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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 Curated skill Quality Excellent · 90 v1.0.0

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Download .skill Standard format with system_prompt and model_config, ready for any agent framework
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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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Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
The same skill can be exported in different platform formats.
.skill Standard format with system_prompt and model_config, ready for any agent framework Download
.skillpro Enhanced format with scripts, tools, dependencies and hooks Download
.json Plain JSON export with system_prompt and model parameters only Download
Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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