4aaeb42035
- CLIP embedding (9086) + Milvus VDB (8886) + NetworkX graph (9092) - BGE-Reranker (9090) for result reranking - Hybrid retrieval: vector search + graph expansion + RRF fusion - API: /api/ingest, /api/search, /api/pipelines, /api/plugins, /api/status - Two pipelines: kg-rag-standard (full) and kg-rag-lite (vector only) - Tested E2E: ingest + search with rerank_score=0.99
128 lines
3.6 KiB
Markdown
128 lines
3.6 KiB
Markdown
# RAG Pipeline Service
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可插拔 RAG 编排服务，支持知识图谱增强的多模态检索。
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## 架构
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```
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┌─────────────────┐
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│ RAG Pipeline │
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│ (9093 CPU) │
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└────────┬────────┘
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│
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┌──────────────────┼──────────────────┐
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│ │ │
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┌─────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐
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│ CLIP │ │ VDB │ │ Graph │
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│ 9086 │ │ 8886 │ │ 9092 │
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│ GPU 2 │ │ CPU │ │ CPU │
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└───────────┘ └───────────┘ └───────────┘
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│ │ │
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┌─────▼─────┐ ┌─────▼─────┐
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│BGE Rerank │ │ LLM │
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│ 9090 │ │harnessed │
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│ GPU 2 │ │ │
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└───────────┘ └───────────┘
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```
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## 插件槽位
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| 槽位 | 当前实现 | 可替换为 |
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|------|---------|---------|
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| embedding | CLIP-ViT-H/14 (9086) | BGE-M3, OpenAI embedding |
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| vdb | Milvus VDB (8886) | Qdrant, Weaviate |
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| graph | NetworkX (9092) | FalkorDB, Neo4j |
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| reranker | BGE-Reranker (9090) | Cohere, LLM rerank |
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| llm | harnessed_agent | 任意 LLM |
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| chunker | recursive/sentence | 自定义分块策略 |
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| extractor | LLM-structured | spaCy, GraphRAG |
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| retriever | hybrid/vector_only | 自定义检索策略 |
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| face | InsightFace (待部署) | FaceNet, DeepFace |
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## Pipeline 配置
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### kg-rag-standard（标准版）
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- CLIP embedding + Milvus + NetworkX 图 + BGE Rerank
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- 向量召回 + 图扩展 + RRF 融合 + 精排
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### kg-rag-lite（轻量版）
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- CLIP embedding + Milvus + BGE Rerank
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- 纯向量检索，无图谱
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## API
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### GET /api/status
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服务状态和可用插件列表。
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### POST /api/ingest
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入库文档。
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```json
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{
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"document": "文本内容...",
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"pipeline": "kg-rag-standard",
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"collection": "knowledge",
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"graph_name": "knowledge"
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}
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```
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流程: 分块 → CLIP embedding → VDB 存储 → 实体抽取 → 图存储
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### POST /api/search
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混合检索。
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```json
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{
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"query": "搜索问题",
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"pipeline": "kg-rag-standard",
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"collection": "knowledge",
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"graph_name": "knowledge",
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"top_k": 5
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}
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```
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流程: CLIP embed → VDB 向量召回 → 图扩展 → RRF 融合 → BGE Rerank
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### GET/POST /api/pipelines
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管理 pipeline 配置。
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```json
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POST: 创建自定义 pipeline
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GET: 列出所有 pipeline
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GET ?name=xxx: 获取指定 pipeline
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```
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### GET /api/plugins
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列出所有可用插件及状态。
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## 部署
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```bash
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cd /data/ymq/rag-pipeline
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bash build.sh deploy
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bash build.sh stop
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bash build.sh status
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```
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## 端到端测试
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```bash
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# 1. 入库
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curl -X POST http://localhost:9093/api/ingest \
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-H "Content-Type: application/json" \
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-d '{"document":"张三在ABC公司担任技术总监，他和李四是同事关系。","pipeline":"kg-rag-standard"}'
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# 2. 检索
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curl -X POST http://localhost:9093/api/search \
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-H "Content-Type: application/json" \
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-d '{"query":"张三在哪家公司工作？","pipeline":"kg-rag-standard"}'
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```
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## 端口
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9093 (CPU only)
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## Git
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git@git.opencomputing.cn:yumoqing/rag-pipeline.git
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