReasoningIncrement, mergeConsecutiveThinking, type ExecutionLogEntry } from '../utils/reasoningParser.js';
|
||||
import { parseContent } from '../utils/resultParser.js';
|
||||
|
||||
const MAX_DURATION_MS = 15 * 60 * 1000;
|
||||
const LOG_FLUSH_INTERVAL_MS = 2000;
|
||||
|
||||
export async function processDeepResearchV2(job: { data: { taskId: string } }) {
|
||||
const { taskId } = job.data;
|
||||
|
||||
logger.info('[DeepResearchV2Worker] Starting', { taskId });
|
||||
|
||||
const task = await prisma.aslResearchTask.findUnique({
|
||||
where: { id: taskId },
|
||||
});
|
||||
|
||||
if (!task) {
|
||||
logger.error('[DeepResearchV2Worker] Task not found', { taskId });
|
||||
return;
|
||||
}
|
||||
|
||||
const searchQuery = task.confirmedRequirement || task.query;
|
||||
const targetSources = (task.targetSources as string[]) || ['https://pubmed.ncbi.nlm.nih.gov/'];
|
||||
|
||||
try {
|
||||
await prisma.aslResearchTask.update({
|
||||
where: { id: taskId },
|
||||
data: { status: 'running', executionLogs: [] as any },
|
||||
});
|
||||
|
||||
const hasChinese = targetSources.some(
|
||||
s => s.includes('cnki') || s.includes('medjournals')
|
||||
);
|
||||
const outputPrompt = buildOutputPrompt(hasChinese);
|
||||
|
||||
const allLogs: ExecutionLogEntry[] = [{
|
||||
type: 'info',
|
||||
title: '任务已提交',
|
||||
text: '正在连接深度检索引擎...',
|
||||
ts: new Date().toISOString(),
|
||||
}];
|
||||
await updateLogs(taskId, allLogs);
|
||||
|
||||
const startTime = Date.now();
|
||||
let reasoningAccumulated = '';
|
||||
let contentAccumulated = '';
|
||||
let lastFlushTime = Date.now();
|
||||
let previousReasoningLength = 0;
|
||||
let pendingLogEntries: ExecutionLogEntry[] = [];
|
||||
let contentStarted = false;
|
||||
|
||||
const stream = streamDeepSearch({
|
||||
query: searchQuery,
|
||||
domainScope: targetSources,
|
||||
maxDepth: 25,
|
||||
referenceStyle: 'link',
|
||||
outputPrompt,
|
||||
});
|
||||
|
||||
allLogs.push({
|
||||
type: 'info',
|
||||
title: '连接成功',
|
||||
text: 'AI 开始深度检索，正在分析检索策略...',
|
||||
ts: new Date().toISOString(),
|
||||
});
|
||||
await updateLogs(taskId, allLogs);
|
||||
|
||||
for await (const chunk of stream) {
|
||||
if (Date.now() - startTime > MAX_DURATION_MS) {
|
||||
throw new Error('Task exceeded 15-minute timeout');
|
||||
}
|
||||
|
||||
if (chunk.type === 'reasoning') {
|
||||
reasoningAccumulated += chunk.text;
|
||||
|
||||
const newText = reasoningAccumulated.slice(previousReasoningLength);
|
||||
if (newText.length > 200) {
|
||||
const newEntries = parseReasoningIncrement(newText, previousReasoningLength);
|
||||
if (newEntries.length > 0) {
|
||||
pendingLogEntries.push(...newEntries);
|
||||
}
|
||||
previousReasoningLength = reasoningAccumulated.length;
|
||||
}
|
||||
|
||||
if (pendingLogEntries.length > 0 && Date.now() - lastFlushTime >= LOG_FLUSH_INTERVAL_MS) {
|
||||
const merged = mergeConsecutiveThinking(pendingLogEntries);
|
||||
allLogs.push(...merged);
|
||||
pendingLogEntries = [];
|
||||
await updateLogs(taskId, allLogs);
|
||||
lastFlushTime = Date.now();
|
||||
}
|
||||
} else if (chunk.type === 'content') {
|
||||
contentAccumulated += chunk.text;
|
||||
|
||||
if (pendingLogEntries.length > 0) {
|
||||
allLogs.push(...pendingLogEntries);
|
||||
pendingLogEntries = [];
|
||||
}
|
||||
|
||||
if (!contentStarted) {
|
||||
contentStarted = true;
|
||||
allLogs.push({
|
||||
type: 'summary',
|
||||
title: '思考完毕',
|
||||
text: '正在生成综合分析报告...',
|
||||
ts: new Date().toISOString(),
|
||||
});
|
||||
await updateLogs(taskId, allLogs);
|
||||
}
|
||||
} else if (chunk.type === 'done') {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (pendingLogEntries.length > 0) {
|
||||
const remaining = reasoningAccumulated.slice(previousReasoningLength);
