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