embedding-strategies

SKILL.md

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