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 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

embedding-strategies