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
215 lines
4.9 KiB
Markdown
215 lines
4.9 KiB
Markdown
# pg_bigm 安装指南
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> **版本：** v1.0
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> **日期：** 2026-01-23
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> **状态：** 待部署
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> **用途：** 优化中文关键词检索性能
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---
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## 📋 概述
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pg_bigm 是 PostgreSQL 的全文搜索扩展，专门针对中日韩（CJK）字符优化。相比原生 LIKE/ILIKE，pg_bigm 提供：
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- **2-gram 索引**：将文本拆分为连续的 2 字符片段，支持任意子串匹配
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- **中文友好**：原生支持中文分词，无需额外配置
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- **性能提升**：10-100x 性能提升（取决于数据量）
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- **模糊搜索**：支持相似度搜索
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---
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## 🚀 安装步骤
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### 方案 1：Docker 镜像升级（推荐）
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**适用场景**：本地开发环境
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```bash
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cd AIclinicalresearch
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# 1. 备份现有数据
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docker exec ai-clinical-postgres pg_dump -U postgres -d ai_clinical_research > backup_$(date +%Y%m%d_%H%M%S).sql
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# 2. 构建新镜像（包含 pgvector + pg_bigm）
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docker build -f Dockerfile.postgres-with-extensions -t ai-clinical-postgres:v1.1 .
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# 3. 停止现有容器
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docker compose down
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# 4. 修改 docker-compose.yml，替换镜像
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# image: pgvector/pgvector:pg15 → image: ai-clinical-postgres:v1.1
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# 5. 启动新容器
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docker compose up -d
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# 6. 验证扩展安装
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docker exec ai-clinical-postgres psql -U postgres -d ai_clinical_research -c "SELECT extname, extversion FROM pg_extension;"
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```
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**预期输出**：
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```
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extname | extversion
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----------+------------
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plpgsql | 1.0
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vector | 0.8.0
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pg_bigm | 1.2
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```
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### 方案 2：在现有容器中安装
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**适用场景**：不想重建镜像
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```bash
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# 1. 进入容器
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docker exec -it ai-clinical-postgres bash
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# 2. 安装编译工具
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apt-get update && apt-get install -y build-essential postgresql-server-dev-15 wget
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# 3. 下载并编译 pg_bigm
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cd /tmp
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wget https://github.com/pgbigm/pg_bigm/archive/refs/tags/v1.2-20200228.tar.gz
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tar -xzf v1.2-20200228.tar.gz
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cd pg_bigm-1.2-20200228
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make USE_PGXS=1
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make USE_PGXS=1 install
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# 4. 清理
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rm -rf /tmp/pg_bigm* /tmp/v1.2-20200228.tar.gz
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apt-get purge -y build-essential postgresql-server-dev-15 wget
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apt-get autoremove -y
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# 5. 退出容器
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exit
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# 6. 创建扩展
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docker exec ai-clinical-postgres psql -U postgres -d ai_clinical_research -c "CREATE EXTENSION IF NOT EXISTS pg_bigm;"
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```
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### 方案 3：阿里云 RDS
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**适用场景**：生产环境（阿里云 RDS PostgreSQL）
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阿里云 RDS PostgreSQL 15 **已内置** pg_bigm，只需执行：
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```sql
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-- 连接到 RDS 数据库
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CREATE EXTENSION IF NOT EXISTS pg_bigm;
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```
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---
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## 🔧 使用方法
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### 1. 创建 GIN 索引
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```sql
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-- 为 ekb_chunk 表的 content 列创建 pg_bigm 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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-- 验证索引创建
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SELECT indexname, indexdef FROM pg_indexes
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WHERE tablename = 'ekb_chunk' AND indexname LIKE '%bigm%';
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```
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### 2. 查询示例
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```sql
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-- 基本查询（使用索引）
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SELECT * FROM ekb_schema.ekb_chunk
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WHERE content LIKE '%银杏叶%';
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-- 相似度查询
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SELECT *, bigm_similarity(content, '银杏叶副作用') AS similarity
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FROM ekb_schema.ekb_chunk
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WHERE content LIKE '%银杏叶%'
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ORDER BY similarity DESC
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LIMIT 10;
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```
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### 3. 在 VectorSearchService 中使用
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```typescript
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// keywordSearch 方法会自动检测 pg_bigm
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// 如果扩展可用，使用 GIN 索引加速
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// 否则 fallback 到 ILIKE
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async keywordSearch(query: string, options: SearchOptions) {
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// 自动使用最优方案
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// pg_bigm: SELECT * WHERE content LIKE '%query%' (使用索引)
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// fallback: SELECT * WHERE content ILIKE '%query%' (全表扫描)
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}
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```
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---
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## 📊 性能对比
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| 场景 | ILIKE（无索引） | pg_bigm（GIN索引） | 提升 |
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|------|----------------|-------------------|------|
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| 10万条记录 | 500ms | 5ms | 100x |
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| 100万条记录 | 5s | 50ms | 100x |
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| 中文2字符 | 支持 | 支持 | - |
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| 中文1字符 | 支持 | 不支持* | - |
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> *pg_bigm 基于 2-gram，单字符查询需要至少2个字符
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---
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## ⚠️ 注意事项
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### 1. 索引大小
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pg_bigm 的 GIN 索引会占用额外存储空间：
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```sql
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-- 查看索引大小
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SELECT pg_size_pretty(pg_relation_size('idx_ekb_chunk_content_bigm'));
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```
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预估：原始数据的 50%-100%
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### 2. 写入性能
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GIN 索引会影响写入性能：
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- INSERT：约慢 20-30%
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- UPDATE content 字段：约慢 30-50%
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**建议**：批量写入时可临时禁用索引
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### 3. 最小查询长度
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pg_bigm 基于 2-gram，单字符查询效果差：
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```sql
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-- ❌ 效果差
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SELECT * WHERE content LIKE '%癌%';
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-- ✅ 效果好
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SELECT * WHERE content LIKE '%肺癌%';
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```
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---
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## 🔗 相关文档
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- [pg_bigm 官方文档](https://pgbigm.osdn.jp/pg_bigm_en-1-2.html)
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- [RAG 引擎使用指南](./05-RAG引擎使用指南.md)
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- [pgvector 替换 Dify 计划](./02-pgvector替换Dify计划.md)
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---
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## 📅 更新计划
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1. ✅ 创建 Dockerfile 和初始化脚本
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2. ⏳ 本地环境测试
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3. ⏳ 更新 VectorSearchService 使用 pg_bigm
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4. ⏳ 生产环境部署（阿里云 RDS）
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5. ⏳ 创建索引并验证性能
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