Agent-Skills.md

Agent Skills: 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.

UncategorizedID: wshobson/agents/embedding-strategies

Repository

wshobson/agents
wshobsonLicense: MIT
32,1353,507

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pnpm dlx add-skill https://github.com/wshobson/agents/tree/HEAD/plugins/llm-application-dev/skills/embedding-strategies

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plugins/llm-application-dev/skills/embedding-strategies/SKILL.md

Skill Metadata

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