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embedding-strategies

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.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=wshobson-agents-plugins-llm-application-dev-skills-embedding-strategies-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
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. Embedding Strategies Guide to selecting and optimizing embedding models for vector search applications. When to Use This Skill Choosing embedding models for RAG Optimizing chunking strategies Fine-tuning embeddings for domains Comparing embedding model performance Reducing embedding dimensions Handling multilingual content Core Concepts 1. Embedding Model Comparison (2026) Model Dimensions Max Tokens Best For voyage-3-large 1024 32000 Claude apps (Anthropic recommended) voyage-3 1024 32000 Claude apps, cost-effective voyage-code-3 1024 32000 Code search voyage-finance-2 1024 32000 Financial documents voyage-law-2 1024 32000 Legal documents text-embedding-3-large 3072 8191 OpenAI apps, high accuracy text-embedding-3-small 1536 8191 OpenAI apps, cost-effective bge-large-en-v1.5 1024 512 Open source, local deployment all-MiniLM-L6-v2 384 256 Fast, lightweight multilingual-e5-large 1024 512 Multi-language 2. Embedding Pipeline Document → Chunking → Preprocessing → Embedding Model → Vector ↓ [Overlap, Size] [Clean, Normalize] [API/Local] Templates and detailed worked examples Full template library and detailed worked examples live in references/details.md . Read that file when you need the concrete templates. Best Practices Do's Match model to use case : Code vs prose vs multilingual Chunk thoughtfully : Preserve semantic boundaries Normalize embeddings : For cosine similarity search Batch requests : More efficient than one-by-one Cache embeddings : Avoid recomputing for static content Use Voyage AI for Claude apps : Recommended by Anthropic Don'ts Don't ignore token limits : Truncation loses information Don't mix embedding models : Incompatible vector spaces Don't skip preprocessing : Garbage in, garbage out Don't over-chunk : Lose important context Don't forget metadata : Essential for filtering and debugging
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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