embedding-strategies
Comprehensive guide for selecting, implementing, and optimizing embedding models for vector search and RAG applications.
- Covers 10+ embedding models with dimensions, token limits, and domain specialization (Voyage AI, OpenAI, open-source options for code, finance, legal, and multilingual content)
- Provides four chunking strategies: token-based, sentence-based, semantic sections, and recursive character splitting with overlap handling
- Includes three implementation templates for Voyage AI, OpenAI, and local Sentence Transformers with specialized query/document prefixes
- Features domain-specific pipelines for general documents and code, plus evaluation metrics (precision, recall, MRR, NDCG) for retrieval quality assessment
- Best practices section covers model selection, preprocessing, batching, caching, and common pitfalls
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 |
|---|
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