Skip to main content 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.
npx skills add wshobson/agents --skill embedding-strategies agents claude claude-code subagents anthropic automation
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
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).