|
||||
if (remaining.length > 0) {
|
||||
const lastEntries = parseReasoningIncrement(remaining, previousReasoningLength);
|
||||
pendingLogEntries.push(...lastEntries);
|
||||
}
|
||||
allLogs.push(...mergeConsecutiveThinking(pendingLogEntries));
|
||||
pendingLogEntries = [];
|
||||
}
|
||||
|
||||
allLogs.push({
|
||||
type: 'summary',
|
||||
title: '检索完成',
|
||||
text: 'AI 深度检索已完成，正在整理结果...',
|
||||
ts: new Date().toISOString(),
|
||||
});
|
||||
await updateLogs(taskId, allLogs);
|
||||
|
||||
const { synthesisReport, resultList } = parseContent(contentAccumulated);
|
||||
|
||||
await prisma.aslResearchTask.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
status: 'completed',
|
||||
rawResult: contentAccumulated,
|
||||
reasoningContent: reasoningAccumulated,
|
||||
synthesisReport,
|
||||
resultList: resultList as any,
|
||||
resultCount: resultList?.length || 0,
|
||||
executionLogs: allLogs as any,
|
||||
completedAt: new Date(),
|
||||
},
|
||||
});
|
||||
|
||||
logger.info('[DeepResearchV2Worker] Completed', {
|
||||
taskId,
|
||||
resultCount: resultList?.length || 0,
|
||||
reasoningLen: reasoningAccumulated.length,
|
||||
contentLen: contentAccumulated.length,
|
||||
duration: Date.now() - startTime,
|
||||
});
|
||||
|
||||
} catch (error: any) {
|
||||
logger.error('[DeepResearchV2Worker] Failed', {
|
||||
taskId,
|
||||
error: error.message,
|
||||
});
|
||||
|
||||
await prisma.aslResearchTask.update({
|
||||
where: { id: taskId },
|
||||
data: {
|
||||
status: 'failed',
|
||||
errorMessage: error.message,
|
||||
},
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function buildOutputPrompt(hasChinese: boolean): string {
|
||||
let prompt = `请按以下格式输出：
|
||||
|
||||
## 综合分析报告
|
||||
（对检索到的文献进行综合分析，包括研究现状、主要发现、研究趋势等。引用文献时请使用内联链接格式 [标题](url)，不要使用脚注式引用。）
|
||||
|
||||
## 文献清单
|
||||
请以 JSON 数组格式输出所有检索到的文献元数据：
|
||||
\`\`\`json
|
||||
[
|
||||
{
|
||||
"title": "文献标题",
|
||||
"authors": "第一作者 et al.",
|
||||
"journal": "期刊名",
|
||||
"year": 2024,
|
||||
"doi": "10.xxxx/xxxxx",
|
||||
"pmid": "12345678",
|
||||
"url": "文献完整链接",
|
||||
"abstract": "摘要前200字...",
|
||||
"studyType": "RCT"
|
||||
}
|
||||
]
|
||||
\`\`\``;
|
||||
|
||||
if (hasChinese) {
|
||||
prompt += `\n\n重要提示：用户选择了中文数据库（CNKI/中华医学期刊网），请务必使用中文关键词检索中文文献，确保结果中包含中文文献。如有中文文献，请保留中文标题和作者信息，url 使用原始中文数据库链接。`;
|
||||
}
|
||||
|
||||
return prompt;
|
||||
}
|
||||
|
||||
async function updateLogs(taskId: string, logs: ExecutionLogEntry[]) {
|
||||
await prisma.aslResearchTask.update({
|
||||
where: { id: taskId },
|
||||
data: { executionLogs: logs as any },
|
||||
});
|
||||
}
|
||||
@@ -8,6 +8,7 @@
|
||||
import { jobQueue } from '../../../common/jobs/index.js';
|
||||
import { logger } from '../../../common/logging/index.js';
|
||||
import { researchService } from '../services/researchService.js';
|
||||
import { processDeepResearchV2 } from './deepResearchV2Worker.js';
|
||||
import type { Job } from '../../../common/jobs/types.js';
|
||||
|
||||
/**
|
||||
@@ -79,4 +80,18 @@ export function registerResearchWorker() {
|
||||
});
|
||||
|
||||
logger.info('[ResearchWorker] ✅ Worker registered: asl_research_execute');
|
||||
|
||||
// ── Deep Research V2.0 Worker ──
|
||||
jobQueue.process<{ taskId: string }>('asl_deep_research_v2', async (job: Job<{ taskId: string }>) => {
|
||||
logger.info('[ResearchWorker] Starting V2 deep research', {
|
||||
jobId: job.id,
|
||||
taskId: job.data.taskId,
|
||||
});
|
||||
|
||||
await processDeepResearchV2(job);
|
||||
|
||||
return { success: true, taskId: job.data.taskId };
|
||||
});
|
||||
|
||||
logger.info('[ResearchWorker] ✅ Worker registered: asl_deep_research_v2');
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user