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.
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- 2026年9月8日 07:12
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- 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
```
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
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