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Embedding strategies

Skill bg-szy/TOP-SKILLS/skills/awesome-skills/embedding-strategies

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Install
npx -y skills add bg-szy/TOP-SKILLS --skill embedding-strategies

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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.

SKILL.md

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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)

ModelDimensionsMax TokensBest For
voyage-3-large102432000Claude apps (Anthropic recommended)
voyage-3102432000Claude apps, cost-effective
voyage-code-3102432000Code search
voyage-finance-2102432000Financial documents
voyage-law-2102432000Legal documents
text-embedding-3-large30728191OpenAI apps, high accuracy
text-embedding-3-small15368191OpenAI apps, cost-effective
bge-large-en-v1.51024512Open source, local deployment
all-MiniLM-L6-v2384256Fast, lightweight
multilingual-e5-large1024512Multi-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

Keep looking

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