描述
为语义搜索和 RAG 应用选择和优化嵌入模型。
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
embedding-strategies
description
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Tags
相关技能
rag-implementation
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。
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