HaHafeng
96290d2f76
Sprint 1-3 Completed (Backend + Frontend): Backend (Sprint 1-2): - Implement 5-layer Agent framework (Query->Planner->Executor->Tools->Reflection) - Create agent_schema with 6 tables (agent_definitions, stages, prompts, sessions, traces, reflexion_rules) - Create protocol_schema with 2 tables (protocol_contexts, protocol_generations) - Implement Protocol Agent core services (Orchestrator, ContextService, PromptBuilder) - Integrate LLM service adapter (DeepSeek/Qwen/GPT-5/Claude) - 6 API endpoints with full authentication - 10/10 API tests passed Frontend (Sprint 3): - Add Protocol Agent entry in AgentHub (indigo theme card) - Implement ProtocolAgentPage with 3-column layout - Collapsible sidebar (Gemini style, 48px <-> 280px) - StatePanel with 5 stage cards (scientific_question, pico, study_design, sample_size, endpoints) - ChatArea with sync button and action cards integration - 100% prototype design restoration (608 lines CSS) - Detailed endpoints structure: baseline, exposure, outcomes, confounders Features: - 5-stage dialogue flow for research protocol design - Conversation-driven interaction with sync-to-protocol button - Real-time context state management - One-click protocol generation button (UI ready, backend pending) Database: - agent_schema: 6 tables for reusable Agent framework - protocol_schema: 2 tables for Protocol Agent - Seed data: 1 agent + 5 stages + 9 prompts + 4 reflexion rules Code Stats: - Backend: 13 files, 4338 lines - Frontend: 14 files, 2071 lines - Total: 27 files, 6409 lines Status: MVP core functionality completed, pending frontend-backend integration testing Next: Sprint 4 - One-click protocol generation + Word export
71 lines
2.1 KiB
SQL
71 lines
2.1 KiB
SQL
-- ============================================================
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-- EKB Schema 索引创建脚本
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-- 执行时机：prisma migrate 之后手动执行
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-- 参考文档：docs/02-通用能力层/03-RAG引擎/04-数据模型设计.md
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-- ============================================================
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-- 1. 确保 pgvector 扩展已启用
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CREATE EXTENSION IF NOT EXISTS vector;
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-- 2. 确保 pg_bigm 扩展已启用（中文关键词检索）
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CREATE EXTENSION IF NOT EXISTS pg_bigm;
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-- ===== MVP 阶段必须创建 =====
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-- 3. HNSW 向量索引（语义检索核心）
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-- 参数说明：m=16 每层最大连接数，ef_construction=64 构建时搜索范围
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_embedding
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ON "ekb_schema"."ekb_chunk"
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- ===== Phase 2 阶段使用（可预创建）=====
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-- 4. pg_bigm 中文关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_content_bigm
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ON "ekb_schema"."ekb_chunk"
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USING gin (content gin_bigm_ops);
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-- 5. 文档摘要关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_summary_bigm
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ON "ekb_schema"."ekb_document"
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USING gin (summary gin_bigm_ops);
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-- 6. 全文内容关键词索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_text_bigm
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ON "ekb_schema"."ekb_document"
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USING gin (extracted_text gin_bigm_ops);
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-- ===== Phase 3 阶段使用（可预创建）=====
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-- 7. JSONB GIN 索引（metadata 查询加速）
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_metadata_gin
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ON "ekb_schema"."ekb_document"
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USING gin (metadata jsonb_path_ops);
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-- 8. JSONB GIN 索引（structuredData 查询加速）
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_structured_gin
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ON "ekb_schema"."ekb_document"
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USING gin (structured_data jsonb_path_ops);
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-- 9. 标签数组索引
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CREATE INDEX IF NOT EXISTS idx_ekb_doc_tags_gin
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ON "ekb_schema"."ekb_document"
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USING gin (tags);
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-- 10. 切片元数据索引
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CREATE INDEX IF NOT EXISTS idx_ekb_chunk_metadata_gin
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ON "ekb_schema"."ekb_chunk"
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USING gin (metadata jsonb_path_ops);
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-- ===== 验证索引创建 =====
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-- SELECT indexname, indexdef FROM pg_indexes WHERE schemaname = 'ekb_schema';
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