描述
使用向量数据库和语义搜索为 LLM 应用构建检索增强生成 (RAG) 系统。
如何使用
- 访问 GitHub 仓库获取 SKILL.md 文件
- 将文件复制到您的项目根目录或 .cursor/rules 目录
- 重启您的 AI 助手或编辑器以应用新技能
完整技能说明
name
rag-implementation
description
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Tags
相关技能
embedding-strategies
为语义搜索和 RAG 应用选择和优化嵌入模型。
hybrid-search-implementation
结合向量和关键词搜索以改进检索。在实现 RAG 系统或构建搜索引擎时使用。
similarity-search-patterns
使用向量数据库实现高效的相似性搜索。在构建语义搜索或最近邻查询时使用。
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; desig
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to