rag-service
高性能 RAG 多路检索服务。集成 Milvus 向量数据库进行语义检索，并结合 Rerank 模型进行精准重排序，支持海量文档的高效存储与历史内容召回。
npx skills add majiayu000/claude-skill-registry --skill rag-service-lin-a1-skills-agent --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 功能 RAG 多路检索服务，提供： 1. 向量语义检索 - 基于 Milvus 的向量相似度搜索 2. Rerank 重排序 - 对检索结果进行精排 3. 文档存储 - 保存文档到向量数据库 ## 调用方式 ```python from services.rag_service.client import RAGServiceClient client = RAGServiceClient() # 健康检查 status = client.health() # 语义检索 result = client.retrieve( query="Python 异步编程最佳实践", top_k=5, min_score=0.85, rerank=True ) print(result["results"]) # 便捷方法：只获取文本列表 texts = client.retrieve_texts(query="Python 异步编程", top_k=5) # 保存文档 client.save(documents=[ {"text": "文档内容...", "metadata": {"title": "标题", "url": "..."}} ]) ``` ## 返回格式 ### retrieve ```json { "query": "Python 异步编程", "results": [ { "id": "abc123", "text": "Python异步编程基于asyncio库...", "score": 0.92, "metadata": {"title": "Python官方文档", "url": "..."} } ], "total": 3, "elapsed_ms": 45.2, "from_cache": false } ``` ### save ```json { "saved_count": 5, "collection_name": "websearch_results" } ```
- 功能
- 调用方式
- 返回格式
- retrieve
- save
What does the rag-service skill do?
高性能 RAG 多路检索服务。集成 Milvus 向量数据库进行语义检索，并结合 Rerank 模型进行精准重排序，支持海量文档的高效存储与历史内容召回。
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rag-service-lin-a1-skills-agent --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.