embedding-strategies is agent-read markdown (skill) from rudycity/superagent: 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..
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What the file says
# 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 |
…
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pass: Frontmatter declares a name
pass: Frontmatter declares a description
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Source
GitHub
rudycity/superagent · 23 stars · license MIT · pushed 2026-09-22 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ukwwufpjsdumf4ej
GET https://markdownregistry.com/api/v1/resolve?ref=rudycity/superagent/embedding-strategies
GET https://markdownregistry.com/api/v1/blob/d87e8c3e519cdc1d4d4578412aeb681e931ada45de143159610d8fe3cda20648
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