Agent skill · research science · wshobson
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.
Why this skill is useful
Provides domain-specific knowledge on embedding model selection and optimization strategies that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-05. 38,570 stars on the source repository.
What this skill does
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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills wshobson/embedding-strategies